The ground truth program used simulations as test beds for social science research methods. The simulations had known ground truth and were capable of producing large amounts of data. This allowed research teams to run experiments and ask questions of these simulations similar to social scientists studying real-world systems, and enabled robust evaluation of their causal inference, prediction, and prescription capabilities. We tested three hypotheses about research effectiveness using data from the ground truth program, specifically looking at the influence of complexity, causal understanding, and data collection on performance. We found some evidence that system complexity and causal understanding influenced research performance, but no evidence that data availability contributed. The ground truth program may be the first robust coupling of simulation test beds with an experimental framework capable of teasing out factors that determine the success of social science research.
Measures of simulation model complexity generally focus on outputs; we propose measuring the complexity of a model’s causal structure to gain insight into its fundamental character. This article introduces tools for measuring causal complexity. First, we introduce a method for developing a model’s causal structure diagram, which characterises the causal interactions present in the code. Causal structure diagrams facilitate comparison of simulation models, including those from different paradigms. Next, we develop metrics for evaluating a model’s causal complexity using its causal structure diagram. We discuss cyclomatic complexity as a measure of the intricacy of causal structure and introduce two new metrics that incorporate the concept of feedback, a fundamental component of causal structure. The first new metric introduced here is feedback density, a measure of the cycle-based interconnectedness of causal structure. The second metric combines cyclomatic complexity and feedback density into a comprehensive causal complexity measure. Finally, we demonstrate these complexity metrics on simulation models from multiple paradigms and discuss potential uses and interpretations. These tools enable direct comparison of models across paradigms and provide a mechanism for measuring and discussing complexity based on a model’s fundamental assumptions and design.
This project studied the potential for multiscale group dynamics in complex social systems, including emergent recursive interaction. Current social theory on group formation and interaction focuses on a single scale (individuals forming groups) and is largely qualitative in its explanation of mechanisms. We combined theory, modeling, and data analysis to find evidence that these multiscale phenomena exist, and to investigate their potential consequences and develop predictive capabilities. In this report, we discuss the results of data analysis showing that some group dynamics theory holds at multiple scales. We introduce a new theory on communicative vibration that uses social network dynamics to predict group life cycle events. We discuss a model of behavioral responses to the COVID-19 pandemic that incorporates influence and social pressures. Finally, we discuss a set of modeling techniques that can be used to simulate multiscale group phenomena.
There is a wealth of psychological theory regarding the drive for individuals to congregate and form social groups, positing that people may organize out of fear, social pressure, or even to manage their self-esteem. We evaluate three such theories for multi-scale validity by studying them not only at the individual scale for which they were originally developed, but also for applicability to group interactions and behavior. We implement this multi-scale analysis using a dataset of communications and group membership derived from a long-running online game, matching the intent behind the theories to quantitative measures that describe players’ behavior. Once we establish that the theories hold for the dataset, we increase the scope to test the theories at the higher scale of group interactions. Despite being formulated to describe individual cognition and motivation, we show that some group dynamics theories hold at the higher level of group cognition and can effectively describe the behavior of joint decision making and higher-level interactions.
This paper discusses relevant findings and theories regarding the role of ideology, culture, and context in shaping the behaviors of individuals within violent social movements. Accordingly, this focus concerns the comparative weight placed on ideology and culture (expressed principles and motives) versus external factors as chief influencers for the propensity of individuals to act outside of the norms of society and politics by resorting to violent behaviors. In doing so, we have drawn upon theory from anthropology, behavioral economics, political science, psychology, and sociology to better understand how these variables give birth to and nurture militant social movements. F u r t h e r d i s s e m i n a t i o n o n l y a s a u t h o r i z e d t o U . S . G o v e r n m e n t a g e n c i e s a n d t h e i r c o n t r a c t o r s ; o t h e r r e q u e s t s s h a l l b e a p p r o v e d b y t h e o r i g i n a t i n g f a c i l i t y o r h i g h e r D O E p r o g r a m m a t i c a u t h o r i t y .
This paper seeks to explore the conditions where leaders from open democracies to authoritarian states become more or less popular in response to perceived economic and social threats to society, along with increases in societal (economic and social) hardship and group polarization effects. To further explore these conditions, we used a psycho-social approach to develop a preliminary conceptual model of how the perception of threats, changes in societal conditions, and the polarization of society can concurrently influence the popularity of a government leader.
A common problem associated with the effort to better assess potential behaviors of various individuals within different countries is the shear difficulty in comprehending the dynamic nature of populations, particularly over time and considering feedback effects. This paper discusses a theory-based analytical capability designed to enable analysts to better assess the influence of events on individuals interacting within a country or region. These events can include changes in policy, man-made or natural disasters, migration, war, or other changes in environmental/economic conditions. In addition, this paper describes potential extensions of this type of research to enable more timely and accurate assessments.
