This article explores the use of ‘agent-based computational economics’ (ACE) for modelling the development of transactions between firms. Transaction cost economics neglects learning and the development of trust, ignores the complexity of multiple agents, and assumes rather than investigates the efficiency of outcomes. Efficiency here refers to minimum cost or maximum profit. We model how co-operation and trust emerge and shift adaptively as relations evolve in a context of multiple, interacting agents. This may open up a new area of application for the ACE methodology. A simulation model is developed in which agents make and break transaction relations on the basis of preferences, based on trust and potential profit. Profit is a function of product differentiation, specificity of assets, economy of scale and learning by doing in ongoing relations. Agents adapt their trust in a partner as a function of his loyalty, exhibited by his continuation of a relation. They also adapt the weight they attach to trust on the basis of realized profit. The model enables an assessment of the efficiency of outcomes relative to the optimum, as a function of trust and market conditions. We conduct a few experiments to illustrate this application of ACE.
Inter-firm relations in general, and buyer–supplier relations in particular, have increasingly been analyzed by means of transaction cost economics (TCE). However, as has been widely acknowledged, TCE does not include any dynamics of learning, adaptation or innovation. Williamson himself (1985, p. 144) admitted that ‘the study of economic organization in a regime of rapid innovation poses much more difficult issues than those addressed here’. A more fundamental problem is that as in economics more in general it is assumed rather than investigated that efficient outcomes will arise. Here, in inter-firm relations, it is assumed that optimal forms of organization or governance will arise, suited to characteristics of transactions such as the need for transaction-specific investments, frequency of transactions, and uncertainty concerning conditions that may affect future transactions (Williamson 1975 and Williamson 1985). Two arguments are used for this: an argument of rationality and an argument of selection.
Williamson granted that rationality is bounded and transactions are subject to radical uncertainty, which precludes complete contingent contracting. But he proceeded to assume a higher level of rationality: people can rationally, calculatively deal with conditions of bounded rationality. Aware of their bounded rationality and radical uncertainty, people rationally design governance structures to deal with those conditions. However, if rationality is bounded, then rationality of dealing with bounded rationality is bounded as well. To rationally calculate economizing on bounded rationality, one would need to know the marginal (opportunity) costs and benefits of seeking further information and of further calculation, but for that one would need to decide upon the marginal costs and benefits of the efforts to find that out. This yields an infinite regress (Hodgson, 1998; Pagano, 1999). Here we accept bounded rationality more fully and deal with it on the basis of the methodology of adaptive agents.
When confronted with arguments against rationality, economists usually concede that assumptions of full rationality are counterfactual, and then resort to the argument of economic selection. We can proceed as if agents make rational, optimal choices, because selection by forces of competition will ensure that only the optimal survives (Alchian, 1950; Friedman, 1953). Williamson was no exception in this respect. He held that in due course, inefficient forms of organization will be selected out by market forces. However, that argument of selection has been shown to be dubious. For example, Winter (1964) showed that in evolution it is not the best conceivable but the best that happens to be available that survives. Due to effects of scale a large firm that is inefficient for its size may win out over efficient small firms. Furthermore, the selection efficiency of markets may be hampered by entry barriers. Koopmans (1957) concluded long ago that if the assumption of efficient outcomes is based on an argument of evolutionary process, its validity should be tested by explicit modelling of that process. Then, particularly in the study of inter-firm relations, we have to take into account the complexities and path-dependencies that may arise in the making and breaking of relations between multiple agents. That is what we aim to do in this article. As Coase (1998) recently admitted:
he analysis cannot be confined to what happens within a single firm. The costs of co-ordination within a firm and the level of transaction costs that it faces are affected by its ability to purchase inputs from other firms, and their ability to supply these inputs depends in part on their costs of co-ordination and the level of transaction costs that they face which are similarly affected by what these are in still other firms. What we are dealing with is a complex interrelated structure.
Following up on Epstein and Axtell's (1996) suggestion, we let the distribution of economic activity across different organizational forms emerge from processes of interaction between these agents, as they adapt future decisions to past experiences. The system may or may not settle down and if it does, the resulting equilibrium may or may not be transaction cost economic. In any case, ‘[i]t is the process of becoming rather than the never-reached end points that we must study if we are to gain insight’ (Holland, 1992, p. 19).
The methodology of artificial adaptive agents, in ACE, seems just the right methodology to deal with this ‘complex interrelated structure’ of ‘processes of interaction in which future decisions are adapted to past experiences’. We use that methodology to model interactions between firms, in the making and breaking of relations on the basis of a boundedly rational, adaptive, mutual evaluation of transaction partners that takes into account trust, costs and profits. We model a system of buyer–supplier relations, because that best illustrates transaction cost issues.
