Convergence of Learning Dynamics in Information Retrieval Games

Ben-Porat, Omer; Rosenberg, Itay; Tennenholtz, Moshe

doi:10.48550/arxiv.1806.05359

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nevertheless, they neither a) provide a systematic approach for doing so under PNE profiles; nor b) show that a PNE always exists. Recently, Ben-Porat, Rosenberg, and Tennenholtz (2018) claim in favor of no intervention in a similar model, and show that no intervention policy leads to convergence of any better-response dynamics; thus, a PNE is guaranteed to exist. Importantly, Ben-Basat, Tennenholtz, and Kurland (2017) as well as Ben-Porat, Rosenberg, and Tennenholtz (2018) do not consider the task of mediator design for better outcomes.…”

Section: Related Workmentioning

confidence: 99%

“…The work of Ben-Porat and Tennenholtz (2018a) is the first to consider mediator design in recommendation systems with strategic content providers, in a mathematical model that extends that of Ben-Basat, Tennenholtz, and Kurland (2017) and Ben-Porat, Rosenberg, and Tennenholtz (2018). They highlight several fairness-related properties that a mediator should arguably satisfy, along with the requirement of PNE existence.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

From Recommendation Systems to Facility Location Games

Ben-Porat

Goren

Rosenberg

et al. 2019

AAAI

Self Cite

View full text Add to dashboard Cite

Recommendation systems are extremely popular tools for matching users and contents. However, when content providers are strategic, the basic principle of matching users to the closest content, where both users and contents are modeled as points in some semantic space, may yield low social welfare. This is due to the fact that content providers are strategic and optimize their offered content to be recommended to as many users as possible. Motivated by modern applications, we propose the widely studied framework of facility location games to study recommendation systems with strategic content providers. Our conceptual contribution is the introduction of a mediator to facility location models, in the pursuit of better social welfare. We aim at designing mediators that a) induce a game with high social welfare in equilibrium, and b) intervene as little as possible. In service of the latter, we introduce the notion of intervention cost, which quantifies how much damage a mediator may cause to the social welfare when an off-equilibrium profile is adopted. As a case study in high-welfare low-intervention mediator design, we consider the one-dimensional segment as the user domain. We propose a mediator that implements the socially optimal strategy profile as the unique equilibrium profile, and show a tight bound on its intervention cost. Ultimately, we consider some extensions, and highlight open questions for the general agenda.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%