Publications

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The political dynamics associated with an election are typically a function of the interplay between political leaders and voters, as well as endogenous and exogenous factors that impact the perceptions and goals of the electorate. This paper describes an effort by Sandia National Laboratories to model the attitudes and behaviors of various political groups along with that population's primary influencers, such as government leaders. To accomplish this, Sandia National Laboratories is creating a hybrid system dynamics-cognitive model to simulate systems- and individual-level political dynamics in a hypothetical society. The model is based on well-established psychological theory, applied to both individuals and groups within the modeled society. Confidence management processes are being incorporated into the model design process to increase the utility of the tool and assess its performance. This project will enhance understanding of how political dynamics are determined in democratic society.

Working with leading experts in the field of cognitive neuroscience and computational intelligence, SNL has developed a computational architecture that represents neurocognitive mechanisms associated with how humans remember experiences in their past. The architecture represents how knowledge is organized and updated through information from individual experiences (episodes) via the cortical-hippocampal declarative memory system. We compared the simulated behavioral characteristics with those of humans measured under well established experimental standards, controlling for unmodeled aspects of human processing, such as perception. We used this knowledge to create robust simulations of & human memory behaviors that should help move the scientific community closer to understanding how humans remember information. These behaviors were experimentally validated against actual human subjects, which was published. An important outcome of the validation process will be the joining of specific experimental testing procedures from the field of neuroscience with computational representations from the field of cognitive modeling and simulation.

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This 3-year research and development effort focused on what we believe is a significant technical gap in existing modeling and simulation capabilities: the representation of plausible human cognition and behaviors within a dynamic, simulated environment. Specifically, the intent of the ''Simulating Human Behavior for National Security Human Interactions'' project was to demonstrate initial simulated human modeling capability that realistically represents intra- and inter-group interaction behaviors between simulated humans and human-controlled avatars as they respond to their environment. Significant process was made towards simulating human behaviors through the development of a framework that produces realistic characteristics and movement. The simulated humans were created from models designed to be psychologically plausible by being based on robust psychological research and theory. Progress was also made towards enhancing Sandia National Laboratories existing cognitive models to support culturally plausible behaviors that are important in representing group interactions. These models were implemented in the modular, interoperable, and commercially supported Umbra{reg_sign} simulation framework.

Understanding the role of emotional states is critical for predicting the kind of decisions people will make in risky situations. Currently, there is little understanding as to how emotion influences decision-making in situations such as terrorist attacks, natural disasters, pandemics, and combat. To help address this, we used behavioral and neuroimaging methods to examine how emotion states and traits influence decisions. Specifically, this study used a wheel of fortune behavioral task and functional magnetic resonance imaging (fMRI) to examine the effects of emotional states and traits on decision-making pertaining to the degree of risk people are willing to make in specific situations. The behavioral results are reported here. The neural data requires additional time to analyze and will be reported at a future date. Biases caused by emotion states and traits were found regarding the likelihood of making risky decisions. The behavioral results will help provide a solid empirical foundation for modeling the effects of emotion on decision in risky situations.

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This report describes the purpose and results of the two-year, Sandia-sponsored Laboratory Directed Research and Development (LDRD) project entitled Understanding Communication in Counterterrorism Crisis Management The purpose of this project was to facilitate the capture of key communications among team members in simulated training exercises, and to learn how to improve communication in that domain. The first section of this document details the scenario development aspects of the simulation. The second section covers the new communication technologies that were developed and incorporated into the Weapons of Mass Destruction Decision Analysis Center (WMD-DAC) suite of decision support tools. The third section provides an overview of the features of the simulation and highlights its communication aspects. The fourth section describes the Team Communication Study processes and methodologies. The fifth section discusses future directions and areas in which to apply the new technologies and study results obtained as a result of this LDRD.

This project is being conducted by Sandia National Laboratories in support of the DARPA Augmented Cognition program. Work commenced in April of 2002. The objective for the DARPA program is to 'extend, by an order of magnitude or more, the information management capacity of the human-computer warfighter.' Initially, emphasis has been placed on detection of an operator's cognitive state so that systems may adapt accordingly (e.g., adjust information throughput to the operator in response to workload). Work conducted by Sandia focuses on development of technologies to infer an operator's ongoing cognitive processes, with specific emphasis on detecting discrepancies between machine state and an operator's ongoing interpretation of events.