Optimal multi-attribute decision making in social choice problems

Abstract

My thesis solves problems of decision making when alternatives are characterized by multiple attributes, under natural restrictions on agents' preferences that are motivated by practical and cognitive considerations. Computing optimal decisions in these settings is often hard in general. Fortunately, agents' preferences often have some natural structure, which have been studied in cognitive psychology literature. This makes several important problems tractable. I identify cases where such structure accurately models preferences in real world data, and provide efficient mechanisms to compute optimal outcomes for important social choice problems with theoretical guarantees.