I describe 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 data from real exchanges - i.e., the sort of data that is needed in large quantities for Deep Learning - is often very expensive, and sometimes simply impossible; this creates a need for a plausibly realistic synthetic data generator, which is what this simulation now provides. The simulator, named the "Bristol Betting Exchange" (BBE), is intended as a common platform, a data-source and experimental test-bed, for researchers studying the application of 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 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.

中文翻译：

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BBE：通过基于代理的建模模拟进行中的博彩交易所的微观结构动力学

我描述了当代在线体育博彩交易所基于代理的仿真模型的原理和设计：此类交易所与主要金融市场的核心交易所机制密切相关，过去曾彻底改变了赌博业20年了，但是从真实的交易中收集到足够数量的丰富且时间上高分辨率的数据（即，深度学习所需的大量数据）通常非常昂贵，有时甚至是不可能的；这产生了对看起来真实的合成数据生成器的需求，这正是该模拟现在所提供的。该模拟器名为“布里斯托（Bristol）博彩交易所”（BBE），旨在用作通用平台，数据源和实验测试平台，供研究人员研究将AI和机器学习（ML）技术应用于博彩交易中出现的问题的方法；据我所知，BBE是同类产品中的第一个：基于开源代理的免费模拟模型，不仅包括体育博彩交流，还包括跑道体育赛事的最小模拟模型（例如，赛马或赛车）下注，以及一群模拟下注者，他们各自对自己的赔率进行私下评估，并在比赛本身之前和（至关紧要）在交易过程中（特别是在比赛过程中）进行下注（即，即所谓的“进行中”投注），并且随着每次比赛的进行，其投注意见会每秒发生变化。BBE是一种概念验证系统，可通过应用AI / ML和先进的数据分析技术，生成大型高分辨率数据集，以自动发现或改进用于体育赛事博彩的盈利策略。本文对相关文献进行了广泛的调查，并解释了BBE的动机和设计，并给出了简要的说明性结果。