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6 Meso-Level Formal Models I n this chapter we describe and discuss formal models of human behavior at a level of aggregation and detail between the micro and macro levels. Such models are often referred to as meso-level models. Typically the models represent interactions and influences among individuals in groups and cover both individual and group phenomena and their interactions. These models include several voting and social decision models, social net- work models, link analysis, and agent-based modeling (ABM). The models have been developed in varied disciplines, including social psychology, sociology, anthropology, economics, and computer and communications sciences. vOTINg AND SOCIAL DECISION MODELS Understanding and predicting social phenomena requires good models of individuals and groups. The behavior of a group can differ from that of the individuals that comprise it. A science of aggregation is needed to model the behavior and actions of collections of people. There is a need to know how individual beliefs, goals, and skills combine on various tasks, such as problem solving and decision making. This section covers voting models that assume people reveal their true preferences, game theory models that assume people vote strategically, and social psychological models that con- sider how individual preferences might change in a group setting. 5

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 BEHAVIORAL MODELING AND SIMULATION What Are voting Models? The research and models from voting theory provide a natural place to begin an investigation into aggregation for both pragmatic and conceptual reasons.1 Governments, terrorist groups, and alliances all make decisions by “voting.” Some follow formal voting rules and procedures and others informally aggregate competing desires. Thus, our use of the term “vot- ing” goes well beyond formal (e.g., electoral) registering of a preference to much less formal situations in which a preference is exercised or a decision is made with input from multiple individuals. Conceptually, voting models are valuable for three reasons: (1) a sub- stantial body of theory exists, (2) that theory shows no shortage of counter- intuitive results, thus highlighting the challenges of aggregation, and (3) the theory highlights a key point: to model groups well, one must be able to model individuals and the interactions between them. State of the Art in Social Decision Modeling We first describe the basics of preference theory. We then discuss results from social choice theory that reveal the problems created by aggregation as well as briefly comment on game theoretic models of strategic voting. The distinction between social choice theory and game theory hinges on behavioral assumptions. Social choice theory assumes that people truthfully reveal their preferences. Game theory does not. It assumes that people act strategically, which may or may not lead them to reveal their true prefer- ences. The game theory models also enable one to understand how and why various institutional rules matter. We also discuss research from psychology that addresses how choices are made in a group context. Preference Theory Preferences capture how much people value or desire things. They dif- fer from choices, which are what people select. Modelers define preferences over a set of alternatives. These alternatives can be outcomes, or they can be policies that produce outcomes (Page, 2007). Preferences impose an ordering over the alternatives. It is customary to write the preferences of someone who prefers apples (A) to bananas (B) as follows: A > B. Most modelers make two assumptions about individual preferences: that a person can compare any two alternatives (completeness) and that a person does not exhibit any preference cycles or internal contradictions (transitivity). 1 We might have alternatively considered models of riots or collective ecosystem mainte- nance, but the related literature is not as deep or well thought out.

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 MESO-LEVEL FORMAL MODELS If a person claimed to prefer apples (A) to bananas (B), and bananas to coconuts (C), and then claimed to prefer coconuts to apples, one might think that person was irrational. Formally, it would be said that the person exhibits a preference cycle in which A > B > C, but C > A. When individual preferences satisfy both completeness and transitivity (i.e., A > B > C and A > C), then they are called rational. If a person has rational preferences and if the modeler rules out indif- ference, then that person’s preferences can be written as an ordered list from the most to the least preferred alternative. Given a set of five alternatives, A, B, C, D, and E, one person’s preferences might be written A > B > C > D > E, and another person’s might be written E > D > C > B > A. This construction does not represent strengths of preferences. One per- son might strongly prefer A to B and strongly prefer B to C. Another person might have the same preference ordering but strongly prefer A to B and only weakly prefer B to C. To capture these relative strengths, one can assign pay- offs or utilities to each alternative. Payoffs are not considered here because comparing these utilities across people is considered a dubious practice. Social Choice Theory If the members of a group have identical preferences, then aggregating those preferences is straightforward. One can think of the group as one big individual—and for some groups that may not be a bad assumption. The aggregation of preferences becomes problematic when the group members’ preferences are diverse. Preference diversity can be fundamental (people want different outcomes) or instrumental (people want the same outcomes but differ over the means to achieve them). In what follows, that distinction is ignored, but it becomes important when thinking about linking models. If voting models are to be linked with cognitive models, then the source of preference diversity is important to define because information can reduce instrumental preference diversity but has little effect on fundamental prefer- ence diversity. A collection of individuals with rational preferences may fail to have rational preferences as a group. We give an example and then state a gen- eral theorem. In this example, three military leaders have preferences over which city to use as a base of operations. The three candidate cities are Paris, London, and Berlin. The leaders are denoted L1, L2, and L3. Their preferences are as follows: Leader L1: Paris > London > Berlin Leader L2: London > Berlin > Paris Leader L3: Berlin > Paris > London

