Learn to Predict Equilibria via Fixed Point Networks. (arXiv:2106.00906v2 [cs.LG] UPDATED)

Systems of competing agents can often be modeled as games. Assuming
rationality, the most likely outcomes are given by an equilibrium, e.g. a Nash
equilibrium. In many practical settings, games are influenced by context, i.e.
additional data beyond the control of any agent (e.g. weather for traffic and
fiscal policy for market economies). Often only game equilibria are observed,
while the players' true cost functions are unknown. This work introduces Nash
Fixed Point Networks (N-FPNs), a class of implicit neural …

arxiv equilibria networks