Prediction markets are starting to look less like crowd polls and more like electronic markets. The central question is therefore no longer only whether these markets forecast well, but what happens when institutional liquidity enters: do spreads tighten, does price discovery improve, and do those gains actually reach the traders who are slowest to react when information arrives? This paper offers a research design for answering that question. It defines a broad market-quality lens, separates the main channels through which institutional liquidity enters, and maps the identification problems that arise in live venue data. It also uses a synthetic microstructure laboratory as a proof of concept for the measurement pipeline. The main lesson of the synthetic exercise is deliberately narrow. Market-maker coverage, liquidity incentives, and automation do not have to work through the same channel; average liquidity gains do not have to translate into equal gains for all traders; and the sharpest welfare losses are most likely to appear in shock states, when slower takers receive the least pass-through of tighter quoted markets. The synthetic results are useful because they stress-test the design, not because they settle the live empirical question.

翻译：预测市场正日益从群体投票工具演变为电子化市场。因此核心问题已不再局限于这些市场能否做出准确预测，而是当机构流动性进入时会发生什么：价差是否会收窄，价格发现是否得到改善，这些收益是否真正惠及信息来临时反应最慢的交易者？本文提出了一套研究框架来解答该问题。该框架界定了广义的市场质量视角，分离了机构流动性进入的主要渠道，并梳理了实盘数据中存在的识别问题。同时，本文采用合成微观结构实验室作为度量管道的概念验证。本合成实验的核心发现具有明确局限性：做市商覆盖面、流动性激励机制与自动化无需通过同一渠道发挥作用；平均流动性改善不必转化为所有交易者的等量收益；最大的福利损失最可能出现在冲击状态下，此时反应较慢的吃单方获得较少的报价价差传导。合成结果的价值在于对研究设计进行了压力测试，而非解决实盘经验问题。