Local Dynamics in Bargaining Networks via Random-Turn Games

Abstract. We present a new technique for analyzing the rate of convergence of local dynamics in bargaining networks. The technique reduces balancing in a bargaining network to optimal play in a randomturn game. We analyze this game using techniques from martingale and Markov chain theory. We obtain a tight polynomial bound on the rate of convergence for a nontrivial class of unweighted graphs (the previous known bound was exponential). Additionally, we show this technique extends naturally to many other graphs and dynamics.

• Type: Journal
• Year: 2010
• Where: WINE
• Authors: L. Elisa Celis, Nikhil R. Devanur, Yuval Peres
• Keywords: Bargaining Network, Convergence, Economy, Markov Chain Theory, WINE 2010