A sequence mining method to predict the bidding strategy of trading agents

Abstract

In this work, we describe the process used in order to predict the bidding strategy of trading agents. This was done in the context of the Reverse TAC, or CAT, game of the Trading Agent Competition. In this game, a set of trading agents, buyers or sellers, are provided by the server and they trade their goods in one of the markets operated by the competing agents. Better knowledge of the strategy of the trading agents will allow a market maker to adapt its incentives and attract more agents to its own market. Our prediction was based on the time series of the traders' past bids, taking into account the variation of each bid compared to its history. The results proved to be of satisfactory accuracy, both in the game's context and when compared to other existing approaches. © 2009 Springer Berlin Heidelberg.