Krasnow Institute for Advanced Study

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https://hdl.handle.net/1920/6554

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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A Computational Theory of Endogenous Norm Change: The NormSim Agent-Based Model in MASON
(2011-08-22) Rouleau, Mark D.; Rouleau, Mark D.; Cioffi-Revilla, Claudio
The current study presents the NormSim Agent-Based Model in MASON. NormSim conducts a computational analysis of the International Relations theory of constructivism. NormSim explores the metastable dynamics of norms through the interactions of heterogeneous agents embedded within a complex social system. The goal is to explain how the social complexity of international relations generates metastability. The use of ABM and the MASON simulation toolkit make it possible to explore this process from a formal experimental perspective. This is advantageous for constructivist research that typically must rely on qualitative analysis alone to justify complex theoretical assumptions. NormSim demonstrates the use of ABM to test the logical consistency of constructivist claims. It also extends constructivist logic to better understand why international norms lead to complex conformity patterns and long run systemic change. NormSim provides a general computational theory to explain this phenomenon.
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.
Agent-Based Simulation of Tax Reporting Compliance
(2012-09-17) Bloomquist, Kim Michael; Bloomquist, Kim Michael; Axtell, Robert L.
Following the global financial crisis of 2008 many national governments have a renewed urgency to collect taxes not paid by noncompliant taxpayers. However, despite decades of theoretical and applied research progress has lagged on the development of computational tools to help tax administrators devise effective compliance improvement strategies. This study aims to bridge this gap by introducing the Individual Reporting Compliance Model (IRCM), an agent-based computational model that simulates tax reporting compliance in a community of 85,000 individual taxpayers, their employers and tax preparers. The model uses detailed tax return information yet maintains taxpayer anonymity by replacing actual tax returns with cases from the Statistics of Income (SOI) Public Use File [Weber 2004]. After reviewing the theoretical and empirical literature on taxpayer compliance, this study describes the development of the IRCM and demonstrates its capabilities in several simulation experiments.
Essays in High-Impact Companies and High-Impact Entrepreneurship
(2012-10-05) Berea, Anamaria; Berea, Anamaria; Tsvetovat, Maksim
Evidence shows that only a small number of entrepreneurial endeavors - high-impact companies - create most of the new employment in the US. This research is looking for the causes of emergence and uses computational methods to analyze speci c aspects of these companies. First, this research proves or disproves some "popular conjectures" regarding the age, location, industry and entrepreneurial character of high-impact companies. Secondly, the agent-based model shows how a company grows in employment and revenue based on two layers of organization: 1) one is the heterogeneous team formation and interaction at the mezzo level of a company organization and 2) another one is the heterogeneous employees skills and interaction at the individual level of a company organization. The model advances and tests the hypothesis that companies that learn the "fastest" from failed projects while retaining access to capital are more likely to become high-impact companies. The experiments replicate the high-impact rate given by the real life data and show that the high-impact phase of a company growth is achieved for speci c learning parameters and failed projects. The purpose is to provide a coherent theory-model-evidence analysis on high-impact entrepreneurship, that adds new insights for researchers, policy makers and business practitioners, in addition to the qualitative information, informal knowledge or hands-on experience that they currently posses.
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.
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.
Computational Modeling of Climate Change, Large-Scale Land Acquisition, and Household Dynamics in Southern Ethiopia
(2013-08) Hailegiorgis, Atesmachew Bizuwerk; Hailegiorgis, Atesmachew Bizuwerk; Cioffi-Revilla, Claudio
COMPUTATIONAL MODELING OF CLIMATE CHANGE, LARGE-SCALE LAND ACQUISITION, AND HOUSEHOLD DYNAMICS IN SOUTHERN ETHIOPIA
Coupled Dynamics of Labor and Firms through Complex Networks
(2013-08-20) Guerrero, Omar A.; Guerrero, Omar A.; Axtell, Robert L.
This dissertation bridges the gap between labor and firm dynamics through the study of complex networks in labor markets. With extensive use of large-scale employer-employee matched micro-data and agent-based modeling, we tap into the effects that networked structures (between individuals or between firms) exert in labor outcomes and employment dynamics. Some of the contributions of this work are: (i) the first characterization of a network of firms for an entire economy (connected through labor flows, i.e. labor flow networks); (ii) the study of the relationship between labor flow networks and employment dynamics; (iii) agent-based models that generate rich stylized facts about labor, firm, and social dynamics from microeconomic behavior; (iv) providing the microeconomic foundations of the formation process of labor flow networks by coupling job search models with models about the formation of complex networks. We show that the study of labor dynamics can be enriched by coupling firm dynamics. Using agent-based modeling is a natural way to deal with the heterogeneous experiences of workers and firms while maintaining a simple representation of the labor market. Despite their simplicity these models are grounded on empirical evidence obtained from large-scale micro-data and are capable of generating numerous stylized facts simultaneously. This approach has great potential for the design and evaluation of labor policies. Therefore, governments, regulators, and policy-makers would be greatly benefited from collecting large-scale labor micro-data, analyzing labor flow networks, and developing agent-based models of labor markets.
