The Warwick Anuual Retreat Projects are back! The Warwick Anuual Retreat Projects, or WARPS as the cool crowd calls them, are study group style projects that we will work on during the retreat. The aim of these projects is to foster a collaborative atmosphere within the department, particularly between year groups. This format was debuted last year, and we hope that this year it will be as enjoyable and informative as the previous one.
If you wish to propose a project then please add a brief description of the problem you want to tackle, as a topic in the forum below. We hope that we can start a discussion on these problems before the retreat starts. Hence, others can express their interest and give comments on the projects. The deadline for project proposals is 7th April 2017.
If you like any of the proposed projects and want to join the group working on it, please specify your preference below, so we can allocate the project groups before the retreat starts. If you hate all of the projects below, then please propose one you like! This time we want to allocate projects before the start of the retreat so we allow for more time to work on the projects.
Devising Strategies for multiple iterations of Newcomb's problem
Newcomb's Problem is an interesting problem that has caused great controversy in the field of decision theory. In Newcomb's Problem there are two boxes and two agents. One box (A) contains always contains a small amount of money (utility) while the other (B) contains either a large amount of money or nothing. Each round of the game, one agent, the selector, can either choose to take the contents of both boxes or just box B. Prior to each choice however, the other agent, the predictor, attempts to predict the choice of the selector. A prediction of both boxes means that nothing is placed in box B while a prediction of choosing a single box means the large amount is placed in box B instead.
The aim of the selector is to acquire as much money as possible, while that of the predictor is to simply most accurately predict the choices of the selector. Thus this game provides an interesting case for applied game theory. This is because understanding the game is very easy, but identifying the optimal strategies for the selector and the predictor across a number of games is nontrivial. This project would like benefit from reinforcement learning, and thus it is possible that this WARP could be associated or integrated with the reinforcement learning WARP.