A global optimization heuristic for estimating agent based models

A continuous global optimization heuristic for a stochastic approximation of an objective function, which itself is not globally convex, is introduced. The objective function arises from the simulation based indirect estimation of the parameters of agent based models of financial markets. The function is continuous in the variables but non-differentiable. Due to Monte Carlo variance, only a stochastic approximation of the objective function is available. The algorithm combines features of the Nelder-Mead simplex algorithm with those of a local search heuristic called threshold accepting. The Monte Carlo variance of the simulation procedure is also explicitly taken into account. We present details of the algorithm and some results of the estimation of the parameters for a specific agent based model of the DM/US-$ foreign exchange market. (C) 2002 Elsevier Science B.V. All rights reserved.