uncertain futures

A CRITICAL ASSESSMENT OF PLANNING APPROACHES FOR

MATEUSZ REJ

UNCERTAIN FUTURES


UNCERTAIN FUTURES


UNCERTAIN FUTURES

BY MATEUSZ REJ

Thesis submitted in partial fulfilment of the requirements for the degree of Master of Science (M. Sc.)

Master of Urban Design Technische Universität Berlin

SUPERVISORS: Prof. Dr.-Ing. ANGELA MILLION (UTTKE) DAAD Guest Prof. ARUN JAIN Institute of Urban and Regional Planning / Institut für Stadt- und Regionalplanung Chair of Urban Design and Urban Development / Fachgebiet Städtebau und Siedlungswesen

BERLIN, DECEMBER 2017

EIGENSTÄNDIGKEITSERKLÄRUNG

Hiermit erkläre ich, Mateusz Rej, dass ich die vorliegende Arbeit selbstständig und eigenhändig sowie ohne unerlaubte fremde Hilfe und ausschließlich unter Verwendung der aufgeführten Quellen und Hilfsmittel angefertigt habe.

Berlin, den 12.12.2017

Mateusz Rej

ABSTRACTS

A CRITICAL ASSESSMENT OF PLANNING APPROACHES FOR UNCERTAIN FUTURES Current megatrends such as accelerating urbanization, climate change and the constant advancements in information technology are challenging the ability of urban planners to design adequate plans and solutions. Growing complexity and uncertainty require more flexible and adaptive planning approaches. This work addresses those issues by assessing and comparing three qualitative planning methodologies and their ability to address uncertain futures: scenario planning, framework planning and adaptive planning. A literature review about complexity, uncertainty and decision-making theories is constituting the foundation for this assessment. All three methodologies share the ability to address epistemological uncertainty (knowledge-guided decisions). They explore different ways of adopting anticipated events. Scenario planning is discovering future outcomes and testing their consequences, while framework planning is developing a flexible strategy envisioning the future development. Adaptive planning on contrast is emphasizing adaptability and adjustment to perform well across a range of futures, but has not been applied yet in urban design and planning contexts. This study highlights the need for more integrated planning and decision-making to take informed decisions that lead to improved outcomes. It considers cities as complex adaptive systems and emphasizes that planners need to acknowledge cities as such. This work sets a stage for a more practically oriented inquiry, that could further evaluate the performance of the different methodologies and investigate influencing factors that support their success for the long-term.

KRYTYCZNA OCENA METODOLOGII PLANOWANIA DŁUGOTERMINOWEGO W WARUNKACH NIEPEWNOSCI Obecne megatrendy, takie jak przyspieszenie urbanizacji, zmiana klimatu i postępująca digitalizacja stanowią wyzwanie dla zdolności urbanistów do projektowania odpowiednich planów i rozwiązań. Rosnąca kompleksowość i niepewność wymagają bardziej elastycznego i adaptacyjnego podejścia. Praca ta zajmuje się tymi kwestiami, oceniając i porównując trzy jakościowe metodologie planowania długoterminowego oraz ich zdolność do radzenia sobie z niepewną przyszłością: planowanie w oparciu o scenariusze, planowanie ramowe i planowanie adaptacyjne. Przegląd literatury na temat kompleksowości, niepewności i teorii podejmowania decyzji stanowi podstawę tej oceny. Wszystkie trzy metodologie mają wspólną zdolność reagowania na niepewność epistemologiczną (decyzje kierowane wiedzą) oraz w różny sposób akceptują potencjalne wydarzenia. Planowanie poprzez scenariusze odkrywa przyszłe rezultaty i testuje ich konsekwencje, podczas gdy planowanie ramowe rozwija elastyczną strategię wyobrażającą przyszły rozwój. Planowanie adaptacyjne z kolei podkreśla zdolność do przystosowania się, aby zapewnić dobre wyniki w różnych obliczach przyszłości. Praca ta to podkreśla potrzebę bardziej zintegrowanego planowania i decydowania w celu podejmowania świadomych decyzji prowadzących do poprawy dla miast i ich obywateli. Praca ta rozpatruje miasta jako złożone systemy adaptacyjne i podkreśla, że planiści muszą rozpoznać miasta jako takie. Wyniki tych badań wyznaczają podstawę dla następnego, bardziej praktycznego etapu prac, mogącego dodatkowo ocenić skuteczność różnych metodologii i zbadać czynniki wpływające na ich sukces w perspektywie długoterminowej.

EINE KRITISCHE BEWERTUNG VON PLANUNGSANSÄTZEN FÜR EINE UNGEWISSE ZUKUNFT Aktuelle Megatrends wie die zunehmende Urbanisierung, Klimawandel und die digitale Transformation stellen eine große Herausforderung für Planer und Urban Designer dar, zukunftsgerechte Pläne und Lösungen zu entwickeln. Die zunehmende Komplexität und Unsicherheit unsere Zeit erfordert flexiblere und anpassungsfähigere Planungsansätze. Im Hinblick auf diese Entwicklung vergleicht diese Arbeit die Planungsmethodologien Szenarioplanung, Frameworkplanung und adaptive Planung, und untersucht deren Fähigkeit für eine uns unbekannte Zukunft zu planen. Eine Literaturanalyse zu den Themen Komplexität, Ungewissheit und Entscheidungsfindung stellt dabei die theoretische Grundlage für diese Bewertung dar. Die untersuchten Methodologien haben gemeinsam, dass sie epistemologische Unsicherheit berücksichtigen und unterschiedliche Ansätze explorieren sich erwarteten Ereignissen anzupassen. Der Szenarioplanungs-Ansatz spielt die Auswirkungen unterschiedlicher Planungsrichtungen durch, während Frameworkplanung eine insgesamt flexible Strategie mit Blick auf eine mögliche Zukunft zum Ziel hat. Adaptive Planung fokussiert sich auf eine anpassungsfähige Strategie mit Blick auf unterschiedliche Entwicklungen, wurde allerdings noch nicht im Kontext von Urban Design und Planung angewandt. Diese Arbeit zeigt auf, dass integrierte Planungs- und Handlungsansätze nötig sind um bedachte Entscheidungen treffen zu können – mit dem Ziel, relevantere Lösungen für Städte und deren Bewohner_innen zu gestalten. Städte dabei als komplexe Systeme wahrzunehmen ist dafür eine Voraussetzung. Weitere Forschung in diesem Bereich könnte stärker praktisch ausgerichtet Erfolgskriterien von Planungsansätzen untersuchen – mit Hinblick auf Faktoren, welche nachhaltig erfolgreiche Planung ermöglichen und unterstützen.

TABLE OF CONTENTS

1.

INTRODUCTION AND PROBLEM STATEMENT

1.1. CONTEXT

3

1.1.1. ACCELERATING CHANGE

4

1.2.

MOTIVATION AND OBJECTIVE OF THE THESIS

11

1.3.

RESEARCH QUESTION AND METHODOLOGY

12

1.4.

TERMS AND DEFINITIONS

14

1.5.

RESEARCH LIMITATIONS

16

THEORETICAL FOUNDATIONS 2.

COMPLEXITY AND COMPLEX SYSTEMS

2.1. DEFINITIONS

21

2.2.

COMPLEXITY IN URBAN DESIGN AND PLANNING

23

2.3.

RELATED THEORETICAL PROCESSES

26

2.3.1. SYSTEMS THINKING

26

2.3.2. CREATIVE PROBLEM-SOLVING JOURNEY

29

3. UNCERTAINTY 3.1. DEFINITION

37

3.1.1. TYPES OF UNCERTAINTY

38

3.1.2. LEVELS OF UNCERTAINTY

39

3.2.

UNCERTAINTY IN URBAN DESIGN AND PLANNING

42

4.

DECISION-MAKING AND HUMAN BEHAVIOR

4.1.

DECISION-MAKING IN URBAN DESIGN AND PLANNING

47

4.2.

HUMAN BEHAVIOR IN URBAN DESIGN AND PLANNING

48

4.2.1. COGNITIVE APPROACH

49

4.2.2. HEURISTIC APPROACH

50

4.3.

51

THE CHALLENGE OF PUBLIC PARTICIPATION

ASSESSMENT 5.

CURRENT AND EMERGENT PLANNING METHODOLOGIES

5.1.

ASSESSMENT CRITERIA

60

5.2.

METHODOLOGY 1: SCENARIO PLANNING

61

5.3.

METHODOLOGY 2: FRAMEWORK PLANNING

69

5.4.

METHODOLOGY 3: ADAPTIVE PLANNING

76

5.5.

COMPARATIVE ASSESSMENT

84

FINDINGS 6.

CONCLUSIONS AND DISCUSSION

6.1.

THEORETICAL IMPLICATIONS FOR IMPROVED PLANNING

95

6.2.

ISSUES AROUND EXISTING METHODOLOGIES

97

6.3.

AREAS FOR FURTHER RESEARCH AND INQUIRY

98

LIST OF FIGURES

101

LIST OF TABLES

102

REFERENCES 102 APPENDIX 1

108

APPENDIX 2

109

Chapter 01



1.1. CONTEXT We live in a fast paced and urbanizing world (Walker et al., 2003; Rowland, 2006; Castells, 2010). These rapid changes make it hard for researchers and planners to act reliably, limiting the effectiveness of common and traditional planning means and practices (ref: PwC, 2013 report1). Today’s planners must address high levels of complexity (de Roo and Hillier, 2016; Portugali, 2016). As complexity and interconnectedness increase, public departments that cannot work outside their assigned areas of focus (i.e. transportation, the environment, parks, etc.), tend to address planning problems in an oversimplified manner. Master plans have increasingly limited shelf life as the assumptions under which they are formulated do not remain valid over the plan period (Jain, 2012). This implies that our current models of planning are increasingly unable to plan for uncertain futures. We need new approaches that are more responsive (Haasnoot et al., 2013). This thesis is based on the premise that current planning methodologies are increasingly inadequate to address a rapidly changing world. It starts by examining the nature of complexity and complex systems as they apply to cities. Uncertainty, human behavior and decision-making are also explained as a basis for evaluating the adequacy of current and emergent planning methodologies.

1 Five megatrends dominating in the world change (PwC, 2013): (1) demographic and social change, (2) shift in global economic power, (3) accelerating urbanization, (4) climate change and resource scarcity, (5) technological breakthroughs.

3

1.1.1. accelerating change The intertwined trends of increasing migration, climate change, global economic power shifts and the rise of information and communications technology (derived from PwC’s five megatrends) amplify uncertainty and intensify complex conditions for urban design and planning. MIGRATION

The increase in internal and international migration is a challenge for development of cities (Coaffee and Lee, 2016). In 2015 over 250 million people were migrants in transit (living in countries other than their country of birth). The reasons of migration are diverse and include natural disasters and scarcity in food and water, economic reasons (i.e. labor migration), political reasons (i.e. political instability) and war (OHCHR, 2016; Sherwood, 2017). In 2015 65 million people have been displaced by war and persecution - the largest number since World War II (UN Refugees and Migrants, 2016). Many asylum seekers fled countries at war or conflicts, including Syria, Afghanistan or Eritrea and sought a refuge in Europe. Looking at the example of Germany, the so-called ‘refuge crisis’ with more than 475.000 people seeking refuge in 2015 and 745.000 in 2016 (BAMF, 2017), illustrates that planning authorities are often unable foresee and react to such unexpected events. Cities struggled to provide humanitarian accommodation facilities for the refugees. Growing inequities cause fear amongst the society and lead to the rise of the right-wing nationalist parties (i.e. entry of AfD into the German Bundestag in 2017 with 12.6% of the votes (Bundeswahlleiter.de, 2017)). This makes the challenge with migration even more dramatic. As the causes for migration are complex and likewise fueled from uncertainty and the five megatrends – the consequences of migration increase the need for adequate planning models in urban design even more – in order to cope with this rapid development. CLIMATE CHANGE

Earth’s climate has changed throughout its history – mostly due to small variations in Earth’s orbit. However, the frequency of extreme weather events has increased significantly during the last 60 years (Figure 1) – global warming being quoted as the main reason. According to most scientists, the cause for global warming is of human nature. The industrial activities that our modern civilization depends upon have raised carbon dioxide levels from 280 parts per million to 400 parts per million in the last 150 years (NASA, 2017) – trapping Earth’s radiated heat in the atmosphere.

4


The increase in human-produced greenhouse gases led to the increase of Earth’s temperatures over the past 50 years. The effects of global warming have been estimated to have cost the G20 countries $309 billion from 2005 till 2014 (Holodny, 2016), but the consequences of global warming go way further.

drought

COUNT

extreme temperature

flood

wildfire

120 100 80 60 40

2010

2000

1990

1980

1970

1970

0

1970

20

TIME

Figure 1: The frequencies of extreme weather events. (adapted from Holodny, 2016; Data sources: EM-DAT; The CRED/OFDA International Disaster Database)

Sea-level rise i.e. is a major challenge for human settlements. In the year 2000, 634 Million people have been living in Low Elevation Coastal Zones (zones defined as the area along the cost that is less than 10 meters above sea level). These zones cover 2% of the world’s land area but contain 13% of the world’s urban population with the least developed countries holding a higher share of their population living in the zone than OECD countries (Mcgranahan et al., 2007). Whereas wealthier coastal settlements are characterized by a good infrastructure, levees and early warning systems, poorer communities are more vulnerable to extreme weather events (Figure 2). The estimates of global sea-level rise indicate that by 2080, millions of people would experience flood and the fact that current population is moving towards coasts poses an even bigger challenge. McGranahan, Balk and Anderson conclude that “More appropriate measures are sorely needed, and the earlier the better” (Mcgranahan et al., 2007, p. 20).

5

Urban extents by population size, 2000 5K - 100K BEIJING

100K - 500K TIANJIN

500K - 1M 1 - 5M 5M+ Low Elevation Coastal Zone

SHANGHAI

0

100

200km

Figure 2: Yellow Sea coastal region. (reprinted from Mcgranahan et al., 2007)

GLOBAL ECONOMIC POWER SHIFTS

The restructuring of the global economy is giving rise to new conditions where global markets are even more intertwined. In 2008, emerging economies’ share of the world total GDP (gross domestic product) based on purchasing power parity became larger than the share of developed economies (IMF, 2017b). In 2011 they already accounted for over half of the global consumption of most commodities and 82% of mobile phone subscriptions (Figure 3), (The Economist Online, 2011). Emerging economies (current GDP growth of 4.6% vs. 2.25% of developed economies (IMF, 2017a)) are transitioning from labor and production to consumption-oriented societies. Although forecasts of growth and population distribution are being communicated as unquestionable, governments as well as businesses are feeling a high level of uncertainty with the current economic development. Unpredictable events such as the 2008 economic crisis as well as the United Kingdom’s decision to leave the European Union in 2016 have left their marks and according to the Global CEO Survey 2017, 83% of CEOs (chief executive officers) are concerned about uncertain economic growth, followed by their concern about over-regulation, geopolitical uncertainty

6


and the speed of technological change (Chesley and Crow, 2017). Business, planning institutions and governments will have to rethink opportunities as well as risks. The global shift in economic power therefore will not only require more agile and accountable approaches from businesses and government institutions, but also from urban designers.

Economies’ share of world GDP, % At market exchange rates

Emerging economies’ world share 2010, % 0 100

25

50

75

100

Population Mobile-phone subs.

FORECAST 90

Forex reserves

80

Copper consumption

70

Motor-vehicle sales

Steel consumption

developed

CO 2 emission

Oil consuption 60

GDP at PPP* Inward FDI**

50

Exports Fixed investments

40

Import Retail sales

30

GDP at market rates

20

Consumer spending

Stockmarket cap. emerging

Outward FDI** 10

Financial assets Fortune Global 500

2020

2015

2010

2005

2000

1995

1990

0

Public-sector debt

* Purchasing-power parity ** Foreign direct investment

Figure 3: Global balance of developed and emerging economies, 2011. (adapted from The Economist Online, 2011; Data sources: AT Kearney, Bloomberg, BP, dotMobi, Fortune, IMF, UBS, World Bank, World Steel Association, WTO)

7

RISE OF INFORMATION AND COMMUNICATIONS TECHNOLOGY

Since the 20th century the amount of innovations has been increasing exponentially (Figure 4). Today change happens faster than ever before (Lee, 2013), in particular because of digitization. With currently 2.6 billion smartphone users (36% of the world’s population) in 2017 (Ericsson, 2017), the world is more connected than ever. This results in a great amount of data collected. From the beginning of 2014 till the end of 2015 more data has been created than in the previous history of human race (Marr, 2015). Those amounts of data are still raising and enable a completely new way of decision-making but also come with risks.

