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Research map / Learning theory and algorithms / Submodular and game algorithms

The Limits of Optimal Pricing in the Dark

NeurIPS 2021 · Learning theory and algorithms › Submodular and game algorithms · approach: Learned auction design · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Commit to the optimal imitative value function under linear pricingPADD pricing game · General production costsseller revenueMixed
Use convex differentiable production costsPADD equilibrium · Convex production costsseller revenueHelped
Use concave production costsPADD equilibrium · Concave production costsseller revenue: 0Hurt
Use concave production costsPADD equilibrium · Concave production costsbuyer surplusHelped
Expand the seller's pricing class from linear to concave pricingPADD pricing game · General production costsseller revenueNo clear effect
Expand from linear pricing to an intermediate richer pricing classPADD pricing game · Single-good example with c(x) = x^2seller revenueHurt
Expand from linear pricing to an intermediate richer pricing classPADD pricing game · Single-good example with c(x) = x^2buyer surplusHelped
Imitate the seller's cost functionBuyer manipulation in PADD · Examples discussed in the paperbuyer surplusHurt

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