Decision Trees for Understanding Trading Outcomes in an Information Market Game

We present an experimental information market designed to aggregate IT job related information distributed among the traders in the market. The payoffs of the shares in this market were tied to true IT job demand in the real world. This paper focuses on the market outcomes of profit or loss made in this market, and more specifically the factors that may lead to either outcome. We explore the use of decision trees to predict the outcome of the market based on three different predictors: the individual traders’ personal preferences, the collective ranking of the shares, and the market prices resulting from the traders’ interaction during the market game. The decision tree illustrates that traders who agree with the market ranking of the best share incur losses, whereas those who don’t agree have a higher chance of making a profit provided they then agree with the collective ranking of the best share or remain consistent in their personal preferences.