ArXiv TLDR

Strategy Rescaling and the Stability of Kantian Optimization

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2605.00692

Igor Sloev, Gerasimos Lianos

econ.TH

TLDR

Kantian optimization, through strategy rescaling, can neutralize Nash free-riders and achieve stable cooperation, proving evolutionarily stable.

Key contributions

  • Multiplicative Kantian Equilibrium (MKE) lacks strategic equivalence; measurement scale impacts outcomes.
  • Proposes strategy rescaling for Kantians to neutralize Nashers' free-rider advantage while preserving Pareto-efficiency.
  • Endogenous choice in dynamic games leads all players to prefer Kantian over Nash optimization.
  • Kantian optimization is shown to be an evolutionarily stable strategy (ESS).

Why it matters

This research highlights a novel mechanism for achieving cooperation in games, even when facing self-interested Nash players. By demonstrating the stability and evolutionary advantage of Kantian optimization, it offers new insights into designing robust cooperative systems.

Original Abstract

This study investigates the properties and stability of the Multiplicative Kantian Equilibrium (MKE) in symmetric games. We first demonstrate that MKE lacks strategic equivalence: the Kantian best-response function is not invariant under monotonic strategy rescaling. This strategic non-equivalence implies that the choice of measurement scale - a subjective interpretation of the game - materially impacts equilibrium outcomes. Exploiting this non-equivalence, in a game where players may be Kantian or Nasher, we propose an efficient strategy rescaling that allows Kantians to neutralize the free-rider advantage of Nashers, while preserving Pareto-efficient outcomes among themselves. In a dynamic framework, we show that the subgame-perfect Nash equilibrium with endogenous choice of optimization type leads all players to prefer Kantian optimization over Nash optimization. In an evolutionary setup, we show that Kantian optimization is an evolutionarily stable strategy (ESS). Our results suggest that the inherent strategic non-equivalence of Kantian optimization provides a robust pathway to stable cooperation.

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