120

100 TECHNOLOGICAL ADVANCEMENT cumulative number of significant inventions

80

60

40

20

2400 BC

500 BC

0

1000

1200

1600

2000

TIME

Figure 4: The accelerating growth of technology. (adapted from Lee, 2013)

During the last decades, advancements in the area information and communications technology have disrupted industries and deeply transformed our lives – a trend which is ongoing and likely to accelerate. Up to date, two companies had particularly large influence on cities: Uber and Airbnb. Uber is a global transportation technology company which was launched in 2008. It offers a comfortable way of ordering a ride via it’s app – and enables private persons

8


to register as drivers and provide their services to those seeking a ride. Uber quickly gained enormous traction, (Blyston, 2017) and today operates in more than 630 cities worldwide (Uber, no date). The company’s success hasn’t been left without critiques i.e. regarding the exploitation of the drivers which are being denied many of the social benefits that registered taxi drivers enjoy. The effects of this on-demand economy caused a range of protests in many cities, including San Francisco, London or Berlin (Blyston, 2017). In August 2017 the company launched Uber Movement – a product aiming at city officials, planners and policy-makers. It provides anonymized mobility data extracted from over two billion Uber trips to inform better planning and decision-making about infrastructure and businesses (Uber, 2017). The data, which is currently available for a few cities including Bogotá, Boston, Manila, Sydney and Washington, D.C (Figure 5), shows i.e. travel times broken down by time of day of week, aim at improving city infrastructure and mitigate traffic congestion. Although the data is available for download under Creative Commons licenses, Uber’s motives behind the project are not altruistic – improvements in city infrastructure ultimately will bring improvements for Uber’s drivers and customers (Geospatial World, 2017).

Figure 5: Uber Movement, Data for Boston. (reprinted from Geospatial World, 2017)

9

It is without doubt, that those technology giants that once started as start-ups have discovered needs of their customers which established companies didn’t (Brooke, 2016). However, due to the possibility to reach millions of people through the internet, the negative consequences of such disruptive innovations also often lead to oversimplified reactions. In its early days, the goal of Airbnb’s founders was just “to make a few bucks” (Carson, 2016), listing short-term stays on air mattress for those who were unable to pay for or find a hotel room in a saturated market (McCann, 2015). Today, the online marketplace for homestays is active in more than 191 countries with over 3 million listings on its platform (Airbnb, no date) and a current valuation of $30 billion, more than the current market capitalization of Marriot International, the world’s largest hotel chain (Morris, 2016). Airbnb acts as a broker and receives commissions from both guests and hosts with every booking – today it’s main revenue coming from hosts offering multiple units (Brennan, 2011) and running hotel-like establishments. In 2016, the platform’s revenue from those hosts reached 1.8 billion in the US market - generating the main part of the platform’s income (CBRE, 2017). In San Francisco (Booth, 2015) and Berlin (Oltermann, 2016), protesters accused Airbnb of contributing to rising rents and displacements, as landlords keep properties off the longer-term rental market aiming for higher profit with short-term housing (Figure 6).

Figure 6: An Anti Airbnb Campaign in Berlin in 2016. (reprinted from Heureka Online, 2016)

10


Students from the group airbnbvsberlin.de analyzed rental data retrieved through Airbnb’s API (application programming interface) in 2015 and found that 10% of hosts offering rooms or flats in Berlin offer more than one unit, some hosts up to 44. In Mai 2014 the government of Berlin introduced the ‘Zweckentfremdungsverbot’, a law fining people who let more than 50% of their apartment on a short-term basis without a permit from the city of Berlin (Krex, 2016). However, other factors influencing on rising rents, gentrification and displacements must be considered carefully, in order to not fall for oversimplified cause-effect thinking or focusing only on symptoms instead of actual reasons (Holmes, 2015). In the above examples planning has been limited to the ability to respond only to the emerged issues. It can be concluded, that in the meantime, further consequences of new situations emerge in a constant, dynamic process. The far-reaching consequences of disruptive innovations – may they be positive or negative therefore require adaptive and human-centered approaches. Moreover, it means that those approaches have to fit the problem’s context and depend on its setting, and not be based on universal formulas and believes.

1.2. MOTIVATION AND OBJECTIVE OF THE THESIS Urban design and planning as a profession is increasingly affected by growing levels of complexity and uncertainty, challenging the planners’ ability to address them. This thesis is based on the hypothesis, that current planning is increasingly unable to deal with such conditions. Such views on traditional planning approaches are gaining popularity in the scientific discourse. During the 2015 AESOP (Association of European Schools of Planning) conference in Prague, Gert de Roo of the University of Groningen questioned traditional means of planning and called for more versatile planning methodologies, addressing uncertainty and complexity (Flint, 2015). Arun Jain states, that nowadays planning is often based on three pillars: (1) planning advocacy, (2) public participation, and (3) use of simulations.

Planning advocacy describes that planners seek to represent the interests of various groups within society. Its selective character focuses mostly on the stronger more convincing ideas of experts and planners with more power. This is not directly reflecting the social issues needed to be considered. Public participation gives the public more deliberative tools to contribute in planning. The democratization of planning processes shifts the focus closer to soft social issues, but misses the holistic picture of concerns needed to be discussed. Finally, simulations show trends

11

in the population growth, but those are strictly valid only for the assumptions behind certain simulation (Jain, A., personal communication, November 7, 2017). Pursuing these three forces results in a rigid frame how we plan, and is far away from the ideals. It misses the flexibility needed to quickly but responsibly react to ongoing changes (Jain, A., personal communication, November 7, 2017). Consequently, the author states the hypothesis that current planners are often unable to capture and cope with complexity. The effects of accelerating change or disruptive innovations such as Airbnb and Uber however, require of urban designers and planners taking responsive and informed decisions. Currently, plans are often schematic and decisions taken ad hoc which might lead to solutions that no longer address the latest circumstances. Considering these problems, it seems to be the right moment to evaluate methodologies of long-term planning addressing uncertainty. The motivation and purpose of this thesis is to explore the strengths and limitations of selected methodologies. This aims at recommendations for better and more relevant planning approaches for uncertain futures. While it is not certain planning can keep up with the pace of change, the author feels an obligation to explore how we might anticipate, plan and act more responsively. Comparing existing and emergent methodologies should help to identify possible new directions for action and further inquiry.

1.3. RESEARCH QUESTION AND METHODOLOGY This thesis in an interdisciplinary research that looks at the bigger and general picture of planning while seeking pragmatic implications on urban design and planning for the futures. It adopts a qualitative evaluation research approach to assess selected planning methodologies (Kelly, 2008). This approach is undertaken by defining objectives and actions (Johannesson and Perjons, 2014), in order to answer related research question:

What is the ability of existing planning methodologies to address uncertain futures in urban design and planning?

This thesis is divided into three parts (Figure 7): a literature reviews on complexity, uncertainty and decision-making, an assessment of planning methodologies and recommendations for more responsive planning.

12


The literature review on complexity and uncertainty brings together existing findings and reveals implications for improved urban design and planning practices. The review of decision-making and human behavior in chapter 4 gives additional insights on the irrationality of human choices affecting public participation and planning methodologies. The analysis of current and emergent planning methodologies in chapter 5 reviews published theoretical discussions and complements them with practical examples. Each methodology is described according to developed assessment criteria. This aims at identifying their values and capacities to anticipate uncertain futures. Finally, the discussion and conclusions in chapter 6 are leading to delineating areas needing further research for improved planning directions.

PROBLEM STATEMENT trends and amplifiers of change planning inadequacy to adress a rapidly changing world

ch. 2 and 3 literature review on complexity and uncertainty ch. 4 literature review on decision-making and human behavior

ch. 5 assessment of current and emergent planning methodologies

ch. 6 discussion and speculations for adaptive and responsive planning

ACTIONS

theoretical implications for improved urban design and planning

values and capacities of existing methodologies

areas for further inquiry into more responsive planning

GOALS

Figure 7: Research design. (author’s own representation)

13

1.4. TERMS AND DEFINITIONS Across different research papers and journal articles, researchers use interchangeably several terms in different contexts and meanings. In order to avert misunderstanding, this section presents essential vocabulary used in this thesis.

adaptation – a “process of change by which an organism or [system]

becomes better suited to its environment” (Oxford Dictionaries, no date a) adaptive policy – a policy able to “adapt to changing (unforeseen) future

conditions” (Walker, Haasnoot and Kwakkel, 2013, p. 956) approach – an assumption-based general way of seeing and thinking,

presented in a set of steps toward the accomplishment of a particular purpose. It reflects a collection of theories and principles that shape the researcher point-of-view. (Merriam Webster Online, no date a) behavioral studies – “a branch of science (such as psychology, sociology,

or anthropology) that deals primarily with human action and often seeks to generalize about human behavior in society” (Merriam Webster Online, no date b) business as usual – an ongoing and unchanging state of affairs despite

difficulties or disturbances. chaos – “the property of a complex system whose behaviour is so

unpredictable as to appear random, owing to great sensitivity to small changes in conditions.” (Oxford Dictionaries, no date b) cognition – “the mental action or process of acquiring knowledge and

understanding through thought, experience, and the senses” (Oxford Dictionaries, no date c) computational model – also used as computer assisted model; a computer

based mathematical representation of a real situation or thing created in order to understand the behavior of complex phenomena through computer simulations or experiments. (Business Dictionary, no date a) disruptive innovation – the process of converting an idea into a good,

service or product generating value and displacing the traditional or established market (Christensen, no date).

14


emergence – a process in which larger parts arise from interactions among smaller parts of a system equilibrium – “a state of the urban system which is balanced and

unchanging.” (Batty, 2009) feedback – a process in which the output of a systems comes back as input

with positive or negative effects (Batty, 2009); an information useful for “(…) modification or control of a process or system by its results or effects” (Oxford Dictionaries, no date d) framework – a basic clear structure for cumulative and strategic

improvements (Jain, 2012) iteration – act of repeating a process to create a range of outcomes or to

achieve better outcomes closer to a desired goal or target (Merriam Webster Online, no date c) mental model – a representation of someone’s thought process. (Business

Dictionary, no date b) method – a systematic and specific procedure for accomplishing a particular

purpose. It is normally accompanied by a definite plan outlining the procedure. (Merriam Webster Online, no date d) methodology – “a set of methods used in a particular area of interests.”

(Merriam Webster Online, no date e) model – a representation of a situation, thing or process. (Merriam Webster

Online, no date f) plan – detailed proposal of doing or achieving something, defined by

structured actions toward set goals (Merriam Webster Online, no date g), used both in public and private sector policy – “broad statement of purpose and process for addressing a

particular social, economic or environmental issue (…) implemented via policy instruments such as regulatory (for example, laws and regulations); economic (for example, taxes, subsidies); expenditure (for example, research and development, education and awareness, targeted projects and programs); and institutional instruments (for example, sector strategies).” (Swanson et al., 2009, pp. 13–14)

15

robust policy – a policy able to “perform satisfactorily under a wide variety

of futures” (Walker, Haasnoot and Kwakkel, 2013, p. 956) self-organization – a process in which some form, structure or pattern

emerge from local interactions between initially disordered parts of a systems. (Boonstra, 2015) simulation – imitation of a process or situation, i.e. “(…) generating synthetic

populations from data which is collated from several sources.” (Batty, 2009) tool – a device or implement, used in practice to accomplish a task, i.e.

software (Merriam Webster Online, no date h) urban system – “a city represented as a set of interacting subsystems or

their elements.” (Batty, 2009)

1.5. RESEARCH LIMITATIONS Because the topic of uncertainty and complexity embraces a big range of planning issues, this thesis takes a much more targeted approach with a smaller scope. This study is limited in the following ways.

Only a few planning methodologies are compared to provide an early indication of the trade-offs between current and emergent methodologies. This work has been kept theoretical and did not include interviews that could further illustrate practical examples of planning dilemmas. Only documented research has been used, and relied upon. This thesis did not not analyze computer models and simulations although it does assume they are used in various methodologies. It is a preliminary inquiry into a complicated and emerging planning concern.

16

THEORETICAL FOUNDATIONS

Chapter 02



2.1. DEFINITIONS Cities are the main subject of urban design and planning (Madanipour, 1997) and have been meaningful examples of systems since the emergence of system sciences (Batty, 2010). Our understanding of cities as systems started in the 1950’s with the beginning of general system theory and has evolved over time (Batty, 2010). Systems theory was shaped by biologist Ludwig von Bertalanffy, who conceived systems as “(...) having subsystems tied together by interactions, thus invoking the idea of a network, but recursively ordered invoking the idea of hierarchy” (Batty, 2010, p. 102).

Batty argues that this comprehension induced an equilibrial state of the system, kept in balanced and unchanging conditions. Following Batty’s arguments, a city - as a static and ordered system - could easily be controlled in case of change. The further development of systems theories led to the evolution of complexity theories and emphasized the complex behavior of some systems (Byrne and Callaghan, 2014). Due to the scope of this work, a general approximation of complexity theory will be referenced, although acknowledging that there is a variety of systems and complexity theories present in the academic discourse. Unlike general systems theory, complexity theories propose an understanding of cities as complex systems. Those theories conceive system as “(...) a set of objects together with relationships between the objects and between their attributes” (Hall & Fagen 1956, p. 8 in Boonstra, 2015, p. 51).

Such systems are open, with large amounts of interacting components (subsystems) whose behavior is non-linear and hard to control. This means that their activity cannot be described as a summation of the activity of individual components (Byrne and Callaghan, 2014).

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Imagining a city as a complex system, Ackoff describes three properties which such a system exhibits: “(1) Each part affects the behavior or properties of the whole, (2) The properties and behavior of each element depend on the properties and behavior of the whole (3) A combination of parts, or subsystem has the same properties as the parts.” (Ackoff, 1974)

Ackoff emphasizes, that therefore a deconstruction of a system into its parts is impossible. Unlike analytical thinking, which brakes the whole into parts and explains the behavior of the whole by the behavior of its parts, the behavior of a system depends on the information contained in the relationships of its parts (Ackoff, 1974). The appropriation of complexity theories applied to cities shifts the focus towards complexity of complex systems (Grobman, 2005). Complexity is an amorphous term with no clear definition. For simplicity, this thesis communicates a set of properties complex systems exhibit. Amongst other researchers, these properties has been elaborated by Byrne and Callaghan (2014).

UNPREDICTABILITY: complex systems are dynamic and evolve over time (Sengupta, Rauws and de Roo, 2016) given all initial conditions, the system cannot be controlled in light of a change, thus the outcome is uncertain and open-ended (de Roo and Hillier, 2016) the dynamic state of a complex system relates to a situation far from equilibrium, that can change rapidly and create temporal conditions such states, described as tipping points, are characterized as small, critical changes to the system causing a large-scale, often irreversible change (Boonstra, 2015)

CHAOS: components of complex systems are in a constant process of interaction and hereby cause irreversible changes to the themselves and the whole system (Boonstra, 2015) hierarchical orderings within the system allow interaction between different levels through bottom-up and top-down influences (Crawford, 2016)

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ADAPTATION AND CO-EVOLUTION: a system reacts to external factors a system’s elements change due to evolution of the whole system a system adapts its attributes due to positive and negative feedback loops – mechanisms of variable results of change (Boonstra, 2015)

EMERGENCE AND SELF-ORGANIZATION: a system reacts to the internal dynamics interactions between a system’s components lead to emergence of new complex patterns and structures in a self-organized, bottom-up way (Boonstra, 2015)

Concluding, this thesis continues with the view on cities as large complex systems built of many subsystems, which are interrelated and interact with each other. A more practical oriented view on the complexity of cities is represented by the multilayered, interconnected construction of cities, visible in their form, function and structure. All the visual aspects of a city, including physical components such as buildings, infrastructure, landscape but also people, have their connecting nodes and are hard to think of separately. This is depicted by the structure of a city constituting of interactions and relations of its components in space-time. The third facet, function, relates to the manner, in which interactions and relations happen. The compound interactions of cites’ elements bring dynamism to the system and generate constant modifications (Perdicoulis, 2013). Ramalingam et al. (2008) state, that planning approaches have to consider unpredictable and dynamic properties of complex systems in order to lead to effective outcomes. They argue, that the advanced comprehension of complex systems and their characteristics enable a better understanding of cities and their behavior.

