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Using Rare Event Modeling & Networking to Build Scenarios and Forecast the Future Abstract—Recent history has reminded society that prediction in the forms of scenario generation, red-teaming, war-gaming, and future planning must incorporate innovative perspectives such as rare, but impactful, events and multiple phased events. Rare event modeling is based on the premise that important events can be dominated by outliers. Current forecasting focuses on more probable events. Likewise, multiple-phased actions can create paradigm shifts that move events in a new direction or push them to higher levels of impact. Therefore, impact potential must be understood, calculated, and utilized. Our networkbased scenario system provides for state-change and high-impact scenario events that enable impact measures to be calculated and used by decision makers. Keywords—black swan, tipping point, scenario, wargame, future planning
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I NTRODUCTION
Our Deep Red project’s goals are to build a viable framework, develop effective tools, and assemble algorithms to develop realistic military-related scenarios for technological and doctrinal preparations that could affect DoD planning 1030 years in the future. This ability to project into the future is important to ensure that planning systematically covers the important contingencies and to ensure that scenarios are based on validated data that produce effective forecasting. At present, scenarios for wargames that ultimately lead to important highlevel and high-cost decisions are developed through ad hoc methods, typically in response to short suspense taskings. Our methodology takes into account new analytic techniques that incorporate multidisciplinary perspectives and use integrated network database structures and processes. The ultimate goal is to develop an effective capability which provides a scientifically and empirically justifiable basis for force planning decisions (military unit size and structure) as well as a rigorous framework in which future technologies, capabilities, doctrines and force structures are formulated, verified, tested, refined, and validated. If recent history has taught us anything, it is that we need flexible thinking where planning considers the unexpected by developing scenarios that incorporate the elements of rare event and tipping point modeling. Our framework incorporates novel analytic perspectives such as rare events and tipping points in the process. The rare-event model involves the idea that our lives are not only restricted to predictable or central, tendencies but also affected by potent outliers – extreme events that lie outside the realm of regular expectations. Taleb [1] popularized the phrase black swan in his book with that title. This is not a new idea, as Gould [2] wrote: “Our culture encodes a strong bias to neglect or ignore variation. We tend to focus instead on measures of central tendency, and as a result we make some terrible
mistakes, often with considerable practical import.” Even more to the point, Gould [3] added: “Variation is the hard reality, not a set of imperfect measures for a central tendency.” Since these kinds of events are considered improbable, they are usually not considered by future planners. Our framework is based on the belief that scenarios should consider the extremes as feasible events. We attempt to overcome this normalcy bias by modeling reality with distributions of heavier tails, such as those in inverse power law or Pareto distributions. Tipping points are a similar phenomenon. This term was popularized by Gladwell [4], where specific incidents or level of activity create environmental or operational paradigm shifts. These kinds of shifts move scenario events in an entirely new direction or push them to a different level of impact. Tipping points and state shifts are related to the complexity concept developed by mathematicians like Poincar´e [5] over a century ago. Examples of black swan/tipping point events include Hitler’s rise to power; collapse of the Soviet Union; 9-11-2001 hijackings; 2003 suicide bombing of UN HQ in Bagdad; 2008 Mumbai attacks; 2005 London subway bombings; 2011-2012 Arab Spring riots. DoD futures planning system uses predictions developed from scenario generation, red-teaming, and war-gaming in hope of mitigating the impact of strategic surprise. We are building a culture of flexible thinking where futures planners consider the unexpected. The reality of our framework is that it goes beyond forecasting the future to helping to prepare for it. Our scenario-generation system uses network structures to create alternative dimensions of the future that reflects a variety of potential driving forces, interactions among those forces, and threshold scenarios that allow for significant paradigm shifts. Red-teamers and war-gamers use these scenarios to help think more creatively and accurately about the future. Good scenarios challenge decision makers’ mental models about the world and help lift the blinders that limit creativity. II. T HREAT TO NATIONAL D EFENSE Simultaneous fragmentation and globalization of society has produced a large collection of non-state actors and networked organizations. Some are terrorists who exploit violent ideologies into political or economic power. These terror elements often have no hierarchical command and therefore fall on the edge of society’s norms. As adaptable networks, they coalesce and metastasize as needed to produce unpredictable events, appear in unanticipated places, and create numerous shifts in paradigms. Through violence and surprise, these events can be high impact and produce significant second and third-order effects. DoD needs to anticipate these high impact events, some produced by scheming belligerents and some through innocent situations. DoD’s decision making and future planning systems
