Geoffrey P. Morgan

A preliminary model of participation for small groups

We present a small-group model that moderates agent behavior using several factors to illustrate the influence of social reflexivity on individual behavior. To motivate this work, we review a validated simulation of the Battle of Medenine. Individuals in the battle performed with greater variance than the simulation predicted, suggesting that individual differences are important. Using a light-weight simulation, we implement one means of representing these differences inspired in part by Grossman’s (On Killing: The Psychological Cost of Learning to Kill in War and Society. Little, Brown and Company, New York, 1995) participation formula. This work contributes to a general theory of social reflexivity by offering a theory of participation as a social phenomenon, independent of explicit agent knowledge. We demonstrate that our preliminary version of the participation model generates individual differences that in turn have a meaningful impact on group performance. Specifically, our results suggest that a group member’s location with respect to other group members and observers can be an important exogenous source of individual differences.

Social Network Modeling and Agent-Based Simulation in Support of Crisis De-Escalation

Decision makers need capabilities to quickly model and effectively assess consequences of actions and reactions in crisis de-escalation environments. The creation and what-if exercising of such models has traditionally had onerous resource requirements. This research demonstrates fast and viable ways to build such models in operational environments. Through social network extraction from texts, network analytics to identify key actors, and then simulation to assess alternative interventions, advisors can support practicing and execution of crisis de-escalation activities. We describe how we used this approach as part of a scenario-driven modeling effort. We demonstrate the strength of moving from data to models and the advantages of data-driven simulation, which allow for iterative refinement. We conclude with a discussion of the limitations of this approach and anticipated future work.

On the Coevolution of Stereotype, Culture, and Social Relationships: An Agent-Based Model

The theory of constructuralism describes how shared knowledge, representative of cultural forms, develops between individuals through social interaction. Constructuralism argues that through interaction and individual learning, the social network (who interacts with whom) and the knowledge network (who knows what) coevolve. In the present work, we extend the theory of constructuralism and implement this extension in an agent-based model (ABM). Our work focuses on the theory’s inability to describe how people form and utilize stereotypes of higher order social structures, in particular observable social groups and society as a whole. In our ABM, we formalize this theoretical extension by creating agents that construct, adapt, and utilize social stereotypes of individuals, social groups, and society. We then use this model to carry out a virtual experiment that explores how ethnocentric stereotypes and the underlying distribution of culture in an artificial society interact to produce varying levels of social relationships across social groups. In general, we find that neither stereotypes nor the form of underlying cultural structures alone are sufficient to explain the extent of social relationships across social groups. Rather, we provide evidence that shared culture, social relations, and group stereotypes all intermingle to produce macrosocial structure.

Increasing efficiency of the development of user models

This paper introduces Herbal, a high-level behavior representation language for creating AI agents and cognitive models. It describes the lessons from other high-level modeling languages that informed the design of Herbal and that will inform other high-level behavior representation languages. We describe a model built in Herbal to illustrate its use and application. The paper concludes that languages like Herbal can help explain the design intent of intelligent agents and cognitive models, and make them easier to create, modify, and understand. These results appear to be particularly true where the model reuses a lot of its own structures.

Computational Organizational Theory

Research in computational organizational theory explores the complex interactions between organizations and their members. Organizations are legally autonomous entities that structure the efforts of individuals to achieve large goals. Interesting behavior often emerges from the interactions and demands of modeled primitives. This introduction describes the common ground and recent advances in multimodeling, multilevel modeling, and rapid model development. We conclude this summary with discussion of issues of model fidelity and model validation.

An Agent-Based Framework for Active Multi-level Modeling of Organizations

Agent-based models of organizations have traditionally had a single level of agency, whether at the individual or organizational level, but many interesting organizational phenomena, including organizational resilience and turnover, involve agency at multiple organizational levels. We propose an extensible multi-modeling framework, realized in software, to model these phenomena and many more. Two applications will be given to demonstrate the framework’s versatility.

