Krasnow Institute for Advanced Study
Permanent URI for this collection
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.
Browse
Browsing Krasnow Institute for Advanced Study by Author "Crooks, Andrew T."
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item 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.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.