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A major goal in Algorithmic Game Theory is to justify equilibrium conceptsfrom an algorithmic and complexity perspective. One appealing approach is toidentify robust natural distributed algorithms that converge quickly to anequilibrium. This paper addresses a lack of robustness in existing convergenceresults for discrete forms of tatonnement, including the fact that it need notconverge when buyers have linear utility functions. This work achieves greaterrobustness by seeking approximate rather than exact convergence in large marketsettings.  More specifically, this paper shows that for Fisher markets with buyershaving CES utility functions, including linear utility functions, tatonnementwill converge quickly to an approximate equilibrium (i.e. at a linear rate),modulo a suitable large market assumption. The quality of the approximation isa function of the parameters of the large market assumption.

Balancing the Robustness and Convergence of Tatonnement