Simulation and Use of Heuristics for Peripheral Economic Policy

TL;DR

This paper evaluates the effectiveness of simulated annealing versus simple search algorithms in a model of an artificial economy with geographically dispersed companies, where a government agent uses these algorithms to optimize taxation and balance supply across markets.

Contribution

It introduces a government agent employing simulated annealing to influence market supply, extending previous models with a new optimization approach.

Findings

Simulated annealing outperforms simple search in balancing market supply.

The government agent effectively uses algorithms to adjust taxation and influence economic outcomes.

The study compares two algorithms' performance in a simulated economic environment.

Abstract

Recent trends in Agent Computational Economics research, envelop a government agent in the model of the economy, whose decisions are based on learning algorithms. In this paper we try to evaluate the performance of simulated annealing in this context, by considering a model proposed earlier in the literature, which has modeled an artificial economy consisting of geographically dispersed companies modeled as agents, that try to maximize their profit, which is yielded by selling an homogeneous product in different cities, with different travel costs. The authors have used an evolutionary algorithm there, for modeling the agents' decision process. Our extension introduces a government agent that tries to affect supply and demand by different taxation coefficients in the different markets, in order to equate the quantities sold in each city. We have studied the situation that occurs when a simulated annealing algorithm and a simple search algorithm is used as the government's learning algorithm, and we have evaluated the comparative performance of the two.