Human Terrain Data – What Should We Do With It?

What are we in the Modeling & Simulation (M&S) community to do with the volumes of \u27human terrain\u27 data now being published by the military and others in databases of the demographics and needs/values/norms of populations of interest? This paper suggests that the M&S community would be remiss if it did not rise to this challenge and suggest next steps for the use of this Human Terrain (HT) data resource. These datasets are a key asset for those interested in synthesis of two major agent-based modeling paradigms – the cognitive and the social – as this paper argues. We pursue this argument with a case study integrating a cognitive agent environment (PMFserv) and a social agent environment (FactionSim) and applying them to various regions of interest (Iraq, SE Asia, Crusades) to assess their validity and realism

Toward Realism in Human Performance Simulation

This chapter focuses on challenges to improving the realism of socially intelligent agents and attempts to reflect the state of the art in human behavior modeling with particular attention to the impact of values, emotion, and physiology/stress upon individual and group decision-making. The goal is to help those interested in constructing more realistic software agents for use in human performance simulations in both training and analysis settings. The first two sections offer an assessment of the state of the practice and of the need to make better use of human performance moderator functions (PMFs) published in the behavioral literature. The third section pursues this goal by providing an illustrative framework for integrating existing PMF theories and models, such as those on physiology and stress, cognitive and emotive processes, individual differences, and group and crowd behavior, among others. The fourth section presents asymmetric warfare and civil unrest case studies to examine some of the concerns affecting implementation of PMFs such as verification, validation, and interoperability with existing simulators, artificial life emulators, and artificial intelligence components. The final section of this chapter concludes with lessons learned and with some challenges if the field is to reach a greater level of maturity

Systems Social Seience: A Design Inquiry Approach for Stabilization and Reconstruction of Social Systems

This paper explores novel approaches under the design inquiry paradigm that promise to help organizations better understand and solve socio-technical dilemmas. Design inquiry is contrasted with scientific inquiry (Section 1). Section 2 presents a meso-scale model of models methodology for design inquiry that synthesizes systems science, agent modeling and simulation, knowledge management architectures, and domain theories and knowledge. The goal is to focus computational science on exploring underlying mechanisms (white box modeling) and to support reflective theorizing and discourse to explain social dilemmas and potential resolutions. Section 3 then describes an evolving agent modeling and simulation testbed while Section 4 offers two gameworld applications that implement this approach and that serve as an example of the new types of instruments useful for systems social science. The conclusions wrapup by reviewing lessons learned about 10 criteria that have guided this research

Toward an expert project management system

The purpose of the research effort is to prescribe a generic reusable shell that any project office can install and customize for the purposes of advising, guiding, and supporting project managers in that office. The prescribed shell is intended to provide both: a component that generates prescriptive guidance for project planning and monitoring activities, and an analogy (intuition) component that generates descriptive insights of previous experience of successful project managers. The latter component is especially significant in that it has the potential to: retrieve insights, not just data, and provide a vehicle for expert PMs to easily transcribe their current experiences in the course of each new project managed

Affordances in AI

Affordances in AI refer to a design methodology for creating artificial intelligence systems that are designed to perceive their environment in terms of its affordances (Sahin et al. 2007). Affordances in AI are adapted from affordances introduced in The Ecological Approach to Visual Perception by James J. Gibson (1979). Design methodologies in the applied sciences use affordances to represent potential actions that exist as a relationship between an agent and its environment. This approach to artificial intelligence is designed for autonomous agents, making it suitable for robotics and simulation

