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Agent-Based Modeling and Learning in Economics: An Overview

In: Machine Learning Perspectives of Agent-Based Models

Author

Listed:
  • Pedro Campos

    (FEP, LIAAD-INESC TEC, University of Porto)

  • Anand Rao

    (Carnegie Mellon University, Heinz College of Information Systems and Public Policy)

  • Pavel Brazdil

    (FEP, LIAAD-INESC TEC, University of Porto)

Abstract

The fields of Agent-Based Modelling (ABM) and Multi-Agent Systems (MAS) have attracted the attention of many researchers over the past two or three decades, as they model the way our society is organized. So, developing such systems could help us to advance not only the general understanding, but also be used to help to guide decisions. In addition, crises have followed one another, from financial crises to pandemics and wars, with significant economic and social impacts. So, a question arises how these ABM e MAS can be developed. Using a manual approach does not sound right in the twenty-first century, when machine learning invaded virtually all areas of science. In this chapter we reflect on the role of learning, or in particular, machine learning, in multi-agent systems (MAS) and also the effects of crisis.

Suggested Citation

  • Pedro Campos & Anand Rao & Pavel Brazdil, 2025. "Agent-Based Modeling and Learning in Economics: An Overview," Springer Books, in: Pedro Campos & Anand Rao & Joaquim Margarido (ed.), Machine Learning Perspectives of Agent-Based Models, chapter 0, pages 9-48, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-73354-3_2
    DOI: 10.1007/978-3-031-73354-3_2
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