This paper discusses and seeks to synthesize theories regarding the role of ideology and psychosocial contextual factors in shaping motivations and behaviors of individuals within violent extremist movements. To better understand how these factors give birth to and nurture extremist social movements, theory from a multitude of disciplines was incorporated into a conceptual model of the drivers associated with terrorist behaviors. This model draws upon empirically supported theoretical notions, such as the violation of socioeconomic and geopolitical expectations, the concept of perceived threat, one’s mental construction of the world and group polarization. It also draws upon the importance of one’s social identity, sense of belonging, and the perceived “glamour” associated with extremist group behaviors.
We created a simulation model to investigate potential links between the actions of violent extremist organizations (VEOs), people in the VEO’s home country, and diaspora communities from that country living in the West. We created this model using the DYMATICA framework, which uses a hybrid cognitive system dynamics modeling strategy to simulate behaviors based on psycho-social theory. Initial results of the model are given, focusing on increases to VEO funding and recruiting resulting from an invasion of the VEO’s home country. Western intervention, prejudice, and economic drivers are also considered.
The Republic of Estonia faced a series of cyber attacks and riots in 2007 that seemed to be highly coordinated and politically motivated, causing short-lived but substantial impact to Estonia's cyber and economic systems. Short-Term harm from these hybrid incidents led to long-Term improvements and leadership by Estonia in the cyber arena. We created a causal model of these attacks to simulate their dynamics. The model uses the DYMATICA framework, a cognitive-system dynamics structure used to quantify and simulate elicited information from subject matter experts. This historical case study underscores how cyber warfare can be a major threat to modern society, and how it can be combined with information operations and kinetic effects to create further disruption. Given states' potential vulnerability to cyber attacks, a deeper understanding of how to analyze, prevent, defend, and utilize the aftermath of these for improvement to systems is critical, as is insight into the fundamental rationale of the outcomes.
We created a cognition-focused system dynamics model to simulate the dynamics of smoking tendencies based on media influences and communication of opinions. We based this model on the premise that the dynamics of attitudes about smoking can be more deeply understood by combining opinion dynamics with more in-depth psychological models that explicitly explore the root causes of behaviors of interest. Results of the model show the relative effectiveness of two different policies as compared to a baseline: A decrease in advertising spending, and an increase in educational spending. The initial results presented here indicate the utility of this type of simulation for analyzing various policies meant to influence the dynamics of opinions in a population.
The Integrated Human Futures Project provides a set of analytical and quantitative modeling and simulation tools that help explore the links among human social, economic, and ecological conditions, human resilience, conflict, and peace, and allows users to simulate tradeoffs and consequences associated with different future development and mitigation scenarios. In the current study, we integrate five distinct modeling platforms to simulate the potential risk of social unrest in Egypt resulting from the Grand Ethiopian Renaissance Dam (GERD) on the Blue Nile in Ethiopia. The five platforms simulate hydrology, agriculture, economy, human ecology, and human psychology/behavior, and show how impacts derived from development initiatives in one sector (e.g., hydrology) might ripple through to affect other sectors and how development and security concerns may be triggered across the region. This approach evaluates potential consequences, intended and unintended, associated with strategic policy actions that span the development-security nexus at the national, regional, and international levels. Model results are not intended to provide explicit predictions, but rather to provide system-level insight for policy makers into the dynamics among these interacting sectors, and to demonstrate an approach to evaluating short- and long-term policy trade-offs across different policy domains and stakeholders. The GERD project is critical to government-planned development efforts in Ethiopia but is expected to reduce downstream freshwater availability in the Nile Basin, fueling fears of negative social and economic impacts that could threaten stability and security in Egypt. We tested these hypotheses and came to the following preliminary conclusions. First, the GERD will have an important short-term impact on water availability, food production, and hydropower production in Egypt, depending on the short- term reservoir fill rate. Second, the GERD will have a very small impact on water availability in the Nile Basin over the longer term. Depending on the GERD fill rate, short-term (e.g., within its first 5 years of operation) annual losses in Egyptian food production may peak briefly at 25 percent. Long-term (e.g., 15 to 30 year) cumulative losses in Egypt's food production may be less than 3 percent regardless of the fill rate, with the GERD having essentially no impact on projected annual food production in Egypt about 25 years after opening. For the quick fill rates, the short-term losses may be sufficient to create an important decrease in overall household health among the general population, which, along with other economic stressors and different strategies employed by the government, could lead to social unrest. Third, and perhaps most importantly, our modeling suggests that the GERD's effect on Egypt's food and water resources is small when compared to the effect of projected Egyptian population and economic growth (and the concomitant increase in water consumption). The latter dominating factors are exacerbated in the modeling by natural climate variability and may be further exacerbated by climate change. Our modeling suggests that these growth dynamics combine to create long-term water scarcity in Egypt, regardless of the Ethiopian project. All else being equal, filling strategies that employ slow fill rates for the GERD (e.g., 8 to 13 years) may mitigate the risks in future scenarios for Egypt somewhat, but no policy or action regarding the GERD is likely to significantly alleviate the projected water scarcity in Egypt's Nile Basin. However, general beliefs among the Egyptian populace regarding the GERD as a major contributing factor for scarcities in Egypt could make Ethiopia a scapegoat for Egyptian grievances -- contributing to social unrest in Egypt and generating undesirable (and unnecessary) tension between these two countries. Such tension could threaten the constructive relationships between Egypt and Ethiopia that are vital to maintaining stability and security within and between their respective regional spheres of influence, Middle East and North Africa, and the Horn of Africa.
The purpose of this work is to create a generalizable data- and theory-supported capability to better understand and anticipate (with quantifiable uncertainty): 1) how the dynamics of allegiance formations between various groups and society are impacted by active conflict and by third-party interventions and 2) how/why extremist allegiances co-evolve over time due to changing geopolitical, sociocultural, and military conditions.
The construction of the Grand Ethiopian Renaissance Dam (GERD) has generated tensions between Egypt and Ethiopia over control of the Nile River in Northern Africa. However, tensions within Egypt have also been pronounced, leading up to and following the Arab Spring uprising of 2011. This study used the Behavior Influence Assessment (BIA) framework to simulate a dynamic hypothesis regarding how tensions within Egypt may evolve given the impacts of the GERD. Primarily, we addressed the interplay between four parties over an upcoming ten-year period: the Egyptian Regime, the Military-Elite, the Militant population, and the non-Militant population. The core tenant of the hypothesis is that rising food prices was a strong driver to the unrest leading up to the Arab Spring events and that this same type of economic stress could be driven by the GERD—albeit with different political undertones. Namely, the GERD offers the Regime a target for inciting nationalism, and while this may buy the regime time to fix the underlying economic impacts, ultimately there exists a tipping point beyond which exponentially increasing unrest is unavoidable without implementing strong measures, such as state militarization.
There is a recognized need to develop computational models that can represent and simulate the decision making process of various groups across socio-cultural domains [5]. Yet, developing such models can be greatly hampered by the need to acquire and represent information pertaining to the psychological and social aspects of decision-making within these groups. Currently, there are numerous techniques and tools to help facilitate the elicitation and structuring of knowledge within expert-type systems—particularly those that focus on technical processes such as mechanical troubleshooting [3]. However, few techniques and tools have been developed for models that are intended to represent and assess the decision making of groups within different societies—particularly including cultural elements within these societies. This paper seeks to help address this challenge by discussing an approach to eliciting and structuring cross-cultural psychosocial and behavioral-economic elements within a theory-based assessment model. This work was developed to address the needs of Sandia National Laboratories’ Behavioral Influence Assessment modeling capability, which assesses decision-making within societies. The main component of the knowledge engineering effort is what we call the “knowledge structure.” The knowledge structure acts as scaffolding for the organization of psychosocial processes underlying decision-making, as well as the actual content of that knowledge with respect to a modeled society.
Adult neurogenesis in the hippocampus region of the brain is a neurobiological process that is believed to contribute to the brain's advanced abilities in complex pattern recognition and cognition. Here, we describe how realistic scale simulations of the neurogenesis process can offer both a unique perspective on the biological relevance of this process and confer computational insights that are suggestive of novel machine learning techniques. First, supercomputer based scaling studies of the neurogenesis process demonstrate how a small fraction of adult-born neurons have a uniquely larger impact in biologically realistic scaled networks. Second, we describe a novel technical approach by which the information content of ensembles of neurons can be estimated. Finally, we illustrate several examples of broader algorithmic impact of neurogenesis, including both extending existing machine learning approaches and novel approaches for intelligent sensing.
This report describes the laboratory directed research and development work to model relevant areas of the brain that associate multi-modal information for long-term storage for the purpose of creating a more effective, and more automated, association mechanism to support rapid decision making. Using the biology and functionality of the hippocampus as an analogy or inspiration, we have developed an artificial neural network architecture to associate k-tuples (paired associates) of multimodal input records. The architecture is composed of coupled unimodal self-organizing neural modules that learn generalizations of unimodal components of the input record. Cross modal associations, stored as a higher-order tensor, are learned incrementally as these generalizations form. Graph algorithms are then applied to the tensor to extract multi-modal association networks formed during learning. Doing so yields a novel approach to data mining for knowledge discovery. This report describes the neurobiological inspiration, architecture, and operational characteristics of our model, and also provides a real world terrorist network example to illustrate the model's functionality.
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.
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.
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.