Today I presented my research done in the context of my master's thesis. I presented a novel product pricing approach for the TAC SCM game, where products are sold through reverse auctions with sealed bids (i.e., traders bid on customer requests for quotes and cannot observe their competitors' pricing behavior).
The new approach is based on price distribution estimations, where the relation between on-line available data and distribution parameters is dynamically modeled using economic regimes (characterizing market conditions) and error terms (accounting for customer feedback). Given the parametric approximations of price distributions, acceptance probabilities are estimated using a closed-form mathematical expression. These probabilities that a customer accepts a price offered by a trader can be used to determine the price yielding a desired quota. The approach has been implemented in the MinneTAC agent and tested against a price-following product pricing method in the TAC SCM game. The novel approach significantly improves performance; more orders are obtained against higher prices. Profits more than double.
During the presentation, (adaptations to) the game specifications of the TAC SCM game were discussed in the group. We briefly discussed the possible impact of new entrants during a game, more competitors, and a randomized game length. Apparently, according to game theory, end-of-game effects would be reduced when the game end is randomized.
Alexander
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