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The Coconut Model with Heterogeneous Strategies and Learning

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Abstract

In this paper, we develop an agent-based version of the Diamond search equilibrium model - also called Coconut Model. In this model, agents are faced with production decisions that have to be evaluated based on their expectations about the future utility of the produced entity which in turn depends on the global production level via a trading mechanism. While the original dynamical systems formulation assumes an infinite number of homogeneously adapting agents obeying strong rationality conditions, the agent-based setting allows to discuss the effects of heterogeneous and adaptive expectations and enables the analysis of non-equilibrium trajectories. Starting from a baseline implementation that matches the asymptotic behavior of the original model, we show how agent heterogeneity can be accounted for in the aggregate dynamical equations. We then show that when agents adapt their strategies by a simple temporal difference learning scheme, the system converges to one of the fixed points of the original system. Systematic simulations reveal that this is the only stable equilibrium solution.

Suggested Citation

  • Sven Banisch & Eckehard Olbrich, 2017. "The Coconut Model with Heterogeneous Strategies and Learning," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(1), pages 1-14.
    • Handle: RePEc:jas:jasssj:2016-38-2
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    1. Hommes,Cars, 2015. "Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems," Cambridge Books, Cambridge University Press, number 9781107564978, October.
    2. Simone Landini & Mauro Gallegati & Joseph Stiglitz, 2015. "Economies with heterogeneous interacting learning agents," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(1), pages 91-118, April.
    3. Aoki, Masanao & Shirai, Yoshimasa, 2000. "A New Look At The Diamond Search Model: Stochastic Cycles And Equilibrium Selection In Search Equilibrium," Macroeconomic Dynamics, Cambridge University Press, vol. 4(4), pages 487-505, December.
    4. Robert Axtell & Robert Axelrod & Joshua M. Epstein & Michael D. Cohen, 1995. "Aligning Simulation Models: A Case Study and Results," Working Papers 95-07-065, Santa Fe Institute.
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    Cited by:

    1. Christian Stummer & Lars Lüpke & Markus Günther, 2021. "Beaming market simulation to the future by combining agent-based modeling with scenario analysis," Journal of Business Economics, Springer, vol. 91(9), pages 1469-1497, November.

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