Memory and Selfishness in the Prisoners Dilemma
Cory Seelenfreund · New Rochelle, New York
Peggy Scripps Award for Science Communication ($10,000) · Regeneron International Science and Engineering Fair · 2025
Simulation and mathematical modelling of how memory length and degree of self-interest change outcomes in the iterated prisoner's dilemma, framed around implications for AI agent design.
Two parameters, varied systematically, in a game whose baseline behaviour is already well understood. Working inside a canonical problem means every result has something to be compared against.
- Iterated prisoner's dilemma simulation
- Memory-length parameter sweep
- Self-interest parameter sweep
- Mathematical modelling of strategy outcomes
- A well-studied model is a gift: your result immediately has a reference point
- Varying two parameters across their range beats testing two hand-picked configurations
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