During previous pandemics, social distancing was organized top-down, through the imposition of a minimum distance. An alternative approach toward social distancing asks individuals to try to maximize their distance to others. Here, we ask whether people can thus efficiently self-organize spatial arrangements. We studied 953 social distancing decisions made in 150 groups under controlled conditions. Results show that subject behavior approximates what optimal mathematical strategies achieve. At scale, the observed behavior produces greater distancing than the mere respecting of an externally imposed minimum distance. These findings suggest that the encouragement of maximal distancing may reduce the propagation of viruses that spread through close-range contact beyond what is achieved with minimum-distance policies alone.

We consider importance sampling simulation for estimating the probability of reaching large total number of customers in an (Formula presented.) tandem queue, during a busy cycle of the system. Our main result is a procedure for obtaining a family of asymptotically efficient changes of measure based on subsolutions. We explicitly show these families for two-node tandem queues and we find that there exist more asymptotically efficient changes of measure based on subsolutions than currently available in literature.