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Markets of Minds: A 51-Billion-State Economy of Intelligence — arXiv:2610.32190

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.32190
License (as archived): CC0-1.0
Archived: 2026-10-26T02:05:11.873Z
SHA-256 of the archived text: 5baa3b9adbec36fc77d895d76e20b943e30914a824d8bda2180122e435aa282e
Stable URL of this page: https://metraipolis.com/documento/dc_eb0579f99c4e

How to cite

Markets of Minds: A 51-Billion-State Economy of Intelligence — arXiv:2610.32190 — arXiv (en), https://arxiv.org/abs/2610.32190 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eb0579f99c4e (Metraipolis archive dc_eb0579f99c4e, sha256 5baa3b9adbec36fc77d895d76e20b943e30914a824d8bda2180122e435aa282e)

BibTeX

@misc{dceb0579f99c4e,
  title = {Markets of Minds: A 51-Billion-State Economy of Intelligence — arXiv:2610.32190},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.32190},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eb0579f99c4e; sha256 5baa3b9adbec36fc77d895d76e20b943e30914a824d8bda2180122e435aa282e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.32190. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

When intelligence is shared, reasoning becomes a constrained allocation problem. In a population of agents with limited access to expert models, an intervention can benefit its recipient yet still be a poor use of shared capacity if another feasible intervention has greater expected value. Assistance must therefore be evaluated through both its marginal benefit and the opportunity cost induced by budgets, access, scarcity, and service capacity. To this end, we introduce Markets of Minds, a framework for allocating expert reasoning across agents before expert outputs are observed. For each feasible agent-service pair, the framework estimates the expected gain from assistance relative to the agent's default continuation, explicitly separating repair benefit from regression risk, and jointly clears assistance contracts under shared resource constraints. We then study this problem in a dependency-aware agent economy with 51.2 billion addressable persona-market states spanning capabilities, cognitive budgets, provider access, scarcity, and network roles. These dependencies couple allocation feasibility across agents rather than the outcome of a fixed contract. Empirically, in a matched replay of 240 cases under a common 30% assistance quota, the frozen allocator reaches 85.42% task-level persona adherence versus 73.86% for equal-capacity random allocation, a gain of 11.56 percentage points. The results show that assistance value is heterogeneous and can be negative, and that scarcity changes which interventions are worth purchasing. Together, these findings motivate a population-level view of intelligence where the value of assistance depends not only on what it can achieve, but also on which alternative uses of scarce cognitive capacity it displaces.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.32190 — 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 2026-10-26T02:04:36.280Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).