creativity in decision-making

A. Vaezipour, “Creativity in Decision-Making,” Tech. Rep. 2013. Research Description, Jönköping University, School of Engineering, 2013.

CREATIVITY IN DECISION-MAKING A. Vaezipour Jönköping University, School of Engineering

Abstract. Today’s commercial visual analytics software packages, as the common applications to business intelligence, with the aid of recent advancements in artificial intelligence along with providing numerous data visualization tools, have been also equipped with a number of data mining, machine learning, optimization algorithms and big data technologies, which can even further enhance the intuitive decision-making. This in fact has been the novel idea that has led this research to benefit from the concept of predictive analytics. For achieving the highest level of creativity in enterprise decision-making, here the visual analytics software packages have been discussed to happen to be the most reliable human-computer interaction (HCI) tools which can highly manipulate and effectively enhance the intuitive mind via the human’s sense of vision.


“There is no logical way to the discovery of elemental laws. There is only the way of intuition, which is helped by a feeling for the order lying behind the appearance... The intuitive mind is a sacred gift and the rational mind is a faithful servant. We have created a society that honors the servant and has forgotten the gift.” Albert Einstein

“The next phase for business is one that competes on innovation. Innovation emerges from organizations that nurture creativity…The first step is to understand creativity.” Rud, Olivia Parr

Human is the creature of emotion. Consequently in most of his organizational behavior, including decision-making, plenty of feelings are involved. Therefore in the process of aligning IT with business and further aligning business into the global economy considering the human factors would be essential for a successful and beneficial transmission. Here it is discussed that the rational approaches to decision-making can only be successful when the human factors are well considered and carefully interacted within the solution procedures.


In the particular realm of decision-making within enterprise the focus of this research is concerned with making most of the IT by taking into account

the

complexity

of

human

brain

and

its

intuitive

characteristics in order to come up with the creative decisionmaking solutions. Here it is argued that when even a simple decisionmaking task in the real-life applications, due to the uncertainties, cannot be treated effectively by using the conventional rational tools and fact-based decision support systems, a well informed intuitive mind can act creatively in dealing with the most complicated decisionmaking scenarios leading to the novel-nonlinear approaches, fast decisions and speedy reaction to the changes. For achieving the highest level of creativity in enterprise decisionmaking, here the visual analytics software packages have been discussed to happen to be the most reliable human-computer interaction (HCI) tools which can highly manipulate and effectively enhance the intuitive mind via the human’s sense of vision, facilitating the creation of heuristic methods even in the toughest cases. Today’s commercial visual analytics software packages, as the common applications

to

business

intelligence,

with

the

aid

of

recent

advancements in artificial intelligence along with providing numerous data visualization tools, have been also equipped with a number of data mining, machine learning, optimization algorithms and big


data technologies, which can even further enhance the intuitive decision-making. This in fact has been the novel idea that has led this research to benefit from the concept of predictive analytics. As the main contribution of this literature along with presenting a number of case studies, mainly focused on the decision-making tasks in the fields of our interests i.e. optimal engineering design, production decision-making, materials selection, health and life science, the effectiveness of the visual analytics, and later, predictive analytics in dealing with the real-life decision-making problems is described and a number of success facts behind this approach are reviewed. In carrying out the case studies, furthermore, the concepts of business modeling, requirement

specification,

algorithms

implementation

and

software testing are also well practiced.

Keywords: Intuitive Mind, Decision-Making, Multiple Criteria DecisionMaking, Enterprise Decision Management, Business Intelligence, Reactive Business Intelligence, Predictive Analytics, Data Mining, Machine Learning, Decision Support System, Artificial Intelligence, Heuristic, Intuition.


Introduction “Truth is ever to be found in the simplicity, and not in the multiplicity and confusion of things.” Isaac Newton

In real-life decision-making a problem has to be considered from very different perspectives. The scientific solution into such problem has been the approach what we call it today multiple criteria decision-making (MCDM) where multiple criteria are simultaneously analyzed. In fact in the human’s daily life including his organizational problem-solving duties, there are typically multiple conflicting and nonlinear criteria as well as uncertainties that need to be evaluated in making decisions. Consequently a vast number of MCDM methods, surveyed in e.g. (Turskis et al. 2011, Henderson et al. 1993, Gandibleux et al. 2002, Marler et al. 2004, Pohekar et al. 2004 Figueira et al. 2005), have been developed, since 1654, for rationally suiting the decision-making problems for instance in economics, managements, engineering, design, energy, business, etc. The French mathematician Blaise Pascal in 1654, in order to deal with uncertainties in real-life decision-making problems proposed the initial form

of

expected

value

theory.

The

theory

in

which

could


simultaneously consider the probabilities as well as values and consequences. The methods on the basis of the Pascal’s theory has been used ever since in different problem solving realm as a rational approach.