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must become as innovative and flexible as the challenges it faces. While there have been many innovative ideas since 9-112001, the threat detection system is not yet in full working order. The goal of the system is to anticipate emergent threats. The World Economic Forum annually produces the Global Risks Report [6], which is long-term in its approach and engages in “what if?” scenarios. This is in line with our Deep Red network-based methodology by categorizing responses according to two measures, the likelihood of the event and its potential impact. The 2012 report specifically highlights connections between economics, demographics, and migration and the interplay between information security, data fraud, and digital misinformation. The report also aggregates and builds connections between the potential threats. In sync with our model, the report prepares society for the future. III. M ILITARY AND M ATHEMATICAL T HEORY A. Military complexity Carl von Clausewitz’s [7] theory of warfare established the unexpected element, often caused by the fog of war or the friction of combat, as the primary challenge for the commander. The complex social, political and cultural nature of warfare had many significant descriptions, roles and considerations in Clausewitzian theory. Careful analysis of asymmetric structures and elements of unpredictability by Clausewitz produced a calculus of warfare that is still relevant in modern operations and cries out for network models and rare event and tipping point analysis. B. Mathematical complexity Henri Poincar´e [5], developer of nonlinear science and complexity theory, showed that unpredictability can stem from statistically random phenomenon or the nature of a complex system where small permutations cause larger unanticipated results. By its nature, complexity cannot be controlled, but it can be measured and tracked. In complex systems, feedback loops, meshed interactions and network connections cause unexpected behaviors to emerge. Under these phenomena, normal is not the normal—outliers are just as important. In complex systems, there is often surprise, fluctuation, and uncertainty. Complexity in our scenarios is non-linear and non-reductive and does not respond to authority or force. In red-teaming and war-gaming such scenarios, intensity and strength are often not as important as influence and cogency. C. Myth of normalcy The bell-shaped probability distribution constructed to explain variability in the results of experimental measurements is often erroneously assumed to be applicable to many data sets in the informational, social and behavioral sciences. Barab´asi [8] determined such distributions to be unrealistic for many human-based networks; that is, using real-world data, he showed that the number of connections between nodes on the Internet deviate markedly from the normal distribution. Others have found that properties of complex networks in general
have inverse power-law distributions. The inverse power law was presented in the systematic study of data on income as analyzed by Pareto [9]. Our Deep Red framework attempts to undo the overuse of the normal distribution and builds a system that counters the normalcy bias by looking at the tails of the probability distributions. IV. D EEP R ED F RAMEWORK The schema for our framework places scenario development as the foundation for the higher-level analysis, such as redteaming, war-gaming, and intelligence production. Figure 1 provides a chart of the basic elements of the Deep Red futures-planning framework. Futures planning is the proactive combination of efforts to deduce concepts and strategies, across military functions, and missions. With the understanding that we are looking at a capability to guide investment to meet future demands, we seek to eliminate capability gaps and blind spots. Futures planning has suffered from its focus on mainstream elements based solely on economics, technology, kinetic events, and diplomacy. We now realize that we must consider the entire landscape of the feasible. Topics need to be considered in such diverse areas as human social behavior; weather and ecological disasters; resource (rare earth minerals, food and water) allocation; social, religious, and political movements; energy needs; and biological, chemical and health epidemics. A. Red-Teaming and War-Gaming General Paul Van Riper’s famous red-team exploits during the Millennium Challenge 2002 (MC02) enabled the military to consider how smaller, less powerful forces, can defeat larger, highly technical forces. This was a breakthrough moment for red-teaming and outlier-enhanced scenario development. In the MC02 wargame, tipping points were uncovered where the smaller red force was able to place the larger blue force into an unexpected, uncomfortable and unplanned paradigm. V. T IPPING P OINTS IN S CENARIO F ORECASTING Tipping point and rare event methods are beginning to effect system development in planning and forecasting. For instance, Clauset and Woodward [10] use rare event analysis to estimate a 50% chance of a major terrorist attack over the next 10 years. Lagi, Bertrand, and Bar-Yam [11] use complexity analysis to address the role of food price increases as a tipping point during the Arab Spring protests. These examples are the tip of the iceberg in using rare event and tipping point modeling. Another aspect of scenario development is the inclusion of political/geographic perspectives. For example, the Rajaratnam School of International Studies (RSIS) in Nanyang Technological University in Singapore seeks to “Ponder the Improbable” and has developed studies that lie outside the mainstream of international relations. These outlier studies are good sources for data collection. The World Values Survey [12] also provides sources of data for potential events that could help in this effort. DoD-sponsored Minerva Initiative reports are also a viable source of applicable information. The most significant
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Fig. 1. Deep Red Futures Framework: Schematic of the role of scenario development, red teaming, and war gaming in futures planning.