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 BEHAVIORAL MODELING AND SIMULATION Were these three leaders to vote on their choice between each pair of cities, London defeats Berlin two votes to one, Berlin defeats Paris two votes to one, and Paris defeats London two votes to one. Thus, the collec- tive preferences exhibit a cycle. Although the collective consists of rational individuals, the collective is not rational. In theoretical terms, the property of rationality does not aggregate. The possibility of a cycle is not an artifact of majority rule voting. Kenneth Arrow proved that any rule for aggregating preference orderings that is not a dictator produces cycles (Arrow, 1951). It requires only that preferences are diverse, rankings between two alternatives do not depend on a third irrelevant alternative, and rankings reflect unanimity—if every- one prefers A to B, then so does the collective. Arrow’s theorem does not imply that cycles are unavoidable, only that if one wants to avoid cycles, one has to sacrifice one of the other conditions of his claim—appoint a dictator, sacrifice unanimity, or violate indepen- dence of irrelevant alternatives. In general, as argued by Donald Saari, pref- erence cycles are more a function of the voting system than the voter. He suggests that voting paradoxes arise when the voting system fails to respect the natural cancellations of votes and so generates preference cycles (Saari, 2001). For example, one such voting system or scoring rule, the Borda rule (Marchant, 2000), does not create cycles. Under the Borda rule with three alternatives, a person’s top choice gets three points, her second gets two points, and her third gets only one point. Each alternative gets a score, making cycles impossible. Borda rule can, however, result in a tie, which is what would occur in the example of voting over cities. A tie isn’t neces- sarily a bad thing. It reflects equal support for each alternative. Borda rule may thus seem to be better than majority rule, but we must keep Arrow’s theorem in mind. Borda rule must violate one of his conditions, and, in fact, Borda does not satisfy independence of irrelevant alternatives. In the example above, a fourth, irrelevant city could be introduced and change the outcome under Borda rule. The fact that by introducing irrelevant alterna- tives someone could change the outcome argues against using Borda rule. The debate thus moves from a discussion of the voter to a discussion of the scoring rules (Saari, 2006). Given that regardless of the voting rule individual agents may fail to reach a stable aggregation, organizational and institutional structures take on great importance. The rules for how a group makes decisions can have large effects on outcomes. For example, if someone has the power to set the agenda, then that person may have substantial power. Thus, even if an organization is democratic in principle, it may not be democratic in prac- tice, especially if one person controls the agenda.

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 MESO-LEVEL FORMAL MODELS Strategic voting In aggregating preferences, it can be assumed either that people vote sincerely or that they vote strategically. Strategic voting occurs even in large groups in mature democracies; people vote for candidates who they think can win rather than the candidates whom they most prefer. Alan Gibbard and Mark Satterthwaite have shown this incentive to misrepresent to be universal (e.g., Satterthwaite, 1975). Why does strategic voting further complicate matters? We have shown that rational individual preferences need not aggregate into a rational col- lective preference. Thus, it may not be possible to discern what a group would decide even if one knew the preferences of every member of that group. Given that people have incentives to misrepresent their preferences, they wouldn’t reveal their true preferences anyway. Thus, one must discern how people’s true preferences get mapped into their actions—in this case, their votes. And that requires a model of individual behavior in groups. The possibility of coalitions further complicates the analysis of vot- ing models. Subgroups may have an incentive to form a coalition to steer outcomes toward desired ends. This is seen in parliamentary systems, with Israel as an example. It may not be possible to predict which coalitions will form: politics makes strange bedfellows, and predicting those bedfellows can be difficult. In a group setting, social influence dynamics can muddy the picture even further, as people may change their preferences to align with the real or the inferred preferences of others. Concern for the preferences of others and for one’s own standing in a group creates more indeterminacy in collective decisions. A striking example of such social influence effects is provided by the Abilene paradox, in which each person privately prefers X but believes that others prefer Z. If all group members revealed their true preferences, the group would clearly choose X. However, the desire to conform to what is (incorrectly) perceived to be the normative opinion can lead a member to suggest Z and others to agree (Harvey, 1974). While this counterintuitive outcome is probably rare in practice, it highlights the importance of realizing that social influence is not sim- ply a matter of one person seeking to change the preference of another. People also actively seek to align their preferences with important others. Both computational models and empirical studies have demonstrated that the impact of others on individual preferences tends to create uniformity of preferences among people who are closely connected. Dynamic social impact theory (Latané, 1996) predicts that people will change their prefer- ences to match those of others, with the impact based on both the strength (status) and the immediacy (social closeness) of others. The result is emerg- ing pockets of uniform attitudes based on social network clusters. Research

