The Coconut Model with Heterogeneous Strategies and Learning

TL;DR

This paper extends the Coconut Model by incorporating heterogeneous strategies and learning mechanisms in an agent-based framework, revealing that the system converges to the original model's stable equilibrium.

Contribution

It introduces an agent-based version of the Coconut Model with heterogeneous and adaptive agents, analyzing non-equilibrium dynamics and convergence to equilibrium.

Findings

Heterogeneity can be integrated into the aggregate dynamical equations.

Agents using simple learning schemes converge to fixed points.

The system's stable equilibrium matches the original model's prediction.

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.