Generating fraud: Agent based financial network modeling
Proceedings of the North American Association for Computation Social and Organization Science (NAACSOS 2005), , , 2005
Abstract: In this paper, we describe research and an application of agent based modeling to create financial network data. Creating a dataset of this type presented some unique challenges. First, the dataset we are trying to emulate is large and sparsely connected (20 million nodes, 20 million edges, in 500GB). Second, it includes multiple types of entities and relationships. A system made up of multiple types of entities with various relationships is tailor made for agent based modeling. Third, this dataset is being created as part of a larger project that is creating graph analysis tools that will work with massive, dynamic datasets. Therefore, it is important that we be able to control what the generated dataset contains so we can test various parts of our graph analysis system. An initial agent based model has been created using Netlogo. This prototype is being created iteratively as we continue to investigate the patterns and other features within the actual dataset. The domain in which the graph analysis tools are to be used is, understandably, of a sensitive nature. We wish to keep the datasets we produce unclassified so they can be released to the academic and analytic communities to aid in collaboration. This presents its own challenges as we need to produce a dataset that is a reasonable facsimile of the actual data for meaningful collaboration, however not so similar as to represent any sort of unreasonable disclosure of information.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).