Prediction Markets (PMs) are markets in which agents trade event contingent assets. Enterprises use PMs to forecast revenues and project deadlines. This paper presents an Agent-based model, called Logarithmic Market Scoring Rule-Automated Market Maker (LMSR-ASM), to evaluate Prediction Markets. Our model is capable of testing different types of Automated Market Makers (AMMs), which are mathematical functions or computational mechanisms needed to provide liquidity in Prediction Markets. The model offers insights into how to set parameters in a PM and how profits react to contrasting settings and AMMs. In addition, we simulate different probability processes, distinct AMMs, and agent behaviors. This paper also utilizes the LMSR-ASM to evaluate the impact of choosing initial prices in profits and revenue opportunities regarding AMM computational implementation. We show that we can use the LMSR-ASM to find optimal parameters for maximizing profits in PMs and how different AMMs affect market results under a variety of settings.

预测市场（PM）是代理人交易事件或有资产的市场。企业使用 PM 来预测收入和项目期限。本文提出了一种基于代理的模型，称为对数市场评分规则自动做市商 (LMSR-ASM)，用于评估预测市场。我们的模型能够测试不同类型的自动做市商（AMM），它们是在预测市场中提供流动性所需的数学函数或计算机制。该模型提供了有关如何在 PM 中设置参数以及利润如何对对比设置和 AMM 做出反应的见解。此外，我们还模拟不同的概率过程、不同的 AMM 和代理行为。本文还利用 LMSR-ASM 来评估选择初始价格对 AMM 计算实施的利润和收入机会的影响。我们表明，我们可以使用 LMSR-ASM 来找到最大化 PM 利润的最佳参数，以及不同 AMM 在各种设置下如何影响市场结果。