Spatial interactions in agent-based modeling

TL;DR

This paper reviews approaches to modeling spatial interactions in agent-based models, emphasizing the importance of emergent patterns and illustrating the use of spatial ABM in economic geography and policy analysis.

Contribution

It provides a comprehensive overview of modeling spatial interactions in ABM, including recent developments and applications like the Eurace@Unibi Model for economic spatial dynamics.

Findings

Different approaches for modeling spatial interactions are discussed.

Spatial ABM can effectively describe economic activity distribution.

The Eurace@Unibi Model demonstrates policy analysis potential.

Abstract

Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution of economic activities, - out of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with spatial structure, is used to illustrate the potential of such an approach for spatial policy analysis.