Meeting Minutes - 25 March 2009

In the presentation, Romke and Otto presented the outcomes from the master seminar in computational economics. The research was focused on improving the price prediction mechanism of the MinneTAC agent. The MinneTAC agent is an agent-based computer model that is developed by the university of Minnesota in cooperation with the Erasmus University.

The MinneTAC agent competes with other Agent based computer models in the Trading Agent Competition for Supply Chain Management (TAC SCM). The TAC SCM game was designed to come to the best solution for an Agent based computer model that is capable of dealing with the problems of a dynamic supply chain.

In the MinneTAC agent, there is an ensemble, consisting of multiple price predictors used to predict the future market prices. The function of the model selection mechanism is to determine the most accurate price based on the predictions from all the individual predictors making up the ensemble. The advantage of using multiple predictors is the ability to capture more features in the data then a single predictor. The disadvantage of using multiple predictors is that different features are captured that causes different predictions. A second disadvantage is that not every price predictor is performing optimal for every time horizon and quantity of training data. To overcome these disadvantages, there is a dynamic weighting mechanism with adaptive weights developed for the MinneTAC agent. This weighting mechanism has to find to the optimal weights for every price predictor for every time horizon. The weights are learned during the game, while the agent is competing with its competitors for customer orders. When the agent starts, every price predictor has an equal weight. During the game, the MinneTAC agent starts using the optimal weights. This means that the price prediction mechanism is not working with the optimal weights during the first phase of the game.

In our seminar research, we found the optimal weights for every price predictor during the game. This data is used to bootstrap the agent to increase the performance in the first phase of the game.

R.J. Romke de Vries
O.B. ter Haar

Meeting Minutes – 11 February 2009

Today I presented some preliminary results of a research project that is intended to be part of my PhD thesis. I discussed an economic model of price competition between spatially organized firms. I discussed the Nash equilibrium solution of the model, but most of the time I focused on the outcomes achieved by so-called evolutionary dynamics. These outcomes were analyzed mathematically (partly analytically, partly numerically) but also through computer simulations. Interestingly, on average firms turned out to cooperate (or collude) with each other under the evolutionary dynamics.

We had a very fruitful discussion about the conditions necessary/sufficient to achieve the cooperative outcome, focusing in particular on the plausibility of various assumptions. We also discussed on a general level the methodology typically used in economic/game-theoretic research.

Ludo