Narrative Economics of the Racetrack: An Agent-Based Model of Opinion Dynamics in In-play Betting on a Sports Betting Exchange

We present first results from a new agent-based model (ABM) of a sports-betting exchange (such as those operated by BetFair, BetDdaq, and SMarkets, among other companies) in which each agent holds a dynamically-varying opinion about some uncertain future event (such as which competitor will win a particular horse race) and in which all agents interact with the betting exchange to find counterparties holding an opposing view with whom they can then enter into a bet with. We extend methods from Opinion Dynamics (OD) research to give each agent an opinion at any particular time which is influenced partially by local interactions with other agents (as is common in the OD literature), partially by globally available information (as published to all by the betting exchange) and partially by the progressive reduction in uncertainty in the system (i.e., eventually all agents know which horse has won the race). Our work here is motivated by the prize-winning ICAART2021 paper of Lomas & Cliff, who integrated OD methods with ABMs of financial markets to explore issues in Narrative Economics, an approach recently proposed and popularised by Nobel Laureate Robert Shiller, but here we explore a significantly different type of market: a betting market (which has strong similarities to a financial market for tradeable derivative contracts such as futures or options). The novel contributions of this paper are centred on the extension of OD methods to situations in which there is a mix of local and global influence, and in which uncertainty progressively reduces to zero. We present results from our initial proof-of-concept implementation. The Python source-code for our ABM is freely available on Github for other researchers to replicate and extend the work reported here.