Optimal prediction of decisions and model selection in social dilemmas using block models
arXiv:2004.12151 · doi:10.1140/epjds/s13688-018-0175-3
Abstract
Advancing our understanding of human behavior hinges on the ability of theories to unveil the mechanisms underlying such behaviors. Measuring the ability of theories and models to predict unobserved behaviors provides a principled method to evaluate their merit and, thus, to help establish which mechanisms are most plausible. Here, we propose models and develop rigorous inference approaches to predict strategic decisions in dyadic social dilemmas. In particular, we use bipartite stochastic block models that incorporate information about the dilemmas faced by individuals. We show, combining these models with empirical data on strategic decisions in dyadic social dilemmas, that individual strategic decisions are to a large extent predictable, despite not being "rational." The analysis of these models also allows us to conclude that: (i) individuals do not perceive games according their game-theoretical structure; (ii) individuals make decisions using combinations of multiple simple strategies, which our approach reveals naturally.
References in corpus (9)
- Structure and inference in annotated networks
- On Universality in Human Correspondence Activity
- Model selection and hypothesis testing for large-scale network models with overlapping groups
- Humans display a reduced set of consistent behavioral phenotypes in dyadic games
- Consistencies and inconsistencies between model selection and link prediction in networks
- Justice blocks and predictability of US Supreme Court votes
- Accurate and scalable social recommendation using mixed-membership stochastic block models
- Network structure, metadata and the prediction of missing nodes and annotations
- Market Imitation and Win-Stay Lose-Shift strategies emerge as unintended patterns in market direction guesses