TOURNAMENT-WINNING STRATEGY FOR ITERATED OPTIONAL PRISONER'S DILEMMA
type: CreativeWork
description: Iterated optional prisoner's dilemma (IOPD) is an adversarial game that can be used to model several real-world scenarios, from mutual grooming between primates to alliances between business firms. This study utilizes simulation techniques to determine winning strategies for IOPD tournaments in a variety of initial conditions. Machine learning techniques are used to iteratively improve upon the winning strategy, culminating in a single undefeated strategy. The outcome of this study is a single strategy that we claim is likely to win an IOPD tournament for most reasonable initial conditions.
abstract: Iterated optional prisoner's dilemma (IOPD) is an adversarial game that can be used to model several real-world scenarios, from mutual grooming between primates to alliances between business firms. This study utilizes simulation techniques to determine winning strategies for IOPD tournaments in a variety of initial conditions. Machine learning techniques are used to iteratively improve upon the winning strategy, culminating in a single undefeated strategy. The outcome of this study is a single strategy that we claim is likely to win an IOPD tournament for most reasonable initial conditions.
author: Shamma, Ahmed A.
date published: 2000-09
date modified: 2026-07-27T20:24:57Z
provider: Naval Postgraduate School
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Monterey, CA; Naval Postgraduate School

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10945/66137