Gossip Based Prediction Market Simulation

In this paper I present a software tool for simulating a dynamic parimutuel market (DPM) based on an underlying model for the spread of information through a community. I use a standard gossip model for the information flow through the community. Typically these models are used to model network congestion, but I have taken a more literal interpretation of the “gossip”. The typical scenario for this simulation involves a rumor that spreads through the community; the rumor gives some information about the content of the DPM auction. When an individual hears the rumor, he will place a bet in the DPM in response to the content of the rumor. The prices of the assets in the DPM change according to the selected price function. The simulation tracks both the change in prices of the assets and the spread of the information through the community. At the end, the simulation ranks each of the individuals in order of influence on the community using a PageRank type metric. In the first section, I present a terse theoretical background on the primary components of the simulation: gossip models, DPM’s, and PageRank. Following the theory, I give detailed documentation of the simulation including where to access it and how to use it. In the third section, I give some typical experiments the simulation can perform. And in the final section, I give some preliminary conclusions and ideas for future work and development of the simulation.