Behavioral Modeling and Simulation From Individuals to Societies (2008) / Chapter Skim
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6 Meso-Level Formal Models
6 Meso-Level Formal Models
Pages 215-260
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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 network models, link analysis, and agent-based modeling (ABM)
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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.
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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.
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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.
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And that requires a model of individual behavior in groups. The possibility of coalitions further complicates the analysis of voting models.
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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 voting 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.
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. This empirical work suggests 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)
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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)
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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 clustering.
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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.
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Centrality Models A frequent analytical strategy in network modeling has been the identification of key players who are disproportionately important due to their structural position in the network (Borgatti and Everett, 2006)
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Similarly, closeness centrality gives the expected values of the time to first arrival of a token flowing through a network, again using exclusively shortest paths. Degree centrality gives the frequency of arrival of a token in a process in which tokens travel along unrestricted random walks through the network.
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Cohesive Subgroup Models An active area in network modeling is the identification of cohesive subsets -- dense regions of a network that have more ties within than to the rest of the network -- that operate as units. The fundamental assumption in this work is that members of a cohesive subset will have more in common with each other than with nodes outside the subset (Borgatti, Everett, and Shirey, 1990)
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or of the emergent structural properties of the whole network (e.g., why some network structures are more robust than others)
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The cognitive/communicative complexity of the model is limited by available computational capacity and processing time. Relevance, Limitations, and Future Directions Social network models and dynamic network analysis models can be used to identify key actors or groups.
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In the long term, this line of work promises to yield continuous time models of network evolution, as opposed to current approaches to longitudinal analysis, which are limited to comparing snapshots of the network at discrete intervals in time. Similarly, most network models that are based on graph theory (and most are)
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Historically, the term "link analysis" was used, particularly in the law enforcement area, to refer to approaches that let the analyst display and reason about the links between multiple types of nodes. Modern link analysis is a new subfield largely centered in computer science and statistics.
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This mathematical representation or "model" of the underlying social behavior is discovered from the data and can then be utilized in other types of models, such as multiagent systems, to characterize a type of behavior. In modern link analysis, there are three fundamental concepts and two more general related concepts.
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Finally, link analysis as a theory of anomaly detection is agnostic about the types of links and nodes that form the paths, whereas social network modeling has historically focused on networks in which the nodes are information-processing entities, such as people, organizations, or groups, and the links are the various factors by which they are connected, such as friendship, mentoring, financial transactions, or marriage. There are a growing number of link analysis tools, many of which are available on the web.
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. Relevance, Limitations, and Future Directions Link analysis has widespread military applications in the creation of actionable intelligence from large diverse data sources and in the development of network models -- such as terrorist network models -- from partial and incomplete transaction data.
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When a link analysis approach is taken, in which links are viewed one at a time and treated as independent, a special-purpose and extremely complex model would need to be constructed. In principle, link analytic tools can be used to locate and construct the networks, and then social network or dynamic network metrics can be applied for predictive purposes.
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In addition, any simultaneous advances in automated data collection and computational algorithms for very large networks would significantly improve the usefulness of link analysis for the problems at hand. It is already possible to construct communication networks based on telephone logs, e-mails, etc.
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. When the interaction network formed by agents is contingent on past experience, and especially when the behaviors of agents in this interaction network continually adapt to past experiences, standard mathematical and statistical tools typically have only limited ability to derive the dynamic consequences.
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State of the Art The goals pursued by ABM researchers take six general forms: empirical description, empirical prediction, normative analysis, behavioral understanding, heuristic understanding, and methodological advancement. Researchers pursuing empirical description ask: Why have particular macro-level structures and social behaviors evolved and persisted, even when there is little top-down control?
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ABM researchers pursuing this objective are interested in evaluating whether policies and institutional arrangements proposed for various types of social systems result in desirable system performance over time. Examples include the design of auction systems, voting rules, and law enforcement practices.
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Five distinguishing structural properties of particular interest are as follows: the number of agents, the basic manner in which agents are represented, the cognitive sophistication of the agents, the social sophistication of the agents, and whether or not the agents are situated in a relational or spatial grid. Table 6-2 illustrates how these five structural properties differ across four classes of models currently used by ABM researchers: cognitive ABMs; dynamic network ABMs; cellular automaton ABMs; and rule-based ABMs.
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However, they are generally not appropriate for more societal or cultural issues, such as state failure, crowd control, or adaptation in terrorist networks. On the other hand, an ABM could comprise tens of thousands or millions of cognitively simplistic agents doing relatively simple tasks.
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For example, in models comprising millions of cognitively simplistic agents, real social networks are typically not modeled. Instead, agents are differentiated using from two to five sociodemographic dimensions and "network" links are characterized by nearness in a grid.
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or to capture social network effects in any great depth. In particular, then, ABM frameworks with grid layouts are currently of limited utility for modeling military situations requiring high levels of realism.
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A third critical issue concerns the disruption of harmful social networks. For example, research on terrorist networks suggests that they are difficult to destabilize when they have a cellular organization, with participant agents in communication only on a need-to-know or similarity basis.
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In summary, applications that require the generation of actionable intelligence in social situations will generally require careful consideration of social network effects along with structural and institutional effects. ABM Development Issues A computational laboratory (CL)
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In summary, great care must be taken in the development of ABM frameworks. Although CLs permit rapid individual development of ABM frameworks, detailed, sophisticated ABM frameworks that produce actionable results often need to be developed by a team working collectively for three to five years.
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The issue is whether it has reached a sufficient stage of development to provide practical support for military operations. Major Limitations ABM frameworks as currently constructed have limitations that could affect their ability to meet critical military needs.
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Model Trade-Offs ABMs using cognitively sophisticated agents tend to require the use of knowledge engineering techniques. Such models tend to be special purpose and permit minimal reuse.
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Now consider dynamic network ABMs tied to empirical data. Such models utilize agents with moderate levels of cognitive sophistication and high levels of social sophistication.
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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.
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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)
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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.
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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 frameworks in a data farming environment, the number of computational experiments conducted, the space of possibilities examined, and the scope of A " wrapper 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.
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. Research initiatives that explore the link of ABM social behavioral modeling to gaming tools may be valuable.
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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, engineering, or physics should work in teams with social scientists to avoid duplicating work already done or making commonsense assumptions about social processes that have no empirical bases.
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Social Networks, 28(4)
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. Dynamic network analysis.
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In Proceedings of the 1998 AAAI Fall Symposium on Artificial Intelligence and Link Analysis. Available: http://kdl.cs.umass.edu/events/ aila1998/goldberg-wong.pdf [accessed April 2008]
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. Dynamic social network modeling and analysis: Workshop summary and papers.
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Judd (Eds.) , Handbook of computational economics, volume 2: Agent-based computational economics.
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