Paper Info
Title
A Probabilistic Model of Social Decision Making based on Reward Maximization.
Abstract
A fundamental problem in cognitive neuroscience is how humans make decisions, act, and behave in relation to other humans. Here we adopt the hypothesis that when we are in an interactive social setting, our brains perform Bayesian inference of the intentions and cooperativeness of others using probabilistic representations. We employ the framework of partially observable Markov decision processes (POMDPs) to model human decision making in a social context, focusing specifically on the volunteer's dilemma in a version of the classic Public Goods Game. We show that the POMDP model explains both the behavior of subjects as well as neural activity recorded using fMRI during the game. The decisions of subjects can be modeled across all trials using two interpretable parameters. Furthermore, the expected reward predicted by the model for each subject was correlated with the activation of brain areas related to reward expectation in social interactions. Our results suggest a probabilistic basis for human social decision making within the framework of expected reward maximization.
Year
Venue
Field
2016
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 29 (NIPS 2016)
Bayesian inference,Computer science,Partially observable Markov decision process,Markov decision process,Influence diagram,Artificial intelligence,Public goods game,Probabilistic logic,Social decision making,Reward-based selection,Machine learning
DocType
Volume
ISSN
Conference
29
1049-5258
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Koosha Khalvati131.79
Park, Seongmin A.211.37
Jean-Claude Dreher3306.86
Rajesh P. N. Rao41390203.31