Assistant Proffesor at the Institute of Socio-Economic Geography, Adam Mickiewicz University, Poland
I’m trying to fing a simulation for Multiagent system based search engine by NetLogo
Senior (Tenure-Track) Assistant Professor in Work and Organizational Psychology (WOP) at the Human Sciences Department of Verona University. My expertise lies in organizational behavior, individual differences and decision-making at work, and social dynamics in the applied psychology field. In the field of fundamental research my studies explore the role of individual antecedents (e.g., Personality traits, Risk attitudes, etc.) in relation to classic I/O models (e.g., Job Demands-Resources model, Effort-Reward model, etc.). My applied research focuses on the development of interventions and policies for enhancing decision-making, and in turn well-being and job performance. Finally, in industrial research, my research aims to better integrate cognitive and behavioral theories (e.g., Theory of Planned Behavior, Prospect theory, etc.) for designing predictive models – based on agents – of social and organizational behaviors.
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I live in Salento (Italy) the tiny land between two seas, where I work as a teacher in a school for adults. My education includes a degree in Life Sciences; in my post-graduate training, I have been involved in searching for the genetic and molecular responses of some cellular systems to environmental and genomic stresses. Now, one of my great interests is the approach to theoretical biology through agent-based modeling techniques, even if - I know - nothing can be more surprising than the complexity of Nature and the cognition about it.
Complex Adaptive Systems, Agent Based Simulation, Technology Enhanced Learning, and Theoretical Biology
I studied Mathematics at Oxford (1979-1983) then did youth work in inner city areas for the Educational Charity. After teaching in Grenada in the West Indies we came back to the UK, where the first job I could get was in a 6th form college (ages 16-18). They sent me to do post16 PCGE, which was so boring that I also started a part-time PhD. The PhD was started in 1992 and was on the meaning and definition of the idea of “complexity”, which I had been pondering for a few years. Given the growth of the field of complexity from that time, I had great fun reading almost anything in the library but I did finally finish it in 1999. Fortunately I got a job at the Centre for Policy Modelling (CfPM) in 1994 with its founder and direction, Scott Moss. We were doing agent-based social simulation then, but did not know it was called this and did not meet other such simulators for a few years. With Scott Moss we built the CfPM into one of the leading research centres in agent-based social simulation in the world. I became director of the CfPM just before Scott retired, and later became Professor of Social Simulation in 2013. For more about me see http://bruce.edmonds.name or http://cfpm.org.
All aspects of social simulation including: techniques, tools, applications, philosophy, methodology and interesting examples. Understanding complex social systems. Context-dependency and how it affects interaction and cognition. Complexity and how this impacts upon simulation modelling. Social aspects of cognition - or to put it another way - the social embedding of intelligence. Simulating how science works. Integrating qualitative evidence better into ABMs. And everything else.
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.