Mutually beneficial behavior in repeated games can be enforced via the threat of punishment, as enshrined in game theory’s well-known “folk theorem.” There is a cost, however, to a player for generating these disincentives. In this work, we seek to minimize this cost by computing a “Stackelberg punishment,” in which the player selects a behavior that sufficiently punishes the other player while maximizing its own score under the assumption that the other player will adopt a best response. This idea generalizes the concept of a Stackelberg equilibrium. Known efficient algorithms for computing a Stackelberg equilibrium can be adapted to efficiently produce a Stackelberg punishment. We demonstrate an application of this idea in an experiment involving a virtual autonomous vehicle and human participants. We find that a self-driving car with a Stackelberg punishment policy discourages human drivers from bullying in a driving scenario requiring social negotiation.
CITATION STYLE
Cooper, M., Lee, J. K., Beck, J., Fishman, J. D., Gillett, M., Papakipos, Z., … Littman, M. L. (2019). Stackelberg Punishment and Bully-Proofing Autonomous Vehicles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11876 LNAI, pp. 368–377). Springer. https://doi.org/10.1007/978-3-030-35888-4_34
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