2.2. COMPLEXITY IN URBAN DESIGN AND PLANNING Considering the characteristics of complex systems, Christopher Alexander (1965) emphasizes the need of addressing complexity in urban planning. He claims that “a city is not a tree” formed out of the collection of sets, but a semi-lattice (Figure 8). The latter consists of components which may interact chaotically on different hierarchical levels. This structure is much more complex. In his reflections,

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he juxtaposes designed and controlled artificial cities (i.e. Chandigarh) representing a tree structure and organically established natural cities (i.e. Manhattan) with a semi-lattice. Christopher argues, that their semi-lattice structure makes those cities more livable and human-centered. He also states that top-down planned cities lack the overlapping of patterns or functions of their components in their simplified structures. He concludes, that this lack eventually leads to a disassociation of cities’ internal elements and in the end, their destruction.

TREE STRUTURE

SEMILATTICE STRUTURE

Figure 8: Tree vs. Semi-lattice structure. (reprinted from Alexander, 1965)

Dealing with the complexity of cities is a challenge. The large number of components of which cities are built and the interactions between them is challenging our comprehension of them. Ackoff (1974) calls this condition a mess, that leads to a general dissatisfaction about the understanding of how those systems work. The recognition of complexity in urban systems led him to the conclusion, that the whole big picture has to be considered when dealing with problems. He sees an opportunity in planning to stage a strategic path to achieve the designed idealized future, and not only to look for simplistic solutions to individual problems. Following this fashion, Mattern states that “a city is not a computer” and argues that under current trends of disruptive innovations changing the cities faces, we need to be careful with technological optimization. She warns of a danger of conditioning the cities’ improvement on “information processing” and dependence on data, which could lead to unwanted, unanticipated outcomes (Mattern, 2017). The broad spectrum of urban design and planning fields affects a wide range of stakeholders and planners, bringing into play objective and rational as well as subjective and expressive points-of-view (Madanipour, 1997). This concerns a mix of scales, visual, spatial and social aspects, and public or private sectors. All these aspects need to be linked to proceed holistically and coherently. In addition, the urban profession is interested both in regulations as well as in processes of spatial transformations of the urban condition (Madanipour, 1997). Because of those dualities, planning approaches embrace complexity in various levels and directions.

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Planning approaches reflecting complexity with its properties range from instrumental towards communicative approaches (Figure 9). As stated by de Roo and Hillier (2016) top-down planning ignores complexity and tries to control the urban system in an ordered and dominative way. It relies on supposed hard facts and pays no attention to the actual needs of potential stakeholders. With the acknowledgement of complexity, uncertainty starts playing a significant role. This makes the planning process a complex inquiry and requires the inclusion of a whole network of stakeholders considering both facts and values and seeking a consensus between various interests (de Roo and Hillier, 2016).

INSTRUMENTAL PLANNING

‘CORPORATE’ PLANNING

SCENARIO PLANNING

ACTOR CONSULTING

PARTICIPATIVE PLANNING

COMMUNICATIVE ACTION

TECHNICAL PLANNING

COMMUNICATIVE PLANNING

certainty facts order closed blue print

uncertainty values very complex open network

complex semi open scenario

Figure 9: Examples of planning approaches in a spectrum. (adapted from Zuidema and de Roo, 2004; de Roo and Hillier, 2016; author’s own representation)

Both, the understanding of a city as an open-ended system and the scientific knowledge on complex systems foster the planners’ ability to develop more reasonable actions leading to informed decisions. Acknowledging cities as complex systems helps planners to consider facts and interests, which might not be obvious to them at first sight. Understanding the theories about systems and their properties leads to more reflected and holistic planning (understanding problems). This leads to the question, to what extend a plan or design needs to be detailed and tightly controlled and to what extend it needs to be flexible and adaptive.

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2.3. RELATED THEORETICAL PROCESSES Two theoretical processes grounded in systems theory have been selected as exemplary approaches relevant to planning in complex situations: systems thinking and creative problem-solving journey.

2.3.1. systems thinking Systems thinking is an approach that holistically understands and manages systems, and has been emerging since the 1940’s. It developed in face of the decease of the Machine Age, in which analytical thinking dealt with independent sets of variables of mindless machines’ systems. This discipline was based on breaking the whole into parts and solving the problem of its parts to solve the problem of the whole (Ackoff, 1974). The acknowledgement of the complexity of systems required a new paradigm of holistic thinking to deal with interdependent sets of variables of multi-minded sociocultural systems (Gharajedaghi, 2011). However, after almost 50 years since the emergence of systems thinking, it seems still not very popular in planning (Orr, 2014). ENVIRONMENT boundary

OPEN SYSTEM

Figure 10: System, Boundary and Environment. (adapted from Gharajedaghi, 2011; author’s own representation)

Systems thinking sets and deploys the properties of a system based on a system’s dynamics. These insights show constraints and opportunities, and give the ability to influence the effectiveness of the system’s behavior. Gharajedaghi (2011) distinguishes five general principles of a system: (1) openness, (2) purposefulness, (3) multi-dimensionality, (4) emergent property and (5) counter-intuitiveness,

all interacting together. The concept of openness states, that the behavior of a system can be only understood in the context of its environment. The boundary of a system is a subjective construct defined by the interests of the planner/participating actors, and is hard to determine objectively. All things which are not included within

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the system’s boundaries are in the system’s environment (Figure 10). A purposeful system can learn and adapt under changing conditions. Those changes may have opposing tendencies, making a system multidimensional while the outcome of interactions and ongoing changes of a system indicate its emergent property. Lastly, counter-intuitive behavior describes, that the actions taken to reach a goal may lead to an opposite outcome or even failure (Gharajedaghi, 2011). Burge states that a system can be better understood by using visual tools such as system maps and a range of diagrams. A system map tries to model a system’s structure (components, boundaries and it’s environment), while influence diagrams and multiple cause diagrams add further depth to the map such as interactions and changes that happen within the system (Burge, 2013). System maps or visual representations of a system however should never be seen as an accurate representation of reality but rather as a simplification. Nevertheless, such tools help to make complex issues visible and support a shared understanding in planning and other domains. Utilizing systems thinking to examine and understand a system enables planners to outline possible consequences of actions taken or planned and to better understand how things unfold over time (Senge, 1990). Ackoff’s planning process model for the future (Figure 11) is based on this comprehension with five delineated steps: (1) action, (2) transformation, (3) outcome, (4) feedback loop and (5) goals (Ackoff, 1974).

MEANS / RECOURCES How to get there? What is needed?

ACTION

TRANSFORMATION

OUTCOME

GOALS

FEEDBACK LOOP experimentations implementation and control Figure 11: Systems thinking design process model. (based on Ackoff, 1974; author’s own representation)

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This process model emphasizes the importance of the goal definition. Ackoff (1974) suggests that designing for an idealized future as an ultimate state can to be enabled by short, intermediate and long-term actions (inputs). In this learning process the efficiency of the actions taken is increasing with every iteration. The insights gained from the outcomes from the transformation step generate feedback and direct the overall process to achieve the ultimate goals. Feedback for one action enables the planner to recognize when the system is deviating from the desired path and to take corrective actions. While a positive feedback loops increases the deviations of the desired future a negative feedback loop enables the system to counterbalance the deviations. This process is an iterative inquiry with iterations advancing the understanding of complexity and facilitating the validation of assumptions. A primary element of this process is experimentation and learning-by-doing, including also learning from error. Concerning this idea, Ackoff (1974) states implications on planning approaches and distinguishes a typology of planning regarding the reaction to change (Figure 12): (1) inactivism, (2) reactivism, (3) preactivism and (4) interactivism.

The first two types are not interacting directly with the future and rather protect from changes. Preactive planning in its nature is problem-oriented and focuses on forecasts and statistical predictions. It tries to prepare to and deal with problems

INACTIVISM

REACTIVISM unmaking previous changes

“do nothing” approach

returning to the past (“the good old times”)

keeping changes from being made

PREACTIVISM

predicting and preparing for the future

dissatisfaction with present conditions

dealing with problems before they become serious

designing desirable future to be achieved

optimizing current conditions to become better

idealizing the future

planning for the future by solving problems in a logic, scientific way exploiting opportunities through research and development

Figure 12: Planning approaches regarding change. (based on Ackoff, 1974; author’s own representation)

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INTERACTIVISM

planning the future by preventing threats and creating opportunities accelerating the future controling and redirecting it if needed


before they become serious. On the other hand, interactive planning defines goals that have a pivotal role to achieve ideal state. This puts emphasis on the means of experimentations that allow dynamic and adaptive planning. Knowing more about the system and its behavior fosters taking more informed decisions. This requires a deep understanding of a system’s initial condition followed by a range of interdisciplinary and collaborative actions between different stakeholders and sections, instead of “smart individuals” (Senge, 2011). The higher the complexity of a system, the harder it is to understand its behavior and thus the more uncertain the outcomes of planning become (Wysocki, 2014). This process is often used in engineering, ecology or biology and is not common in urban planning and design. However, the use of the systems thinking approach for planning promises the ability to anticipate change and avoid inaccuracies in the long-term. It aims at developing a mind-set capable of seeing connections, patterns, and structures, far beyond the political election periods or yearly budgeting. Applying systems thinking in the domain of urban design and planning shifts the focus from allegedly controlled and quantifiable elements toward people’s activities, community engagement and livability, all being unquantifiable aspects. Both, hard and soft problems of cities need to be seen as interrelated creating an organic whole. This approach helps to better organize the raw data describing cities, understand resource flows, improve planning and decision-making, and lead to greater realism and awareness on the real problems. However, as feedback plays an essential role in this process, the approach won’t lead to satisfying outcomes if the willingness to gather, react to and implement feedback is missing (Orr, 2014).

2.3.2. creative problem-solving journey Creative problem-solving journey is another planning process based on iterations and learning from feedback. Presented by Koberg and Bagnall (1974) this process aims to provide solutions for human needs by applying convergent and divergent modes of thinking (Figure 13). Divergent thinking facilitates a broad set of initial possibilities and multiplies options to create choices (Brown, 2009; Thoring and Müller, 2011). When analysing a situation, divergent thinking aims to create an understanding of multiple insights from different perspectives, while it aims at coming up with multiple alternatives when ideating. Convergent thinking aims at synthesising the various insights and decide on possible solutions.

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The creative problem-solving journey supports the creative behavior of planners by following unique characteristics which are the method’s foundation: “recognition (acknowledgement of a situation or thing), sympathy (caring about situation or thing), and empathy (deep personal sharing of the attributes of the situation or thing).” (Koberg and Bagnall, 1974, p. 15)

These principles enable the planner/designer to immerse into a situation or a person and gain a deeper and more holistic understanding, focusing on the big picture instead of small fragments.

Exploration of the problem space

v di

g er

e

co

nv

er

Exploration of the solution space

co ge

?

!

d

iv

er

ge

nv

er

ge

Iterative alignment of both spaces

Figure 13: Divergent and convergent thinking. (Lindberg, 2009)

Koberg and Bagnall argue that the process of creative problem-solving is “(...) synonymous with design process; a sequence of unique actions leading to the realization of some aim or intention.” (Koberg and Bagnall, 1974, p. 10)

In their approach, they expand the formerly common two stages problem-solving process mainly based on convergent thinking into the following seven steps: (1) (2) (4) (6)

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accept situation – enthusiastically and empathically taking the challenge, analyze – open mindedly researching, (3), define – identifying key issues, ideate – looking for alternative options, (5) select – making decision(s), implement – taking the actions, and (7) evaluate – assessing the outcome.


This process starts with convergent consensus on the challenge and goals. Each following step is represented by interchangeably divergent or convergent thinking that informs the problem definition and facilitate the synthesis. Koberg and Bagnall (1974) illustrate different pathways of the process to reach the goals: (1) linear, (2) circular, (3) feedback-oriented, (4) branching, and (5) natural (organic).

Although most of planners would expect the process to be linear, it is rarely so straight-forwarded (Figure 14). As already argued in the first chapter, the reality is much messier, complex and ambiguous. The process hardly ever fits expectations illustrated by the linearity. Dealing with behavior of complex systems requires the preparedness for unexpected events represented in the natural process path (D.school, 2017).

EXPECTATIONS

REALITY

Figure 14: Expectations vs. Reality – linear and organic paths of a process. (adapted from D.school, 2017)

The approach by Koberg and Bagnall (1974) has been further developed and popularized by Stanford’s d.school as Design Thinking (D.school, 2017). Nowadays, this concept is intensely deployed to complex problems in uncertain contexts (Brown, 2008) and it’s popularity in business contexts has been growing intensely during the last decade, as if it was “a panacea for the economy” (Johansson-Sköldberg, Woodilla and Çetinkaya, 2013, p. 1).

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Today, planning and designing cities is considered as a creative process. In architectural design, planners and architects use the process of creative problemsolving, but internally and unwarily, making it implicit. Philosopher and urban planner Donald Schön described this practice as an “(...) intuitive processes which some practitioners do bring to situations of uncertainty, instability, uniqueness, and value conflict” (Cross, 2001).

Applying the creative problem-solving process in urban design and planning leads to more holistic analysis by considering the perspective of multiple stakeholders (phases 1,2,3) and reduces risk by exploring and testing multiple solutions before actually implementing them (phases 4,5,6,7). According to Cross (2006) “Designers attempt to solve ill-defined problems by proposing and trying solutions rather than by seeking all possible information” (Meyer, 2015).

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SUMMARY

Cities are adaptive, socio-technical systems, undergoing dynamic changes over time (Portugali, 2016). The problems they face are highly complex and composed of interconnected relationships (Voulvoulis, 2016). Planning for such conditions needs to consider holistic problem analyses, iterative processes and seek and act on feedback. This means the departure from top-down planning towards communicative and collaborative actions between planners, stakeholders and public participants. Such planning needs to reflect the properties of a complex adaptive system, i.e. represented in a decentralized, local interventions fostering self-organization (de Roo and Hillier, 2016). Systems thinking aims at a better comprehension of a system’s properties, thus leading to a more accurate problem definition (Ackoff, 1974). Having knowledge about systems supports such an approach and enables planners to pay attention to relationships and possible impacts of actions taken, which could have been overseen in simplistic convergent decision-making. Moreover, the iteration in creative problem-solving is leading to more accurate solutions (Koberg and Bagnall, 1974). It also offers a model of interchangeable convergent and divergent thinking, revealing insights from different perspective and exploring different solutions. This makes it an organic journey, giving the planners the ability to rethink and take corrective action at each moment. Success in planning for complex adaptive systems depends to a great extent on our ability to understand complex situations and our willingness to consider uncertain change and have an openness for experimentation. Normal planning processes do not lend themselves to this mix. Its therefore important to decide if we want to interact with the future and be part of natural processes of change or if we should remain pragmatic and simply react when we are more certain of what needs to be addressed. These decisions impact how much we test and experiment. They are driven by how much flexibility and adaptation we consider necessary to be incorporated into a plan.

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Chapter 03

UNCERTAINTY

UNCERTAINTY

The behavior of a city - a complex urban system - is hard to predict. This makes uncertainty an essential property when planning or designing for a city. The means of dealing with uncertainty have a direct influence on the success of planning approaches. This chapter aims to provide an understanding of uncertainty and describes possible implications on planning.

3.1. DEFINITION Walker, Lempert and Kwakkel (2013, p. 1) state that uncertainty relates to as a “situation with limited knowledge about future, past or current events”

and has already been explored in ancient times, i.e. in the philosophy of ancient Greeks (Funtowicz and Ravetz, 1990; Walker, Lempert and Kwakkel, 2013). The contemporary understanding of uncertainty began in the early 20th century. In 1921 the economist Frank H. Knight pioneered in formalizing the difference between risk and uncertainty. He stated that it is appropriate to speak of risk in situations, in which the outcome is unknown but the information is accurate enough to calculate and control the probability of the outcome. On the other hand, uncertainty is considered when all information cannot be known, thus the outcome is incalculable, uncontrollable and unpredictable (Knight, 1921; Walker, Lempert and Kwakkel, 2013). Uncertainty has been widely researched in different fields and has taken different meanings (Agusdinata, 2008; Walker, Lempert and Kwakkel, 2013). Its study is found in scientific disciplines such as: statistics (fuzzy set theory, probability), sociology and psychology (uncertainty reduction theory), engineering (design safety), information science (artificial intelligence), finance and entrepreneurship (effectuation), metrology (measurement errors), policy-making (integrated assessment) or ecology (environmental assessment). However, this understanding of uncertainty is mostly considered quantitatively. Contrarily, uncertainty in urban design and planning is mostly qualitative, and its comprehension is still developing (Portugali, 2016). However, the discipline of urban design could use the knowledge from other disciplines to better understand and address uncertainty. The existing methodologies for urban planning considering uncertainty are the subject of their examination in chapter 5. For this reason, following theoretical considerations of uncertainty are crucial.