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0 BEHAVIORAL MODELING AND SIMULATION on minority influence (e.g., Nemeth, 1986) also shows that the views of a cohesive minority, when clearly and consistently stated, can also change the opinions of majority members. Hence, knowing what the majority of individuals prefer at time 1 may not allow one to predict confidently what a group will choose at time 2. Relevance, Limitations, and Future Directions for Social Decision Models While voting models per se, especially those that compare specific vot- ing rules like Borda and majority rule, may seem more relevant to political science than to the situations that concern us here, the insights that can be drawn from these models are of critical importance. Two of the main rec- ommendations of this report are that modelers should recognize diversity of background, activity, and preferences and that they should embrace uncer- tainty. Nowhere does that advice ring more clearly and loudly than in under- standing the link from individual incentives to group behavior. Moreover, one can link diversity of background, activity, and preferences and uncer- tainty about all three into a general insight: the more diverse the members of a group in their general makeup (their background), their preferences, and their actions, the more uncertain one should be about their collective decisions and actions. For example, models that attempt to make predictions about the attitudes and behaviors of a group of noncombatant civilians must consider the diversity of that group in terms of the sociocultural- ethnographic-economic background, preferences, and available actions. The more diverse the group on any of these three dimensions, the less certain the predictions. At a very practical level, the implication of recognizing diversity is to make the models more complex. Another practical implication is that model results should often be characterized in terms of how the diversity of the population being modeled impacts the results (e.g., show entropy or diversity indices to characterize the initial population and show how out- comes change as the initial population varies on this metric). Even if group models cannot be expected to make point predictions, they can provide a way to predict sets of possible outcomes. If one has even crude approximations of preferences, possible coalitions, and a set of possible voting rules, he can write game theoretic or agent-based models, and those models can provide some guidance for what might happen and, equally important, what probably will not happen. For a recent survey of these methods, see Kollman and Page (2006). The ability to apply voting theory depends on data, knowledge, and theory. For many of the problems relevant to this study, one would not have information about individual-level preferences. And, even if one did have access, the theory tells us that it is not possible to predict outcomes with certainty from that data. Equally important, one may not have knowledge

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 MESO-LEVEL FORMAL MODELS of the voting rules. And, as discussed above, the voting rule has a substantial influence on the outcome. Thus, even with information about preferences, one would also need to know something about the process of preference aggregation in the group of interest. Finally, to apply these models, one needs models of how people behave in groups. Are the group members strategic? Do coalitions form, and who belongs to those coalitions? Imagine a model that includes the actions of a terrorist organization or of a nascent nation-state. One could make a black box assumption about how that organization or government acts. In other words, one could treat the group as an individual, presumably an individual who is the average of the group members. The voting models reveal the problems with that approach. Groups do not make choices as though they were a single individual. The natural way to improve the model would be to make the black box transparent and to allow for multiple characterizations of the collective decision-making pro- cesses that produce those outcomes. This will require data, knowledge, and models of the group of interest, but the potential payoff is large, as it will provide a more accurate assessment of the likely distribution of behaviors over the set of possible actions. Finally, empirical voting studies demonstrate that humans do not act in a strictly rational or strategic manner, hence calling into question the formal mathematical “rational” and “strategic” voting models. Summaries of the empirical literature point to the social rather than rational nature of vot- ing behavior; for example, people vote primarily along ethnocultural lines rather than according to their economic interests and display widespread voter ignorance (Friedman, 2005). As another example, research on the “voter participation paradox”—in which it is asked why people vote at all, as each individual has virtually zero probability of affecting the out- come (Converse, 1964; Green and Shapiro, 1994)— both demonstrate this lack of rationality and suggests that there are huge variations in individual behavior. Turnout depends on a number of social factors, including the size of the electorate (as the size of the electorate grows, fewer voters turn out), the closeness of the competition (the closer it is, the higher the turnout), and the presence of an underdog (more turnout). This empirical work sug- gests both that the formal models and simplistic game theoretic models are inadequate and that the more detailed and nuanced behaviors possible in agent-based models (ABMs) are better at capturing the complexities of voting behavior. SOCIAL NETWORK MODELS Networks are ubiquitous, and many techniques have been developed for analyzing, predicting, and understanding the world in terms of the set

