The influence of seller learning and time constraints on sequential bargaining in an artificial perishable goods market

In this paper we test some hypotheses about the formation of prices through sequential bilateral bargaining on a perishable goods market under the assumption of behavioural learning by buyers and sellers. We constructed a multi-agent simulation model based on a survey concerning the fruit and vegetables market in Marseille, France. In our model, the agents bargain the price of a perishable good. The representation of agents' rationality is inspired by a literature relative to markets of perishable goods (Kirman [6] [7] and Rouchier [18] [19] [20]) as well as on artificial bargaining (Brenner[2], Weisbuch[22]). We study the influence of three parameters (the sellers' initial beliefs concerning the buyers' willingness to pay, the time spent by buyers on the market, and the heterogeneity in the sellers' limit value) on the evolution of prices and of agents' representations of others agents. We compare our results with empirical observations and some existing literature on bargaining. We find that our assumptions concerning the learning process give consistent results and lead the transactions prices to converge toward the sellers' limit value.

[1]  Andrew B. Abel,et al.  Inventories, Stock-Outs, and Production Smoothing , 1985 .

[2]  John Duffy,et al.  Internet Auctions With Artificial Adaptive Agents: A Study on Market Design , 2007 .

[3]  M. Janssen,et al.  Learning, Signaling, and Social Preferences in Public-Good Games , 2006 .

[4]  P. Diamond A Search-Equilibrium Approach to the Micro Foundations of Macroeconomics , 1984 .

[5]  C. Hommes Heterogeneous Agent Models in Economics and Finance , 2005 .

[6]  William A. Brock,et al.  A rational route to randomness , 1997 .

[7]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[8]  Thomas Brenner,et al.  A Behavioural Learning Approach to the Dynamics of Prices , 2002 .

[9]  B. LeBaron Post Walrasian Macroeconomics: Agent-Based Financial Markets: Matching Stylized Facts with Style , 2006 .

[10]  Nicolaas J. Vriend,et al.  Evolving market structure: An ACE model of price dispersion and loyalty , 2001 .

[11]  Dale T. Mortensen,et al.  The Matching Process as a Noncooperative Bargaining Game , 1982 .

[12]  Ana Paula Rocha,et al.  Agents Advanced Features for Negotiation in Electronic Commerce and Virtual Organisations Formation Processes , 2001, AgentLink.

[13]  John Joseph McCall,et al.  The Economics of Information and Uncertainty , 1982 .

[14]  Nicholas R. Jennings,et al.  Determining successful negotiation strategies: an evolutionary approach , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[15]  Juliette Rouchier,et al.  Information perception and price dynamics in a continuous double auction , 2006 .

[16]  G. Weisbuch,et al.  Market Organisation and Trading Relationships , 2000 .

[17]  John H. Miller,et al.  Auctions with Artificial Adaptive Agents , 1995 .

[18]  W. Härdle,et al.  Transactions that did not happen and their influence on prices , 2005 .

[19]  L. Tesfatsion,et al.  Preferential partner selection in an evolutionary study of Prisoner's Dilemma. , 1994, Bio Systems.

[20]  Leigh Tesfatsion,et al.  Agent-Based Computational Modeling And Macroeconomics , 2006 .

[21]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[22]  Jim R. Oliver A Machine-Learning Approach to Automated Negotiation and Prospects for Electronic Commerce , 1996, J. Manag. Inf. Syst..

[23]  John Duffy,et al.  Agent-Based Models and Human Subject Experiments , 2004 .