Bayesian learners can drive out no-regret learners despite logarithmic regret in stochastic markets, but no-regret is more robust; hybrids are proposed to combine strengths.
Amortized Analysis of Asynchronous Price Dynamics
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abstract
We extend a recently developed framework for analyzing asynchronous coordinate descent algorithms to show that an asynchronous version of tatonnement, a fundamental price dynamic widely studied in general equilibrium theory, converges toward a market equilibrium for Fisher markets with CES utilities or Leontief utilities, for which tatonnement is equivalent to coordinate descent.
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2025 1verdicts
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Markets with Heterogeneous Agents: Dynamics and Survival of Bayesian vs. No-Regret Learners
Bayesian learners can drive out no-regret learners despite logarithmic regret in stochastic markets, but no-regret is more robust; hybrids are proposed to combine strengths.