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 BEHAVIORAL MODELING AND SIMULATION of connections among entities—a network. As the focus here is on social and behavioral modeling, we limit our discussion of network modeling techniques to those that have been and are being used to address individual, social, organizational, political, or cultural issues, rather than, say, gene interaction networks or computer networks. For a review of the field of net- work analysis, see Freeman (2004), and for the methodology, see Freeman, White, and Romney (1991) and Wasserman and Faust (1994). What Are Social Network Models? Social network models view groups as consisting of a set of nodes (the members of the group) and a set of ties that connect them, which link together to form a network. The ties are often seen as pipes or roads along which various kinds of traffic flow, such as informational and material resources, as well as influences and coordination. Thus, a key aspect of net- work modeling is concerned with predicting (and controlling) what flows to whom at what time. Ties are also seen as providing a kind of underlying structure or topology that has effects on the performance of the group or individuals. A fundamental proposition of social network models is that a node’s position in the network (in conjunction with its attributes) deter- mines the opportunities for and constraints on action that it will encounter. A group-level corollary of this proposition is that the network structure of a group (together with other attributes of the group), determines the perfor- mance or outcomes of the group. Thus, network models differ from other models in placing less emphasis on characteristics of the nodes and more emphasis on the structure of connections between the nodes. Social network analysis (SNA) has received a great deal of attention since the terrorist attacks of September 11, 2001 (Borgatti and Foster, 2003). Phrases for fighting terrorism, such as “disconnect the dots” and “it takes a network to fight a network,” and for doing business, such as “it’s not who you know but who or what who you know knows” and “are you networking?” have appealed to the imagination and raised awareness of this area. In addition, there have been successful applications of this approach. For example, social network information was used to locate Saddam Hussein, and several SNA tools have been used in various criminal investigations. Social network information is used in popular social net- working web services, like Friendster, to help students vet their dates. Traditionally, most SNA has focused on the analysis of relatively simple datasets involving a small number of social relations (often of just one kind) connecting a set of persons in some kind of group at a single point in time. Analysts in this area use computational techniques primarily to statistically analyze these networks. This area has a long tradition, predating World War II. It emerged from the social sciences, particularly from social psychology,

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 MESO-LEVEL FORMAL MODELS anthropology, and sociology, and has now spread to organization science, economics, physics, and computer science. More recent work has focused on more complex networks involving large numbers of nodes of differing types (see section on Multimode Net- works below). For example, Carley (2003) has developed social network metrics that take into account not only relations among individuals, but also relations among tasks, relations among items of knowledge, assign- ments of tasks to individuals, relations of knowledge to individuals and other relationships. In addition, a key research interest today is in understanding network dynamics, both in the sense of how networks change over time (especially in response to attacks) and in the sense of how things flow over the network links. Carley (2003) has used multiagent models in a network context to predict and reason about change in social and other networks. State of the Art in Social Network Models In this section, we lay out the key concepts of SNA, starting with a discussion of the nature of the data and followed by an outline of the key analytical constructs, namely cohesion, centrality, equivalence, and cluster- ing. The section ends with a discussion of network evolution. Nodes and Ties The set of actors or agents that form the nodes of a network can consist of either individuals or collectives, such as organizations, cities, or countries. Nodes are assumed to possess characteristics that define their goals and affect their ability to achieve and exploit their network positions. These characteristics are modeled as a set of categorical and/or continuous attributes. In general, relations among nodes are modeled as dyadic 2-tuples (called ties, links, or edges) that bind exactly two nodes to each other. Therefore, a conversation among three people A, B, and C is typically modeled as three separate dyadic interactions consisting of A with B, B with C, and A with C. For the most part, the ties modeled among nodes typically belong to a general class known as social relations. These include such things as acquaintance (e.g., knows), kinship (e.g., brother of, father of), other social roles (e.g., friend of, teacher of), and affective relations (e.g., likes, dislikes). Each type of tie can be further characterized by relevant characteristics or attributes. For example, a friendship tie can be characterized in terms of intensity, closeness, and duration. In addition, network modelers often represent interactions over time— such as in-person meetings, communication, or fighting—as ties. Hence a tie