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3.1.1. types of uncertainty Uncertainty is not simply characterized by a lack of information. Funtowicz and Ravetz (1990) argue that uncertainty is a situation of inadequate information, classified into three categories: (1) inexactness, (2) unreliability or (3) ignorance.

This inadequacy of information can be resulting from different types of uncertainty. Tannert, Elvers and Jandrig (2007) differentiate two fundamental forms of uncertainty (Figure 15): objective and subjective. The first one is of bigger importance in terms of design and planning, because it focuses on knowledge and facts instead of believes and ethics (Jain, 2017b). Following the distinction made by van Asselt & Rotmans (2000, p. 15) in their research in Integrated Assessment Modelling, two types of objective uncertainty can be categorized due to their sources.

UNCERTAINTY DUE TO A LACK OF KNOWLEDGE:

also known as epistemological or structural gaps or imperfection of knowledge that can be theoretically improved by further research (i.e. by reasoning or comparison) appears under the names of incompleteness of information or informative uncertainty UNCERTAINTY DUE TO VARIABILITY:

also known as ontological or aleatory uncertainty considers variability as an attribute of reality, it is also referred to as objective uncertainty, where the system/process can behave randomly without the influence is caused by non-linear behavior of complex systems appears also under the names of stochastic uncertainty, external uncertainty or random uncertainty.

Epistemological uncertainty refers to the missing information, multiplicity of divergent anticipated events and the inability to say in which direction the future will go. Ontological uncertainty puts the emphasis on the need of means to address and prepare for the variable and unanticipated changes as the future unfolds.

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UNCERTAINTY

UNCERTAINTY

objective

subjective

epistemological / structural

ontological / aleatory

moral

subjective

knowledge guided decision

quasi-rational decision

rule guided decision

intuition guided decision

Figure 15: Types of uncertainty. (adapted from Tannert, Elvers and Jandrig, 2007; authors own representation)

3.1.2. levels of uncertainty The types and sources of uncertainty have a direct influence on the level of uncertainty. Walker et al. (2013) states, that uncertainty can be divided into two extreme levels of uncertainty (complete certainty and total ignorance, that in fact not appear in the reality) and five intermediate levels. The five levels (Figure 16) initially adapted from economics (Courtney, Kirkland and Viguerie, 1997) has been extended by Walker et al. (2013) and are elaborated as follows. Each level of uncertainty refers to the type of uncertainty, its source, and the (dis) recognition of or the (un)willingness to deal with the uncertainty. It has a direct influence on the amount of possible futures considered. The three first levels focus on the the uncertainties that can be managed quantitatively and reduced by simple gathering of information. However, higher levels (especially levels four and five) refer to deep uncertainty, in which uncertainties cannot be measured. They can be only anticipated or unanticipated (Lyons and Davidson, 2016).

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The concept of deep uncertainty emerged during the last two decades as a response to the inability of representation of radical uncertainties in quantitative form in decision-making (Agusdinata, 2008). According to the definition by Hallegatte et al. “deep uncertainty is a situation in which analysts do not know or cannot agree on (1) models that relate key forces that shape the future, (2) probability distributions of key variables and parameters in these models, and/or (3) the value of alternative outcomes.“ (Hallegatte et al., 2012, p.2)

With the emergence of deep uncertainty, a need for new planning methods dealing with unquantifiable uncertainties has been recognized (Hallegatte et al., 2012; Walker, Lempert and Kwakkel, 2013).

COMPLETE CERTAINTY

alternative futures with probabilities

LEVEL 2

alternative futures with ranking

LEVEL 3

multiplicity of plausible futures

LEVEL 4

unknown future

LEVEL 5

diversity of opinions and behaviors societal randomness invention

inexactness ‘we roughly know’

DEEP UNCERTAINTY

natural randomness

LEVEL 1

LEVEL OF UNCERTAINTY

UNCERTAINTY DUE TO VARIABILLITY:

a clear enough future

indeterminacy ‘we do not know’

TOTAL IGNORANCE

Figure 16: Comparison of levels of uncertainty (Walker, Lempert and Kwakkel, 2013) and nature of uncertainty (van Asselt and Rotmans, 2000). (author’s own representation)

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UNCERTAINTY

5 LEVELS OF UNCERTAINTY (FIGURE 17) A

Level 1 – a clear enough future – A situation, in

which the absolute certainty is not available, but an estimated future (with sensitivities) can be predicted.

LEVEL 1

level 2 – alternative futures (with probabilities)

– A situation with uncertainties that can be described statistically; possible futures can be estimated in form of a trend-based single forecast or multiple forecasts with associated possibilities. alternative futures (with ranking) – A situation, in which several alternative futures can be estimated and ranked in terms of perceived likelihood. Those possible futures can be estimated in a set of trend-based scenarios with alternative assumptions about the future.

A1 A

A2

B

level 3 –

LEVEL 2

A+ B0

level 4 – multiplicity of plausible futures –

A situation, in which several alternative futures can be estimated but not ranked in terms of perceived likelihood. This can be due to a lack of knowledge or information about the mechanism and functionalities of the process or the inability to agree upon the ranking by decision-makers.

C-

LEVEL 3

E

level 5 – unknown future – A situation of the

highest recognized uncertainty under ignorance . In this situation, possible futures cannot be estimated due to unpredictable, surprising and mostly painful events. Such a state can be embraced by the mechanisms of adaptation to unanticipated events as they occur or are able to estimate as the future unfold.

Figure 17: Possible futures in levels of uncertainty. (adapted from Walker, Lempert and Kwakkel, 2013; author’s own representation)

A

B D

C

LEVEL 4

LEVEL 5

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3.2. UNCERTAINTY IN URBAN DESIGN AND PLANNING In urban design and planning, different methodologies include methods of dealing with uncertainty in various levels. This includes different treatments of uncertainty as following: (1) ignoring, (2) eliminating, (3) reducing, (4) coping with and (5) embracing (Wheaton, 2008).

For instance, a single master plan presents a rigid and static vision for the future working only for the assumptions behind it (Burckhardt, 1982). However, the need of approaches coping and embracing uncertainty has been recognized (Jain, 2017b). In the practice of urban planning, epistemological uncertainty is the kind of uncertainty which can be tackled primarily. Understanding where are possible gaps of information helps to incorporate necessary flexibilities and adaptabilities into a plan. This helps to act and react more agile and resilient over time (Root, Jones and Wild, 2015). It also requires a deep understanding on the initial state with the knowledge and evaluation of natural, human and economic resources and assets, but also planning instruments cities have. It highlights the significance of the collection, understanding and analysis of data into organized, contextual information for more informed decision-making (Jain, 2017a). However, next to epistemological uncertainty, the existence of ontological uncertainty challenges planners to address and prepare for variable natural and man-made conditions and events. These go beyond seasonalities and add to inherent uncertainty. According to van Asselt & Rotmans (2000), ontological uncertainty has five sources: (1) natural randomness; (2) value diversity; (3) behavioral variability; (4) societal randomness; (5) technological surprise.

Each of those sources can be further elaborated in this particular manifestations in existing urban conditions, that is (1) unforeseen or infrequent weather events; (2) differences and alterations of values and cognition, i.e. generational differences (Sturt and Nordstrom, 2016); (3) changes in lifestyles; (4) migration, governmental instability, economic crises; (5) influences of disruptive innovations on cities.

Accepting uncertainty and understanding how, where and when we are vulnerable improves the planning processes and their outcomes (Holmes, 2015). The representation of uncertainties in urban design and planning is difficult and constrains the planning process. Van Asselt & Rotmans (2000) argue that in

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UNCERTAINTY

well-understood systems or processes uncertainties can be quantified, i.e. for a simulation of population growth, and add up to reasonable projections. However, planners and decision-makers often mistake simulations as predictions. Simulations are only as good as the assumptions behind them. Consequently, a lot of policy, regulation and infrastructure investment is made assuming simulations will happen. This ends up being a costly mistake for cities that must use their shrinking resources carefully (Jain, 2012). Nevertheless, the most crucial uncertainties for urban design and planning remain qualitative (Portugali, 2011) and difficult to measure. SUMMARY

The unpredictable behavior of complex, open-ended systems such cities demands for ways of dealing with uncertainty (Walker, Haasnoot and Kwakkel, 2013). Uncertainty has always been a part of our world and as not a new concept in the planning domains (Walker, Lempert and Kwakkel, 2013). The accuracy of planning outcomes depends on our ability to address and embrace uncertainty in the planning process. Thus, long-range planning requires acknowledging the existence and possible influence of uncertain futures. The two different types of uncertainty considered in urban design and planning require different types of action. Epistemological uncertainty refers to our ability to take knowledge-guided decisions. It can be offset with additional information or addressed by actions that fill information gaps. Ontological uncertainty requires means to address variable and unanticipated changes as the future unfolds. Although we collect increasingly more data, most of the uncertainties in urban design and planning remain of qualitative nature (Portugali, 2016). Relying on forecasts and calculating the probability of possible outcomes might be sufficient if these efforts are put in proper perspective – i.e. as long as they are not seen as predictions. Planning based on incomplete knowledge should be approached as a staged and strategic path to more plausible futures (Wilkinson and Kupers, 2013) and deploy adaptation to constantly changing environments. The ability to cope with uncertainty and to understand the behavior of complex systems varies across planning professions. Conventional planning approaches rely on static methods of planning, and tend to view change as a direct consequence of actions. This is reflected in the approaches referring to levels 1 and 2 of uncertainty (Walker, Lempert and Kwakkel, 2013), and the use of statistical and predictive models for the future. However, this is at odds with the complex behavior of urban systems, for which we need to plan adaptively and communicatively. If we focus on the qualitative issues of what cities and their citizens need, then we have a better chance of addressing uncertain and rapidly evolving conditions.

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Chapter 04



Urban design and planning under uncertainty refer to the unpredictability and irrationality of behaviors and decision’s outcomes of both experts as well as the rest of the affected society. People’s choices, but also unforeseen behavior impact a plan’s performance and the success of spatial transformations. Change is considered for everyone as a stress factor and majority of people tend to avoid it (Thaler and Sunstein, 2008). They prefer to keep everything as it is or even try to go back to the “old good times” (Ackoff, 1974). For instance, many residents are afraid of changes in their neighborhoods and oppose those developments in so called NIMBYism (Not In My Back Yard) (Figure 18).

Figure 18: If not in your neighborhood, then where? (reprinted from Toronto Star)

Being aware of cognitive decision-making process in humans is crucial for improved outcomes of design and planning for change. Understanding human behavior aims at a better view of people’s problems, needs and wishes. The insights from behavioral studies reveal how this knowledge may improve planning approaches, in particularly these looking for solutions to uncertain futures (Thaler and Sunstein, 2008).

4.1. DECISION-MAKING IN URBAN DESIGN AND PLANNING Planning and decision-making are two interrelated and inseparable actions. The general definition of a planning process refers to the action of identifying goals and objectives and formulating plans to achieve them (Business Dictionary, no date c). This relates to a process of thinking before doing, which deals with looking for a solution on an abstract level. It demonstrates the investigation of alternative paths to reach the goals, on which planners and decision-makers need to agree upon. It is

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a rather long-term process, which may include short and intermediate term actions. It requires planners to control the plan’s performance and also take corrective actions. On the other hand, the process of decision-making can be defined as a thought process of making decisions. This is further complicated when dealing with uncertain conditions, but not impossible. Most significantly, this process includes the evaluation of the identified alternatives and finally the choice of the most preferable or plausible one (Simon et al., 1986; Power, 2017). The instrumental and top-down approach has been strongly criticized due to its centralized and aggregated decisive power leading to a lack of direct democracy throughout the process (Gualini, 2015). In this traditional approach, planning and decision-making are separated. In this setting, the decision-maker picks a plan from a range of alternatives prepared by the planners (Burckhardt, 1974). However, such technocratic visions incorporated into a single master plan rarely lead to adequate and effective solutions (Burckhardt, 1982). The critique of this approach shifted the center of attention toward process-oriented deliberative model of planning and more decentralized decision-making. However, people do not always make rational choices (Kaplan and Kaplan, 1982). It is reasonable to assume then that decision-making in planning is not always logical in its translation to action. Our ability and willingness to cope with uncertainty in complex situations is limited because of inherited governmental and fiscal constraints and our collective inability to assess and define the problem appropriately (Jain, 2017b).

4.2. HUMAN BEHAVIOR IN URBAN DESIGN AND PLANNING The art of the responses of individuals or groups to internal and external stimuli describes human behavior. These include every physical action and observable emotion of which some are consistent throughout the lifetime, and some are changing over time. Human behavior in the classical theory of economics was believed to be perfectly rational and logic. In fact, the behavioral studies in cognitive psychology reveals people’s decision-making is far from rational (Simon, 1959). This insight helps to better understand our actions when making decisions, planning as professionals or taking part in participatory planning as novices (Kaplan and Kaplan, 1982). The cognition of urban conditions depends on the developed mental models and the experience of urban space at different times (years, seasons, days, daytimes). Following Lynch’s idea, people engage in the city in way-finding and require to be able to recognize and organize urban elements forming them into a logical pattern.

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“In the process of way-finding, the strategic link is (...) the generalized mental picture of the exterior physical world that is held by an individual. This image is the product both of immediate sensation and of the memory of past experience, and it is used to interpret information and to guide action.” (Lynch, 1960, p. 4)

This cognitive image of a city everyone develops on their own and it may change over time. The limits and causes of human decision-making are elaborated as below. Thaler and Sunstein (2008) explain that our decisions processes rely on divergent perception and automatic and reflective cognition. This explains how humans tend to make irrational choices. Furthermore, the heuristic approach adds to that with biased rationality and the incompleteness or inadequacy of human knowledge (Kaplan and Kaplan, 1982; Campitelli, 2010).

4.2.1. cognitive approach Thaler and Sunstein (2008) distinguish between two systems of cognitive thinking in human decision-making. The first one, called an “automatic system”, refers to gut reaction, which is an uncontrolled and unconscious reflex in which we make decisions spontaneously. The second is called a “reflective system”. This refers to conscious thought, where decisions are made deliberately 2. The dependence on the automatic system leads to making decisions without ‘consulting’ them with the reflective one. Such performance leads to making “bad” decisions or those that are removed from what may be considered an optimum solution or outcome (Thaler and Sunstein, 2008). Moreover, people tend to neglect the analysis of possible alternatives in order to find the best one. They tend to behave as “satisfiers” (Simon, 1956) and choose the good enough option, instead of deciding on the best (“maximizers”). As a result, missing evaluation of gathering of additional information increase uncertainty. Rash, premature decisions far from an optimum induce non-rational human behavior. In our complex world, many people are busy and simply don’t have time or will to reflect on their decisions, and rely on their automatic thinking. People often don’t question social norms or fashions, and rather tend to stick to them in a conformist

2 To make this distinction clear, Thaler and Sunstein (2008) give an example, where most of the world uses automatic system as a reaction to temperature given in Celsius, while using reflective system to proceed the temperature given in Fahrenheit.

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way (Thaler and Sunstein, 2008). Consequentially, this behavior impacts urban design and planning, that looks for solutions based on the needs of people. As they might be wrong or non-rational, the ability to comprehend the real needs is decreasing (Jain, 2017b).

4.2.2. heuristic approach Another reason of human disability to take more informed decision is the tendency to assess a situation in a biased way. While making decision we tend to rely on heuristics, which strongly influence our ability to deliberately judge over alternatives (Figure 19). This influence come from the experience and the knowledge of dealing with difficulties in our life (Kaplan and Kaplan, 1982). Using so called rules of thumb, we look for solutions based on our know-how instead of theory. This approach (Figure 19) includes three major rules (Tversky and Kahneman, 1974 in; Thaler and Sunstein, 2008): (1) representativeness, (2) availability, and (3) anchoring.

