Not Yet: Humans Outperform LLMs in a Colonel Blotto Tournament
The emergence of large language models (LLMs) has spurred economists to study how humans and LLMs behave in strategic settings. We organized a series of round-robin tournaments in the Colonel Blotto game. This game attracts game theorists' attention due to high-dimensional action space and the absence of pure strategy Nash equilibria. In the first tournament, more than 200 human participants competed against one another. In the second tournament, several popular LLMs were invited to submit strategies. In the third tournament, we matched the number of LLM strategies to the number submitted by humans. We find that humans more often employ better-calibrated intermediate-level allocation heur...
| Authors | Dmitry Dagaev, Egor Ivanov, Petr Parshakov et al. |
|---|---|
| Primary category | econ.GN |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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