Silverman, Barry G

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Now showing 1 - 10 of 45
  • Publication
    Holistically Evaluating Agent Based Social System Models
    (2012-01-01) Bharathy, Gnana K.; Silverman, Barry G.
    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.
  • Publication
    StateSim: Lessons Learned from 20 Years of A Country Modeling and Simulation Toolset
    (2020-01-01) Silverman, Barry G; Silverman, Daniel M; Bharathy, Gnana; Weyer, Nathan; Tam, William
    A holy grail for military, diplomatic, and intelligence analysis is a valid set of software agent models that act as the desired ethno-political factions so that one can test the effects of alternative courses of action in different countries. This article explains StateSim, a country modeling approach that synthesizes best-of-breed theories from across the social sciences and that has helped numerous organizations over 20 years to study insurgents, gray zone actors, and other societal instabilities. The country modeling literature is summarized (Sect 1.1) and synthetic inquiry is contrasted with scientific inquiry (Sect. 1.2 and 2). Section 2 also explains many fielded StateSim applications and 100s of past acceptability tests and validity assessments. Section 3 then describes how users now construct and run ‘first pass’ country models within hours due to the StateSim Generator, while Section 4 offers two country analyses that illustrate this approach. The conclusions explain lessons learned.
  • Publication
    Blackboard System Generator (BSG): An Alternative Distributed Problem-Solving Paradigm
    (1989-03-01) Silverman, Barry G; Chang, Joseph S; Feggos, Kostas
    The classical blackboard model employs a number of relaxations of team decision theory that are commonly organized into three panels of AI heuristics, including: 1) a shared information panel that offers a capability for ensuring agent knowledge sharing, 2) a contract formalism for the agent and event scheduling, coordinating, and control panel, and 3) a blackboard panel for metalevel planning and guidance that offers whole situation recognition, top down reasoning, and adaptive learning. The nature and implications of these relaxations are explained in terms of the blackboard system generator (BSG) and via comparisons to what is done in other blackboard shells. Particular attention is paid to theoretical relaxations inherent in the classical blackboard model and to research opportunities arising as a result. Progress made to date to counteract adverse effects of some of these relaxations is described in terms of a project management/work breakdown paradigm adopted in BSG that: 1) alleviates the knowledge engineering bottlenecks of traditional blackboards and that provides BSG with a semantic rather than just syntactic understanding of blackboard control and scheduling; 2) allows a distributed problem-solving capability for connecting agents at virtual addresses on a logical network and that permits concurrent processing on any machine available on the network; 3) establishes an open architecture that includes techniques for integrating preexisting agent methods (e.g., expert systems, procedures, or data bases) while laying the foundation for assessing the impact of “black boxes” on the global and local objective functions; and 4) utilizes project management techniques for team agents planning as well as an analogical reasoner subsystem for BSG metaplanning and generic controlled learning. This latter item is supported by a connectionist scheme for its associative memory. The techniques of each of the three panels and of the four sets of paradigm-related advances are described along with selected results from classroom teaching experiments and from three applications using BSG to date.
  • Publication
    What is a Good Pattern of Life Model? Guidance for Simulations
    (2018-08-01) Silverman, Barry G; Bharathy, Gnana K.; Weyer, Nathan
    We have been modeling an ever-increasing scale of applications with agents that simulate the pattern of life (PoL) and real-world human behaviors in diverse regions of the world. The goal is to support sociocultural training and analysis. To measure progress, we propose the definition of a measure of goodness for such simulated agents, and review the issues and challenges associated with first-generation (1G) agents. Then we present a second generation (2G) agent hybrid approach that seeks to improve realism in terms of emergent daily activities, social awareness, and micro-decision making in simulations. We offer a PoL case study with a mix of 1G and 2G approaches that was able to replace the pucksters and avatar operators needed in large-scale immersion exercises. We conclude by observing that a 1G PoL simulation might still be best where large-scale, pre-scripted training scenarios will suffice, while the 2G approach will be important for analysis or if it is vital to learn about adaptive opponents or unexpected or emergent effects of actions. Lessons are shared about ways to blend 1G and 2G approaches to get the best of each.
