Conclusions

We have presented a framework for the development of agents with incremental modeling/learning capabilities, in an economic society of agents. These agents were built, and the execution of different agent populations lead us to the discovery of the lessons summarized in Table 1. The discovery of volatility and price distributions as predictors of the benefits of deeper models, will be very useful when building agents. Moreover, we believe that other similar metrics will allow deeper modeling agents to make similar predictions. We are also encouraged by the fact that increasing the agents' capabilities changes the system in ways that we can recognize from our everyday economic experience.

Some of the agent capabilities shown in this paper are already being implemented into the UMDL [2] MAS. Our results showed how sellers with deeper models fare better, in general, even when they produce less valuable goods. This means that we should expect those type of agents to, eventually, be added into the UMDL. Fortunately, this advantage is diminished by having buyers keep deeper models. We expect that there will be a level at which the gains and costs associated with keeping deeper models balance out for each agent. Our hope is to provide a mechanism for UMDL agents to dynamically determine this cutoff and constantly adjust their behavior to maximize their expected profits given the current system behavior. The lessons in this paper are a significant step in this direction.

We are considering the expansion of the model with the possible additions of agents that can both buy and sell, and sellers that can return different quality goods. Allowing sellers to change the quality returned to fit the buyer will make them more competitive against 1-level buyers. We are also continuing tests on many different types of agent populations in the hopes of getting a better understanding of how well different agents fare in the different populations.

In the long run, another offshoot of this research could be a better characterization of the type of environments and how they allow/inhibit ``cheating'' behavior in different agent populations. That is, we saw how, in our economic model, agents are sometimes rewarded for behavior that does not seem to be good for the community as a whole. The rewards, we are finding, start to diminish as the other agents become ``smarter''. It would be very useful to characterize the type of environments and agent populations that, combined, foster such antisocial behavior (see [8]), especially as interest in multi-agent systems grows.