arXiv subjects

Spyros Galanis

Publications and source records attributed to Spyros Galanis.

3 recordsLinked to original sources

arXiv2026

Information Aggregation with Costly Information Acquisition

We study information aggregation in a dynamic trading model with partially informed traders. Ostrovsky [2012] showed that `separable' securities aggregate information in all equilibria, however, determining whether a security is separable requires knowing the exact information structure of agents. To remedy this problem, we allow traders to acquire signals with cost $κ$, in every period. We show that `$κ$ separable securities' characterize information aggregation and, as the cost decreases, almost all securities become $κ$ separable, irrespective of the traders' initial private information. Moreover, the switch to $κ$ separability happens not gradually but discontinuously, hence even a small decrease in costs can result in a security aggregating information. We provide a complete classification of securities in terms of how well they aggregate information, which surprisingly depends only on their payoff structure.

econ.TH↗

arXiv2026

Information Aggregation with AI Agents

Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by observing price movements? We conduct a controlled experiment where AI agents trade in a prediction market after receiving private signals, measuring information aggregation by the log error of the last price. We find that although the median market is effective at aggregating information in the easy information structures, performance deteriorates in the harder structures, suggesting that AI agents may suffer from similar limitations as humans when reasoning about others. Consistent with our theoretical predictions, market accuracy does not improve from allowing cheap talk communication, changing the duration of the market, or strategic prompting; initial price has little average effect but matters in the very hard structure. We also find that "smarter" AI agents perform better at aggregation and are more profitable. Surprisingly, giving them feedback about past performance does not improve aggregation.

econ.GN↗

arXiv2025

No Trade Under Verifiable Information

No trade theorems examine conditions under which agents cannot agree to disagree on the value of a security which pays according to some state of nature, thus preventing any mutual agreement to trade. A large literature has examined conditions which imply no trade, such as relaxing the common prior and common knowledge assumptions, as well as allowing for agents who are boundedly rational or ambiguity averse. We contribute to this literature by examining conditions on the private information of agents that reveals, or verifies, the true value of the security. We argue that these conditions can offer insights in three different settings: insider trading, the connection of low liquidity in markets with no trade, and trading using public blockchains and oracles.

econ.TH↗