Affordance

1. (n.) An affordance is an action possibility formed by the relationship between an agent and its environment (J. Gibson 1977; J. Gibson 1979). For any combination of agent or environment, any given affordance either exists or does not exist. There is no middle ground. The most inclusive definition of affordances considers only the physical possibility of an action occurring. An agent does not need to be aware of the afforded action, such as the affordance of opening a secret door. This definition is rooted in perceptual psychology and its primary source is The Ecological Approach to Visual Perception by James J. Gibson (1979). 2. (n.) An affordance may refer to a perceived affordance. Perceived affordances are a subset of affordances. A perceived affordance uses a more restrictive definition that requires an agent to be aware of the affordance, either through direct perception or experience. A perceived affordance is a possible action to an agent (Norman 1988). Unlike the traditional definition, a perceived affordance is primarily a relationship between an agent’s cognition and the environment. This definition is commonly used within the humancomputer interaction (HCI) community. 3. (n.) Affordance may refer to how appealing an action possibility is to an agent, as in “this switch has affordance.” While the other definitions are dichotomous, this definition implies a magnitude (continuum) of affordance. This usage combines the ease of perceiving and/or perceived ease of performing a possible action. Since this usage refers to one or both of these qualities, this form is unclear from a theoretical standpoint

Social Learning and Adoption of New Behavior in a Virtual Agent Society

Social learning and adoption of new behavior govern the rise of a variety of behaviors: from actions as mundane as dance steps to those as dangerous as new ways to make IED detonators. However, agents in immersive virtual environments lack the ability to realistically simulate the spread of new behavior. To address this gap, a cognitive model was designed that represents the well-known socio-cognitive factors of attention, social influence, and motivation that influence learning and the adoption of a new behavior. To explore the effectiveness of this model, simulations modeled the spread of two competing memes in Hamariyah, an archetypal Iraqi village developed for cross-cultural training. Diffusion and clustering analyses were used to examine adoption patterns in these simulations. Agents produced well-defined clusters of early versus late adoption based on their social influences, personality, and contextual factors, such as employment status. These findings indicate that the spread of behavior can be simulated plausibly in a virtual agent society and has the potential to increase the realism of immersive virtual environments

Holistically Evaluating Agent Based Social System Models

The philosophical perspectives on model evaluation can be broadly classified into reductionist/logical positivist and relativist/holistic. In this paper, we outline some of our past efforts in, and challenges faced during, evaluating models of social systems with cognitively detailed agents. Owing to richness in the model, we argue that the holistic approach and consequent continuous improvement are essential to evaluating complex social system models such as these. A social system built primarily of cognitively detailed agents can provide multiple levels of correspondence, both at observable and abstract aggregated levels. Such a system can also pose several challenges, including large feature spaces, issues in information elicitation with database, experts and news feeds, counterfactuals, fragmented theoretical base, and limited funding for validation. We subscribe to the view that no model can faithfully represent reality, but detailed, descriptive models are useful in learning about the system and bringing about a qualitative jump in understanding of the system it attempts to model – provided they are properly validated. Our own approach to model evaluation is to consider the entire life cycle and assess the validity under two broad dimensions of (1) internally focused validity/quality achieved through structural, methodological, and ontological evaluations; and (2) external validity consisting of micro validity, macro validity, and qualitative, causal and narrative validity. In this paper, we also elaborate on selected validation techniques that we have employed in the past. We recommend a triangulation of multiple validation techniques, including methodological soundness, qualitative validation techniques, such as face validation by experts and narrative validation, and formal validation tests, including correspondence testing

Validating Agent Based Social Systems Models

Validating social systems is not a trivial task. The paper outlines some of our past efforts in validating models of social systems with cognitively detailed agents. It also presents some of the challenges faced by us. A social system built primarily of cognitively detailed agents can provide multiple levels of correspondence, both at observable and abstract aggregated levels. Such a system can also pose several challenges including large feature spaces, issues in information elicitation with database, experts and news feeds, counterfactuals, fragmented theoretical base, and limited funding for validation. Our own approach to validity assessment is to consider the entire life cycle and assess the validity under four broad dimensions of methodological validity, internal validity, external validity and qualitative, causal and narrative validity. In the past, we have employed a triangulation of multiple validation techniques, including face validation as well as formal validation tests including correspondence testing

Modeling the Personality & Cognition of Leaders

This paper summarizes efforts at adapting a personality profiling framework to model behavior and choices of political and military leaders. This is part of a larger project to create a role-playing, decision-making game to allow you to play out scenarios of interest against other leaders. In this modeling exercise we implement the Hermann leader personality profile tool to create historic leaders (Saladin, Richard I, etc.). We then attempt to validate the leader agents against scenarios of the 3rd Crusade