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4 BEHAVIORAL MODELING AND SIMULATION is considered to exist between two nodes if at least one interaction between them is observed during a given period. The actual number of interactions may be recorded as an attribute of this tie. Interactions are inherently transitory and evanescent but are often seen as revealing the presence of underlying social relations. Interactions, such as conversations, provide the mechanism by which things flow through social relations, as when an actor transmits informa- tion to a friend through communication or when a person infects another with a disease via personal contact. Thus flows represent a third category of tie that a network modeler can choose to model. Typical flows of interest have been information, ideas, infections, material goods (such as guns and money), and such intangibles as energy and motivation. These are often referred to by network analysts as “tokens.” Multimode Networks When a categorical variable exists that distinguishes between different types of nodes, and, in addition, ties exist only between nodes of different types (and not within types), the resulting networks are referred as k-node networks or, in graph theory, as k-partite graphs, where k refers to the number of distinct types of nodes. These kinds of data typically arise in the context of recording affiliations between individuals and groups or events. For example, Davis, Gardner, and Gardner (1941) recorded which women attended which social events in a given season. Ties exist between women and events, but not among women and not among events. Similarly, it is common to record for each person in a group the organizations to which they belong(ed). And in organizational analysis, one can collect the number of hours that each person worked on various tasks or projects. Multinode networks can be analyzed directly or converted into simple 1-node networks by deriving co-occurrence indices. For example, a 2-node women-by-events network can be converted into a 1-node women-by- women network in which a tie between each pair of women is characterized by the number of events they attended in common. With multiple nodes, it is possible to represent the system as a whole as a meta-matrix (Carley, 2003). The meta-matrix is a conceptual device for identifying the set of networks within and among nodes of multiple classes. For example, given the three classes of nodes—people, knowledge, and activities—the set of subnetworks possible is shown in Table 6-1. The second key concept is the entity ontology—for network analysis, this is the set of categories that defines the node classes and the link classes among the nodes used in a particular study. The table illustrates a particular ontology; other ontologies are needed for other applications.

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5 MESO-LEVEL FORMAL MODELS TABLE 6-1 Illustrative Meta-Matrix People Knowledge Activities People Social network Knowledge network Activity network Knowledge Information network Needs network Activities Precedence network Cohesion Models A fundamental concept in network modeling is cohesion. Cohesion refers to the connectedness or structural integrity of a network, and it is often interpreted in terms of the network’s potential for coordinating among its members or exploiting knowledge that is distributed across the network. One aspect of network cohesion is density, which refers to the propor- tion of pairs of nodes that have a direct tie (i.e., are not dependent on an intermediary). A high density implies that, on average, each node is directly connected with many others. If the ties represent something like trust rela- tions, this indicates a group in which information can flow quite freely. Another aspect of cohesion is the average path distance, also known as characteristic path length. Path distance refers to the number of links in the shortest path between two nodes. A network with low average distance is one in which the lengths of the shortest paths between pairs of nodes are quite small, so that things flowing through the network can reach any or all nodes comparatively quickly. In the case of viruses or other infections, this is a measure of the vulnerability of the network to disease. In the case of the spread of best practices, it can be seen as a determinant of the potential performance of a continuously adapting system. Centrality Models A frequent analytical strategy in network modeling has been the iden- tification of key players who are disproportionately important due to their structural position in the network (Borgatti and Everett, 2006). The struc- tural importance of a node in a network is conceptualized as its centrality. One way to think about centrality is in terms of a node’s direct or indirect contribution to the cohesion or structural integrity of the network. For example, degree centrality is defined as the number of ties that a node has. If the total number of ties in the network is a measure of the cohesion of the network, then clearly degree centrality can be seen as each node’s “share” of the total cohesion. In this sense, the centrality measure implies a model of the sources of cohesion.

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50 BEHAVIORAL MODELING AND SIMULATION chains. For example, pattern discovery techniques can be used to derive equations from historical data that can then be used in ABMs to evolve future systems. ABM techniques can be used to evaluate courses of action and to suggest areas for further data collection. Combining these techniques will enable new types of problems to be solved; for example, combining social network metrics with pattern discovery techniques is the key to build- ing an understanding of how networks grow and evolve. This is not to suggest that the military should move to large integrated behavioral models—quite the contrary. What is needed is increased inter- operability of the tools. The development of ABM CLs and the explosion of network analytic tools are putting social behavioral modeling into the hands of the masses. Moreover, these trends are leading to the development of many small, single-purpose tools. This should be taken advantage of by encouraging interoperability (this is also discussed further in Chapter 8). It is important to note that it would not be feasible to require all tools to be written in a single language or to require the use of a single frame- work; rather, the solution needs to enable the integration of models not only from diverse domains but also in diverse languages. Multiple models, visualization tools, and the like should be available to address diverse prob- lems, but in such a way that data (real and virtual) can be shared easily among the various tools. There are a variety of things needed to support such interoperability. Standards for the interchange of relational data need to be developed. Behavioral modeling tools need to be web enabled, and XML input-output (IO) languages need to be developed. A uniform vocabulary for describ- ing relational data also needs to be developed; this is particularly critical because the tools and metrics are coming out of at least 20 different scientific fields.8 For defense and intelligence applications, common platforms and data sharing standards need to be explored and developed so that tools written in the unclassified realm can be rapidly moved, without complete redesign, to the classified realm. Enabling interoperability and providing a platform and common ontologies for these tools will enable novel problems to be more rapidly addressed by regrouping existing models. It will also enable various subject matter experts to interact through the interaction of their models. In turn, this will enable a broader approach to problems, reduce the likelihood of biased solutions, and facilitate rapid development and deployment. 8 These fields include anthropology, sociology, psychology, organization science, marketing, physics, electrical engineering, geology, ecology, economics, biology, bioinformatics, health services, forensics, artificial intelligence, robotics, computer science, mathematics, statistics, information systems, medicine, civil engineering, communication, and rhetoric.

