Inferring Strategies from Observations in Long Iterated Prisoner's Dilemma Experiments

TL;DR

This study investigates human strategies in long Iterated Prisoner's Dilemma experiments with fixed and shuffled partners, revealing how interaction patterns influence strategy development and cooperation.

Contribution

It introduces long-term experiments with different partner matching strategies and applies unsupervised clustering and Hidden Markov Models to infer strategies from human behavior.

Findings

Fixed partners promote behavioral self-organization and cooperation.

Shuffled partners entangle strategy subgroups, hindering cooperation.

Long experiments (over 25 rounds) are necessary to observe stable strategies.

Abstract

While many theoretical studies have revealed the strategies that could lead to and maintain cooperation in the Iterated Prisoner's Dilemma, less is known about what human participants actually do in this game and how strategies change when being confronted with anonymous partners in each round. Previous attempts used short experiments, made different assumptions of possible strategies, and led to very different conclusions. We present here two long treatments that differ in the partner matching strategy used, i.e. fixed or shuffled partners. Here we use unsupervised methods to cluster the players based on their actions and then Hidden Markov Model to infer what are those strategies in each cluster. Analysis of the inferred strategies reveals that fixed partner interaction leads to a behavioral self-organization. Shuffled partners generate subgroups of strategies that remain entangled, apparently blocking the self-selection process that leads to fully cooperating participants in the fixed partner treatment. Analyzing the latter in more detail shows that AllC, AllD, TFT- and WSLS-like behavior can be observed. This study also reveals that long treatments are needed as experiments less than 25 rounds capture mostly the learning phase participants go through in these kinds of experiments.