using systemigrams and fuzzy cognitive maps to … paper - usma.pdf · using systemigrams and fuzzy...

Proceedings of the 2015 Industrial and Systems Engineering Research Conference S. Cetinkaya and J. K. Ryan, eds.

Using Systemigrams and Fuzzy Cognitive Maps to Understand and Quantify Causality

Abstract ID: I101

Robert Prins, John Farr, Kenneth McDonald, Shawn Fitzgerald, and Derek Sanchez

Nuclear Science and Engineering Research Center and the Center for Nation Reconstruction and Capacity Development

United States Military Academy West Point, New York 10996

Abstract

The need to fully understand follow-on effects from a catastrophic event resulting from the use of a weapon of mass destruction has become even more important with the growth of extremism and as the interconnectivity of the world increases. Often described as a network of networks or complex systems, follow-on effects from these catastrophic events can be debilitating far beyond the physical damage of the event. This research involved using systemigrams and fuzzy cognitive mapping (FCM) schemes to develop causal dependencies along with an indication of the importance of factors driving the behavior. This approach was used to define and quantify, though at a very high level, the structure, factors and behavior of the system. Systemigrams were found to be an important first step in understanding causality and structure. The FCM technique was found to be useful in identifying the importance of critical factors and processes that drive the behavior of the system. Ultimately we believe that systemigrams when used with FCM play an important role in developing detailed models using tools such as system dynamics to fully understand and model complex systems behaviors. Keywords Systems thinking, weapons of mass destruction, causality, fuzzy cognitive mapping, complex systems 1. Background 1.1 Consequence Analysis Consequence Analysis (CA) aims to quantify the negative impacts of a hazardous event due to anticipated eventualities [1]. Essentially, CA is used to predict, model, and analyze the higher order effects on a nation of a chemical, biological, radiological and nuclear, and high yield explosives (CBRNE) event. After examining a broad survey of research, gaps in understanding CA become clear: the research is neither large enough in scale nor holistic enough to allow adequate analysis of the conditions of a post hypothetical CBRNE event. Many attempts at CA are too small in scale to truly understand a significant CBRNE events and fail to consider higher order effects. For instance, while the first order effects of nuclear weapons are well documented, the majority of reports fail to extend the analysis on blast, radiation, and immediate casualties to important second and third order effects such as political stability, infrastructure, or social services [1,2]. CA is also conducted for industrial level incidents. Arunraj and Maiti [1] identified a successful process for CA by conducting accident scenario analysis, identifying and classifying losses, and finally estimating losses [1]. Losses are then quantified and integrated so that the cumulative effect becomes clear [1]. However, the methodology is never applied above a single industrial event and the scope poorly translates to the conditions of a large-scale CBRNE event. When the literature does consider higher order variables, it does not examine holistic outcomes or how the variables interact with one another. Several examples highlight this finding to include Carter et al [3] who assessed several higher order effects, such as the effects of individual fears of radiation or how fear of additional strikes may impede a federal response.[Carter] However, there is little attempt to pair the above effects or to include the multitude of other factors that would shape the post CBRNE event landscape. Dodgen et al [4] recognized that while the research


