Journal of Economic Behavior Organization Vol. 41 2000 27–53
On the emergence of exchange and mediation in a production economy
Herbert Dawid
∗
Department of Economics, University of Southern California, Los Angeles, CA 90089, USA Received 14 October 1997; accepted 21 August 1998
Abstract
In this paper we simulate the behavior of a population of boundedly rational agents in a two good economy where all agents can spend their time budget for the production of one or both goods or
trading. Agents update their strategies according to a simple imitation type learning rule with noise. It is shown that in several different setups both direct trade and trade via mediators who specialize in
trading can emerge. Both increasing returns to scale in production and heterogeneity of production technologies facilitates the development of trade. For heterogeneous production technologies we
can also observe the transition from a pure production economy via direct trade to an economy with mediated trade. ©2000 Elsevier Science B.V. All rights reserved.
JEL classification: D83; F10 Keywords: Bounded rationality; Learning; Trade; Mediation
1. Introduction
Mediated trade is a phenomenon which can be observed in many different markets in the real world economy. Examples reach from retail stores to real estate agents and stock
brokers. In all those cases ‘producers’ do not directly deal with the ‘consumers’ of their goods but there are middlemen in-between who facilitate the trades. The basic role of these
middlemen is to reduce the search costs of buyers and sellers needed to find an appropriate trading partner. The middlemen profit from these transactions by marking up their selling
prices but it is quite obvious that trading via mediators nevertheless pays off for all agents in the economy.
∗
Tel.: +1-213-740-8842; fax: +1-213-740-8543 E-mail address: dherbertusc.edu H. Dawid
0167-268100 – see front matter ©2000 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 2 6 8 1 9 9 0 0 0 8 6 - 4
28 H. Dawid J. of Economic Behavior Org. 41 2000 27–53
In most economic models the problem of search and transaction costs in trading is ne- glected and it is assumed that producers and consumers have no problems finding each other
and agreeing on the equilibrium price to carry out the trade
1
. There are several exceptions like Rubinstein and Wolinsky 1987, Day 1994 or Pingle 1997 where middlemen are ex-
plicitly introduced into a model of trade. The perspectives taken in these three contributions vary significantly. Whereas Rubinstein and Wolinsky deal with middlemen in an equilib-
rium theoretic framework, Day studies the out of equilibrium behavior of an economy with market mediators by specifying adaptation rules for the agents. Pingle, on the other hand,
carries out econometric analyses of data obtained by experiments in a model where agents can choose between producing and opening a store i.e. mediating. The data presented in
Pingle 1997 shows that mediation emerges as an important part of the economy also in laboratory experiments.
In this paper we follow a similar line as Day 1994 and deal with the question of the emergence of trade and mediation in a dynamic off-equilibrium framework. We do not
assume that all agents have rational expectations and know their optimal decision but rather consider a learning model where agents are boundedly rational and slowly acquire the
experience and knowledge to improve their performance. It has been argued in several places see e.g. Sargent 1993 or Simon 1983 that the relaxation of the very demanding
rationality and knowledge assumptions underlying general equilibrium theory might allow new insights and the study of more realistic economic models. We adopt this point of view
here and study the behavior on the path towards equilibrium rather than equilibrium behavior itself. Contrary to Day and Pingle’s work we do not represent agents by mathematical
equations or experiment with human agents, but we simulate the adaptive behavior of a population of boundedly rational economic agents on a computer. Computer simulations
have recently gained high importance also in economic research see e.g. Routledge, 1995; Arifovic, 1996; Dawid, 1996; Curzon Price, 1997; McFadzean and Tesfatsion, 1997 and
chances are they will become even more important in the near future. Although such an approach does not permit exact general results like analytical studies do, it enables us to
study complex interactive models which could not be dealt with analytically. The main question posed in this article is: can boundedly rational agents who all start off as pure
producers organize in a way to use the possible profits of production specialization and trading? How do certain parameters like the transactions costs of trade, the inertia of agents
or the returns to scale in production influence the emergence of trade? We will also study the question whether increasing returns to scale in production lead to direct trade without
middlemen or to mediation, respectively.
The learning rule we use to describe the adaptive behavior of the agents is in the spirit of imitation dynamics often used in the evolutionary game theory literature. The underlying
assumptions about the knowledge and computational ability of the agents are weak but we will see that the massive interaction of the agents nevertheless leads to quite efficient
behavior involving mediation and direct trading.
The paper is organized as follows. In Section 2 we present the basic simulation model where all agents in the population have the same production technology and produce with
1
Note however that there is some literature dealing with the emergence of a medium of exchange in trade; see e.g. Jones 1976 or Marimon et al. 1990.
H. Dawid J. of Economic Behavior Org. 41 2000 27–53 29
increasing returns to scale. In this section we also provide some considerations about what outcomes to expect for different parameter setups. Simulation results for this model are
shown in Section 3 where also the influence of parameter variations on the simulation results are explored. We finish with some concluding remarks in Section 4.
2. The model
