This paper describes the rationale for, and design of, an agent-based simulation model of a contemporary online sports-betting exchange: such exchanges, closely related to the exchange mechanisms at the heart of major financial markets, have revolutionized the gambling industry in the past 20 years, but gathering sufficiently large quantities of rich and temporally high-resolution (sub-second interval) data from real exchanges — i.e., the sort of data that is needed in large quantities for leading-edge machine learning techniques such as Deep Learning — is often very expensive, and sometimes simply impossible; this creates a need for a plausibly realistic synthetic data generator (SDG), which is what the simulation described here is intended to provide. The simulator, named the Bristol Betting Exchange (BBE), is intended to become a common platform, a data-source and experimental test-bed, for any/all researchers studying the application of artificial intelligence (AI) and machine learning (ML) techniques to issues arising in betting exchanges; and, as far as I have been able to determine, BBE is the first of its kind: a free open-source agent-based simulation model consisting not only of a sports-betting exchange, but also a minimal simulation model of racetrack sporting events (e.g., horse-races or car-races) about which bets may be made, and a population of simulated bettors who each form their own private evaluation of odds and place bets on the exchange before and — crucially — during the race itself (i.e., so-called "in-play'' betting) and whose betting opinions change second-by-second as each race event unfolds. BBE is offered as a proof-of-concept simulator system that enables the generation of large high-resolution data-sets for automated discovery or improvement of profitable strategies for betting on sporting events via the application of AI/ML and advanced data analytics techniques. This paper offers an extensive survey of relevant literature and explains the motivation and design of BBE, and presents brief illustrative results. In a companion paper, I and my co-authors describe three independent implementations of BBE that each serve different audiences, for which the source-code is being released on GitHub, and show comparative results