provides a thorough look at psychological consequences, an approach that integrates a broader survey of variables and effects is needed to conduct CA. Eun et al [5] used Bayesian Network theory and system dynamics simulation to conduct an assessment of key infrastructure after Hurricane Katrina. The literature is intended to be a decision support tool for identifying key infrastructure to protect in future natural disasters, but is limited in scope to the immediate aftermath regarding infrastructure. Finally Smith et al [6] used a general equilibrium model to disaggregate the United Kingdom financial market into 12 related sectors for pandemic influenza. Modeling these sectors through simulated pandemics lead to observations about system behavior such as the agriculture, mining and food-processing sector, and food production sectors being least affected. The research is limited, as it only examines the financial sector, but is promising as it offers a method for holistic look of a system that may be applicable to larger systems of systems. Ultimately, while some research exists conducting CA for individual higher order effects of a CBRNE event, there is a gap in analyzing the holistic and cumulative effect of said consequences. Analyzing a single variable limits CA, as system level intricacies and interactions are lost. While the above research is useful, actual CA cannot exist in a vacuum where all other variables are held constant. 1.2 Consequence Management In Joint Publication 3-41 [7], the U.S. military defines CM as “…the overarching U.S. capability and the strategic national direction, to prepare for, respond to, and recover from the effects of CBRNE incident at home or abroad, and whether or not it is attributed to an attack using weapons of mass destruction (WMD).” In other words, CM is the systematic process used by a nation or state to react to a CBRNE event. The challenges associated with CA and CM are best illustrated in a modern example. On March 11, 2011, the Great East Japan Earthquake caused exponential radioactive material to be discharged from the Fukushima Daiichi plant. Structural damage caused by the earthquake and flooding from the follow on tsunami resulted in explosions at several plant reactors [8]. The explosion resulted in the release of approximately 5.5-10% of the radiation released at Chernobyl. Additionally, the plume created by the explosion resulted in the deposition of radioactive materials into the Pacific Ocean and northwest onto the Japanese main land [9]. The CM for the Fukushima disaster began with the Japanese Prime Minister declaring a state of emergency and instigating an evacuation of an estimated 200,000 people [9]. Japan then moved to the arduous process of managing refugees, providing medical care, and rebuilding after a disaster projected to cost over $300 billion dollars with more than 20,000 killed [9]. The presence of 150,000 U.S. citizens in Japan consequently lead to significant U.S. involvement including the Department of Defense (DoD), Nuclear Regulatory Commission (NRC), Department of Energy (DOE), Office of Foreign Disaster Assistance (USOFD), and the Department of Health and Human Services (DHHS); the primary role of these agencies included both clean up support and research for nuclear and radiation safety [10]. The CM had many significant problems. To begin, there was large loss of life and enormous physical damage to coastal villages in Fukushima complicated radiological cleanup [10]. Additionally, radiation estimation technology regarding the radioactive plume proved to be inaccurate and needed constant updating [11]. Another troubling failure was that despite attempts to control radiation distribution, statistically significant levels of radiation were found in several food groups such as vegetables, fish, and beef [12]. Finally, poor communication by Japanese officials, combined with panic by the Japanese citizens, caused some evacuating personnel to unnecessarily enter the fallout zone based on faulty information [8]. Of great importance was that “the biggest crisis communication error of the Japanese government was failure to answer the second and third questions satisfactorily; i.e., its failure to forewarn people about tomorrow’s and next week’s probable headlines, and its failure to guide people’s fear about worst-case scenarios. [13]” 2. Interconnected Networks WMDs can pose deadly and destructive threats to the security of any nation state. Implied within the desire to reduce collateral damage brought upon by increased accessed and accuracy of WMD systems is the need to better understand the overall outcomes and objectives for both offensive (targeting and employment) and defensive (resiliency assessment and preparations) actions. These actions help to frame both the threat environment (from a military and terrorist perspective) where the immediate effects of the weapon system are felt and the operational


environment which extends beyond the immediate area and involves other areas which are either positively or adversely impacted by the action. As shown in Figure 1, the operational environment becomes very large very quick due to the interconnected nature of networked economies, social media, infrastructure, and political alliances among others.

Figure 1: Example depiction of the Interconnected Operational Environment [2] The effects on the interconnected networks are felt at varying degrees among all, and even the slightest insult to the system can have far reaching adverse effects well beyond the challenge of planning the initial objectives. Complexity and uncertainty are at the heart of this challenge and as stated in the 2013 Army Strategic Planning Guidance, “Today’s global connectivity and its effect on the changing environment reinforce that lasting strategic results can only be achieved by effectively influencing people. Success depends as much on understanding the social and political fabric of the situation as it does on the ability to physically dominate it”[14]. This research attempts to quantify the interconnectedness of the operational environment by initially developing a visual representation (systemigram) and then applying FCM techniques to understand how a change to one network affects the other connected networks. This approach was used to define and quantify, though at a very high-level level, the structure, factors and behavior of the system. Ultimately systemigrams, when used with FCM, play an important role in developing detailed models using tools such as system dynamics to fully understand and model systems behaviors. The U.S. DoD defines a system as a functional, physical, and/or behaviorally related group of regularly interacting or interdependent elements forming a unified whole and then further defines an operational environment as a complex interaction of political, military, economic, social, information, and infrastructure (PMESII) domains [15]. A systemigram is based upon the notation of a systemic diagram. Systemigrams are a valuable tool when trying to convert rich text concepts at a high level into a structured diagram to visualize complexity and dependencies. Systemigrams can then be used to visually represent an operational environment of this complexity for our WMD problem. Figure 2 shows one version of a systemigram generated to help understand the interdependencies of the operational environment for a WMD. The systemigram was intended to convey a synergy of prose and pictures, thus embodying the best features of each [16,17]. We have, or will generate, systemigrams for a host of WMD events in order to understand the interconnected operational environment to include terrorist attacks, offensive and defensive military operations, etc., across the entire WMD profile to include CBRNE.