HEURISTICS representativness predicting likelihood by looking for similarities

availability determining the frequency by relying on experience

BIASED DECISIONS

anchoring fixating on initial information

Figure 19: Heuristic rules leading to biased decisions. (adapted from Tversky and Kahneman, 1974, author’s own representation)

The rule of representativeness states that people tend to look for similarities between a current situation and on they have experienced in the past. People lack accurate perception, which causes stereotyping or not looking for existing patterns in randomness (Thaler and Sunstein, 2008). Secondly, the recognition and concern of risks or dangers depends on the availability of personal experiences. For example,

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if a person experienced an earthquake once, he or she will be more sensitized to that possibility in the future. Lastly, anchoring influences decision-making by estimating differently in situations based on given specific initial information (Thaler and Sunstein, 2008). These internal processes illuminate how decisions are made. In terms of risks or challenges cities are dealing with, “biased assessment (...) can perversely influence how we prepare for and respond to crises, business choices, and the political problems.” (Thaler and Sunstein, 2008, p. 28)

This affects policies and plans being developed to fit people’s fears rather than help us respond to real or likely dangers. Additionally, the choice to do nothing is a decision with its own consequences. This is caused by a preference of current state or a fear of the unknown (Kaplan and Kaplan, 1982). Though people lean towards behaving unrealistically optimistic, they can also sustain a fear of a loss, producing inertia – a resistance to change (Thaler and Sunstein, 2008). By leaving familiar environment people show a fear of loss of support, what pushes them towards familiarity bias. However, choosing to do nothing may be a cause of the inability to make a decision without obtaining more information or input. This can be a very rational and measured choice (Kaplan and Kaplan, 1982).

4.3. THE CHALLENGE OF PUBLIC PARTICIPATION The lack of experience is the biggest barrier hindering people from making more rational and logical decisions. As Kaplan & Kaplan argue, “people tend to deal with difficulties arising from the physical environment very much as they deal with difficulties in general.” Kaplan & Kaplan (1982, p. 118)

However, the rareness of damaging events or the lack of knowledge lead to the lack of positive and productive behaviors, resulting in incorrect decisions. Complex situations make problems hard to understand and synthesize, and the delayed effects of long-term planning are difficult to frame (Thaler and Sunstein, 2008). With growing experience, experts often tend to approach problems in a way that has already proven to be successful for them – which comprises the risk of ignoring aspects the make every problem unique. Such routinized approaches may turn out to be biased, and ignoring the actual needs of stakeholders.

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This professional problem-solving differentiates from the way novices frame and try to solve problems (Kaplan and Kaplan, 1982; Campitelli, 2010). Because of that, participatory approaches play a significant role in planning. From a simple informing or consulting function, it can incorporate participants into the decision-making process and lead to more human-centered outcomes (Kaplan and Kaplan, 1982). Shifting the decisive power from institutions in top-down approach to people in more deliberative approaches urges us to rethink how humans behave and make choices. As noticed by Kaplan and Kaplan “(...) the biggest obstacle to a more humane world for people is – people.” Kaplan and Kaplan (1982, p. 224)

Participatory approaches cannot guarantee a particular outcome, but they are increasingly deployed in the practice (Kaplan and Kaplan, 1982). They bring essential input into the process and encourage feedback, learning and communication on various levels. It helps experts to develop sympathy and empathy (Koberg and Bagnall, 1974). However, some of the diverse and biased points-of-view of different stakeholders may be impossible to converge. Generally, participatory approaches offer ways of collective decision-making, thus dealing with biases. The ability to realize, when, how and why they are doing it can yield with better decisions. Furthermore, the quality and the moment of the participatory process has a significant role in the participatory design. To get desirable outcomes, participatory approaches need better and clearer formats with improved facilitation. Badly performed processes tend to lead to a loss of faith in the planners, or negative perceptions of the plan’s outcome (Preiser and Vischer, 1991).

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SUMMARY

Traditional planning models keep planning and decision-making apart, leading to inadequate and inefficient solutions (Burckhardt, 1974). As a result, planning shifts toward deliberative, participatory approaches, interacting more with people (Kaplan and Kaplan, 1982). However, people’s diverse needs and wishes in pluralistic society add to complexity, with which urban design and planning deal with (Gualini, 2015). Additionally, people’s views can be biased, and their behavior can be irrational. Their lack of experience in the world of accelerating change contributes to the inability to reasonably response to challenges that planners and designers face (Kaplan and Kaplan, 1982; Thaler and Sunstein, 2008). The acknowledgement of humans systematically failing in decision-making highlights the need to influence the effectiveness of planning and facilitate better human-centered design. For that, planners need to consciously structure the problems or the setting of the decision-making, and tailor the outcome to its context. To avert biased decisions leading to unsuccessful and poor plans, participatory design processes confront individual biased points-of-view and advance the assemblage of diverse issues. The improved communication and correct facilitation fosters taking improved decisions and leverages the consciousness and sense of the urban environment across stakeholders. Creating choice architecture encouraging beneficial behaviors, can improve the design of user-friendly environments and contribute to the solution of major social problems.

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ASSESSMENT

Chapter 05



This study examines three different planning methodologies that are either common, or have future potential for long term planning. These are: SCENARIO PLANNING FRAMEWORK PLANNING ADAPTIVE PLANNING

Scenario planning is a current and common approach to long term planning. Framework planning has had limited use, and adaptive planning is emergent but still largely theoretical approach. The selected methodologies are driven by qualitative concerns, and have been selected for their contrasting evolutions, process models, and distinctive points-ofview. Because complexity is a difficult subject to measure, hardly any comparative basis evaluating how methodologies deal with complexity and uncertainty exists. For this chapter, the typology of uncertainty from van Asselt and Rotmans (2000) is applied in combination with the ranking of uncertainty from Walker, Lempert and Kwakkel (2013) in section 3.1.1. Because of the complex nature of cities, all three of these methodologies deal with uncertainty levels from three (alternative futures with ranking) to five (unknown future) as defined by Walker, Lempert and Kwakkel (2013). The availability of adequate documentation about these methodologies was also influencing factors in their selection. The absence of any comparative analysis of them has encouraged this thesis to do so.

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5.1. ASSESSMENT CRITERIA Each methodology has been first assessed individually and later compared to the others in a table. This assessment is undertaken according to seven criteria concluding with the methodology’s ability to address uncertainty. The seven criteria include:

BACKGROUND

The origin and basic ideas behind each methodology is explained. SCOPE AND GOALS

This section describes the methodology’s focus and scope. PROCESS

The most important steps of each methodology are shown as diagrams and described. METHODS AND TOOLS

The methods and tools applied in each methodology are explained, describing its general approaches (i.e. qualitative, participatory or holistic). PARTICIPATION AND ACTORS

The typical manner of public participation specific to each methodology is described. APPLICATION IN URBAN DESIGN AND PLANNING

When possible, project showing how each methodology was used is described. ABILITY TO ADDRESS UNCERTAINTY

The ability of each methodology to address uncertainty is assessed according to following principles: approach to epistemological and ontological uncertainty as described in section 3.1.1 consideration of multiplicity of futures stakeholder involvement actions defining and assuring success ability to respond quickly to change level of uncertainty as described in section 3.1.2.

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5.2. METHODOLOGY 1: SCENARIO PLANNING Scenario planning is commonly used to develop medium to long-term strategic plans and policies. It is mostly prevalent in regional and national planning, but is also common at the local scale. It considers various scenarios to test the impact and consequences of possible strategies. This methodology has been first introduced in 1940’s and since than has been developed into several different styles. This study assesses a type of scenario planning called intuitive logics3, because it is dominantly used and it has the most qualitative approach compared to the other existing approaches to scenario planning (Amer, Daim and Jetter, 2013).

BACKGROUND

The roots of scenario planning originally stem from the military research for defense management by Rand Corporation for the US Airforce (Bradfield, Wright and Cairns, 2005). In the 1940’s, Herman Kahn, one of the ranking authorities in Rand Corporation, came up with a technique he titled "future-now" thinking. This technique sought to connect detailed analyses with a visionary imagining of the future (Chermack, Lynham and Ruona, 2001). The articulation of the imaginary and stimulating thinking in the “future-now” approach has found its expression as scenarios, which borrows from the film industry, and reflects a developed form of storytelling (van Notten, 2006). Scenario planning is used to explore plausible futures. Van Notten (2006) states that scenarios represent the “(...) consistent and coherent descriptions of alternative that reflect different perspectives on past, present, and which can serve as (van

hypothetical futures future developments, a basis for action.” Notten, 2006, p. 70)

He describes scenarios as the mental models of scenario makers, and representative of their visions of possible futures. Because many futures can be described in this methodology, it is common that no clear or obvious one is revealed (van Notten, 2006). This reinforces van Notten’s assertion that the scenario method is not a forecast, and that its function is not predictive (Amer, Daim and Jetter, 2013).

3 According to Amer, Daim and Jetter, (2013) three main approaches to scenario planning exists: (1) intuitive logics, (2) probabilistic modified trends (PMT) methodology and (3) the French approach of La prospective.

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By the 1960’s, the interest in scenario planning attracted the attention of the Hudson and Stanford Research Institutes. Both of these institutions looked for a cooperation with big corporations willing to invigorate the future in their strategies (Chermack, Lynham and Ruona, 2001). Both sought an elaboration of long-range planning methods for businesses, anticipating massive societal changes at the time. The first most known and cited example is the Royal Dutch Shell program of “Long-Term Studies” started in 1965. (Chermack, Lynham and Ruona, 2001; Wilkinson and Kupers, 2013). Their use of scenario planning engaged the corporation to think of uncertain futures and consider unexpected events in place of planning their strategy, based on predictive forecasting and assuming the future will be mostly like the present. This encouraged the management board of Shell to link “(...) strategy making, innovation, risk management, public affairs, and leadership development” (Wilkinson and Kupers, 2013)

and examine their future flexibility to address unforeseen circumstances. The success of scenario planning as applied by Shell established a trend for others to follow. Currently, scenario planning is used by large corporations as well as small and medium-sized enterprises and other institutions (van Notten, 2006). Eventually it found its way in the field of urban planning and design (Chakraborty, Mcmillan and Guide, 2015; Zapata and Kaza, 2015).

SCOPE AND GOALS

Scenario planning aims at developing strategic and flexible long-term plans. Their aim is to explore possible futures as realistically as possible. Often there is disagreement on the right settings in which scenario planning is appropriate to apply (Schoemaker, 1991). The general criteria for doing so include: (1) high uncertainty, (2) experience of significant change (in the past, present or coming) and, (3) strong differences of opinion within the institution/team.

Each scenario generates new questions and exposes possible inconsistencies in related arguments and underlying assumptions (Schoemaker, 1991). Scenario planning in planning serves as a medium to communicate between stakeholders and agencies and does not deliver a single plan at the end of the process (Bradfield, Wright and Cairns, 2005). However, it is not always clear how this process influences the final decisions of decision-makers. It is also not clear if the overall performance of institutions undertaking the scenario planning method is improved as a result of using it (Wright, Bradfield and Cairns, 2013; Zapata and Kaza, 2015).

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Normally, this methodology does not clearly define the planning timeframe for a process, plan or project, neither it defines a planning horizon. Primarily, this methodology focuses on plausible events for which the precise date and planning horizon is not important. However, the planning horizon is typically defined as 10 years and more (below a horizon of 10 years scenario planners tend to extrapolate from current trends instead of considering alternative futures) (Wright, Bradfield and Cairns, 2013).

PROCESS

Typically, the most common steps of a scenario planning process are:

1. AGENDA

understand and define situation and problem area

2. CHANGE FORCES

identify and rank key change-driving forces

(1) UNDERSTAND AND DEFINE THE SITUATION AND PROBLEM

The first step aims at understanding and defining the situation, clarifying the problem and the defining the challenges of the development. This process is often supported by the organization of available data and the identification of the setting’s stakeholders. Additionally, it sets an appropriate planning horizon (according to i.e. the rate of changes) (Schoemaker, 1995). (2) IDENTIFY AND RANK THE KEY CHANGE-DRIVING

3. UNCERTAINTIES

identify critical uncertainties

4. SCENARIO BUILDING

FORCES

The second step aims at identifying the key change-driving forces based on i.e. the observation of societal, economic, political and technological trends (as applied to corporate scenario planning). Consequently, these drivers are being ranked in order to evaluate the scenarios easier and put the focus on the most important ones (Schoemaker, 1995).

develop multiple, maximally different scenarios

5. EVALUATION

evaluate scenario Figure 20: Steps of scenario planning. (based on Schoemaker, 1995; van Notten, 2006; Wright, Bradfield and Cairns, 2013; author’s own representation)

implications and tradeofs

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(3) IDENTIFY THE CRITICAL UNCERTAINTIES

In the third step the key uncertainties are being recognized and described (Schoemaker, 1995). (4) DEVELOP MULTIPLE SCENARIOS (PRE-DETERMINED)

The fourth step discovers multiple scenarios according to the previously identified change-driving forces and uncertainties. The number of scenarios is not limited, but usually varies between two (the least amount to reflect uncertainty) and eight, however mostly four scenarios are created. Developed scenarios are checked for internal consistency and relevancy to the selected area of concern (Chermack, Lynham and Ruona, 2001). The aim of building multiple scenarios is to create maximally different scenarios, and not only view them as best, worst and business-as-usual cases (Foresight Horizon Scanning Centre, 2009). (5) EVALUATE CREATED SCENARIOS

The last step aims at the evaluation that discusses the scenarios’ implications and tradeoffs, and describes guidelines for decision-making.

METHODS AND TOOLS

The scenario planning methodology is a qualitative approach aiming to improve thinking of the future by adding qualitative inputs (Chakraborty, Mcmillan and Guide, 2015). It represents a process-oriented approach (Amer, Daim and Jetter, 2013). This process is supported by a range of methods which can be grouped according to their application throughout the process. (A) PRE-WORKSHOP METHODS (DATA COLLECTION AND SYNTHESIS)

Including stakeholder mapping and analysis as well as personas, defining the meaningful partnerships, as well as methods of information and feedback gathering such as interviews with stakeholders or other analytical tools, i.e. GIS analysis. (B) WORKSHOP METHODS (SCENARIO BUILDING)

A main part of the process, which is conducted with facilitated workshop(s), including brainstorming. This purposes in creating different types of scenarios: due to data type

qualitative and participatory scenarios: Two axes method (includes only two uncertainties (Amer, Daim and Jetter, 2013), Branch analysis method, Cone of plausibility method (Foresight Horizon Scanning Centre, 2009), (Figure 21);

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further methods include STEEP analysis, Delphi survey (Wright, Bradfield and Cairns, 2013), sensitivity analysis, checklist (van der Sluijs et al., 2004) quantitative scenarios4 (model-based analytical examination of huge sets of variables): Interactive Cross Impact Simulation, Interactive Future Simulations, Trend Impact Analysis, Fuzzy Cognitive Maps, (Amer, Daim and Jetter, 2013), Scenario Discovery (Lempert, 2013) due to values role (Chakraborty, Mcmillan and Guide, 2015, p. 22):

normative – scenarios developed for precise targets predictive – scenarios depicting most likely futures according to data and stakeholders’ input exploratory – scenarios identifying not only likely but plausible and possible futures

PARTICIPATION AND ACTORS

The methodology of scenario planning, especially the workshop method, allows for a great diversity of actors and stakeholders to be participating in the process. It includes the involvement of experts, consultants, regional policy-makers, major and minor stakeholders and the participation of community members (Chakraborty, Mcmillan and Guide, 2015; Wihbey, 2016). The diversity of the team producing scenarios that deal with collective biases and heuristics by confronting the biased points-of view of individual team members (Wright, Bradfield and Cairns, 2013). The participation of a diverse range of stakeholders further more addresses (Chermack, Lynham and Ruona, 2001) a narrow, biased thinking bandwidth of experts (Lempert, 2013). As a result of the confrontation with various models from different viewpoints, scenario planning expands or alters mental models of individual scenario-makers (van Notten, 2006). Additionally, it addresses ignorance and prejudiced assumptions (van der Sluijs et al., 2004), while the immersion towards wider possibilities of the future enhances learning and communication within the scenario-making teams (van Notten, 2006), and the identification of previously not noticed problems or issues (Amer, Daim and Jetter, 2013).

4 Intuitive logics scenario planning usually does not make use of quantitative methods of scenario building, but some examples can be found in the literature (Amer, Daim and Jetter, 2013).

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event 1A

uncertainty A+

scenario 4

scenario 1 event 1 event 1B

uncertainty B-

uncertainty B+

scenario 3

now

event 1C

scenario 2

uncertainty A-

event 2

TWO AXES METHOD

BRANCH ANALYSIS METHOD

drivers

assumptions

1.

1.

2.

2.

3.

3.

etc.

etc.

scenarios 2.

1.

3.

