Community

Mohammad Achachlouei Member since: Wednesday, January 23, 2013

MSc in Information Systems, MSc in Information Technology Engineering, BSc in Computer Engineering

Modeling and simulation of future impacts of information and communication technologies on environmental sustainability using agent based modeling and system dynamics

cchrist Member since: Monday, July 09, 2012

Bachelors of Science in Civil Engineering

Agent Based Modeling–Researching Infrastructure Interdependencies

Susan Boerma Member since: Wednesday, October 23, 2013

MSc

Using Bayesian statistics for improving Agent based models and visa versa.

Arend Ligtenberg Member since: Thursday, April 09, 2015

PhD

Agent Based Modelling for spatial systems

Brent Auble Member since: Friday, December 17, 2010

B.S. Computer Science, Lafayette College, MAIS, Computational Social Science, George Mason University

Dissertation: Narrative Generation for Agent-Based Models

Abstract: This dissertation proposes a four-level framework for thinking about having agent-based models (ABM) generate narrative describing their behavior, and then provides examples of models that generate narrative at each of those levels. In addition, “interesting” agents are identified in order to direct the attention of researchers to the narratives most likely to be worth spending their time reviewing. The focus is on developing techniques for generating narrative based on agent actions and behavior, on techniques for generating narrative describing aggregate model behavior, and on techniques for identifying “interesting” agents. Examples of each of these techniques are provided in two different ABMs, Zero-Intelligence Traders (Gode & Sunder, 1993, 1997) and Sugarscape (Epstein & Axtell, 1996).

Fernando Santos Member since: Tuesday, December 10, 2019 Full Member

Agent-based Simulation, Artificial Intelligence, Multiagent Systems

Talal Alsulaiman Member since: Friday, February 27, 2015

Bachelor of Science in Systems Engineering, Master of Science in Industrial Engineering, Master of Science in Financial Engineering

In this paper, we explore the dynamic of stock prices over time by developing an agent-based market. The developed artificial market comprises of heterogeneous agents occupied with various behaviors and trading strategies. To be specific, the agents in the market may expose to overconfidence, conservatism or loss aversion biases. Additionally, they may employ fundamental, technical, adaptive (neural network) strategies or simply being arbitrary agents (zero intelligence agents). The market has property of direct interaction. The environment takes the form of network structure, namely, it takes the manifestation of scale-free network. The information will flow between the agents through the linkages that connect them. Furthermore, the tax imposed by the regulator is investigated. The model is subjected to goodness of fit to the empirical observations of the S\&P500. The fitting of the model is refined by calibrating the model parameters through heuristic approach, particularly, scatter search. Conclusively, the parameters are validated against normality, absence of correlations, volatility cluster and leverage effect using statistical tests.

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