  • Publication
    A Demonstration of the PMF-Extraction Approach: Modeling The Effects of Sound on Crowd Behavior
    (2002-05-01) Cornwell, Jason; Silverman, Barry G; O'Brien, Kevin; Johns, Michael
    The vast majority of psychology, sociology, and other social-science literature describing human behavior and performance does not reach the eyes of those of us working in the modeling and simulation community. Our recent work has been concerned with the extraction and implementation of Human Behavior Models(HBMs)/ Performance Moderator Functions(PMFs) from this literature. This paper demonstrates how our methodology was applied to extract models of the effects of music and sound on both individuals and groups and to implement them in a simulated environment. PMFs describing how several classes of sound affect decision-making and performance were constructed based on well-established psychological models. These PMFs were implemented in a simulation of protesters and security guards outside a prison that demonstrates how the presence of chanting and music changes the response of protesters to police aggression. The extraction of PMFs from the literature, the synthesis of a coherent, cohesive model, and the implementation and results of the simulation are discussed.
  • Publication
    Social Learning and Adoption of New Behavior in a Virtual Agent Society
    (2013-01-01) Nye, Benjamin D.; Silverman, Barry G.
    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 well-known socio-cognitive factors of attention, social influence, and motivation that influence learning and 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.
  • Publication
    What is a good pattern of life (PoL) model? – Guidance for simulations
    (2019-01-01) Silverman, Barry; Bharathy, Gnana; Weyer, Nathan; Sun, Qiwei
  • Publication
    Athena's Prism - A Diplomatic Strategy Role Playing Simulation for Generating Ideas and Exploring Alternatives
    (2005-05-01) Silverman, Barry G; Rees, Richard L; Toth, Jozsef A; Cornwell, Jason; O'Brien, Kevin; Johns, Michael; Caplan, Marty
    Intelligence analysts must clear at least three hurdles to get good product out the door: cognitive biases, social biases and self-imposed organizational impediments. Others (e.g., Gilovich, et al., Heuer, and Kahneman and Tversky), explain the cognitive processes that can help or trip us. A less well mapped set of dangers arises in the social dynamics of communicating tasking, working with other analysts, editing and customer interaction. Finally, the mere fact of a unit's published record creates analytic inertia - an argument at rest tends to stay at rest and one in motion (i.e., ambiguous or uncertain) tends to stay in motion. (A variation of this includes groupthink.)
  • Publication
    Human Terrain Data – What Should We Do With It?
    (2007-12-01) Silverman, Barry G
    What are we in the Modeling & Simulation (M&S) community to do with the volumes of 'human terrain' 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.
  • Publication
    Constructing Virtual Asymmetric Opponents From Data and Models in the Literature: Case of Crowd Rioting
    (2002-05-01) Silverman, Barry G; Johns, Michael; O'Brien, Kevin; Weaver, Ransom; Cornwell, Jason
    This paper describes an effort to integrate human behavior models from a range of ability, stress, emotion, decision theoretic, and motivation literatures into a game-theoretic framework appropriate for representing synthetic asymmetric agents and scenarios. Our goal is to create a common mathematical framework (CMF) and an open agent architecture that allows one to research and explore alternative behavior models to add realism to software agents - e.g., physiology and stress, personal values and emotive states, and cultural influences. Our CMF is based on a dynamical, game-theoretic approach to evolution and equilibria in Markov chains representing states of the world that the agents can act upon. In these worlds the agents' utilities (payoffs) are derived by a deep model of cognitive appraisal of intention achievement including assessment of emotional activation/decay relative to value hierarchies, and subject to (integrated) stress and related constraints. We present the progress to date on the mathematical framework, and on an environment for quickly editing opponents in terms of the various elements of the cognitive appraiser, utility generators, value hierarchies, and Markov chains. We illustrate the approach via an example training game for counter-terrorism and crowd management. Future research needs are elaborated including validity issues and ways to overcome the gaps in the behavioral literatures that confront developers of asymmetric forces.