Hypothesis generation using network structures on community health center cancer-screening performance

RESEARCH OBJECTIVES Nationally sponsored cancer-care quality-improvement efforts have been deployed in community health centers to increase breast, cervical, and colorectal cancer-screening rates among vulnerable populations. Despite several immediate and short-term gains, screening rates remain below national benchmark objectives. Overall improvement has been both difficult to sustain over time in some organizational settings and/or challenging to diffuse to other settings as repeatable best practices. Reasons for this include facility-level changes, which typically occur in dynamic organizational environments that are complex, adaptive, and unpredictable. This study seeks to understand the factors that shape community health center facility-level cancer-screening performance over time. This study applies a computational-modeling approach, combining principles of health-services research, health informatics, network theory, and systems science. METHODS To investigate the roles of knowledge acquisition, retention, and sharing within the setting of the community health center and to examine their effects on the relationship between clinical decision support capabilities and improvement in cancer-screening rate improvement, we employed Construct-TM to create simulated community health centers using previously collected point-in-time survey data. Construct-TM is a multi-agent model of network evolution. Because social, knowledge, and belief networks co-evolve, groups and organizations are treated as complex systems to capture the variability of human and organizational factors. In Construct-TM, individuals and groups interact by communicating, learning, and making decisions in a continuous cycle. Data from the survey was used to differentiate high-performing simulated community health centers from low-performing ones based on computer-based decision support usage and self-reported cancer-screening improvement. RESULTS This virtual experiment revealed that patterns of overall network symmetry, agent cohesion, and connectedness varied by community health center performance level. Visual assessment of both the agent-to-agent knowledge sharing network and agent-to-resource knowledge use network diagrams demonstrated that community health centers labeled as high performers typically showed higher levels of collaboration and cohesiveness among agent classes, faster knowledge-absorption rates, and fewer agents that were unconnected to key knowledge resources. Conclusions and research implications: Using the point-in-time survey data outlining community health center cancer-screening practices, our computational model successfully distinguished between high and low performers. Results indicated that high-performance environments displayed distinctive network characteristics in patterns of interaction among agents, as well as in the access and utilization of key knowledge resources. Our study demonstrated how non-network-specific data obtained from a point-in-time survey can be employed to forecast community health center performance over time, thereby enhancing the sustainability of long-term strategic-improvement efforts. Our results revealed a strategic profile for community health center cancer-screening improvement via simulation over a projected 10-year period. The use of computational modeling allows additional inferential knowledge to be drawn from existing data when examining organizational performance in increasingly complex environments.

Building social networks out of cognitive blocks: factors of interest in agent-based socio-cognitive simulations

This paper examines how cognitive and environmental factors influence the formation of dyadic ties. We use agent models instantiated in ACT-R that interact in a social simulation, to illustrate the effect of memory constraints on networks. We also show that environmental factors are important including population size, running time, and map configuration. To examine these relationships, we ran simulations of networks using a factorial design. Our analyses suggest three interesting conclusions: first, the tie formation of these networks approximates a logistic growth model; second, that agent memory quality (i.e., perfect or human-like) strongly alters the network’s density and structure; third, that the three environmental factors all influence both network density and some aspects of network structure. These findings suggest that meaningful variance of social network analysis measures occur in a narrow band of memory strength (the cognitive band); the threshold for defining tie criteria is important; and future simulations examining generative social networks should control and carefully report these environmental and cognitive factors.

Data-driven diffusion modeling to examine deterrence

The combination of social network extraction from texts, network analytics to identify key actors, and then simulation to assess alternative interventions in terms of their impact on the network is a powerful approach for supporting crisis de-escalation activities. In this paper, we describe how researchers used this approach as part of a scenario-driven modeling effort. We demonstrate the strength of going from data-to-model and the advantages of data-driven simulation. We conclude with a discussion of the limitations of this approach for the chosen policy domain and our anticipated future steps.

Socio-Cultural Cognitive Mapping

We introduce Socio-cultural Cognitive Mapping (SCM), a method to characterize populations based on shared attributes, placing these actors on a spatial representation. We introduce the technique, taking the reader through an overview of the algorithm. We conclude with an example use-case of the Hatfield-McCoy feud. In the Hatfield-McCoy case, the SCM process clearly delineates members of the opposing clans as well as gender.