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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Learning not to Trade: On Scarcity, Emergence and Failure of Markets

Authors:

Oezge Dilaver

Published in:

 

(2014).ECMS 2014 Proceedings edited by: Flaminio Squazzoni, Fabio Baronio, Claudia Archetti, Marco Castellani  European Council for Modeling and Simulation. doi:10.7148/2014

 

ISBN: 978-0-9564944-8-1

 

28th European Conference on Modelling and Simulation,

Brescia, Italy, May 27th – 30th, 2014

Citation format:

Oezge Dilaver (2014). Learning not to Trade: On Scarcity, Emergence and Failure of Markets, ECMS 2014 Proceedings edited by: Flaminio Squazzoni, Fabio Baronio, Claudia Archetti, Marco Castellani  European Council for Modeling and Simulation. doi:10.7148/2014-0732

DOI:

http://dx.doi.org/10.7148/2014-0732

Abstract:

This paper addresses the substitution of virgin resources used in industrial processes with by-products that would otherwise be regarded as waste and questions why some byproduct markets fail to emerge. It highlights the long-forgotten distinction between the use-value and exchange-value and points out that markets can only work for assigning the latter. It argues that in some industrial settings, waste resources are not scarce and their supply schedule in the classical market plot are at the right of and far from the demand schedule. Hence, even though the waste resource has some positive use-value, a positive price level cannot be established at the intersection of demand and supply schedules. The paper employs agentbased simulations with zero-intelligence traders to illustrate that when agents are unable to learn, transactions do occur in the abovementioned context but when agents have simple adaptive capabilities, the market fails to emerge. Thereby, the paper employs the zero-intelligence traders in a different way than usual; whereas the literature on zero-intelligence traders aim to show markets can emerge even when agents lack rationality, this paper illustrates that markets can fail to emerge when agents have some ability to learn.

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