CONE OF PLAUSIBILITY METHOD

Figure 21: Representation of common methods of qualitative scenario building. (adapted from Foresight Horizon Scanning Centre, 2009; author’s own representation)

APPLICATION IN URBAN DESIGN AND PLANNING

The use of scenario planning in urban design and planning is evident in scientific publications in the form of many applied examples. Zapata and Kaza (2015) compared four cases of regional scale from the USA: Envision Utah, Region Forward 2050 (Washington, DC), the Maryland Scenario Project, and the Valley Future

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Project (California). The common goal of these cases was to improve regional

planning and decision-making under rapid growth by defining shared regional goals, and coordinating investments and regulations in the region. Each of the cases predominantly depended on professional stakeholders. In each process, different scenarios have been created, but only in Envision Utah one preferable scenario has been chosen. In some cases, further mixing and modification of scenarios has been seen after the process. Generally, in each of the cases the process has been successful, as it aimed rather at the communication of possible futures than at the creation of an implementable plan (Zapata and Kaza, 2015). In a different approach, Wihbey (2016) examined a community-oriented scenario planning process for five counties in Southwest Colorado. The goal of this process was to identify uncertainties by collaborating with a diverse group of stakeholders. It aimed at defining possibilities for a more adaptable economy, considering the community’s vulnerability to extended long-term drought. This scenario planning process successfully brought a range of diverse stakeholders together, who produced eight different scenarios. Those scenarios helped to work out a compilation of actions for diverse groups in the community. It also helped to understand uncertainties and further yield with less surprises (Wihbey, 2016). However, scenario planning has proved to have several disadvantages when used outside of business settings. These problems varied from the inability of planning authorities to take collective decisions as single entities, a resistance to deal holistically, and an inability to address the contradictory goals and needs of diverse actors (Zapata and Kaza, 2015). Furthermore, the multiplicity of scenario planning methodologies challenged planners and designers to yield successful outcomes (Chakraborty, Mcmillan and Guide, 2015). The biggest obstacle discovered by the researchers was the ambiguous use of the scenario planning outcomes in the decision-making process. Scenarios, besides being good mechanisms for communication and analysis, have been misused to romanticize and distort reality (Salewski, 2010). The actual implementation of a scenario has been hard as well. Contributing factors included an unwillingness to implement a preferred option due to a lack of funding. Often the ability and timing of publicly financed agencies to undertake such efforts has been a barrier (Zapata and Kaza, 2015). These hurdles can be amplified when there is confusion in the combined value of quantitative and qualitative metrics and goals, even though van Notten (2006) asserts that such combinations yield better outcomes.

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ABILITY TO ADDRESS UNCERTAINTY

Scenario planning is able to deal with epistemological uncertainty (see Figure 15), because of its ability to draw upon a diverse range of sources for knowledge in step 1 (understand and define the situation and problem) and therefore gain a rather holistic understanding of the situation and the challenge. This comprehension is further developed in step 4 (scenario building), where stakeholders share their knowledge and address critical uncertainties. On the other hand, scenario planning doesn’t address ontological uncertainty because it focuses primarily on anticipated conditions and does not consider unanticipated, variable ones. Scenario planning directly considers the multiplicity of futures which is seen in the creative process of scenario building (van Notten, 2006). The scenarios help understand the impact of uncertain conditions (Wright, Bradfield and Cairns, 2013) and also foster holistic thinking (Amer, Daim and Jetter, 2013). This methodology involves a range of stakeholders from the beginning to the end of the planning process which helps to generate feedback and deal with heuristics (Chakraborty, Mcmillan and Guide, 2015), thus offsetting uncertainties. Scenario planning does not explicitly define success. The generated scenarios serve as a basis for further decision-making, sometimes with one favorable scenario being chosen. This methodology doesn’t state how to assure the desired outcomes. Furthermore, the use of the most favorable scenario can lead to ignorance of the potential impacts of uncertainties (as i.e. defined in other scenarios created) and might lead to a misidentification of decisions leading to outcomes less vulnerable to change (Zapata and Kaza, 2015). As the outcomes of scenario planning only serve as a basis for further decision-making, this methodology does not embody methods and tools to quickly respond to change. According to the levels of uncertainty (see section 3.1.2), scenario planning responds to level 3, when the decision-makers are able to agree on ranking of scenarios and can choose one favorable (Zapata and Kaza, 2015). On the other hand it respond to level 4, when scenarios cannot be ranked and are used as a communicative tool for advocacy planning (Chermack, Lynham and Ruona, 2001).

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5.3. METHODOLOGY 2: FRAMEWORK PLANNING Framework planning is a new approach for creating long-term strategies to guide the development of cities and regions (Jain, 2012). The Australian Local Government Association (1999; Planning Practice Note 17, 2015) asserts that future visions, that are developed using flexible frameworks offer a range of potential options instead of defined and exact plans that do not typically have the same flexibility to adapt over time (Jain, 2017).

BACKGROUND

Framework planning is relatively new and is a practical methodology for city development. It is a response to the heavy reliance on urban design guidelines that tend to focus primarily on the implementation of regulations and standards (UDF, 2008; Jain, 2012). The objective of this methodology is to create an open and adaptable framework, that is able to connect a planning area’s existing assets with its development goals dynamically and as they might change over time. It provides strategy and direction for interventions and aims at building resilience and adaptation of cities to informed change (Jain, 2012). Examples of urban design frameworks can be found in Australia (i.e. Perth (City of Perth, 2010), Victoria (State Government Victoria, 2016), USA (i.e. Portland (Jain, 2012), Seattle (City of Seattle, 2014), and Canada (i.e. Edmonton (City of Edmonton, 2015)). However, these examples have not been extensively described in academia. This is perhaps explained by the fact that the applied nature and orientation of such framework planning makes it hard to use a single distinctive process model. For the purposes of this evaluation, the Urban Design Framework for Portland, Oregon is considered as an appropriate example (Jain, 2012).

SCOPE AND GOALS

According to Jain, the typical long-range development plan has an increasingly limited shelf life. This is due to our increasing inability to anticipate and predict future conditions in a rapidly dynamic and changing world. Further, Jain states that creating adaptive “frameworks” instead of traditional plans make use of the considerable resources (time, money and public input) more efficiently. The essential elements of such frameworks are to: (1) identify the value and importance of urban infrastructure (including spatial), and (2) establish “performance criteria” of what the essential outcomes of each intervention should be.

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These outcomes can have many manifestations, and can be adapted to meet the context at the time when there is a nexus of need, political will and the financial ability to make such improvements. This is a radically different approach from current long-range planning that seeks to establish defined outcomes that are likely to become increasingly inappropriate over time (due to changed context) (Jain, 2012). As seen in the Urban Design Framework for Portland, a framework plan can create the basis for more holistic refinements to zoning, housing, open spaces, mobility, infrastructure, but also partnerships (both public and private), leveraging the benefits (Jain, 2012). This approach creates flexibility and helps identify the most important objectives and mechanisms to achieve them. This creates a reliable framework for decision-making (Planning Practice Note 17, 2015). The act of creating “performance criteria” for high value locations in a framework can also incorporate the criteria necessary for great places and livable urban spaces. Its flexible character makes it easier to address uncertain conditions since each set of performance criteria can be adapted to meet the conditions at the time intervention is possible. It is necessary to stay adaptive to political realities while still being able to direct desired outcomes. (Jain, 2012).

PROCESS

Jain’s Urban Design Framework for Portland, Oregon is described in detail to explain the various steps of this methodology. A more generic framework planning would not look exactly like this, but would still have similarities of an assessment creating a body of knowledge for further development. The framework plan for Portland (Figure 22) follows a systematic structure of four main steps (Jain, 2012), including: (0) BROAD OBJECTIVES

This step sets the agenda and aims at identification of broad objectives of the framework, including its scope, boundaries and influences. (1) URBAN DESIGN ASSESSMENT

The step aims at reviewing and analyzing historical and existing conditions. This is elaborated under the research of precedents (historical – relevant case studies; and contemporary – urban design plans (review)) as well as the analysis of historical and existing plans and conditions influencing the future development. This analysis looks for current issues, concluding with development opportunities and threats. In this step, critical strategic actions are identified, i.e. “creating connective urban tissue (including landscaping) that connects disparate parts of the city’s spaces and places” (Jain, 2012, p. 9)

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BROAD OBJECTIVES

1. URBAN DESIGN ASSESSMENT Understand potential by assessing

PRECEDENTS

HISTORY

historical &

historical plans

EXISTING CONDITIONS AND PLANS

CURRENT (FOCUS) ISSUES

contemporary development opportunities

2. BASIS FOR PLACE-MAKING Identify needs and wants

WANTS:

NEEDS:

Desired Locations

Places of Necessity

CONVERGENCE Composite Overlay

3. URBAN DESIGN FRAMEWORK Converge needs and wants

CONCEPT

URBAN DESIGN FRAMEWORK locations with performance criteria

Figure 22: Steps of framework planning. Urban Design Framework for Portland, Oregon (adapted from Jain, 2012; author’s own representation)

(2) A BASIS FOR PLACE-MAKING

This step is very specific to the example of the framework for Portland, Oregon. It identifies the areas worthiest of development focus through an evaluation of needs (i.e. places of necessity) and wants (i.e. high value and desirable

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locations). Places of necessity (need) are places where people have no better alternatives (i.e. railway stations). Desirable places (wants) are usually a mix of unique “reasons to be there” and include desirous or uniquely attractive places such as unique food markets, great architecture or extraordinary views). This assessment is then shared with stakeholders together with tools that include cognitive mapping. All these inputs are consolidated into a single composite in order to clearly define areas with greatest opportunity for place-making and development potential. (3) URBAN DESIGN FRAMEWORK

The results of the above steps are expressed using concept maps and diagrams, as well as performance criteria for high value and strategically important areas. This allows desired outcomes to be then grouped in to short, medium and longterm actions. This structure provides continued guidance not only in times of growth, but also during poorer periods.

METHODS AND TOOLS

This methodology has a strong analytical and holistic character. It uses methods of data collection, assessment, and explanation (representation) in the following manner. (A) METHODS OF DATA COLLECTION

interviews and public consultations technical and historical studies of city’s plans cognitive mapping GIS mapping and analysis (B) ASSESSMENT METHODS

analyses of existing plans, existing conditions, and issues (i.e. movement, use patterns, urban forms, etc.) analyses of needs and wants based on citizens’ perception and cognitive mapping furthermore, the McHargian layer cake5 analysis is a key tool to create a composite analysis of individually analyzed layers of both technical and sociological aspects. This tool helps spatially identify cumulative development opportunities and constraints (Jain, 2012).

5 McHargian layer cake is an analysis in which several layer of natural, man-made or contextual aspects are analyzed and combined geographically to identify different suitability for various types of development (McHarg, 1995).

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(C) EXPLANATORY METHODS

concept maps and diagrams reference projects, precedent case studies


Public participation in framework planning is preceded by a preliminary analysis that frames problems and making complex planning conditions easier to understand. The effort to do such detailed analysis and making it easy to understand creates a stronger foundation for stakeholder comprehension and involvement. This comprehension of complex conditions allows for a more robust discussion between stakeholders and decision-makers allowing better compromises and tradeoffs. From the very start of this process professionals, experts, consultants or politicians and public administrators and finally stakeholders remain involved (Jain, 2012).


The example of deploying framework planning has been well documented by Arun Jain for creating a specific development strategy for the city of Portland. The objective of this framework was to “create a basis for strategic investments to maximize public benefit” (Jain, 2012, p. 7).

Figure 23: Composite Site Analysis for Portland, Oregon. (Jain, 2012) Reprinted with owner’s permission; no re-use without permission from the owner

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Figure 24: Urban Design Framework for Portland, Oregon. (Jain, 2012) Reprinted with owner’s permission; no re-use without permission from the owner

As part of the first step of the assessment, previous master plans for Portland were analyzed in detail and collated with current issues. The outcome of this phase was a list of development issues, opportunities and challenges. In the next phase, the desired and necessary locations were identified to establish the planning area’s “urban bones” (i.e. a combination of necessary development sites, perceived districts and boundaries, preferred and potential green corridors, development opportunities and transit ridership concentrations). These layers were then assembled to reflect the early analysis phase (Figure 23). The final concept diagram showed important nodes, green rings, major corridors and attractors, urban design framework elaborated further those elements (Figure 24). The resulting framework identified the city’s assets and gave strategies that were relatively free from the prevailing political agenda. The actual process took place during two different political periods, with substantial support during the first political administration and very little from the second one. Consequently, the early steps of this effort including the urban design assessment and basis for placemaking had great political support. The final framework however, struggled due to poor interest in a new administration that had little orientation towards this new process.

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In summary, this framework planning methodology seems to yield very flexible results useful at both local and regional scales. It embraces a bigger and more comprehensive base of inquiry providing clear directions for spatial development. Finally, it creates a transparent and clear basis for decision-making. Although, its deep analytical character can be time and money-constraining, the methodology itself provides an important path to minimize the planning implications of political change over time.


Framework planning embraces epistemological uncertainty (knowledge guided decision-making, see Figure 15) by a deep, holistic understanding of complex issues and potentials city has in step 1 (urban design assessment). Additionally, it defines human needs and wants in a human-centered way in step 2 (a basis for placemaking). Unlike scenario planning framework planning do not generate multiple and alternative possibilities, it considers the multiplicity of futures indirectly by creating a flexible framework that works across diverse conditions. It allows choice and change to occur at the time interventions are possible. This flexibility in when and how development happens addresses the ontological uncertainty. The built comprehension of complex conditions shared by every participant allows more informed and robust discussions. This helps to reduce uncertainty coming from incomprehension or miscomprehension of complex problems and aims to yield more informed feedback (Jain, 2012). Framework planning defines and assures success by creating performance criteria. This well-defined criteria do not prescribe what the outcomes should look like, but they leave a room for interpretation i.e. what the result should accomplish (Jain, 2012). These criteria allow also quick responses in case of future change. According to the levels of uncertainty (see section 3.1.2), this methodology deals with uncertainties in level 4 by addressing anticipated future events.

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5.4. METHODOLOGY 3: ADAPTIVE PLANNING Adaptive planning is a methodology designed to make robust policies but the author sees a great potential of this methodology for spatial implementation. This evaluation looks at two methodologies of adaptive planning. First, adaptive policymaking as defined by Walker et. al and second, adaptive policy-making as promoted by Swanson et al. Adaptive policy-making as laid out by Walker et al. (2000), is also known as dynamic adaptive policy-making. Although it is relatively popular in the scientific research, examples of its deployment in practice are scarce. Adaptive policy-making according to Swanson et al. (2010) has been developed as a result of a broad research of several other approaches representing adaptive thinking (including Walker’s approach) and the analysis of over 10 case studies using adaptive policies. It presents a practical methodology like Walker’s, but is expanded by a range of tools backing policy-makers to improve the adaptability of policies. Additionally, it also incorporates scenario planning (Swanson et al., 2010). The term of adaptation has been popularized by Darwin in the 19th century, but it took over hundred years to seriously enter planning theory (Walker, Haasnoot and Kwakkel, 2013). Adaptive planning has a relatively short development history, starting in 1990’s (Swanson et al., 2009). It denies the static predictions-oriented approaches based on single models and optimization (Swanson et al., 2010). It represents a point-of-view exploiting adaptation, deploying modifications to changes or new conditions through implementing responsive capacities and provisioning learning (Walker, Rahman and Cave, 2001).

BACKGROUND

The adaptive policy-making has been developed by Warren Walker6 in 2000 (later in cooperation with Vincent Marchau) over critic on the insufficiency of assumption based scenario planning. According to Walker, the major disadvantage of scenario planning is the implementation of policies based on the best option scenario. That approach might be successful only when the future would be exactly as the best option scenario describes, which in fact is hardly possible. Recognizing a need for

6 Walker’s original research area comprises operations research and integrated assessment. He developed and tested the adaptive policy-making originally in the field of transportation infrastructure planning such as strategic planning of Schiphol Airport extension (Walker and Marchau, 2003) or introduction of automated taxis (Walker and Marchau, 2017). Later this method has been applied in the energy sector (Hamarat, Pruyt and Kwakkel, 2012) and water management affected by climate change (Haasnoot et al., 2013). All of the described literature cases employing his model are fictional but based on real information. No research on the real implementation of adaptive policies has been found.

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experimental policies which are able to react rapidly to change, he suggested a new paradigm of making policies with already existing adaptation actions (Walker, 2000), instead of relying on so called “muddling through” – changing policies ad hoc after the change has already happened (Lindblom, 1959). Since the development of this methodology it has been combined with other methods that led to creating i.e. quantitative dynamic adaptive policy pathways used for spatial planning responses to rising sea level (Haasnoot et al., 2013; Kwakkel, Haasnoot and Walker, 2015). The adaptive policy-making according to Swanson et al. has been based on a comprehensive research of academic and applied paradigms in adaptive policies and complex systems in various fields. This methodology has been first published as guidebook in 2009 (Swanson et al., 2009) and later in a synthesized version of a research paper (Swanson et al., 2010) and was meant to provide guidelines for policymaking in “dynamic and unpredictable socio-economic and ecologic circumstances” (Swanson et al., 2010, p. 925). Their methodology recognizes a need of policies made for conditions that are known, possible or unknown but with a human-centered approach.

