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

- We formally define the perfect stranger matching problem (PSM) for experiments.
- We propose a heuristic for maximizing the length of matching sequences.
- Solution is provided in the form of matching tables, pseudo, and source code.
- Run time comparisons with existing approaches are provided.
- The solution contributes by tackling an increased problem size and number of matches.

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.