  We present a novel anytime heuristic (ALMA), inspired by the human principle
of altruism, for solving the assignment problem. ALMA is decentralized,
completely uncoupled, and requires no communication between the participants.
We prove an upper bound on the convergence speed that is polynomial in the
desired number of resources and competing agents per resource; crucially, in
the realistic case where the aforementioned quantities are bounded
independently of the total number of agents/resources, the convergence time
remains constant as the total problem size increases.
  We have evaluated ALMA under three test cases: (i) an anti-coordination
scenario where agents with similar preferences compete over the same set of
actions, (ii) a resource allocation scenario in an urban environment, under a
constant-time constraint, and finally, (iii) an on-line matching scenario using
real passenger-taxi data. In all of the cases, ALMA was able to reach high
social welfare, while being orders of magnitude faster than the centralized,
optimal algorithm. The latter allows our algorithm to scale to realistic
scenarios with hundreds of thousands of agents, e.g., vehicle coordination in
urban environments.