Expected value theory; the consequences’ values and probabilities are multiplied and summed, then the different decisions’ utilities are compared for an optimal decision

Politician, Benjamin Franklin, the founding father of the United States widely used and promoted basically the same method, yet he called it moral algebra, described in details in (Gigerenzer 1999, 2007, 2008). As he simplifies and explains the method further; for coming up with a rational decision, you should make a list of all that might happen as a result of your choosing a particular option, and then decide how good or bad each of these possible outcomes would be (probability). According to Gigerenzer (2007), this has been one of the earliest way of rational and scientific decision-making in real-life application. However it has been argued that whether the inclusion of probability theories and complicated mathematical modelings in calculating the decision values actually worth implemetation in real-life applications where uncertainties are way challenging.


The theory of decision-making had been progressed over the years from the further advances in expected value theory, and later, expected utility theory starting by the works of e.g. Swiss mathematicians, Bernoulli’s family; Nicholas and Daniel from the years 1713-1740 up until now. In fact the idea of weighting and adding scheme of the expected value theory had been highly influencing the rational and logical thinking in modeling the decision-making problems over the years in numerous areas e.g. moral behavior, motivational behavior, managements, health and life sciences. Subsequently some times around and after world war II other theories and disciplines e.g. game theory, graph theory, operational research and other analytical methods as well as probability theory have become more popular and further well contributed to the progressing of the rational and logical decision-making. In the modern days the major advancements in decision-making theory have been accomplished by the genius works of Herbert Simon, from about 1950 up until 2001, on artificial intelligence (AI) and its interactions with psychology, sociology, economics, and human behavior in organization. Considering a definition to organizational decision, Herbert Simon states that “any decision involves a choice selected from a number of alternatives, directed toward an organizational goal or subgoal.” (Herbert 1976). Following figure describs the Herbert


Simon’s graph of decision-making; the three steps, pointing out the important role of AI in organizational decision-making tasks.

Herbert Simon’s the graph of decision-making (Herbert 1976)

Acording to Simon the task of rational decision-making is to select the alternative that results in the more preferred set of all the possible consequences. This task is divided into three required steps: firstly the identification of all the alternatives; secondly the determination of all the consequences resulting from each of the alternatives; and finaly the comparison of the accuracy and efficiency of each of these sets of consequences.


As practical as it may sound however Simon further clarifies that due to uncertainties involved in real situations, any organization attempting to implement such model would be unable to fully satisfy the three requirements. Although still a group of scientists e.g. (Russell 1997), have a strong belief that Simon’s three steps toward a rational decision can be accomplished along with the progressing of AI tools, described in e.g. (Russell 2003), yet it has been argued in e.g. (Shafer 2013 & 1987) and (Horvitz 1988), that it is highly improbable that one could study all the alternatives, and all the consequences relying only on AI tools. They conclude that AI actualy can not be adequate and, one should therefore carry out the law of probability e.g. Bayes' theorem to analyse the total uncertainties involved, along with benefiting from the AI convenient tools. However doing so, crearly makes solving the task even more complicated involving too many mathematical modeling. In the other words despite all the advanements that AI has brought to the new approaches and software tools of decision-making it has been well argued (Gigerenzer, 2007, 2008, 2011) that with the ever increasing complexity of today’s decision-making problems at the presence of huge uncertainties, multicriteria and dynamic nature of big data (subjected to change), the conventional procedures to rational decision-making simply can not be the answer. With this, Gigerenzer


strongly criticizes the efficiency of the most logical and statisticalbased MCDM tools ever been produced for rational deision-making. Worth mentioning that both Gigerenzer (2011) and earlier Herbert (1976) belive that using either AI, statistics or laws of probability e.g. Bayes' theorem can be useful for rational decision-making but only considering simple problems at the precence of adequate amount of data which can well describe the problem. However this is often not the case in most of the enterprise decision-making tasks of the uncertain world. This at the first sight seams in fact to be a huge obstacle and concrete limitation to the rational decision-making. On the other hand however the human being striving for rationality and yet with his limited knowledge and shortage in his data processing abilities, which have been well studied in e.g. (March 1978), has been appeared to have a certain ability to develope some working procedures, so called Heuristics, that simply overcome such difficulties and complexities we often face in rational decision-making.