SCOPE AND GOALS

The adaptive policy-making aims at developing adaptive policies that are “designed to function more effectively in complex, dynamic, and uncertain conditions.” (Swanson et al., 2010, p. 924)

This methodology uses insights from complexity theories, and recognizes that complex systems evolve and adapt over time, and so need policies. The goal of such policies is to be “robust across a range of plausible futures” instead of being optimal for one estimated future or chosen scenario (Walker and Marchau, 2017, p. 4). Swanson et al. (2010) define the scope of such policies according to two types of capacity an adaptive policy should have: (1) adaptation to anticipated conditions described by multiple plausible futures; and (2) adaptation to unanticipated conditions arising from unpredictability.

The first one aims to meet a policy’s objectives in a different range of defined plausible circumstances. The goal of the second type of capacity is to implement the ability to accommodate unforeseen issues as they unfold in the future and using information as it becomes available.

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The indications as when to use the adaptive policy-making methodology are not precisely defined. Resulting from the statement, that we live in a world characterized by ongoing changes, Walker and Swanson agree that all policies should be adaptive (Walker, 2000; Swanson et al., 2010). The starting point is set at the presence. The time horizon is not determined and depends solely on the monitoring of a policy’s performance (Swanson et al., 2009; Walker and Marchau, 2017).

PROCESS

The steps of adaptive planning methodology presented below (Figure 25) have been combined in accordance with very similar processes of adaptive policy-making acc. to Walker et al. and adaptive policy-making acc. to Swanson et al (see APPENDIX 2). This methodology has two phases of design and implementation, with the following steps: (1) AGENDA

This step identifies objectives and scope, as well as key change driving forces and uncertainties. It looks for strengths and assets, linkages between the systems elements, and addresses needs and wishes of communities. It also defines constraints and opportunities, and success criteria, i.e. intended outcome. (2) INCREASING ROBUSTNESS

This step is divided into two main parts: shaping actions:

mitigate (reducing exposure to likely damages and vulnerabilities) hedge (transferring or reducing risks) seize (taking likely opportunities) exploit (exploring uncertain profitable opportunities) innovation actions:

defines performance indicators7 needed in the monitoring step experiments with multiple solutions tackling particular problems facilitates innovation through self-organization, social networking or decentralized decision-making.

7 Performance indicators in adaptive planning differ from performance criteria in framework planning. Performance criteria describe and indicate detailed characteristics a plan needs to accomplish/ demonstrate (Jain, 2012). Performance indicators describe measurable values demonstrating the effectiveness of a plan or policy. This means they describe how the plan or policy is achieving its objectives and how it demonstrates the initially defined success (Swanson et al., 2010).

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(3) MONITORING

This step monitors the policy’s or plan’s performance. It collects observations, feedback from stakeholders and other information deemed to be useful as the future change unfolds. It also defines the signposts (information on performance to be tracked) and triggers8 (critical situations where the policy does not meet the objectives anymore and additional actions are needed). (4) LEARNING

This step completes the policy cycle. Continuous learning and the evaluation of a policy’s performance may show a need for major policy adjustments. This requires a revision of goals, the redefinition of adaptive actions and a reassessment.

METHODS AND TOOLS

Adaptive policy-making acc. to Walker et al. in its short development time has not developed many supporting methods yet. The qualitative approach focuses mainly on expert judgments, (Walker and Marchau, 2017), and the quantitative approach is supported by computer assisted modelling, i.e. in the energy sector (Hamarat, Pruyt and Kwakkel, 2012; Kwakkel, Walker and Marchau, 2012). This includes a bundle of supporting computational methods for simulation modelling: exploratory modelling and analysis (Agusdinata, 2008), fast and simple policy models, scenario discovery, robust optimization (Walker, Haasnoot and Kwakkel, 2013) or systems dynamics. These methods aim at analyzing the performance of the plans or policies according to variable uncertainties (Kwakkel, Walker and Marchau, 2012). Furthermore, most of those require additional skillsets, i.e. familiarity with programming languages such as Python (Kwakkel, Haasnoot and Walker, 2016). The lacking public engagement in adaptive policy-planning acc. to Walker et al. has been incorporated in the methodology acc. to Swanson et al. (2010, section 4). They developed a manual of 7 qualitative tools with strong human-centered focus described as: (A) INTEGRATED AND FORWARD-LOOKING ANALYSIS:

aims to clarify goals and better understand the context. It consists of a comprehensive analysis of the present and future situation(s) including a development of multiple plausible futures using the scenario planning methodology. Additionally, it defines success and describes potentially undesirable outcomes with possible improvement actions. 8 This can be also referred to as a tipping point (Haasnoot et al., 2013), as referred to in section 2.1

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DESIGN PHASE

1. AGENDA SETTING - objectives and scope - key change driving forces and uncertainties - constraints and opportunities - definition of success

2. INCREASING ROBUSTNESS - shaping actions: mitigate / hedge seize / exploit - innovative actions: performance indicators variation and decentralisation self-organisation and networking 3. MONITORING

- stakeholder feedback - information on emerging issues - signposts and triggers

4. LEARNING

other’s actions unforeseen events changing preferences

- continous learning - adjustments - formal review and reassessment

IMPLEMENTATION PHASE

Figure 25: Steps of adaptive policy-making. (based on Walker and Marchau, 2017 and Swanson et al, 2010; author’s own representation)

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(B) BUILT-IN POLICY ADJUSTMENTS

aims at building-in automatic policy adjustments reacting to critical changes (tipping points). Those adjustments may be fully-automatic9 in well understood systems, where the change can be to some extent predicted, or fully-manual in unanticipated conditions of not so well understood systems. The manual adjustments can be best handled through the next tool of formal review. (C) FORMAL REVIEW AND CONTINUOUS LEARNING

defines a regular assessment of the policy’s performance detecting emerging issues of unanticipated conditions and improving the policy in the light of new conditions. The review, which could be also seen as learning by testing, provides best feedback when done both analytically and deliberatively. (D) MULTI-STAKEHOLDER DELIBERATION

adding to the participatory process of scenario planning, Swanson et al. (2010) suggest additional participation in actual decision-making. This aims at “building recognition of common values, shared commitment and emerging issues, and by providing a comprehensive understanding of causal relationships” (2010, p. 932). This can serve as a base for identifying

convergences and divergences, that affects decisions. (E) ENABLING SELF-ORGANIZATION AND SOCIAL NETWORKING

focuses on the cooperation on the lowest hierarchical level of the complex adaptive systems by enabling and facilitating bottom-up processes and initiatives. Towards those actions, Swanson et al. (2010, p. 932) count “ensuring that policies do not undermine existing social capital; creating forums that enable social networking; facilitating the sharing of good practices; and removing barriers to self-organization”.

(F) DECENTRALIZATION OF DECISION-MAKING

aim to give the biggest responsibility in decision-making to “the lowest effective and accountable unit of governance” (Swanson et al., 2010, p. 933). According to Swanson, decision-making in close cooperation with affected citizens provides the best and quickest feedback in well-informed conditions. (G) PROMOTING VARIATION

considers implementation of a range of diverse policies regarding same issues. This can be seen as an experiment of implementing policies on small scale and looking for the best solution by testing and learning. 9 Swanson et al. give an example of insurance policies referring to agriculture, where the claims were made conditional to risky weather events compared to historical climate data, instead of manually settling a claim by assessing each case separately.

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Adaptive policy-making acc. to Walker is limited by the lack of participatory approach. It focuses exclusively on the expert and consultant judgments and experiments using computer assisted models. On the contrary, adaptive policy-making acc. to Swanson et al. enhances in various ways the diversity of actors taking part in the policy-making. Next to experts, this includes participatory approaches in scenario planning and in multi-stakeholder deliberation, self-organization and networking, as well as feedback-oriented cooperation in decentralized decision-making. This great diversification aims at influencing the success of the outcomes and lowering the possibility of dissatisfaction as if in the case of a singular participation action.


Currently, adaptive planning remains emergent and is typically applied to policymaking. Some researchers recognize the prospective use of this methodology in urban design and planning (Ahern, Cilliers and Niemelä, 2014). Consequently, the author speculates on the potential of the methodological translation for spatial planning and incorporation of learning-by-doing practice in urban design and planning. The first step of this methodology is very similar to the one in scenario and framework planning. Additionally, it seeks to combine the conclusions from the theoretical field of complexity and uncertainty research with planning. Apparently, planners would need to be capable to grasp these notions in order to be able to successfully use them. However, employing these novelties in planning could bring a range of opportunities, but also limitations. Adaptive planning benefits learning to a great extent, which can be seen as its most significant strength. It not only benefits from feedback in deliberative processes, but also from monitoring and the evaluation of actions implemented. This implies that when a traditional plan would not be able to meet the objectives and a new plan would be needed, the analytical part would have to start again from the beginning. However, if insights from the initial evaluation could be adapted and appropriately used then the analytical part could still yield quick and reliable decisions. Moreover, employing variation and experimentation gives the opportunity to learn from own mistakes. Adaptive planning has an enormous potential in employing approaches dealing with properties of complex adaptive systems such as emergence, networking and selforganization. This could strengthen central planners by the cooperation with local communities. The recognition and appreciation of bottom-up initiatives can reinforce the local incremental practice of neighborhood development. However, cities need to recognize the positive impact of such interventions instead of selling plots off to the highest bidders.

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Adaptive planning sets performance indicators to guarantee success, which is defined in the first step of the process. However, this is different than performance criteria in framework planning, because it focuses mostly on measurable values. These indicators help to ensure the desired performance in the monitoring step, which observes and learns from evaluation of the implementation. However, this raises the question of how to track the design’s or plan’s effectiveness or success. Additionally, this methodology can utilize quantitative computer assisted models. Such models, conditioning outcomes to quantitative variables, require additional resources (i.e. people with knowledge in programming languages). More interdisciplinary approaches would be necessary, in order to benefit from those models. This could be further complicated by the planner’s inability to quantify uncertainties. Moreover, the limitation of such models might be the lack of the necessary transparency for the public. This methodology can also be seriously limited by the time and money necessary for monitoring and evaluation. As in the policy-making, some adjustments may be automatized, conditioning the policy regulations on i.e. weather impacts in spatial implementation could be complicated. Moreover, high public expectations make it harder to overcome the political fear of experimenting and a general unwillingness to try out and test innovative ideas.


Adaptive planning addresses both epistemological and ontological uncertainty (see Figure 15). The first one is addressed by the integrated and forward-looking analysis (in agenda setting done in step 1). The latter one is addressed by actions of increasing robustness and monitoring allowing adaptation to change (step 2 and 3) The framework planning directly considers the multiplicity of futures in the first step which can deploy scenario planning methodology. In the next step it does not consider scenarios directly – the incorporation of adaptation and robustness actions enable the policy to perform well across diverse scenarios. Adaptive planning involves stakeholders in each step of the process. Diverse tools described by Swanson et al. leads to enhanced information gathering through participatory and human-cantered actions, feedback, built-in adjusting actions, and close decentralized cooperation with minor and major stakeholders. All of them reinforce the holistic thinking and encourage feedback gathering improving the plans. Adaptive planning defines success in the first step. The second step defines indicators of performance, which compared to the definition of success are used

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in the third step of monitoring to control the plan’s performance. These indicators together with previously described adjustment actions give the possibility to respond quickly to change in an informed way. According to the levels of uncertainty (see section 3.1.2), adaptive planning deals with deep uncertainty in levels 4 and 5. Dealing with anticipated conditions of uncertain futures refers to level 4, and with unanticipated conditions to level 5. Furthermore, using computational modelling in the quantitative approach of adaptive policymaking acc. to Walker et al. benefits in additional exploration of uncertainties in performing vast numbers of experiments (small scenarios with variables) and assessing their implications (Hamarat, Pruyt and Kwakkel, 2012).

5.5. COMPARATIVE ASSESSMENT This study examined three methodologies: scenario, framework, and adaptive planning. They have been compared and summarized in Table 1. SCENARIO PLANNING

FRAMEWORK PLANNING

ADAPTIVE PLANNING

original field

military

urban design

operations research

fields of deployment

business, urban design and planning

urban design and planning

policy-making

outcomes

a set of scenarios

a highly-flexible framework based on performance criteria

an adaptive policy, plan or strategy

focus

communicative

strategic

strategic / decisive

planning horizon

above 10 years

not defined

not defined

style

linear

non-linear

non-linear

learning

via research, stakeholder feedback and reduction of biases

via research, stakeholder via research, and stakeholder feedback and reduction of feedback biases, monitoring

participation-oriented human-centered heuristic

analytical human-centered

participation-oriented human-centered heuristic

single-issue / holistic

holistic

single-issue / holistic

BACKGROUND

SCOPE AND GOALS

PROCESS

METHODS AND TOOLS approach

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SCENARIO PLANNING

FRAMEWORK PLANNING

ADAPTIVE PLANNING

private and public

private and public

private and public

experts, consultants, regional policy-makers

experts, consultants, regional and local policymakers

experts, consultants, regional and local policymakers

major and minor stakeholders citizens

major and minor stakeholders citizens

basis for decision-making strategic planning spatial development plans

basis for decision-making strategic planning

understanding of initial conditions (step 1) building shared comprehension of complex issues

integrated and forwardlooking analysis

PARTICIPATION AND ACTORS actors

major and minor stakeholders public experts citizens APPLICATION IN URBAN DESIGN AND PLANNING basis for decision-making advocacy planning communication of uncertainties

appropriation

ABILITY TO ADDRESS UNCERTAINTY understanding of initial conditions (step 1) knowledge sharing (step 4)

addressing epistemological uncertainty

addressing ontological uncertainty

-

via flexibility

via adaptation, actions increasing robustness and monitoring

consideration of multiplicity of futures

directly, via scenario building

indirectly, via performance criteria

directly, via scenario building, indirectly, via increasing robustness and variation

stakeholder involvement

generating feedback

generating feedback

generating feedback focus on self-organization and social networking

actions defining and assuring success

-

via performance criteria

definition of success, indicators of success,

ability to respond quickly to change

-

via performance criteria

via monitoring and adjustment actions

Table 1: Comparison of selected methodologies: scenario planning, framework planning and adaptive planning. (author’s own representation)

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Long-term planning has always been set in the context of uncertainty. Following the motivations and hypothesis developed in this thesis, it seems that current planning approaches are increasingly inadequate to address growing levels of complexity and uncertainty. This study examined the following methodologies: scenario, framework, and adaptive planning. Their values and constraints are elaborated as follows.

BACKGROUND

The examined methodologies differ in their origin, implying on their capability to address uncertain futures in urban design and planning. Scenario planning is being applied in urban design and planning mostly on a broader scale and seeks goals and uncertainties constraining the development. It may or may not use spatial analysis and related maps. Framework planning has the most spatial character and its purpose is to create a local or regional strategic development plan illustrated with diagrams and maps. Adaptive planning has not yet been applied outside policymaking, but it demonstrates potential for the deployment for development of spatial strategies and plans.

SCOPE AND GOALS

Each methodology aims at the development of different instruments for long-term planning. Their different scopes and goals imply on diverse reactions to change as compared to Ackoff’s planning approaches regarding change (see section 2.3.1, Figure 12). Scenario planning refers to proactivism, because it describes multiple scenarios which aim to deal with problems before they become serious and prepares for various uncertain developments. Framework and adaptive planning intend to construct an idealized future, both being characterized by interactivism. Framework planning establishes location-specific performance criteria aiming to ensure measurable success. Adaptive planning deploys opportunities in real-time and monitors the implementation process to prevent unwanted outcomes.

PROCESS

Considering the methodologies’ processes, each of them starts with the setting an agenda – a rather analytical phase- though in various extent. Framework planning is shows the highest analytical character of those three. It emphasizes a deep understanding of initial conditions and resources of cities and needs of its citizens. Further steps diverge in each of the methodologies, from scenario building in scenario planning, through the composing diversely analyzed layers to building comprehensive knowledge in assets in framework planning, to monitoring and learning steps in adaptive planning.