Figure 2: Systemigram showing the interdependencies of the operational environment

While definitely not a definitive list of domains, approaching the creation of an operational environment from this macroscopic view serves as a starting point to refine the domains involved. Domains can be further refined as necessary in order to gain better fidelity of the connections and linkages. For example, the social domain can be refined into sub-domains addressing psychology (emotional state, nationalistic identity, behavioral tendencies, fight or flight (populous), cultural factors, etc.) and sociology (population movement, refugee, migration, humanitarian needs, etc.). Often domain-specific models have been developed to explore intra- and inter-dependencies. Fuzzy cognitive techniques have been used to help understand the relationships among many entities where definitive quantification is not possible or desirable. For example, fuzzy cognitive mapping (FCM) has been used to assist leaders in the research and development arena to make predictive comparison and justify decisions and resource considerations [18]. In a similar fashion, governmental agencies have used the technique to study population behavioral affects deriving from established agency priorities [19]. Jones et al [20] have even used FCM techniques to study how infantry platoon leaders use situational awareness methods to gather the necessary information to make decisions. 3. Methods and Results FCM and logic helps to formalize approximate reasoning used in everyday life when the object of the investigation contains vague properties (beautiful, small, plausible, etc.) [21]. Fuzzy logic is often used to also describe the relationship between two entities. For example, the statement “the air is hot” has a relationship between the entities air and hot. The nature of these relationships can be satisfied quantitatively from 0 (False) to 1 (True). The relationship of ‘0’ represents a complete non-relationship between the two entities whereas a relationship of ‘1’ represents a direct and extremely strong relationship. Very few relationships are defined by the two extremes and most will exist somewhere in the gradient between ‘0’ and ‘1’. Yet the influence between the entities can still be defined based upon assumptions derived from subject matter expertise, definitive knowledge, and modeling (both probabilistic and deterministic) [22]. In this research, we explored fifteen interconnected domains and provided a working definition of each; non-proliferation, outreach, PMESII, resilience, security, social media, threats, medical, governmental structure, emergency operations, education, culture, counter proliferation, consequence management, and agriculture. Non-proliferation is defined as those actions preventing the proliferation of weapons of mass destruction by dissuading or impeding access to, or distribution of, sensitive technologies, material, and expertise [23]. Outreach is defined as a willingness to seek and/or provide aid and assistance for military, political, and humanitarian needs. PMESII is used

TransportationNetwork

GovernmentFunctions

InformationSystems

IdealogicalNetworks

SocialNetworks

Manufacturing

KnowledgeBase

Commandand Control

WMD,CBRNE,

ConventionalWeapons

Troops andEquipment

PowerSystems

CommunicationSystems

Finance andBanking

InformalFormal

Hospital Emergency

Food andWater Distro

PopulaceCharacteristics Values

Military Populace

Leadership

Military Complex

Critical Infrastructure

Key and EssentailServices

Power Networks

Population Valuesand Beliefs

Ability and Desireto Wage War

Supports

Controls

Enables

Supports

Affects

Controls

Enables

Shapes

Drives

Enables

Affects

Affects

Affects


as previously mentioned and is a strategic method for understanding the influences of an environment’s systems and capabilities. Resilience is defined as the ability to sustain, recover, and learn from an incident upon an environment that initiates a change from normal ways of life. Resilience also includes the inclusion of lessons learned from an insult on the environment. Security is defined as those measures taken by a military unit, activity, or installation for protection against all acts designed to, or which may, impair its effectiveness [24]. Social media is defined as those communication networks by which people create, share, and observe social interactions and discovery. Social media includes those outlets and participation in by both governments and citizens. Threats are defined as potential future attacks or damage dealt with the most common threats being weapons of mass destruction. Medical is defined as those actions taken to ensure the general welfare of the population as well as the infrastructure needed to care for the population. Medical considerations also include immediate medical capabilities (hospitals, medical professionals, equipment) and public health (those capabilities designed to prevent, prolong, and/or promote positive health). Governmental structure is defined as the infrastructure and capabilities of the government in both a physical sense (governmental organizations) and a known sense (clearly defined communication and guidance channels of information flow). Emergency operations are defined as those operations necessary to support the immediate activities needed to respond to an incident both within and beyond local response capability. Emergency operations often include coordinated information, resources, and guidance to support domestic incident management activities. Education is defined in accordance with the formalized infrastructure support the hierarchy by which official knowledge is disseminated and introduced to populations for inclusion in the social infrastructure of a location. Culture is defined as the shared beliefs of personnel within an environment. Counter proliferation is defined as those actions taken to defeat the threat and/or use of weapons of mass destruction against the U.S., our forces, friends, allies, and partners [8]. Agriculture is defined as the current production and capability for food production, delivery, and consumption within a location including but not limited to mammalian considerations. FCM modeling was performed using a freeware software program called Mental Modeler (www.mentalmodeler.org). Fifteen domains were inserted into the model as components and the relationships among the components were “scored” according to their interdependencies. The scoring was relationship based where “+” or “-“ describes the positive or negative relationship or influence respectively (Figure 2). Likewise the thickness of the connection denotes the strength of the relationship.

Figure 2. Mental Modeler model of the fifteen domains investigated following a radiological or nuclear event denoted as Consequence Analysis


Influence interactions and strengths of the relationship were determined by questioning available subject matter experts and initial research (Table 1). Initial research into the relationships was not meant to provide definitive causal analysis but performed to gain a first-level understanding of the environment. Research is currently being conducted to further refine both the relationships and the strength of the relationships.