Using Social Media Content to Inform Agent-based Models for Humanitarian Crisis Response
(2014-05) Wise, Sarah; Wise, Sarah; Crooks, Andrew T.
Crisis response is a time-sensitive problem with multiple concurrent and interacting subprocesses, applied around the world in a wide range of contexts and with access to varying levels of resources. The movement of individuals with their shifting patterns of need and, frequently, disrupted normal support systems pose challenges to responders trying to understand what is needed, where, and when. Unfortunately, crises frequently occur in parts of the world that lack the infrastructure to respond to them and the information to inform responders where to target their efforts. In light of these challenges, researchers can make use of new data sources and technologies, combining the information products with simulation techniques to gain knowledge of the situation and to explore the various ways in which a crisis may develop. These new data sources - including social media such as Twitter and volunteered geographic information (VGI) from groups such as OpenStreetMap - can be combined with authoritative data sources in order to create rich, synthetic datasets, which may in turn be subjected to processes such as sentiment analysis and social network analysis. Further, these datasets can be transformed into information which supports powerful agent- based models (ABM). Such models can capture the behavior of heterogeneous individuals and their decision-making process, allowing researchers to explore the emergent dynamics of crisis situations. To that end, this research explores the gathering, cleaning, and synthesis of diverse data sources as well as the information which can be extracted from such synthetic data sources. Further, the work presents a rich, behaviorally complex agent-based model of an evacuation effort. The case study deals with the 2012 Colorado Wildfires, which threatened the city of Colorado Springs and prompted the evacuation of over 28,000 persons over the course of four days. The model itself explores how a synthetic population with automatically generated synthetic social networks communicates about and responds to the developing crisis, utilizing real evacuation order information as well as a model of wildfire development to which the individual agents respond. This research contributes to the study of data synthesis, agent-based modeling, and crisis development.
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.
Modeling Adaptive Economic Agents With PID Controllers
(2015) Carrella, Ernesto; Carrella, Ernesto; Axtell, Robert L.
I provide here a counterpoint to the rational agents that dominate economics: rather than adding rigidities and information limits on an otherwise classical feed-forward agent, I build a new feed-back agent that achieves equilibrium without knowledge of the model or the market it is in.
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.
Towards Emergent Social Complexity
(2015) Rouly, Ovi Chris; Rouly, Ovi Chris; Axtell, Robert L.; Crooks, Andrew
Complexity science often uses generative models to study and explain the emergent behavior of humans, human culture, and human patterns of social organization. In spite of this, little is known about how the lowest levels of human social organization came into being. That is, little is known about how the earliest members of our hominini tribe transitioned from being presumably small-groups of ape-like polygamous/ promiscuous individuals (beginning perhaps as early as Ardipithecus or Australopithecus after the time of the Pan-Homo split in the late Pliocene to early Pleistocene eras) into family units having stable breeding-bonds, extended families, and clans. What were the causal mechanisms (biological, possibly cognitive, social, and environmental, etc.) that were responsible for the conversion? To confound the issue, it is also possible the conversion process itself was a complex system replete with input sensitivities and path dependencies, i.e., a nested complex system. These processes and their distinctive social arrangements may be referred to favorably (as one notable anthropologist has called them) as, “the deep structure of society.” This dissertation describes applied research that used discrete event computer modeling techniques in an attempt to model-then-understand a few of the underlying social, environmental, and biological systems present at the root of human sociality; at the root of social complexity.
Climate Change and the Potential for Conflict and Extreme Migration in the Andes: A Computational Approach for Interdisciplinary Modeling and Anticipatory Policy-Making
(2015) Magallanes, Jose Manuel; Magallanes, Jose Manuel; Cioffi, Claudio
I present an agent-based model to support the thesis that extreme migration and social conflict can emerge by simply extending the current social and natural conditions, and by replicating simple mechanisms at the individual level. To carry out this work, every available information on water supply and demand has been collected and organized using official data sources, producing a baseline dated in 2011; and the basic demographics of the population has been implemented using the last official census (2007). Based on these data, many computations have been made to find, calibrate and represent the trends in population growth and water balance. For the basic mechanisms at the individual level, field work guided both by theoretical considerations and ethnographic findings has been done. A key assumption on the processing of information, not identified from the field work, has been introduced via a Bayesian belief updating mechanism.