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METHODS AND TOOLS

Concerning the process of the creative problem-solving journey by Koberg and Bagnall (see section 2.3.2), improved planning for uncertain futures demands holism and iteration. The examined methodology promotes holism in different manners. Scenario planning focuses on broad issues and develops plausible scenarios based on different uncertainties. Framework planning looks at a range of interconnected urban layers and composites them into a cumulated detailed representation. Adaptive planning approaches issues holistically executing integrated and forward-looking analysis, but also utilizes further tools described by Swanson et al. However, in some cases scenario and adaptive planning are used for single-issue situations.


Each methodology engages a multiplicity of actors and approaches stakeholder participation differently. Scenario planning focuses on public participation in scenario building. Framework planning focuses on transparency throughout every phase while executing upon the framework’s ideas. The biggest empowerment of stakeholders is found in adaptive planning represented by the multi-stakeholder deliberation, enabling self-organization and social networking as well as focus on participation in scenario building which is a part of the first step of this methodology. Moreover, scenario and adaptive planning exploit participatory heuristic approaches, resulting in the development of empathy and sympathy for stakeholders as well as the reduction of biases. Although the examined methodologies include steps of convergent and divergent thinking, adaptive planning seems to have an extensive sequence of those to monitor and offset unfavorable results effectively.


The application of the examined methodologies revealed their shortcomings. Scenario planning communicates plausible developments, but lacks a comprehensive approach of decision-making based on constructed scenarios. In some cases, one favorable scenario has been chosen. This could lead to value loss of the other scenarios and lead to decisions disrespecting uncertainties considered in other scenarios, and in fact lead to vulnerable outcomes. Framework planning, although it intended to create a framework free from a political agenda struggled with the implementation due to poor support from the government.

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Although adaptive planning has not been applied in urban design and planning, it was part of the research because of it speculative potential for this field. This methodology could be adequate to reinforce local planning by referring to decentralized decision-making and encouraged networking. Furthermore, it seeks experimentation and variation, which is being monitored to yield feedback and learning.


The methodologies examined address uncertainty in various extents and can offset epistemological uncertainty by collecting and evaluating available information. Furthermore, the flexibility of outcomes from framework planning processes, offers capacities to embrace anticipated events. It approaches ontological uncertainty by setting performance criteria that assure the accomplishment of the desirous outcome across a range of futures. Adaptive planning openly offers additional capacities of embracing unanticipated events, thus dealing with ontological uncertainty. By formulating performance indicators, it is signifying when to execute adaptation or robustness actions. It is taking advantage of information as it becomes available while the future unfolds, and is executed in the learning step in monitoring stage. These capacities give the ability to deal with deep uncertainty.

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SUMMARY

This assessment reveals different values and abilities of current and emergent planning methodologies to address uncertain futures. Designers and planners must be conscious of their strengths and limitations and must be able to choose the best fitting and relevant options to achieve their desired outcomes. The properties of the adaptive planning methodology suggest the biggest viability for creating successful long-range urban design and plans. The methodology’s preliminary, analytical step and later the steps focusing on robustness, monitoring and learning, but also the variety of methods deployed in this methodology, make this planning approach the most adequate to react responsively to change and assure the effectiveness of the plans. However, this methodology has been only tested in the field of policy-making, and it is not obvious whether it would suit the spatial implementations. On the other hand, framework planning produces the most spatial outcomes and has many similarities to adaptive planning. Its deep analytical character aids to identify and comprehend the critical assets of cities and offers flexible development strategies under change. Moreover, its sets performance criteria, securing the successfulness of the outcome. This implies on its appropriation for further development to improve its adaptation and ability to rapidly response to changes.

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FINDINGS

Chapter 06



Nowadays, the world is urbanizing at a fast pace. This and other current trends of change, i.e. migration, climate change, the shift of economic powers or the rise of information and communication technologies are amplifying. The consequences of such developments are hard to foresee and the intertwined character of those changes adds up to the complexity of our world and sets complex conditions for urban design and planning. Those changes influence on our ability to plan for long-term. This work is grounded on the belief that current planning methodologies are increasingly inadequate to address a rapidly changing world. The need for more flexible planning that is able to cope with complexity and uncertainty has been recognized – therefore this study examined the ability of current and emergent planning methodologies to address uncertain futures.

6.1. THEORETICAL IMPLICATIONS FOR IMPROVED PLANNING Aiming to create a basis for more adequate planning approaches, this study looked for implications from complexity and uncertainty theories along behavioral theories on planning and decision-making. The review of those areas serves as a foundation to critically assess existing planning methodologies.

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Since cities are considered complex adaptive systems built of many subsystems, their issues are highly complex and interrelated. These properties are challenging planners and designers to accurately understand their dynamics. In such conditions, a tendency to generalize and simplify actual problems has been observed, which eventually leads to dysfunctional solutions. A profound understanding of systems theory and the dynamic properties of systems allows for a better comprehension of the city as a system, its behavior and challenges. Acknowledging the complexity of urban systems supports the understanding of the system’s components and their interrelations, aiming to outline the possible consequences of actions. Ackoff (1974) proposes to embrace the future and design for idealized goals using feedback to direct the transformations to more desirable outcomes. Furthermore, Koberg and Bagnall (1974) suggest looking for more adequate solutions by exploring the problem space and collecting insights from different perspectives. This helps to gain a deeper and more holistic understanding of problems. They put an emphasis on the need of iteration and an organic process, reflecting the complex problems that are linked with uncertain events. Planners have always been confronted with uncertainty, but there are differences in how they are approaching it. Planners often find themselves in situations with limited knowledge, which requires them carefully validate their assumptions and be mindful about the implications unexpected events might have on their plans. The distinction between epistemological and ontological uncertainty revealed two ways of dealing with them. Collecting, understanding and analyzing data into organized, contextual information for more informed decision-making can help to offset uncertainty. Nonetheless, the randomness of variable and unexpected events is hard to address and requires preparedness and the ability to react quickly but pertinently. These involve adaptive and communicative planning. Furthermore, the use of insights from behavioral studies can yield with better planning outcomes - which entails both interdependent planning and decision-making to ensure responsiveness. Considering pluralistic societies in planning adds to complexity but also shifts the focus towards a more deliberative and participatory practice. Planning collectively can help coping with biases and reduces the risk of experts’ ignorance of aspects which are unique for each problem. Such improved communication can yield with more understanding and better recognition of people’s needs, but it also demands the ability to deal with irrational and heuristic judgments and choices people tend to make.

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6.2. ISSUES AROUND EXISTING METHODOLOGIES Building on those theoretical foundations, this study assesses current and emergent planning methodologies: scenario planning, framework planning and adaptive planning. This evaluation reveals their contrasting potentials and limitations to deal with current and future uncertainties.

SCENARIO PLANNING

Scenario planning is a methodology that creates scenarios – simulations of possible future outcomes based on related assumptions. Exploring different scenarios aims at a better understanding of existing conditions, and at testing possible future actions and their consequences. This process encourages a great diversity of actors, pointing to confront diverse views on the future and providing additional information. This helps to address biased points-of-view, ignorance and prejudiced assumptions. On the other hand, a closer look at a few case studies revealed that often any obvious scenario gets selected for implementation. This methodology is separated from decision-making and therefore is not able to quickly respond to change. It only facilitates communication on various future visions and deals solely with epistemological uncertainty. Concluding, scenario planning excels in understanding and synthesizing problems and exploring various strategies but lacks interdependent character when it comes to decision-making and implementation.

FRAMEWORK PLANNING

Framework planning is a methodology that creates a flexible strategy which envisions and guides the further development. It is based on a holistic and precise assessment of historic and current conditions and aims to identify the value of existing assets. In framework planning, planners do not directly consider a multiplicity of futures as in scenario planning, but they establish specific performance criteria for a location, to ensure the outcome’s success This process intends to build a shared comprehension of complex problems across a diverse range of stakeholders. However, in the examined case, the planning process took place in two different political periods and therefore suffered from the poor political support in the second phase. A critical assessment in framework planning deals with epistemological uncertainty and gives a credible basis for the development. The performance criteria address ontological uncertainty and should also be incorporated into further decision-making.

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ADAPTIVE PLANNING

Adaptive planning is a methodology that creates robust policies. Although the methodology has not been applied in urban design and planning, it demonstrates meaningful potentials. It emphasizes adaptability, allowing for modification of the plan over time and across a range of futures. It involves monitoring of the performance of the actions taken and incorporates feedback and learning across the whole process. Adaptive planning addresses both types of uncertainty by offering capacities to adapt to anticipated events by the means of holistic analysis and multi-stakeholder deliberation as well as unanticipated events by monitoring and provisioning learning. It is interdependent with decision-making and enables planners and designers to take quick and informed decisions. The methodology draws on complexity theories, and acts on self-emergency or variation and experimentation, indicating potential for local neighborhood development. Such actions however could be constraining the methodology because it is dependent on the support of the authorities – especially when it comes to experimentation and the testing of new ideas.

6.3. AREAS FOR FURTHER RESEARCH AND INQUIRY Following the conclusions and discussion of this study findings, areas of further research and inquiry are elaborated as following.

THE INABILITY OF PLANERS TO ADRESS RAPID CHANGES

This work is based on the hypothesis, that current planning is increasingly inadequate in a rapidly changing world. Although having touched upon a few examples that support this hypothesis, further research is necessary. This could include the development of a framework for defining and assessing the success of cases in urban design and planning, based on similar contexts and comparable criteria, i.e. country, culture or scale. Such inquiries, revealing issues constraining long-run planning could make planning more successful. Seeing limitations could help to avoid romanticizing of the planning process as reflected in i.e. over-optimistic attitude.

DEVELOPMENT OF ADAPTIVE PLANNING

The methodology of adaptive planning examined in this study has been used exclusively for policy-making. Further inquiry on its application in urban design and

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planning, and testing of its suitability in real-world settings would be necessary. Looking at the aspects of adaptive planning described in this study, some questions remain open. It is not clear, how the ideas of self-organization or variation aiming to reinforce the incremental practices of local planning could help to influence a citywide transformation. This bottom-up practice based on emergent pattern puts into question, how much control a plan would need. Furthermore, an investigation on the plan’s success definition and performance measurement seems relevant. Designing open, adaptive plans requires a deep understanding of complex issues. We need more interdependencies between planning and decision-making to be able to react rapidly but in an informed way. This sets an additional area of inquiry, which would involve an enhanced interdepartmental, cross-organizational communication but also multi-stakeholder participation, aiming to increase the ability to agree upon convergences and to make balanced decisions.

USE OF DATA IN PLANNING

Despite the growing possibilities of big data, many complex problems still require qualitative approaches. The mix with quantitative approaches is still emergent, and conditions when to deploy such are ambiguous. Further works could investigate current practices of planning for uncertainty based on quantitative models and research where, when and how to deploy such a mix. This could involve research on modelling and quantifying uncertainties in urban design and planning, but also on the utilization of data for more informed decision-making. Nevertheless, as planning increasingly deploys simulations, planners need to develop the awareness that their outcomes are only as good as the assumptions behind them. The quality of data as well as the adequacy to the problem’s context needs to be considered.

FIT-FOR-PURPOSE METHODOLOGIES

Analyzing different planning methodologies can yield with great insights on their potentials and limitations. However, further inquiry could clear up the contextual relevancies of those methodologies. It is important to keep in mind that formula approach to planning doesn’t exist and that planners should recognize the uniqueness of each problem and problem’s setting. Using formulas could lead to generic outcomes, depriving cities from their identity. We need to look for better ways and opportunities to create more accountable planning responses.

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LIST OF FIGURES Figure 1: The frequencies of extreme weather events. (adapted from Holodny, 2016; Data sources: EMDAT; The CRED/OFDA International Disaster Database)

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Figure 2: Yellow Sea coastal region. (reprinted from Mcgranahan et al., 2007)

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Figure 3: Global balance of developed and emerging economies, 2011. (adapted from The Economist Online, 2011; Data sources: AT Kearney, Bloomberg, BP, dotMobi, Fortune, IMF, UBS, World Bank, World Steel Association, WTO)

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Figure 4: The accelerating growth of technology. (adapted from Lee, 2013)

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Figure 5: Uber Movement, Data for Boston. (reprinted from Geospatial World, 2017)

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Figure 6: An Anti Airbnb Campaign in Berlin in 2016. (reprinted from Heureka Online, 2016)

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Figure 7: Research design. (author’s own representation)

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Figure 8: Tree vs. Semi-lattice structure. (reprinted from Alexander, 1965)

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Figure 9: Examples of planning approaches in a spectrum. (adapted from Zuidema and de Roo, 2004; de Roo and Hillier, 2016; author’s own representation)

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Figure 10: System, Boundary and Environment. (adapted from Gharajedaghi, 2011; author’s own representation)

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Figure 11: Systems thinking design process model. (based on Ackoff, 1974; author’s own representation)

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Figure 12: Planning approaches regarding change. (based on Ackoff, 1974; author’s own representation)

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Figure 13: Divergent and convergent thinking (Lindberg, 2009)

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Figure 14: Expectations vs. Reality – linear and organic paths of a process. (adapted from D.school, 2017)

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Figure 15: Types of uncertainty. (adapted from Tannert, Elvers and Jandrig, (2007); authors own representation)

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Figure 16: Comparison of levels of uncertainty (Walker, Lempert and Kwakkel, 2013) and nature of uncertainty (van Asselt and Rotmans, 2000). (author’s own representation)

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Figure 17: Possible futures in levels of uncertainty. (adapted from Walker, Lempert and Kwakkel, 2013; author’s own representation)

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Figure 18: If not in your neighborhood, then where? (reprinted from Toronto Star)

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Figure 19: Heuristic rules leading to biased decisions. (adapted from Tversky and Kahneman, 1974, author’s own representation)

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Figure 20: Steps of scenario planning. (based on Schoemaker, 1995; van Notten, 2006; Wright, Bradfield and Cairns, 2013; author’s own representation)

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Figure 21: Representation of common methods of qualitative scenario building. (adapted from Foresight Horizon Scanning Centre, 2009; author’s own representation)

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Figure 22: Steps of framework planning. Urban Design Framework for Portland, Oregon (adapted from Jain, 2012; author’s own representation)

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Figure 23: Composite Site Analysis for Portland, Oregon. (Jain, 2012)

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Figure 24: Urban Design Framework for Portland, Oregon. (Jain, 2012)

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Figure 25: Steps of adaptive policy-making. (based on Walker and Marchau, 2017 and Swanson et al, 2010; author’s own representation)

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LIST OF TABLES Table 1. Comparison of selected methodologies: scenario planning, framework planning and adaptive planning. (author’s own representation)

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APPENDIX 1 STEP NO.

SCHOEMAKER (1995)

VAN NOTTEN (2006)

WRIGHT ET. AL. (2013)

1

Define the scope

Identification of subject or problem area

Setting the agenda 

2

Identify the major stakeholders

Description of relevant factors

Determining the driving forces

3

Identify basic trends

Prioritization and selection of relevant factors

Clustering the driving forces 

4

Identify key uncertainties

Creation of scenarios

Defining the cluster outcomes

5

Construct initial scenario themes

Impact/uncertainty matrix

6

Check for consistency and plausibility

Framing the scenarios  

7

Develop learning scenarios

Scoping the scenarios

8

Identify research needs

Developing the scenarios

9

Develop quantitative models

10

Evolve toward decision scenarios

Appendix 1: Steps of scenario planning according to: (Schoemaker, 1995; van Notten, 2006; Wright, Bradfield and Cairns, 2013)

108

APPENDIX 2 STEP NO.

WALKER ET AL. (2000/2017)

SWANSON ET AL. (2010)

1

Set the stage and define: - objectives - constraints - definition of success - options set

Understand the policy setting and define: - goals - key change-driving forces - plausible futures - indicators of success - policy options

2

Assemble basic policy - set conditions for success - basic policy actions

Enable policy innovation - conditions for success - basic policy actions

3

Increase robustness Vulnerabilities: - mitigating actions - hedging actions Opportunities - seizing actions - exploiting actions

-

- shaping actions

4

Set up monitoring system - signposts - triggers

Monitor - indicators of performance compared to objective - indicators of key factors and thresholds for triggering policy adjustments - stakeholder feedback - new information on emerging issues

5

Prepare trigger responses - corrective actions - defensive actions - capitalizing actions

Improve policy adjustments to ensure performance

6

Reassessment - other’s actions - unforeseen events - changing preferences

Formal review and reassessment

Appendix 2: Steps of adaptive policy-making according to: (Walker, 2000; Swanson et al., 2010; Walker and Marchau, 2017)

109

uncertain futures

uncertain futures

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