A randomized tabu search-based approach for perfect stranger matching in economic experiments

Experiments in the field of behavioral economics often require repeated matching of participants to groups over multiple periods. Perfect stranger matching requires that no two participants interact more than once during the experiment. Computing a sequence of perfect stranger matches is an NP-hard problem that has received little attention in experimental economics literature beyond brute-force approaches. This work provides a problem definition and an algorithm for perfect stranger matching that outperforms existing approaches in the field of experimental economics in terms of problem size and number of found matches.