An $Ξ±$-regret analysis of Adversarial Bilateral Trade
October 13, 2022 Β· Declared Dead Β· π Neural Information Processing Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yossi Azar, Amos Fiat, Federico Fusco
arXiv ID
2210.06846
Category
cs.GT: Game Theory
Cross-listed
cs.DS,
cs.LG
Citations
19
Venue
Neural Information Processing Systems
Last Checked
6 months ago
Abstract
We study sequential bilateral trade where sellers and buyers valuations are completely arbitrary (i.e., determined by an adversary). Sellers and buyers are strategic agents with private valuations for the good and the goal is to design a mechanism that maximizes efficiency (or gain from trade) while being incentive compatible, individually rational and budget balanced. In this paper we consider gain from trade which is harder to approximate than social welfare. We consider a variety of feedback scenarios and distinguish the cases where the mechanism posts one price and when it can post different prices for buyer and seller. We show several surprising results about the separation between the different scenarios. In particular we show that (a) it is impossible to achieve sublinear $Ξ±$-regret for any $Ξ±<2$, (b) but with full feedback sublinear $2$-regret is achievable (c) with a single price and partial feedback one cannot get sublinear $Ξ±$ regret for any constant $Ξ±$ (d) nevertheless, posting two prices even with one-bit feedback achieves sublinear $2$-regret, and (e) there is a provable separation in the $2$-regret bounds between full and partial feedback.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Game Theory
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
A Motivational Game-Theoretic Approach for Peer-to-Peer Energy Trading in the Smart Grid
R.I.P.
π»
Ghosted
Computing Resource Allocation in Three-Tier IoT Fog Networks: a Joint Optimization Approach Combining Stackelberg Game and Matching
R.I.P.
π»
Ghosted
Fast Convergence of Regularized Learning in Games
R.I.P.
π»
Ghosted
Computation Peer Offloading for Energy-Constrained Mobile Edge Computing in Small-Cell Networks
R.I.P.
π»
Ghosted
Blockchain Mining Games
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted