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.