Delay and traffic rate estimation in network tomography
(2015) Etemadi Rad, Neshat; Etemadi Rad, Neshat; Mark, Brian L.; Ephraim, Yariv
Network tomography deals with estimation of computer network features from measurements on links or terminal nodes. The area was pioneered with the work of Vanderbei and Iannonou in 1994 and Vardi in 1996. Of particular interest are estimation of source-destination traffic rates from link packet counts or from aggregated packet counts in input and output nodes, and estimation of link delay from source-destination delay measurements. Traffic rate estimation, and link propagation delay estimation, are inverse problems which require the solution of under-determined sets of linear equations. Iterative solutions based on moment matching and the expectation-maximization algorithm were proposed for traffic rate estimation, and a maximum entropy approach was developed for link propagation delay estimation. Traffic rate estimation was also performed using a Bayesian estimation approach. Estimation of link delay densities commonly involves exponential mixture models which entail independence of the delay on various links. Network tomography is useful for monitoring the performance of a network, and thus maintaining and expanding the network.
An Agent Based Model of Community Authority Structure Resilience
(2016) Mcfarlane, Hugh James; Mcfarlane, Hugh James; Cioffi-Revilla, Claudio
This dissertation presents a theoretical model based on social exchange theory that explains the resilience and adaptation of authority structures in urban communities. Communities where non-state actors undermine or replace government institutions are a persistent public policy concern in many cities. The structure of instrumental relationships between authorities and residents in these communities is a key variable associated with a wide range of human security and governance challenges. Altering these structures is often necessary to enable other public policy goals. However, there is an absence of theoretical frameworks that address the dynamic characteristics of these structures. This hinders policy development by limiting insights into the effects of efforts to support or undermine particular groups or to alter social conditions on authority structures. The theory developed here describes authority structures as an emergent feature of a community-level complex adaptive social system. In this system, individual actors select relationship partners based on past experiences, preferences, environmental conditions, and information from other actors. Changes to structure are the result of changes to the set of actors in the system and how these actors value particular relationships. A comparative case study of three sub-Saharan African communities located in Nairobi, Cape Town, and Lagos and several additional experiments are performed using an agent-based model implementing this theory. The results demonstrate the practical application of the theory to public policy analysis and support the choice of social exchange theory as the basis for actor decision-making. More broadly, this effort extends existing theoretical and agent-based models of contentious polities and authority structures. It also demonstrates the utility of computational modeling in advancing the research programs in social exchange theory, political authority, and comparative urban politics.
Essays on the Drivers of Political and Ideological Extremism
(2016) Alizadeh, Meysam; Alizadeh, Meysam; Cioffi-Revilla, Claudio
The problem of interest in this dissertation is the phenomenon of drifting toward opinion extremes. The process is called Radicalization and has received a great deal of attention in social psychology and sociology from its inception. Although it is extremism of behavior that is of greatest interest, it is important to study opinion extremism since it has been shown to be a plausible preceding step of violent extremism. One of the longstanding quests in counter-radicalization studies is to know what drives extremists to adopt extreme ideologies. However, it is unlikely that extremists will volunteer for experimental studies.
A General Social Agent-Based Model of Opinion Dynamics with Applications to STEM Education and Radicalization
(2016) Harrison, Joseph Francis; Harrison, Joseph Francis; Cioffi-Revilla, Claudio
Many aspects of our society are affected by how opinions change and ideology spreads (e.g., interest in STEM and political radicalization), but the underlying processes are not well understood. Previous attempts at modeling these phenomena have suffered from a lack of empirical data and/or insufficient grounding in social-psychological theory. Moreover, the field of opinion dynamics would benefit from a broader view of the discipline that captures the commonalities between different domains.
A Computational Social Science Approach to the Social Determinants of Cancer
(2016) Metgher, Cristina; Metgher, Cristina; Axtell, Robert
Cancer is a complex system of systems - in which heterogeneous actors interact dynamically with each other and with the environment across time. It is the second leading cause of death in the U. S. after cardiovascular disease, striking people from all occupations and backgrounds. The U. S. President Nixon declared the “War on Cancer” in 1971. Since then, much progress has been made towards understanding this disease. However, due to the reductionist thinking that has been predominant in cancer research for the last decades, the mortality rates due to cancer have hardly changed.
Explaining Box Office Performance from the Bottom Up: Data, Theories and Models
(2016) Russo, Holly Ann; Russo, Holly Ann; Axtell, Robert
Every week, there are more than 50 movies playing in theaters from which movie-goers can choose. Analyses of the relative box office success of these films shows that it is Pareto-distributed, with roughly 20% of them earning 80% of the overall revenue. Arthur De Vany studied the potential causes of this ‘winner-take-all’ distribution through equation-based analyses, and theorized that the Pareto-distributed box office revenues we observe emerge from the micro-level complex adaptive behavior of movie-goers with imperfect information.

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