Stephen Lieberman

The Cultural Geography Model: Evaluating the Impact of Tactical Operational Outcomes on a Civilian Population in an Irregular Warfare Environment:

The civilian population has been described as ‘the center of gravity in irregular warfare’. Understanding the behavioral response of the civilian population in irregular warfare operations presents a major challenge area to the joint modeling and simulation community where there is a clear need for the development of models, methods, and tools to address civilian behavior response. This paper provides a conceptual and theoretical overview of the Cultural Geography (CG) model, a government-owned, open-source agent-based model designed to address the behavioral response of civilian populations in conflict environments. With an embedded case study, we describe the development of cognitive modules to represent the civilian population and their implementation as Bayesian belief networks (BBNs), the social structure module implemented using homophily, the process of adjudicating the effects of tactical level outcomes on a population segment within the model, and a sample case study analysis using a designed experiment.

Developing cognitive models for social simulation from survey data

The representation of human behavior and cognition continues to challenge the modeling and simulation community. The use of survey and polling instruments to inform belief states, issue stances and action choice models provides a compelling means of developing models and simulations with empirical data. Using these types of data to population social simulations can greatly enhance the feasibility of validation efforts, the reusability of social and behavioral modeling frameworks, and the testable reliability of simulations. We provide a case study demonstrating these effects, document the use of survey data to develop cognitive models, and suggest future paths forward for social and behavioral modeling.

The effects of time advance mechanism on simple agent behaviors in combat simulations

We investigate the effects of time advance mechanisms on the behavior of agents in combat simulations using some simple scenarios relevant to combat and agent-based models. We implement these simulation designs in two modeling packages that illustrate the differences between discrete-time simulation (DTS) and discrete-event simulation (DES) methodologies. Many combat models use DTS as their simulation time advance mechanism. We demonstrate that the presence and size of the time step as a modeling component can have a substantial impact on the basic characteristics of agent and simulation performance. We show that the use of a DTS method can degrade the modeling accuracy of changes in agent sensor range and detection outcomes, and also can compromise the ability of agents to travel to specific target destinations in a spatial simulation environment. We conclude that DES methodology successfully addresses these problems and is preferred as a time advance mechanism in these situations.

Extensible software for whole of society modeling: framework and preliminary results

In order to extend the ongoing academic and practitioner dialogs germane to Human, Social, Cultural, and Behavioral (HSCB) modeling and simulation, we introduce a novel extensible methodology for modeling Complex Adaptive Social Systems (CASS) that centers on capturing the emergence of dynamic Beliefs, Values, and Interests (BVIs) in the individuals and groups that compose a whole society. We follow Peter Blau, Miller McPherson, Lynn Smith-Lovin, Duncan Watts and many others in positing that human behavior is best understood in terms of the complex social systems within which actions are taken, and that the representation of these social systems must make evident the endogenous structural constraints on opportunities for interpersonal contact and group affiliation. We further demonstrate that the ability to seed social simulations with representative social data about the society being modeled (e.g. from social surveys), and the use of social networks generated using principles of homophily from empirical social science, provide a compelling and actionable framework for both interpreting with and theory building from whole society simulations.

Representing dynamic social networks in discrete event social simulation

One of the key structural components of social systems is the social network. The representation of this network structure is key to providing a valid representation of the society under study. The social science concept of homophily provides a conceptual model of how social networks are formed and evolve over time. Previous work described the results of social simulation using a static homophily network. In order to gain the full benefit of modeling societies a representation of how the social network changes over time is required. This paper introduces the implementation of a dynamic homophily network, along with a case study exploring the sensitivity of model outputs to the parameters describing the network and applying social network change detection methods (SNCD) to model output.

Developing social networks for artificial societies from survey data

Authentically representing large social collectivities remains a preeminent challenge throughout the social computing, and modeling and simulation communities. We demonstrate here a simple technique that uses survey and polling data to embed agents with attributes and endogenously elicit an authentic and theory-driven simulation social structure for an artificial society. We furthermore show that a representation of social structure based on internal agent attributes allows for the continuous representation of social dynamics that affect agent cognition and association, and that social structures for artificial societies can be generated without any loss to the granularity of the underlying data or simulation output. We provide a case study using social survey data to demonstrate the method and effects, document the visualization of social structure for the population of Indonesia, discuss the implications and uses of survey data for social simulation, and suggest several paths forward for social and behavioral predictive modeling.

A use-case approach to the validation of social modeling and simulation

The modeling and simulation (M&S) community is faced with the task of informing public policy decision makers, from both the defense community and from the civilian sector, of the impact of their decisions on the beliefs, values and interests (BVIs) of the populations in their areas of influence. The M&S techniques used for these types of analyses by necessity challenge the traditional boundaries and methods regarding validation efforts. Given that the analysis of populations is largely intractable via reductionist methodologies, we posit that insight must be garnered through experiment and iteration using simulated societies. Further, the designs of these simulation experiments must be based on the information needs of the community. We discuss the concept of developing social simulations by use case, the validation of data sources for model development, validation techniques guided by usefulness, and a case study approach to validation of social simulations.

Visualizing the human, social, cultural and behavioral components of a complex conflict ecosystem

Creating meaningful visualizations of multi-dimensional human, social, cultural, and behavioral (HSCB) data will provide greater insights to operational decision makers across a large variety of problem domains. Developing and deploying visualization tools presents a variety of challenges to the analytic community, and these are further compounded when presenting information on the non-traditional dimensions of the battlefield encompassed by HSCB domain. Given that the center of gravity in Irregular Warfare is the population, the need for battlefield commanders to understand this information in operationally relevant ways is clear. Here we provide an overview of the challenges in providing visual displays of HSCB data for decision support, and the methods chosen for communicating the output of a social simulation. We use the Cultural Geography model as an example of a social simulation with accompanying visualizations, discuss the importance of display configurations, and propose several paths forward for future work in HSCB data visualization.

Violent extremist network representation and attack the network course of action analysis in social simulation

The representation of violent extremist networks (VENs) and their behaviors within social simulations is required in order to leverage models and simulations for use in attack the network course of action analysis. VENs appear throughout the world in multiple forms with varying objectives and behaviors. They exist as a subset of society, nested within and leveraging the underlying social network of a culture to further their own ends. The interdiction of these networks to presents a challenging task for law enforcement and military organizations due to their ability to change and adapt to the situation at hand. The use of social simulation with VEN representations and designed simulation experiments can provide decision makers with insights into the potential impact of their actions to attack the threat network to include the likely manner in which the VEN might adapt to counter the attack. This paper discusses the requirements to represent VENs within simulations, provides an overview of the current VEN representation within the Cultural Geography model, and provides a case study analysis of the use of social simulations for the exploration of possible futures in support of course of action analysis.