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Learning Competitive Equilibrium

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Abstract

The epsilon-intelligent competitive equilibrium algorithm is a decentralized alternative to Walras' tatonnement procedure for markets to arrive at competitive equilibrium. We build on the Gode-Spear-Sunder zero-intelligent algorithm in which random generation of bids and offers from agents' welfare-enhancing opportunity sets generates Pareto optimal allocations in a pure exchange economy. We permit agents to know if they are subsidizing others at such allocations, and to veto such allocations, restricting the subsequent iterations of the algorithm only to those trades that are both Pareto-improving and provide strictly greater wealth, and ultimately utility, for such agents. In this simple institution actions of minimally intelligent agents based on local information can lead the market to approximate competitive equilibrium in a larger set of economies than the tatonnement process would allow. This helps address one of the major shortcomings of the Arrow-Debreu-McKenzie model with respect to the instability of tatonnement in an open set of economies. It also addresses the behavioral critique of mathematically derived equilibria for the inability of cognitively-limited humans to maximize. The proof of convergence of the algorithm presented here also provides a way of showing the existence of competitive equilibrium for monotonic, convex exchange economies with heterogeneous agents and many goods without application of a fixed-point theorem.

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  • Sean Crockett & Stephen Spear & Shyam Sunder, 1899. "Learning Competitive Equilibrium," GSIA Working Papers 2003-E18, Carnegie Mellon University, Tepper School of Business.
  • Handle: RePEc:cmu:gsiawp:-2052381325
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    Cited by:

    1. Jean-Marc Bonnisseau & Orntangar Nguenamadji, 2013. "Discrete Walrasian exchange process," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 52(3), pages 1091-1100, April.
    2. Sean Crockett, 2013. "Price Dynamics In General Equilibrium Experiments," Journal of Economic Surveys, Wiley Blackwell, vol. 27(3), pages 421-438, July.
    3. Duffy, John, 2006. "Agent-Based Models and Human Subject Experiments," Handbook of Computational Economics,in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 19, pages 949-1011 Elsevier.
    4. Goeree, Jacob K. & Lindsay, Luke, 2016. "Market design and the stability of general equilibrium," Journal of Economic Theory, Elsevier, vol. 165(C), pages 37-68.
    5. Jacob K. Goeree & Luke Lindsay, 2012. "Stabilizing the economy: Market design and general equilibrium," ECON - Working Papers 092, Department of Economics - University of Zurich.
    6. Paola Tubaro, 2009. "Agent-based Computational Economics: a Methodological Appraisal," EconomiX Working Papers 2009-42, University of Paris Nanterre, EconomiX.
    7. Marco LiCalzi & Lucia Milone & Paolo Pellizzari, 2008. "Allocative efficiency and traders' protection under zero intelligence behavior," Working Papers 168, Department of Applied Mathematics, Università Ca' Foscari Venezia, revised Nov 2009.

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