Reflective Oracles: A Foundation for Game Theory in Artificial Intelligence

  • Benja Fallenstein
  • Jessica Taylor
  • Paul F. Christiano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9394)


Game theory treats players as special: A description of a game contains a full, explicit enumeration of all players. This isn’t a realistic assumption for autonomous intelligent agents. In this paper, we propose a framework in which agents and their environments are both modelled as probablistic oracle machines with access to a “reflective” oracle, which is able to answer questions about the outputs of other machines with access to the same oracle. These oracles avoid diagonalization problems by answering some queries randomly. Agents make decisions by asking the oracle questions about their environment, which they model as an arbitrary oracle machines. Since agents are themselves oracle machines, the environment can contain other agents as non-distinguished subprocesses, removing the special treatment of players in the classical theory. We show that agents interacting in this way play Nash equilibria.


Nash Equilibrium Cellular Automaton Turing Machine Pure Strategy Causal Decision Theory 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Benja Fallenstein
    • 1
  • Jessica Taylor
    • 1
  • Paul F. Christiano
    • 2
  1. 1.Machine Intelligence Research InstituteBerkeleyUSA
  2. 2.UC BerkeleyBerkeleyUSA

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