This is an interesting modeling and model-based analysis study, providing insights into the machinery of human social decision making and possibly its neural correlates. The paper is generally well written and combines advances that could be interesting to both experimental and modeling audience. However, some of its aspects (particularly interpretation of estimated parameters and fMRI analyses) should be improved for it to be acceptable to NIPS. More specific comments: - What is the meaning of groups of 5 individuals if computer generates actions of 19 others? More details about how these actions are generated would be helpful. What are the free parameters/their number for each model?

Minimax, as the name suggest, is a method in decision theory for minimizing the maximum loss. Alternatively, it can be thought of as maximizing the minimum gain, which is also know as Maximin. It all started from a two player zero-sum game theory, covering both the cases where players take alternate moves and those where they made simultaneous moves. It has also been extended to more complex games and to general decision making in the presence of uncertainty. In the above explanation, it has been mentioned that the minimax algorithms started off with the concept of zero-sum.

The past few months, I encountered one term again and again in the data science world: Markov Chain Monte Carlo. In my research lab, in podcasts, in articles, every time I heard the phrase I would nod and think that sounds pretty cool with only a vague idea of what anyone was talking about. Several times I tried to learn MCMC and Bayesian inference, but every time I started reading the books, I soon gave up. Exasperated, I turned to the best method to learn any new skill: apply it to a problem. Using some of my sleep data I had been meaning to explore and a hands-on application-based book (Bayesian Methods for Hackers, available free online), I finally learned Markov Chain Monte Carlo through a real-world project.