Heuristics and gut feeling “What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention.” Herbert Simon


Gigerenzer in “gut feelings: the intelligence of the unconscious” (2007), and later in “gut feelings: short cuts to better decision making” (2008), shows that heuristics are often created based on gut feelings, and the accuracy of the method and its success rate depend on the structure of the organization environment and the experience of the decision-maker. According to the litriture of Herbert’s administrative behavior (1976), heuristic is what a person or organization uses to achieve approximately the best result in a speedy and seamless manner, and often more accuratly compairing to the complex optimization models. Overall the heuristics can be more accurate than more complex strategies even though they process less information. Decision-making in organizations typically involves heuristics because the conditions for rational models utilizing logical and/or statistical rules can not effectivly deal with an uncertain world. Yet developing a systematic theory of the building effective heuristics is proposed by Gigerenzer (2011) as the major challenge for the future research. He further clarifies that for now we know something for certain that with sufficient experience, human can learn to select proper heuristics from his adaptive toolbox. Creativity in this realm is a worthy occurrence of human mind which one can bring to an organization. Yet it is a nonlinear and unexpected


appreach and hard to actualy be planned in the business/IT alignment. Furthermore it clearly can not be produced by increasing the IT usage. However, as it is discussed later in this report it can be further directed and

empowered

with

the

AI

and

Business

Inteligence

(BI)

applications. The procedures of producing the heuristics as the efficient cognitive processes consist in assuming that the decision-making task can be isolated from the rest of the world including a limited number of variables and a limited range of consequences and therefore uncertainties by ignoring some parts of the information. Creating heuristics is considered as a valuable approach and a creative accomplishment in any organization.

Worth

mentioning

that

in

an

organization

indeed

experience of employees weather consciously or unconsciously plays an important role in being creative to produce heuristic methods (Herbert 1976). Companies are so keen on benefiting from the creative minds of their employees who generate such short cuts that potential employees may be encouraged to walk in the woods, listening to their favorite music, having flexible working hours and comforting themselves in their workplaces in order to get more inspiration. Yet as Kandel (2007) argues we are at a very early stage in understanding the creativity and other higher mental processes, and certainly due to the technological advancements of this era one can get a very good insights


into the situations that may lead to increased creativity. To figure out the origins of creativity in organizations or creativity as an individual occurrence the topic has long been considered both from a social, psychological and from a biological point of view.

Unconsciousness, creativity, and brain “Creativity cannot be forced. It can only be allowed. However, much can be done to increase the flow of creativity.” Rud, Olivia Parr

According to the above statement the three major rational approaches to modeling decision-making problems are then identified to be; logic, statistics, and heuristics. Although each of these approaches suited to a particular kind of problem, yet they have not been treated equally. In fact in rational problem solving the heuristics have been often associated with errors, while logical and statistical rules are believed to define rational thinking in the major situations (Gigerenzer 2011). This would contradict the fact that huge amount of decision-making tasks in organizations are often done using heuristics and on the gut feelings weather consciously or unconsciously. Gigerenzer (2011) reviewed studies on decisions by individuals and institutions, including business,


medical, and legal decision-making, showing that heuristics have been often reported to be more accurate than complex rational strategies utilizing AI and/or statistics. Considering the beliefs of Kandel et al. (2000) and Freud (1931) that human makes a lot of decisions by unconscious evaluations makes the situation even more interesting to explore further. Kandel et al. (2000) provide concrete reasons e.g., Libet (1993)’s experiments on free will and unconscious decision, to prove that human is not consciously aware of most of his decisions. Further evidence suggests that unconscious phenomena may include repressed feelings, visual memories, automatic skills, subliminal perceptions, thoughts, habits, and automatic reactions (Westen 1999). The unconscious mind consists of the processes in the mind that occur automatically without introspection. In fact in everyday’s life there are lots of decisions that are made unconsciously than consciously (Freud 1931). Now we know that human makes a lot of decisions by unconscious evaluations (Kandel et al. 2000).


An iceberg; a visual representation of Freud's theory indicating that most of the human mind operates unconsciously; the yet to be known capacity

On the other hand conscious decision-making can function well when one is dealing only with a very limited number of fixed alternatives as it would be possible to focus consciously very effectively on one thing at a time using some rational approaches e.g. moral algebra (explained in Gigerenzer, 1999). Yet at the presence of multiple options, relying on


unconscious mind is very likely to be creative and effective (Kandel et al. 2000). Herbert (1960, 1958, 1972, 1987, 1955) had well studied the concept of unconsciousness and creativity in human organizational behavior and decision-making from the psychological and sociological point of view. According to Herbert’s and the earlier works of Barnard (1938), the creativity of an individual in an organization could be highly effected by the goals and environments of that organization. They further argue that personal choices may be determined whether an individual joins a particular organization. As a member of an organization, an individual makes decisions not in relationship to personal needs, but in an impersonal sense as part of the organizational goals. And one’s experience in an organization using a proper tool can bring him a learning and creativity ability to create heuristics (Gigerenzer, 2001). Along with psychological and sociological factors involved in human creativity in organizations, on the other hand, the anatomical structure and functioning aspects of the brain are aslo identified as one of the major effective success factors to implementing any BI alignment project (Rud 2009). To draw attention to the importance of study the function of the brain worth mentioning that Kandel et al. (2000) in the book principles of neural science argue that all mental functions, including conscious and unconscious decision, whether a creative


heuristic or a logical approach, come from the brain. In this sence studying the structure, function, ability and processing quality of the brain plays an important role in investigation of the creative thinking and problem solving.

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