Working Paper #2014-036
Epsilon-stability and the speed of learning in network games
This paper introduces epsilon-stability as a generalization of the
concept of stochastic stability in learning and evolutionary game
dynamics. An outcome of a model of stochastic evolutionary dynamics is
said to be epsilon-stable in the long-run if for a given model of
mistakes it maximizes its invariant distribution. We construct an
efficient algorithm for computing epsilon-stable outcomes and provide
conditions under which epsilon-stability can be approximated by
stochastic stability. We also define and provide tighter bounds for
contagion rate and metastability as measures for characterizing the
short-run and medium-run behaviour of a typical stochastic evolutionary
model.
- Keywords
-
- Stochastic evolution
- network games
- epsilon-stable sets
- expected waiting time
- metastability
- contagion rate
- JEL codes
-
- C73
- D80
Cite this as
APA 7th ed.Azomahou, T. T., & Opolot, D. (2014). Epsilon-stability and the speed of learning in network games (UNU-MERIT Working Paper No. 2014-036). UNU-MERIT. https://unu-merit.nl/publications/working-papers/abstract/?id=5425