By Philippe Mathieu, Bruno Beaufils, Olivier Brandouy
Agent-based Computational Economics (ACE) is a brand new self-discipline of economics, principally grounded on options like evolution, auto-organisation and emergence: it intensively makes use of computing device simulations in addition to man made intelligence, typically according to multi-agents platforms. the aim of this e-book is to offer an up-to date view of the medical creation within the fields of Agent-based Computational Economics (mainly in industry Finance and online game Theory). according to communications given at AE'2005 (Lille, USTL, France), this publication deals a large landscape of contemporary advances in ACE (both theoretical and methodological) that might curiosity lecturers in addition to practitioners.
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Additional resources for Artificial Economics Agent Based Methods in Finance Game Theory and Their Applications
The initial strategy is chosen randomly. On subsequent periods, each agent learns to change his strategy looking for the best bidding strategy in the following way. To take this decision each trader only knows their own reservation prices and the information generated in the market, but he does not know the bidding strategy or the profit achieved in a market session by other agents. An agent will consider to change his strategy if the profit is less than the profit from the previous period. The agent considers whether he could have reached higher profits following an alternative strategy.
Thus it was not necessary to study real people behaviour since (presumably) there is only one way to be rational. Even after the conditions of rationality and homogeneity were relaxed, many models did it by postulating arbitrary departures not necessarily based on actual experiments. When the connection to the real subjects behaviour was considered , an entire host of puzzles and paradoxes appeared even in the simplest artificial (laboratory) conditions. Thus the inclusion of real trader behaviour in the next generation of models and simulations is hampered by the inexistence of comprehensive, systematic, reliable data.
And when should they accept an outstanding order of some other trader? Artificially intelligent traders have been used to explore the properties of the CDA market. But, in all previous works, agents have afixedbidding strategy during the auction. In our simulations we allow the soft-agents to learn not only about how much they should bid or ask, but also about possible switching between the alternative strategies. We examine the emergence or not of Nash equilibriums, with a bottom-up approach. Our results confirm that although market efficiency is an ecological property, an it is robust against intelligence agents, convergence and volatility depend on the learning strategy.