Matching Markets meet Cumulative Prospect Theory: Towards Optimal and Adversarially Robust Learning (opens in new tab)

We study a multi-agent multi-armed bandit problem in the competitive setup with two-sided matching markets under a human centric decision making model. To capture human preferences, we use cumulative prospect theory (CPT) that weighs the actions of the agent in a nonlinear fashion using a ($\alpha$-H\"older continuous) weight function. CPT has been widely used in behavioral economics and risk sensitive machine learning to emulate human prefere...