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5 MESO-LEVEL FORMAL MODELS Current tools are either very data-greedy or become more valuable as they are linked to real data. However, there is a dearth of relevant data currently available in clean preprocessed form. Thus, to reduce the time analysts spend on data collection and to increase the time they spend on analysis, automated and semiautomated tools for data gathering, cleaning, and sharing are needed. Such tools should include natural language process- ing tools for extracting relational data from audio and text sources, “web- scraping” tools, automatic ontology generators, and visual interpretation tools to extract network data from photographs and visual images. Appropriate subtools for node identification, entity extraction, thesau- rus creation, and other functions are also needed. The development and availability of these tools in an interoperable environment are critical for providing masses of data that can be used for model tuning and validation. Moreover, these tools reduce time spent on data collection and thereby free the analysts’ time for analysis. More rapid data collection would also mean the availability of more datasets for doing meta-analyses, thereby enabling improvements in the theoretical foundations of the field and in the understanding of social behaviors. Finally, these tools are essential for providing the wealth of data needed by social behavioral models to make reasonable forecasts or to provide reasonably accurate analyses of situa- tions and organizations. Improved speed for many of the algorithms could be provided by computer architectures designed for relational data or by the use of special integrated circuits with embedded versions of the less scalable algorithms. Note this would enable a speed savings beyond that afforded by the use of current vector technology. Such technology would facilitate faster pro- cessing and enable more real-time solutions, particularly for large-scale networks. To reduce the “art” aspect of interpretation in this field, a living archive of collected network data is needed, replete with information on metrics for the nodes in each dataset. Such an archive could be used to set context information. For example, such information could be used to evaluate whether the density of particular networks is exceptionally high or low or to identify exceptional values of connectedness of individuals. Such an archive would facilitate meta-analysis and comparative analysis. This is critical for improving the theoretical foundations of the field as well as for the understanding of social behavior. Forecasting and Possibility Analysis Of the models described here, those that have shown the most promise in terms of forecasting are the voting models, the dynamic network models (that combine agent-based technology and meta-matrix of relations), and

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5 BEHAVIORAL MODELING AND SIMULATION the social influence models. These models have had limited success in forecasting voting outcomes, changes in beliefs and attitudes at the macro level, and identifying emergent new leaders. For other modeling techniques, including the ABM and system dynamic techniques for complexity model- ing, the models are best at providing insight into the space of possibilities, that is, demonstrating what possible futures might exist and their relative likelihood. However, for these models to provide an adequate map of the possibilities (a reasonable response surface), the models need to be run a vast number of times under diverse scenarios; hence, as is discussed in the next section, there is a need for placing these models in a data farming environment. One question that arises is: How can these models be made more pre- dictive? This topic, in and of itself, is quite complex and a full treatment is beyond the scope of this study. However, several factors are worth noting. As more of these models are placed in data farming environments, statistical tools are developed for mining the vast data so generated, and repositories of meta-matrices are developed and shared with scientists for testing and validating, one can expect that many of these models will become more reliable in their forecasts. However, there will still be many classes of social phenomena for which prediction, of the form used in engineering and physics, will simply not be possible due to the lack of stationarity in the underlying social processes, the paucity of data, and the lack of continuity in key variables. A second question often arises regarding the concern that, if the models are truly predictive, the mere act of making a prediction public will cause actors to change their behaviors and so alter the outcome. While this issue is addressed in other sections of this report, several key factors directly related to the nature of the models described here are worth mentioning. For most of the models described here, other than the simple voting models, making the models transparent to the public (so that others can infer the predictions) or making the predictions themselves public is not likely to invalidate the predictions. There are three basic reasons for this: lack of temporal forecasting, level of specificity, and hyper-confluence. Temporal forecasting tends to be weak and predictions are often vague in terms of when something will occur; rather than point predictions, most predictions are of the form “A will likely occur after B” or “at some time in the future more than two weeks but less than two years from now.” Most models produce rather general results, such as that a state will fail, civil violence is likely to erupt, or corruption will increase, rather than the more specific “the state will fail due to a regime change where General X takes over” or “civil violence will take the form of riots in these five cities” or “corruption will increase the most in the area of infrastructure development in county X.” Finally, most models generate a prediction due to hyper-confluence,

