出版物与手稿

Chuan August Sun

Prediction markets promise to turn dispersed beliefs into useful probabilities, but that promise depends on the price-formation process remaining informative. We ask when it breaks in a market populated by automated agents that follow local rules and do not collude. The core measurement problem is that live markets do not reveal interim truth. We therefore build a controlled binary-market simulator with a known latent probability path, spanning a microstructure limit order book and scalable PyTorch batch auctions. The paper makes three contributions. First, it provides a testbed for measuring prediction-market failure against ground truth. Second, it shows that composition, not scale alone, is the clearest risk variable in the current evidence: as trend-following agents become more prevalent, prices steadily lose alignment with fundamentals, while matched scale controls rule out a simple “bigger is worse” story. Third, it introduces population-level diagnostics for market safety. Pressure-balance logs show how corrective arbitrage can be overwhelmed at the moments it is needed most; resonance, concentration, and synthetic crash-path tests show how markets can remain active while becoming less informative. The resulting evidence hierarchy reframes automated-market evaluation around strategy mix, synchronization, stabilizing capacity, concentration, and mechanism design, not only individual-agent behavior.

Chuan August Sun

Lucidosophy is a postulate-driven philosophical framework for understanding reality across all domains. It is not religion, not ideology, not self-help: it is a system of postulates that can be examined, challenged, and refined. Built on six foundational postulates — Reality, Unfolding, Pattern and Mystery, and their consequences — the book develops a unified language for reasoning about being, knowledge, and value, and grounds the Institute’s applied research in prediction markets, finance, and AI. This is the foundation volume of the Lucidosophy framework, available in full English and Chinese editions.

Chuan August Sun

This manuscript describes the engineering design behind a reusable simulation stack for studying population-level behavior in prediction markets. The current system combines a microstructure-oriented limit order book engine with a vectorized PyTorch batch-auction engine that scales to one million agents on local CPU hardware. The research goal is not only to produce one manuscript, but to create an instrument that can support repeated paper generation across market mechanisms, agent populations, and intervention designs. We describe the requirements, architecture, agent APIs, experiment orchestration pattern, reproducibility contract, and claim-audit workflow. We also document current gaps: the code is functional research software, but it still needs a package boundary, formal config schemas, stronger provenance manifests, systematic tests, and an interactive simulation layer before it can serve as a durable research platform.