We focus on the role of trust, for two reasons. The first reason is that TCE does not incorporate trust, and this is an area where development of insight has priority. The second reason is that the central feature, in ACE, of adaptation in the light of experience seems particularly relevant to trust (Gulati, 1995; Zucker, 1986; Zand, 1972).
Section 2 briefly characterizes the methodology of agent-based computational economics (ACE) that will be used. Section 3 discusses the issue of trust and opportunism. Section 4 discusses costs and profits of transactions. Then we proceed to explain the design of our model. Section 5 indicates how buyers and suppliers are matched on the basis of their preferences, which include trust next to expected profit. Section 6 shows how we model costs and profits. Section 7 shows how we model trust. Section 8 shows how we model adaptation. Section 9 summarizes the simulation model. Section 10 discusses a few illustrative experiments. Finally, Section 11 discusses limitations and further research.
This study aimed to explore and illustrate the use of the methodology of
agent-based computational economics (ACE) for modelling the emergence of
inter-"rm co-operation and trust. When the full implications of bounded rationality
are accepted, we need such a process-based approach. Rather than
knowing in advance what is optimal, agents need to adapt perceptions and
evaluations on the basis of experience. Perceptions and evaluations of pro"tability
and trustworthiness depend on experience in the sometimes chaotic and
generally unpredictable making and breaking of relations among multiple
agents. Whether this yields e$cient outcomes is not to be assumed but to be
investigated. In view of uncertainties and path dependencies that arise in the
making and breaking of relations, optimal outcomes are not guaranteed.
As the central challenge we took the modelling of adaptive trust: its adaptation
in view of perceived loyalty, its role next to pro"t in the choice of partners,
and adaptation of the weight attached to trust in view of realized pro"ts.
The main conclusion is that such a model is feasible, in a form that reproduces
the core issues of transaction cost economics. The model endogenizes the &make
or buy' issue. It incorporates the positive e!ect of di!erentiated products on
pro"t as well as the associated problem of switching costs. It incorporates the
trade-o! between the higher pro"tability of di!erentiated products and the scale
advantages of a standard product. The model allows us to track how trust and
its role in partner evaluation evolve under di!erent conditions, such as the
degree and pro"tability of product di!erentiation, strength of economy of scale,
opportunities for learning by doing, and basic levels of trust that prevail in
a society.
The model was used only for a "rst round of experiments, aimed at illustrating
how this type of model might work and at testing elementary hypotheses to
assess the plausibility of outcomes. As expected, and predicted by transaction
cost economics, product di!erentiation favors &make' relative to &buy'. This is
because the transaction cost of switching is larger and there is less potential for
economy of scale in large volumes of production for multiple buyers by specialized
suppliers. As also expected, when there is more &make', due to high product
di!erentiation, the weight attached to trust in the evaluation of partners declines
faster. The experiments also illustrate that pro"ts are not always optimal, evenafter adaptation settles down in a stable outcome, and that this outcome varies
between runs. This re#ects the path dependency that arises in the making and
breaking of relations: paths to optimal results can get blocked.
We have hardly scratched the surface of experimentation. We can investigate
the e!ects of di!erent initial conditions concerning the level and weight of trust,
and the level of basic trust, re#ecting di!erences in institutional environment.
We can test the robustness of outcomes, e.g. under the invasion of opportunists
into a setting with high levels and weights of trust. Or vice versa: the perspectives
for altruists in an opportunistic setting.
The model is also amenable to important extensions. We aim to extend the
model with a threshold of resistance to opportunistic temptation of switching to
more attractive partners. An agent will defect from an existing relation only
when the gain in expected pro"t exceeds the threshold. We can then compare the
e!ects of di!erent levels of such a threshold of loyalty. We can build in an
adaptation mechanism of that threshold, similar to the current adaptation of the
weight attached to trust, and observe how loyalty develops under di!erent
conditions and starting values. We feel that this extension should be made
before we proceed to more extensive and systematic testing and experimentation
with the model.
In the current set-up switching costs arise only due to loss of learning by
doing. We also intend to implement switching costs in the form of loss of speci"c
assets. A technical complication is that the switching cost depends on the
residual value of the asset after N periods of utilization, and this allows for
a wide variety of speci"cations. When we add loss of speci"c assets, we can
compare di!erent forms of governance, such as di!erent distributions of switching
costs between partners: the cost is shared equally, or the one who makes the
investment pays, or the one who breaks the relation pays.
While the adaptation process (of the pro"t elasticity of preference scores)
operates only on the experience of the agent himself, in contrast with genetic
algorithms, the process is rather blind, or at least non-cognitive. Another
extension of the model would be to delve more deeply into cognitive processes
involved in adaptation.
