Computational Social Philosophy Seminars

Computational Social Philosophy Seminars (CSPS) is a regular international online seminar series, focusing on research that uses or engages with the use of computational tools to address questions in social philosophy.

Upcoming Seminar Presentation

All seminars are on Mondays at 8:30am PT / 11:30am ET / 4:30pm London / 5:30pm Berlin.

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Date: October 5, 2026

• Speaker: Leonard Kahn

• Title:  Rule Consequentialism: Toward a Game-Theoretic Interpretation with Monte Carlo Simulations

• According to Rule Consequentialism (RC) the moral status of actions is determined by whether they are required, permitted, or prohibited by the rules contained in the code whose acceptance, under the theory's specified conditions, would have the best consequences. In this paper, I explore the game-theoretic and computational groundwork that can be used to improve this theory. I begin with a disputed question among discussants of RC concerning the appropriate acceptance rate for a given code when evaluating it. Advocates of fixed-rate approaches evaluate codes at a specified rate but often disagree about what that rate should be, with 100% and 90% getting the most attention. In contrast, advocates of variable-rate approaches evaluate codes across multiple rates, usually averaging the results at each of the percentage rates from 1% to 100%. This disputed question helps to expose a challenge for RC concerning how candidate rules are to be specified precisely enough for their consequences to be compared. RC theorists typically formulate candidate rules in ordinary, folk-theoretic terms, (for example, in terms of lying or harming). However, rules formulated in this way are often too vague and too disconnected from outcomes to allow us to compare the consequences of codes especially at different levels of acceptance.
  
  My response is two-fold. I suggest that we begin by rethinking codes of rules in terms of game-theoretic strategies because game-theoretic strategies can represent candidate rules with greater precision, while game-theoretic models make the consequences of their interaction more explicit. I follow up on this suggestion by constructing a simple Monte Carlo simulation in which interactions among these strategies can be evaluated at various rates of acceptance. (For reasons of tractability, I study competing rules governing one recurrent interaction, while acknowledging that whole-code evaluation and interactions among rules remain downstream problems to solve.). My test case in this paper is the Stag Hunt. My simulation compares five strategies across ten acceptance rates. It includes two simple strategies (Always Stag and Always Hare), one randomized strategy (Two Face), and two adaptive strategies (Cooperative Adapter and Empirical Best Responder). My results suggest (1) that a comparatively simple cooperative rule can perform remarkably well against the alternatives tested across a broad range of acceptance rates and (2) that, under the baseline payoffs and the present method of aggregating results, high fixed-rate and variable-rate approaches can yield surprisingly similar evaluations of candidate rules. My results illustrate how game theory and computational modeling might begin to clarify questions about rule specification and acceptance rates in RC as well as provide answers to these questions, though a long road remains ahead of us.

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