How To Be Loyal, Rich And Have Fun Too:
The Fun Is Yet To Come

By: Juliette Rouchier and David Hales
Date: 7th October 2003
CPM Report No.: CPM-03-122

Presented at the first international conference of the European Social Simulation Association, Gronigen, the Netherlands, September 2003.


Our paper presents a model that has been elaborated to synthesize the first results of a field study and simulations that were led in the artificial societies that were derived. The study is led at the “Marché d’Intérêt National des Arnavaux”, a wholesale fruits and vegetables market, situated near Marseille, and which represents one of the main hubs of supply for retailers. The elaboration of prices takes place through one-to-one interactions along the morning, with only oral information being given to retailers. To elaborate its prices, each wholesale seller competes with all his colleagues of the market and is dependent on its suppliers’ offers. The indirect competitors of all retailers are the supermarket suppliers, which define the average prices Supermarkets use an alternate (and larger) supply chain.

The study of real markets is not extremely developed yet in the economics field (Kirman, 2002). Classical economic analysis deals with ideal prices that ought to appear when certain buyers and sellers, whose reservation prices are known, meet. Reservation price is a key concept in the study of markets: it stands for the minimum price a seller wants to get for the product he is selling (alternatively the maximum price a buyer is willing to give). Once ones knows all the reservation prices, it is indeed possible to know who will be able to exchange and who has expectations that cannot be fulfilled; then to build an offer and demand curve, which intersect at a value called the equilibrium price. A classical approach enables to interrogate the nature of market institutions (institutions can be for example auctions or paired interactions) by evaluating their efficiency, i.e. the total value produced compared to an ideal value with the best possible matching. An other issue is the one of information that is generated for the agents by exchanges that take place: how much does each transaction reveal of the reservation prices of the others (and hence their preferences).

Once one starts to study how information is generated and processed by individuals, it becomes interesting to study markets in real, so that to find out how individuals actually deal with dynamical offers and demands that can be expressed through transactions, price proposals, apparition of shortages. One trend is the study led by behavioural economists, who put people in artificial controlled market settings (Smith, 2002; Rabin, 2002). They observe the proposals and accepted transactions along the time, and can limit and monitor any information circulation. They analyse how prices dynamics can be related to the institution that is chosen to organise the interactions. They use comparison to establish their hypothesis: they observe real actions in different settings, where the quantity and quality of information can vary.

Another approach is the study of real markets in their natural environment, where no variable can be controlled. Whereas artificial markets enable to study the actions of individuals undergoing short-time interactions in a market activity, the observation of a living market can help understand which actual use of the official institutions humans develop and how they interact in the long term. Indeed, our interest in this study is to see how individuals adapt to their environment, but also arrange it, so that to be efficient in the long-run (Callon, 1998). This is a very different approach to more classical economy, since the motivational aspects of an everyday life activity has to be reconsidered , being necessarily far different from one shot meetings or an ideal computation based on only monetary preferences. There are indeed numerous ethnographic evidences that economic activities are not based solely on an income optimisation attitude (Sahlins, ; Malinowski, 1922). It is also shown by simulation that the motivations of economic attitude have a impact on global results in an economy (Janssen and Jager, 2001). More specifically, we are here interested in studying the apparition of habits within a certain framework, be them relational or calculative: the shape of these habits and the potential interest they represent for the actors. It is indeed common to stress the importance of networks and of the strategic behaviour of actors regarding these networks on market (Fafchamps and Minten, ?). One of the common interpretation, that we will not follow here, is related to a now classical analysis in economics, which explain the reinforcement of relational link because of their effect on the reduction of transaction costs (Poole et al., 1998).

After performing a field study on the Marché des Arnavaux, organised as a series of observations on the market and of interviews with wholesale sellers and retailers, we decided to structure the results by building a multi-agent model. No economic theory can help us analyse our observation, and only a few results have been focusing on the same type of behavioural and institutional issues, like  (Kirman … ) on the fish market in Marseille. Hence we had to produce our own assumptions on what are the motivations for behaviour for the actors, that are based at the same time on our field study and on sociological books that describe trading activities and networks in the area of Marseille (Taurius, 2002). To see if the more significant cognitive processes we identified can be described in a consistent way, simulating interactions in artificial societies seemed to be a reasonable first step. Afterwards, our aim is to relate our research to a larger methodology which is increasingly used to deal ling with economical or coordination behaviour, “companion modelling” (Bousquet et al. 1999, Rouchier et al., 1998, Barreteau and Bousquet, 2000). The model results will be redirected towards the represented actors, whose remarks help to reconsider the model, and hence help to guarantee the qualitative validation of the model.

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