EURACE is a major European attempt to construct an agent-based model of the European economy with a very large population of autonomous, purposive agents interacting in a complicated economic environment. To create it, major advances are needed, in particular in terms of economic modeling and software engineering. In this paper, we describe the general structure of the economic model developed for EURACE and present the Flexible Large-scale Agent Modeling Environment (FLAME) that will be used to describe the agents and run the model on massively parallel supercomputers. Illustrative simulations with a simplified model based on EURACE's labor market module are presented.
JEL classification: C69 D83 E5
Keywords:Time inconsistency Bounded rationality Forecast and agent heterogeneity Cheap talk Evolutionary learning a b s t r a c t This paper studies the potential commitment value of cheap talk announcements in an agent-based dynamic extension of the Kydland-Prescott model. In every period, the policy maker makes a non-binding inflation announcement before setting the actual inflation rate. It updates its decisions using individual evolutionary learning. The private agents can choose between two different forecasting strategies: They can either set their forecast equal to the announcement or use an adaptive learning scheme to (potentially) forecast the true inflation. They switch between these two strategies as a function of information about the associated payoffs they obtain through word-ofmouth, choosing always the currently most favorable one. While all agents using the first strategy make the same forecast, those using the second strategy may generate different individual forecasts.In spite of the complexity of the environment, the boundedly rational policy maker learns to sustain a situation with a positive but fluctuating fraction of believers. This outcome is Pareto superior to the outcome predicted by standard theory. Interestingly enough, the actions taken by the policy maker undergo marked qualitative changes as a function of the prevailing heterogeneity among and learning characteristics of the private agents.
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