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ICM Out! Better Tournament Strategy from Computed Continuations, vs. Solvers and LLMs — arXiv:2608.09586

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.09586
License (as archived): CC0-1.0
Archived: 2026-10-19T06:49:38.243Z
SHA-256 of the archived text: 4951e7e9eed3c63f11840ba7fc591732aa8be317ecf03ea2d08a0450409c31ad
Stable URL of this page: https://metraipolis.com/documento/dc_380eaacfeec0

How to cite

ICM Out! Better Tournament Strategy from Computed Continuations, vs. Solvers and LLMs — arXiv:2608.09586 — arXiv (en), https://arxiv.org/abs/2608.09586 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_380eaacfeec0 (Metraipolis archive dc_380eaacfeec0, sha256 4951e7e9eed3c63f11840ba7fc591732aa8be317ecf03ea2d08a0450409c31ad)

BibTeX

@misc{dc380eaacfeec0,
  title = {ICM Out! Better Tournament Strategy from Computed Continuations, vs. Solvers and LLMs — arXiv:2608.09586},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.09586},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_380eaacfeec0; sha256 4951e7e9eed3c63f11840ba7fc591732aa8be317ecf03ea2d08a0450409c31ad},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.09586. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The Independent Chip Model (ICM) converts tournament chips into reference prize equity, and policies are routinely constructed against those values. Because ICM reads only stack sizes, it omits action order, blind obligations, and seat rotation, and it does not price the elimination pressure a big stack puts on the short stacks it can bust. Those omissions can alter the successor-state contrasts that determine a move. We introduce Strategic-Continuation Optimization (SCO), a policy-construction method that enumerates current-hand outcomes, maps them to successor states, prices those states with continuation values computed from the finite tournament model, and optimizes and freezes the resulting current-hand policy. The fixed-ICM comparison policy changes one thing only: the same optimizer solves the same game with successor states priced by analytic ICM, so the two policies differ only through that pricing. We evaluate the resulting policies in a three-player jam/fold tournament with a \$1M prize pool. Relative to the frozen strategic-continuation benchmark, analytic ICM has \$9{,}036 mean absolute value error across all 2,838 state--seat entries. That value error rewrites the ranges it prices: measured against each decision point's own fixed-ICM jam range, SCO moves the jam frequency by an average of 14.08\%. To price those different moves, we compare all 946 states and three policy owners while changing only the focal policy and holding both opponents and the continuation evaluator fixed. The policy produced by SCO earns \$214.33 more prize equity per hand on average and is favored in 2,433 of 2,838 matched units. The ordering survives replacing the solver-built opponent with two LLMs and with a family of non-modeling threshold players. This value-to-policy-to-cost chain shows directly when ICM becomes an inadequate objective for tournament strategy construction.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-11 in cs.MA).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computer Science and Game Theory (cs.GT); Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2608.09586 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T06:45:31.719Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).