study of this kind was reported in a 2008 white paper [13] entitled “Anticipating Rare Events: Can Acts of Terror, Use of Weapons of Mass Destruction or Other High Profile Acts Be Anticipated?: A Scientific Perspective on Problems, Pitfalls and Prospective Solutions.” This 200-page report written by 52 experts in the field outlines many ideas and methods used in Deep Red. This work is complemented by a EUCOM Deep Futures study [14] that uses a “noise floor” concept that reveals rare events. Schwartz [15] advocates using scenarios to make better current decisions and think more concretely and accurately about what is possible in the long term. VI.
D EEP R ED M ODELING
To properly model unexpected events, it is necessary to understand those that happened in the past, why they happened, and why they were missed. Our database of identified black swan events allows for the deconstruction of events into their qualitative and quantitative characteristics. The analysis provides the basis for the scenario-generation model. Our Deep Red model includes the following: 1. Collaborative approach, network structure, and flexible processes As shown in Figure 1, scenario development is the foundation of the futures planning process. This work uses an interdisciplinary perspective to develop a culture of sharing information, collaborating, and designing methods of information analysis and processing. Scenario collaboration is achieved by building a vision of world events through a network of information and corresponding actions.
2. Modeling and simulation efforts to support future scenarios development The automated network must consider possible extensions of all actions and events by investigating “what if?” questions. For paths created by the “what if” methodology, new scenario developments are modeled and simulations run to ensure the validity of possible combinations. 3. Multi-scale capabilities to fuse tactical to strategic intelligence Scenario development must include various levels of fidelity. While the main thrust of this research is in the operational and strategic levels, it is clear that tactical level elements and actions can synergize into larger-scale effects. This multi-scale nature of scenario development is modeled by our network to ensure small, yet important features are not assumed away by the system. 4. System measures Embracing the complexity of military operational systems is one of the important aspects of systems modeling. In the case of scenario development, it is possible to measure the complexity of the evolving network to ensure the proper level of realism in the scenario. Impact measures show developers and planners the significance of specific scenario events. 5. Assessment Our next step is to design a series of test scenarios to be gamed out with our network model to inform our concept and framework. VII. E XAMPLES OF S CENARIOS The following two examples show the thrust of our ongoing efforts: A. Example: Counterinsurgency scenario Counterinsurgency (COIN) operations are essentially a series of actions that attempt to countermand the surprising strategies and tactics of an insurgency. In order to model a COIN scenario, the events of the insurgency must be understood [16]. Drawing on Galula [17], the traditionalist form of an insurgency generally follows stages, which sometimes blend together. The first stage is blind terrorism in which insurgents conduct seemingly random, high profile attacks to gain publicity for their cause. The second stage is selective terrorism with the goal to separate the counterinsurgents from the population by targeting government officials. The insurgents garner money from the population through fear of reprisal. The next stage, guerrilla warfare, is effective when the counterinsurgency is weakened. Guerilla warfare establishes power for the insurgents so they can contend with the counterinsurgency. The next stage transitions from guerrilla action to more regular warfare. The final stage increases operations against the counterinsurgency to try to force the counterinsurgency to sue for peace or leave the region. This basic COIN network framework is being used to develop test scenarios. As the scenario unfolds, COIN red-teamers and war-gamers seek to counteract each insurgent stage with combinations of kinetic operations, intelligence, security, logistics, civic affairs projects, government cooperation, psychological operations,
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and coalition network models. The goal of red-teaming and war-gaming is to understand how the different combinations of operations are possible at different stages to evaluate COIN doctrinal effectiveness.
