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Agent-Based Simulation as a Useful Tool for the Study of Markets

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Simulating Social Complexity

Part of the book series: Understanding Complex Systems ((UCS))

Abstract

This chapter describes a number of agent-based market models. They can be seen as belonging to different trends in that different types of markets are presented (goods markets, with or without stocks, or financial markets with diverse price mechanisms or even markets with or without money), but they also represent different aims that can be achieved with the simulation tool. For example, it is possible to develop precise interaction processes to include loyalty among actors; try to mimic as well as possible the behaviour of real humans, which have been recorded in experiments; or try to integrate psychological data to show a diffusion process. All these market models share a deep interest in what is fundamental in agent-based simulation, such as the role of interaction, interindividual influence and learning, which induces a change in the representation that agents have of their environment.

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Notes

  1. 1.

    For example, an issue that anyone representing learning (not only on markets) has to face is the exploration-exploitation dilemma. When an action gives a reward that is considered as “good”, the agent performing it has to decide either to continue with this action—and hence possibly miss other, more rewarding actions—or to search for alternative actions, which implies indeterminate results. Leloup (2002), using the multi-armed bandit (Rothschild 1974) to represent this dilemma, showed that a nonoptimal learning procedure could lead to a better outcome than an optimal—but non-computable—procedure.

  2. 2.

    http://www.econ.iastate.edu/tesfatsi/ace.htm.

  3. 3.

    The main objection to Brenner’s exposition is the lack of homogeneity of notation, which makes the algorithms difficult to compare and maybe to implement.

  4. 4.

    Natlab, which can be found at http://www.complexity-research.org/natlab.

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Acknowledgements

I wish to thank Bruce Edmonds for his patience and Scott Moss and Sonia Moulet for their advice.

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  1. LAMSADE, Paris-Dauphine, 75775, Paris Cedex 16, France

    Juliette Rouchier

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Editors and Affiliations

  1. Centre for Policy Modelling, Manchester Metropolitan University, Business School, Manchester, United Kingdom

    Bruce Edmonds

  2. Centre for Policy Modelling, Manchester Metropolitan University, Business School, Manchester, United Kingdom

    Ruth Meyer

Further Reading

Further Reading

Arthur (1991) is one of the first models incorporating learning agents in a market. Lux (1998) describes a model of speculation on an asset market with interacting agents. Duffy (2001) was the first to attempt to link experimental data to simulation results in order to evaluate the kind of learning within a speculative environment. Jefferies and Johnson (2002) give a general overview of market models including their structures and learning by agents. Moulet and Rouchier (2007) use data on negotiation behaviours from a real market in order to fit the parameters of a two-sided learning model. Finally, Kirman (2010) summarizes many interesting dimensions that can be captured using agent-based models.

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Rouchier, J. (2017). Agent-Based Simulation as a Useful Tool for the Study of Markets. In: Edmonds, B., Meyer, R. (eds) Simulating Social Complexity. Understanding Complex Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-66948-9_25

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