Agent-Based Modeling of a Non-tâtonnement Process for the Scarf Economy: The Role of Learning

Shu-Heng Chen, Bin-Tzong Chie (), Ying-Fang Kao () and Ragupathy Venkatachalam ()
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Bin-Tzong Chie: Tamkang University
Ying-Fang Kao: National Chengchi University
Ragupathy Venkatachalam: Goldsmiths, University of London

Computational Economics, 2019, vol. 54, issue 1, No 14, 305-341

Abstract: Abstract In this paper, we propose a meta-learning model to hierarchically integrate individual learning and social learning schemes. This meta-learning model is incorporated into an agent-based model to show that Herbert Scarf’s famous counterexample on Walrasian stability can become stable in some cases under a non-tâtonnement process when both learning schemes are involved, a result previously obtained by Herbert Gintis. However, we find that the stability of the competitive equilibrium depends on how individuals learn—whether they are innovators (individual learners) or imitators (social learners), and their switching frequency (mobility) between the two. We show that this endogenous behavior, apart from the initial population of innovators, is mainly determined by the agents’ intensity of choice. This study grounds the Walrasian competitive equilibrium based on the view of a balanced resource allocation between exploitation and exploration. This balance, achieved through a meta-learning model, is shown to be underpinned by a behavioral/psychological characteristic.

Keywords: Non-tâtonnement process; Co-ordination; Agent-based modeling; Learning (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10614-017-9721-5

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