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5 MESO-LEVEL FORMAL MODELS that is, the strongest predictions are those for which there are a large num- ber of interconnected causes that weave together in complex ways. But single actors can best counter a specific event that is likely to occur at a specific time with only one or two actions or activities. Even with sufficient research funding, improved theory, and available data to overcome the issues of vague temporal forecasting and lack of specificity; the problem of hyper-confluence will remain. That is one of the key reasons why social and behavioral models need to be driven by the science of the possible, rather than the traditional science of point predictions involved in traditional physical science and engineering models. Data Farming ABMs designed for applied settings need to be placed in data farming environments. These environments need to be augmented with special- purpose tools for running massive virtual experiments. These tools should enable improved visualization and analysis and facilitate the development of semiautomated response surface generators. Current data farming tools often are cumbersome to use, require code modification of the ABM, and are limited by the processor speed and storage capabilities of the machines that they run on. In order for ABM frameworks to run routinely in data farming environ- ments, more flexible environments need to be developed and made easily available to researchers. Moreover, ABM frameworks need to be developed with wrappers,9 so that they can be placed in these environments. Standard- ized IO formats need to be developed. By routinely placing ABM frame- works in a data farming environment, a better understanding of the space of possibilities predicted by the frameworks will be derived. This will enable ABM frameworks to better support policy and decision making. Currently, when ABM frameworks are used to inform policy and criti- cal decisions, they are typically run only a few times in carefully controlled computational experiments. While this approach enables the analyst to explore more possibilities more systematically than not using a simulation, it still leaves open the possibility that errors might be made if the results are generalized beyond the scope of the experiment. By placing ABM frame- works in a data farming environment, the number of computational experi- ments conducted, the space of possibilities examined, and the scope of 9 “Awrapper is a software layer used to change the interface of a component or to give new properties, such as fault tolerance or security, to the interaction between components. Software wrappers are often used to glue existing subsystems into a larger system with new properties and functions. The wrappers know the protocols needed to make the subsystems work together, even if they were not originally designed for a common purpose” (Webber, 1997, p. 1).

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54 BEHAVIORAL MODELING AND SIMULATION analyzed conditions can be expanded, often by several orders of magnitude, thus providing a stronger basis for decision making. Furthermore, once an ABM framework has been validated, the response surface equivalent can be used as a “rapid” model in training situations in which the users do not have time to wait for an ABM experiment to finish running. Cross-Disciplinary Initiatives Another avenue that may promote major breakthroughs is the linkage of ABM social behavioral modeling to gaming environments, particularly online multiplayer games such as Everquest and America’s Army (see Chap- ter 7). Research initiatives that explore the link of ABM social behavioral modeling to gaming tools may be valuable. Possible research areas include using agent-based modeling to explore the realism of the social behaviors exhibited in gaming models; using it to provide flexible opponents or to make the apparent number of game players larger and so force players to think about group scale issues; and using agent-based modeling to track and analyze game behaviors using dynamic network analysis techniques. Key benefits here would be improved training tools and visual what-if scenario evaluation. As previously noted, additional ABM development needs to be done in a number of areas. These include attachment of ABM frameworks to data streams, improved ABM visualization, metric ABM robustness studies, and so on. Moving ahead in these areas will require linking social networks to other types of data, such as location and event information, and linking diffusion theory to other forms of theory, such as action and cultural theory. This will require the funding of both basic and applied research. It will also require an increased recognition for, and acceptance of, applied social sci- ence research in universities. Currently there are a number of funded research efforts in the areas of cultural modeling, geospatial link analysis, and adversarial modeling, all of which are supporting work along these lines. A key to much of this work is that it combines dynamic network analysis with geospatial rea- soning or anthropological data-gathering techniques. Much of this work is applied, directed at providing usable systems in several years. This is a positive development, particularly when such modeling efforts are based on strong empirical and theoretical foundations. However, there is still a huge amount of basic research to be done in such areas as the development of an ontology for tasks, a unified model of culture, or even a shared definition of culture. Relatively little research funding is being directed to the basic research questions in this area. The key here is not simply to invest in the social sciences but to invest in the mathematical and computational social sciences to engender the

