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A behavioural learning approach to the dynamics of prices: analysis of citation, usage, and acceptance in the journal literature
Brenner T. Computational Economics19 (1):67-94,2002.Type:Article
Date Reviewed: Jul 9 2003

In economics, agents interact to sell and buy goods in a market with an equilibrium price that depends on the assumptions made about the agent’s behavior. This paper studies the dynamics of market prices under the assumptions that sellers and buyers learn simultaneously while interacting on the market, and that sellers and buyers may bargain and negotiate prices. The buyers learn about choosing sellers and acceptable prices; the sellers may offer a fixed price, make successive reductions, and learn about selling behavior and price levels.

The study is focused on two aspects: whether sellers should sell their products at fixed prices or sell them after a bargain process; and whether the prices converge, converge to an equilibrium with a single price, or with price dispersion. Extensive simulation results are reported in the paper. Based on these results, the author concludes that sellers may profit from the lack of buyers’ information about sellers and bargaining, while sellers learning about buyers’ behavior may prevent bargaining. Another reported result is that the prices always converge to the costs of the goods for the sellers. However, the convergence process is very slow, and, in the medium run, the prices converge to a certain corridor with a defined lower and upper limit.

The paper is well written and contains significant references. However, the author selects the references mainly from the economic research community, failing to compare his work with similar work from the intelligent agent community, such as that of Vidal and Durfee [1]. In addition, there are several typographical errors in the paper.

Reviewer:  A. Florea Review #: CR127933 (0311-1279)
1) Vidal, J.M.; Durfee, E.H. The impact of nested agent models in an information economy. In the Proceedings of the Second International Conference on Multiagent Systems (Menlo Park, CA, ), AAAI Press, 1996, 377–384.
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