Table 1: Example relationship depiction between the radiological (R) or nuclear (N) event and the resulting effect on a domain

From To Type

(+ or -) Strength Descriptions

1 2 + 0.25 An radiological/nuclear (R/N) event would slightly increase a country’s non-proliferation policies

1 3 + 0.50 An R/N event would moderately increase a country’s willingness to ask others for aid

1 4 - 0.50 An R/N event would moderately have a negative impact on all PMESII components

1 5 - 0.50 An R/N event would moderately decrease a country’s resilience after an attack

1 6 - 1.00 An R/N event would greatly decrease a country’s security 1 7 + 0.50 An R/N event would moderately increase social media 1 8 + 1.00 An R/N event would greatly increase a the threat of future harm for a

country 1 9 - 1.00 An R/N event would greatly decrease all medical aspects of a country 1 10 - 0.50 An R/N event would moderately decrease government structure 1 11 + 1.00 An R/N event would greatly increase a country’s emergency operations

The CA of a CBRNE event serves as the catalyzing vector for the model where the matrix effects are subsequently derived. The model calculates the changes from the initial steady state and graphs the results on a histogram of relative change (Figure 3).

Figure 3: Histogram of change from steady state condition following a radiological or nuclear initiation event


Positive results from these histograms show a relative increase from a beginning state. Similarly, negative results depict a relative decrease from the initial state. The histogram depiction of embedded in our model shows that the domains affected the most by a radiological or nuclear event are CM (emergency response), medical, and threats (retaliatory measures). These results can inform senior decision makers to help in allocation of scarce resources. For example, investigation can be conducted into whether specific models exist for these domains or for separate aspects within each domain exist. Computer codes do not tend to cross-governmental lines due to security classification concerns and the inherent physics involved. However, a federated system of models can use tabular inputs/outputs to cross the security classification lines and provide informative analysis. In this manner, technology gaps can be addressed. Some of these technology gaps include political science models (relationships with neighbors, trade partners, political landscape, governmental organization influence, world opinion, etc.), economic models (state of economy, trade partners, level of technology, infrastructure, national/personal wealth, etc.) and medical models (medical distribution, medical management, standards of care, equipment life-cycle replacement determination and security, etc.). Given the challenge of identifying and faithfully modeling the numerous interdependent factors, one might reasonably question the necessity and benefit of representing them all. However, if the past few decades have taught us anything, they have highlighted that ignoring or assuming an interdependent factor away can be prohibitively expensive. In fact, stability and reconstruction costs have been roughly four times the cost of major combat operations (Figure 4) [25].

Figure 4: Stability and reconstruction costs from post war Vietnam until 2004 [25]


4. Discussion FCM and systemigram techniques can present a parsimonious technique for understanding a complex and uncertain environment. Furthermore an in-depth understanding of how the domains are affected following an event can inform leaders seeking to invest resources in building response capabilities and capacities like prediction modeling and training respectively. Currently, domain-centric models are used for analysis yet very seldom are the models linked or connected to explore multi-layered effects. Ideally, a federated system of models would serve as the backbone of operational environment prediction models. A federated system of models would treat each domain-centric model as a network so that multi-layered network analysis can then take place. In order to determine where resources should be applied in order to develop or identify already existing models, FCM techniques can be used. 5. Conclusion Systemigrams, FCM, and ultimately system dynamics type modeling provide a logical means to build complex systems models. This hierarchical approach with regards to complexity is a logical means to develop an understanding of the basic causality and then develop more quantifiable models. The FCM is a powerful technique to bridge diagrams and complex systems modeling. Systemigrams and causal loops have been used to understand casualty with some success. However, FCM provides another tool to bridge diagramming and modeling. We used this approach to investigate the relationships among fifteen interconnected domains and explored the relative follow-on effects following a radiological or nuclear event. Although more research is definitely needed to further define the relationship influence between each domain, initial results can help inform decision makers and leaders as to where resources need to be applied in order to develop a federated system of models. The federated system of models can serve as the underlying structure by which operational plans further refined to include an in-depth understanding of both offensive targeting and defensive responses. Systemigrams were found to be an important first step in understanding causality and structure. The FCM technique was found to be useful in identifying the importance of critical factors and processes that drive the behavior of the system. Conducting this research showed us that when combined these two techniques are valuable in developing models such as system dynamics to conduct “what if” analysis. From this analysis we can help plan, allocate scarce resources, etc. and respond to WMD events. Our experience has shown for some problems that diagraming techniques sometimes do not translate well to modeling techniques such as system dynamics. More research is needed to develop guidelines for crafting systemigrams and FCM for the purpose of complex systems modeling. References

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