Recursive joint simulation in games.
Game-theoretic dynamics between AI agents could differ from traditional human–human interactions in various ways. One such difference is that it may be possible to accurately simulate an AI agent, for example because its source code is known. Such an agent would then be fundamentally uncertain wheth...
| Publicado en: | Synthese Vol. 208; no. 2; pp. 1 - 28 |
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| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Springer Nature
Aug2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=195763681&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 195763681 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00397857 4LI jtl: Synthese issn: 00397857 maglogo: N pubinfo: dt: Aug2026 vid: 208 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 195763681 10.1007/s11229-026-05706-7 ppf: 1 ppct: 27 formats: tig: atl: Recursive joint simulation in games. aug: au: Kovarik, Vojtech Oesterheld, Caspar Conitzer, Vincent affil: https://ror.org/05x2bcf33 Foundations of Cooperative AI Lab (FOCAL), Computer Science Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA https://ror.org/03kqpb082 AI Center, Czech Technical University, Jugoslávských partyzánů 1580/3, 160 00, Prague, Czech Republic https://ror.org/024d6js02 Center for Theoretical Study, Charles University, Ovocný trh 560/5, 116 36, Prague, Czech Republic sug: keyword: AI agents Cooperative AI Folk theorem Information and Computing Sciences Artificial Intelligence and Image Processing Repeated games Self-locating beliefs Simulation ab: Game-theoretic dynamics between AI agents could differ from traditional human–human interactions in various ways. One such difference is that it may be possible to accurately simulate an AI agent, for example because its source code is known. Such an agent would then be fundamentally uncertain whether it is in the real world or in a simulation. Our aim is to explore ways of leveraging this possibility to achieve more cooperative outcomes in strategic settings. In this paper, we study an interaction between AI agents where the agents run a recursive joint simulation. That is, the agents first jointly observe a simulation of the situation they face. This simulation in turn recursively includes additional simulations (with a small chance of failure, to avoid infinite recursion), and the results of all these nested simulations are observed before an action is chosen. We show that the resulting interaction is strategically equivalent to an infinitely repeated version of the original game, allowing a direct transfer of existing results such as the various folk theorems. As evidence that the equivalence is robust, we show that it holds even when we relax some of the assumptions and that it also holds “from the inside” – meaning, for an agent that finds itself inside the game and has self-locating uncertainty. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2026 holdings: @attributes: islocal: N |
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