Krasnow Institute for Advanced Study
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The Krasnow Institute seeks to expand understanding of mind, brain, and intelligence by conducting research at the intersection of the separate fields of cognitive psychology, neurobiology, and the computer-driven study of artificial intelligence and complex adaptive systems. These separate disciplines increasingly overlap and promise progressively deeper insight into human thought processes. The Institute also examines how new insights from cognitive science research can be applied for human benefit in the areas of mental health, neurological disease, education, and computer design.
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Browsing Krasnow Institute for Advanced Study by Subject "Agent-Based Modeling"
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Item Agent-Based Modeling in Intelligence Analysis(2012) Frank, Aaron Benjamin; Frank, Aaron Benjamin; Axtell, Robert L.The United States Intelligence Community (IC) was born out of the experiences and organization of the Office of Strategic Services during World War II and became a permanent fixture of the national security establishment with the passage of the National Security Act of 1947. Since its inception, there has been a strong fascination with the secret aspects of its work, particularly with respect to the clandestine collection of information and covert efforts to influence foreign governments, and to undermine rival intelligence services. By comparison, intelligence analysis, specifically the ways in which intelligence professionals develop and present assessments about the international system to policy makers, has been relatively ignored. As a result, intelligence analysis has remained largely under-theorized within the study of international relations, despite its prominent role in strategic thinking--only receiving significant attention in the aftermath of perceived failures.Item Implementing a Complex Social Simulation of the Violent Offending Process: The Promise of a Synthetic Offender(2016) Dover, Thomas J.; Dover, Thomas J.; Cioffi-Revilla, ClaudioThere are limitations to traditional methods of capturing the dynamics of violent interactions. These limitations are due to outcome driven approaches, data sampling issues, and inadequate means to capture, express, and explore the complexity of behavioral processes. To address these challenges, it is proposed that “violent offending” be re-framed as an emergent feature of a complex adaptive social system. This dissertation abstracts and computationally implements a theoretical framework that forms the basis of a complex social simulation of the violent offending process. The primary outcome of this effort is a viable synthetic offender that emerges from simulated interactions between potential offenders (subjects) and potential victims (targets) within an environment. The results of calibrating this model to a real-world murder series are discussed, as well as, the comparison metrics used to assess goodness-of-fit of simulated and real-world event-sites. A synthetic offender promises valuable insights into individual offending trajectories, offender tactical processes, and the emergence of geospatial and temporal behaviors. Furthermore, this approach is capable of reproducing the violent offending process with sufficient detail to contribute new scientific understanding and insights to criminology and the social sciences.Item Individual and Social Learning: An Implementation of Bounded Rationality from First Principles(2015) Palmer, Nathan Michael; Palmer, Nathan Michael; Axtell, Robert L.This dissertation expands upon a growing economic literature that uses tools from reinforcement learning and approximate dynamic programming to impose bounded rationality in intertemporal choice problems. My dissertation contributes to the literature by applying these tools to the canonical household consumption under uncertainty problem. The three essays explore individual and social approaches to learning-to-optimize and how these may be brought to data.Item Innovation from a Computational Social Science Perspective: Analyses and Models(2013) Casstevens, Randy M.; Casstevens, Randy M.; Axtell, Robert L.Innovation processes are critical for preserving and improving our standard of living. While innovation has been studied by many disciplines, the focus has been on qualitative measures that are specific to a single technological domain. I adopt a quantitative approach to investigate underlying regularities that generalize across multiple domains. I use a novel approach to better understand the innovation process by combining computational models with empirical data on software development, on one hand, and the evolution of the English lexicon on the other. Innovation can be viewed as the recombination and mutation of existing building blocks. I focus on how building blocks are used to generate innovations. The building blocks are pieces of code (e.g., functions or objects) for the software development data and words for the written language. These data lie at extremes of time scales: innovation occurring over the course of a few days or a week in the case of software while language evolution occurs over decades or centuries. This allows the examination of innovation processes that range from highly-constrained to completely open-ended. Computational methods reinforce the findings from the data analyses and permit exploration of the general features of innovation processes through the construction of abstract models.Item Social Preferences, Learning, and the Dynamics of Cooperation in Networked Societies: A Dialogue Between Experimental and Computational Approaches(2016) Cotla, Chenna Reddy; Cotla, Chenna Reddy; Axtell, Robert LIn this dissertation, I empirically investigate cooperative behavior in networks using the framework of network public goods games. To do so, I use a dialogue between behavioral experiments and agent-based models. I design and conduct behavioral experiments to generate data to construct boundedly rational agents that behave like humans and reproduce stylized facts in public goods environments. The human-like agents are deployed in a small-scale agent-based model to make novel quantitative predictions that can be statistically tested using a new set of behavioral experiments. This ensures that the behavioral specification of agents carries predictive value so that quantitative predictions made using it can be reproduced with human subject experiments. The high fidelity agent-based model is then extended to study the dynamics cooperation in networked environments. The dissertation is organized into three chapters.Item The Blind Lawmaker(2013-08) Koehler, Matthew; Koehler, Matthew; Axtell, Robert L.Many have written about how the Common Law should evolve. The few attempts to demonstrate this empirically, however, have not found evidence that this evolution takes place. This study uses a representation of the Article III United States Federal Courts and an agent-based model to demonstrate that a judicial system may evolve while simultaneously emitting signals to the contrary by evolving via a punctuated equilibrium dynamic. The study then proceeds to demonstrate that agent-based modeling is a viable method for understanding the performance of judicial institutions. After reviewing concepts of jurisprudence and computational social science, the development of the model is discussed followed by a presentation of the results of the aforementioned experiments.Item When People Rebel: A Computational Approach to Violent Collective Action(2014-08) Pires, Bianica; Pires, Bianica; Crooks, Andrew T.Why an individual rebels, why an individual joins collective action, and how that manifests to violence are not new questions, but are questions that continue to pose a significant scientific challenge. It is the role of the conflict analyst to answer these questions by exploring the underlying dynamics, interactions, and individual behaviors of the conflict. Violent collective action, a subfield of conflict studies, is a complex system, consisting of individuals with unique attributes that interact with other individuals through interconnected networks on a heterogeneous environment. In order to represent a complex system, we must model it from the "bottom-up," as the only way to generate the macro-behaviors is by modeling the individual, micro-level components of the system. In its ability to model complex systems, a computational approach is ideal. While various computational models have explored the use of agent-based modeling (ABM), social network analysis (SNA), and geographic information systems (GIS) in the field of violent collective action, most have explored the techniques in isolation. The models presented in this dissertation build on the value of integrating these approaches. Computational methods (i.e., ABM, SNA, and GIS) are used to develop three instantiations of more general models of violent collective action. The instantiations, or case studies, were selected for their diversity in terms of geographic location, temporal and spatial scale, and the political and cultural issues underlying the violent collective action. In addition, the case studies serve as building blocks; as I add layers to the environment, develop more sophisticated cognitive frameworks, and create agent-to-agent and agent-to-environment interactions that more closely represent reality. In addition, with the final case study I will demonstrate the value of integrating the three computational methods. Using empirical data for which to create the modeling world and inform the agents, qualitative agreement with actual events modeled are sought. The research question this dissertation addresses is: Can a bottom-up approach provide us with useful insight into the formation, spread, and strength of violent collective action? By covering a variety of different situations of violent collective action while building on the complexity of each computational technique used, the use of a computational approach to gain a better understanding violent collective action is given greater legitimacy. Through such understanding, this dissertation contributes to the existing body of knowledge on the topic of violent collective action.