Advances in Artificial Economics: The Economy as a Complex by Mikhail Anufriev, Valentyn Panchenko (auth.), Professor

By Mikhail Anufriev, Valentyn Panchenko (auth.), Professor Charlotte Bruun (eds.)

Perceiving the economic system as a posh dynamic approach, generates a necessity for brand new instruments for its research. As a optimistic simulation process, Agent-Based Computational Economics (ACE) has in recent times confirmed its power and wide applicability. Fields of analysis are commonly unfold inside economics, with a cluster round monetary markets. This publication is predicated on communications given at AE’2006 (Aalborg, Denmark) – the second one symposium on synthetic Economics, and covers either general questions of economics, just like the life of marketplace potency, in addition to new questions raised via the recent instruments, for instance questions relating to networks of social interplay.

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3 Behavioral Assumptions A major obstacle in the study of microeconomic systems is that their performance is jointly determined by the interactions of traders’ behavior within the market protocol. As traders may react differently to different market protocols, it is difficult to separate the intrinsic characteristics of a market protocol from the properties induced by the traders’ strategies. Our approach is to concentrate on the institutional characteristics of the protocols, by making general-purpose assumptions on traders’ behavior.

5 Conclusions and Further Research We have extended in several ways related works following Gode and Sunder [5] seminal contribution in experimental economics with programmed agents: beyond experimental economics. Price dynamics for a CDA is obtained for non symmetric environments and populations of heterogeneous agent behaviour. We allow the agents to change their strategies endogenously so that we can trace the patterns of the emerging proportion of agents’ behaviour not only with symmetric environment as in Posada et al.

Each agent is given a list of ten limit prices (valuations). To isolate the effects of behaviour under symmetric or asymmetric environments and to prevent some agents having relative initial advantage, the agents’ valuations are the same for all the agents that are in the same side of the market. The simulations can be represented in six scenarios, that accommodate three different environments and two kinds of agent strategies (fixed strategies and changes on strategies. 2). 2. Simulated environments.

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