[6] [7] [8]
B. Example 2: Earth health scenario Society is interested in developing and using models to forecast environmental health conditions of our planet. Scientific studies have concluded there is growing stress on Earth’s environmental systems. The UN-backed Millennium Ecosystem Assessment Synthesis Report [18] found that nearly two-thirds of Earth’s life-supporting ecosystems, including clean water, pure air, and stable climate, are being degraded by unsustainable use. Soaring demands for food, water, fuel, and timber have contributed to environmental changes. Despite considerable research on local habitats and regions, current models do not adequately inform decision makers on the global issues of public interest. Our network model is analogous to a scenario that enables eco-system complexities with multiple interactions, variable feedbacks, emergent behaviors, disaster effects, and impending state changes or tipping points. The recent Nature article [19] written by 22 internationally known scientists entitled “Approaching a state shift in Earth’s biosphere” outlines many of the issues associated with the need for scientific models and the importance of predicting potential state changes of planetary health. Although warning signs are appearing, no one knows if such an extreme state is inevitable. We use this eco-system challenge problem as the basis for a test scenario with: 1. Events that embrace the complexity of Earth’s interrelated systems and take into account rare events that include the networked effects of the local system on global conditions. 2. Factors in the scenario that produce unhealthy global tipping points and state-shifts that involve the use of ecosystem management and military operations to prevent or limit these impending state changes. VIII. C ONCLUSION We are in the initial stages of this Deep Red research effort and, therefore, are actively collecting data, building network models, and establishing test cases. While we realize the task and its challenges are daunting, we remain optimistic that progress in systemic and networked scenario development and ultimately futures planning can be made to benefit our military force. R EFERENCES [1] [2] [3] [4] [5]
Taleb, N.N.: The Black Swan: The Impact of the Highly Improbable. Random House, New York (2007) Gould, S.J.: Full House. Harmony Books, New York (1996) Gould, S.J.: The Median isn’t the Message. http://people.umass.edu/ biep540w/pdf/Stephen%20Jay%20Gould.pdf Gladwell, M.: The Tipping Point: How Little Things Can Make a Big Differenc. Little, Brown, Boston (2002) Poincar´e, H: The Future of Mathematics. Revue generale des Sciences pures et appliques, 19, 23 (1908)
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World Economic Forum: Global Risks 2012 An Initiative of the Risk Response Network (Seventh Edition) (2012) Clausewitz, C.V.: On War, (trans. James John Graham). N. Trbner, London (1873) Barab´asi, A.L.: Linked: How everything is connected to everything else and what it means for business, science, and everyday life. Penguin, New York (2002) Pareto, V: Cours d’Economie Politique. Lausanne and Paris (1897) Clauset, A., Woodard, R: Estimating the Historical and Future Probabilities of Large Terrorist Events. Annals of Applied Statistics (2012) Lagi, M., Bertrand, K.Z., Bar-Yam, Y.: The Food Crises and Political Instability in North Africa and the Middle East. Physics and Society (2011) arXiv:1108.2455 World Values Survey http://www.worldvaluessurvey.org/index findings http://redteamjournal.com/papers/U White Paper-Anticipating Rare Events Nov2008rev.pdf EUCOM Deep Futures: A Method of Piercing the Noise Floor. Technical Report, 2 Jun 2012 Schwartz, P.: The Art of the Long View: Planning for the Future in an Uncertain World. Currency Doubleday, New York (1991) Sloan, S. and Bunker, R.: Red Teams and Counterterrorism Training. Univ. of Oklahoma, Norman (2011) Galula, D.: Counterinsurgency Warfare: Theory and Practice. Praeger, New York (1964) http://matagalatlante.org/nobre/down/MAgeneralSynthesisFinalDraft. pdf Barnosky, A.D., et al.: Approaching a state shift in Earths biosphere. Nature 486, 52–58 (2012)
