drawn following a stationary probability distribution, and a fixed stock, that is decreased at every purchase; they are essentially reactive, their functional role in the simulation being limited to providing an abstract good of variable quality to the buyers. Sellers exit the simulation when the stock is exhausted and are substituted by a new seller with similar characteristics but with a new identity (and as such, unknown to the buyers). The disappearance of sellers makes information necessary; reliable communication allows to discover faster better sellers. This motivates the agents to participate in the information exchange. In a setting with permanent sellers (infinite stock), once all buyers have found a good seller, there is no reason to change and the experiment freezes. With finite stock, even after having found a good seller, buyers should be prepared to start a new search when the good seller's stock ends. At the same time, limited stock makes good sellers a scarce resource, and this constitutes a motivation for the agents not to distribute information. One of the interests of the model is in the balance between these two factors.

## 4 Discovering Good Sellers

If reputation is a means for finding out material cheaters, i.e. non-reciprocators in exchange of material goods, how can such a mechanisms be exploited to find out informational cheaters, i.e. deceitful informants? Solutions to this problem in the MAS field usually rely on learning strategies (see [25] and [6]), by means of which agents more or less gradually discover trustworthy informers and gain accurate information.

Applied to detection of informational cheating, learning techniques are more efficient than in the case with material cheating, since facing deception in reputation is less costly than trading with a cheater. Here the number of sellers and buyers is fixed to 100 and 15, respectively. Goods are represented by a utility factor that we interpret as quality (but, given the level of abstraction used, could as well represent other utility factors as quantity, discount, timeliness) with values between 1 and 100.

In Figure 2 the quality of contracts in the simulated environment per simulation time is shown. In both scenarios (with only image and with image and reputation) the levels are equals.

On the contrary, the two curves present a different cyclic behavior: in L1 the the discovering of good sellers come out by information exchange based only on image circulating in the system. The peaks of each wave in 3 represents, according to the theory, that, once discovered a good seller, buyers start to buy from this one. Hence, the distribution of requests is not stable because of the finite number of stock for each seller. In our view, the minimum value for each wave indicates a very slow process of discovering when only image circulates in the system.

This result suggests that reputation-based information has a stabilizing effect in a world of agents that communicate and learn to evaluate one another under inaccurate information.