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55 MESO-LEVEL FORMAL MODELS development of work that will support defense needs. The benefit will be an improved understanding of basic social and cultural phenomena. Another benefit will be a decrease in the development of misleading models that appear to be social but that are not theoretically or empirically sound. At the same time, most of the research community, particularly in the social sciences, is not focusing on strongly applied problems. The mere idea of hard deliverables, while accepted as common practice in engineering and computer science, is contrary to the basic culture of most social sci- ence departments. Thus, while there is a strong need for quantitative social science modeling on defense issues, there is a dearth of social scientists involved in and trained to do applied work. Building Expertise The lack of highly trained professionals is a key difficulty in this area. Universities need to expand their undergraduate social science curricula to include more of the mathematical and computational social sciences. In particular, undergraduate courses should be routinely taught that cover SNA and agent-based modeling, and that permit the mastery of ABM programming tools. Universities need to encourage and facilitate applied research. New curricula are needed that have an engineering style but that are focused on social and policy applications. Master’s programs that combine social and computational science need to be developed. Military universities, such as West Point and the Naval Postgraduate School, should also offer social network courses and possibly ABM courses, particularly those for evolving networks, and they should integrate dynamic network measures of shared situation awareness, leadership, and power into the standard curriculum. The development of these curricula and degree programs is vital to the nation’s intellectual strength in order to remain at the forefront in this area. The clear benefit of these programs will be a stronger workforce of computational social analysts capable of developing and using social behavioral models. Analysts engaging in ABM but trained in computer science, engineer- ing, or physics should work in teams with social scientists to avoid duplicat- ing work already done or making commonsense assumptions about social processes that have no empirical bases. Corporations need to provide time and resources for selected personnel to become jointly trained in computer and social science, either by increasing the number of personnel sent to master’s programs, bringing in relevant faculty to teach short courses, or engaging in more joint research with universities as equal partners (in which the university provides the missing skill, social or computational). The key advantage of teaming is that it will enable improved model development

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5 BEHAVIORAL MODELING AND SIMULATION and will serve as a stop-gap until more computational social analysts are trained. Expected Outcomes Across the board, success in the activities outlined above would facili- tate the rapid development and deployment of agent-based modeling. The advantage is that it enables systematic reasoning about various courses of action in a wide range of complex environments. More courses of action could be evaluated in less time and more systematically than is done with conventional table-top war-gaming or current non-computer-assisted analy- sis of relational data. The dynamic social network and ABM tools outlined above reduce the time spent on data processing and increase time spent on analysis and interpretation. They would facilitate what-if analysis and could ultimately support near-real-time what-if analysis in the field. This would be a valuable force multiplier. In summary, the activities listed above would increase the maturity of the modeling field, improve scientific theory, facilitate rapid linking of computational models to empirical data, particularly network data in a unified reasoning framework to solve novel problems, and encourage new discoveries. These activities would also promote the development of a new science that combines computation and society, just as the previous join- ing of computer science, design, and psychology led to the new science of human-computer interaction. REFERENCES Arrow, K.J. (1951). Social choice and individual values. Hoboken, NJ: John Wiley & Sons. Arthur, W.B. (2006). Out-of equilibrium economics and agent-based modeling. In L. Tesfatsion and K.L. Judd (Eds.), Handbook of computational economics, volume : Agent-based computational economics. Amsterdam, The Netherlands: Holland/Elsevier. Axelrod, R. (1997). Advancing the art of simulation in the social sciences. In R. Conte, R. Hegselmann, and P. Terna (Eds.), Simulating social phenomena (pp. 21–40). Berlin: Springer. Borgatti, S.P., and Everett, M.G. (1992). Notions of position in social network analysis. Sociological Methodology, , 1–35. Borgatti, S.P., and Everett, M.G. (2006). A graph-theoretic framework for classifying centrality measures. Social Networks, (4), 466–484. Borgatti, S.P., and Foster, P. (2003). The network paradigm in organizational research: A review and typology. Journal of Management, (6), 991–1013. Borgatti, S.P., Everett, M.G., and Shirey, P. (1990). LS sets, lambda sets and other cohesive subsets. Social Networks, (4), 337–357. Breiman, L. (2001). Statistical modeling—The two cultures. Statistical Science, , 199–231. Brenner, T. (2006). Agent-learning representation: Advice on modeling economic learning. In L. Tesfatsion and K.L. Judd (Eds.), Handbook of computational economics, volume : Agent-based computational economics. Amsterdam, The Netherlands: Holland/Elsevier.

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