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Dive into the research topics where Igor Rochlin is active.

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Featured researches published by Igor Rochlin.


adaptive agents and multi agents systems | 2014

Efficiency and fairness in team search with self-interested agents

Igor Rochlin; Yonatan Aumann; David Sarne; Luba Golosman

We consider team-work settings where individual agents incur costs on behalf of the team. In such settings it is frequently the custom to reimburse agents for the costs they incur (at least in part) in order to promote fairness. We show, however, that when agents are self-interested, such reimbursement can result in degradation in efficiency—at times severe degradation. We thus study the relationship between efficiency and fairness in such settings, distinguishing between ex-ante and ex-post fairness. First, we analyze reimbursement policies that reimburse solely based on purchase receipts (as is customary), and show that with such policies the degradation in both efficiency and fairness can be unbounded. We thus introduce two other families of reimbursement policies. The first family guarantees optimal efficiency and ex-ante fairness, but not ex-post fairness. The second family improves (at times) on ex-post fairness, but at the expense of efficiency, thus providing a tradeoff between the two.


web intelligence | 2012

Join Me with the Weakest Partner, Please

Moshe Mash; Igor Rochlin; David Sarne

This paper considers the problem of self-interested agents engaged in costly exploration when individual findings benefit all agents. The purpose of the exploration is to reason about the nature and value of the different opportunities available to the agents whenever such information is a priori unknown. While the problem has been considered for the case where the goal is to maximize the overall expected benefit, the focus of this paper is on settings where the agents are self-interested, i.e, each agents goal is to maximize its individual expected benefit. The paper presents an equilibrium analysis of the model, considering both mixed and pure equilibria. The analysis is used to demonstrate two somehow non-intuitive properties of the equilibrium cooperative exploration strategies used by the agents and their resulting expected payoffs: (a) when using mixed equilibrium strategies, the agents might lose due to having more potential opportunities available for them in their environment, and (b) if the agents can have additional agents join them in the exploration they might prefer the less competent ones to join the process.


international joint conference on artificial intelligence | 2017

Contest Design with Uncertain Performance and Costly Participation.

Priel Levy; David Sarne; Igor Rochlin

This paper studies the problem of designing contests for settings where a principal seeks to optimize the quality of the best contribution obtained, and potential contestants only strategize about whether to participate in the contest, as participation incurs some cost. This type of contest can be mapped to various real-life settings (e.g., selection of background actors based on headshots, photography contest). The paper provides a comparative game-theoretic based solution to two variants of the above underlying model: parallel and sequential contest, enabling a characterization of the equilibrium strategies in each. Special emphasis is placed on the case where the contestants are a priori homogeneous which is often the case in contests where ranking is mostly influenced by some probabilistic factors (e.g., luck) or whenever contestants are evaluated subjectively by a referee whose taste cannot be a priori predicted. Here, several (somehow counter-intuitive) properties of the equilibrium are proved, in particular for the sequential contest, leading to a comprehensive characterization of the principal’s preference between the two.


Artificial Intelligence | 2014

Joint search with self-interested agents and the failure of cooperation enhancers

Igor Rochlin; David Sarne; Moshe Mash


national conference on artificial intelligence | 2013

Information sharing under costly communication in joint exploration

Igor Rochlin; David Sarne


european conference on artificial intelligence | 2012

Coordinated exploration with a shared goal in costly environments

Igor Rochlin; David Sarne; Moshe Laifenfeld


web intelligence | 2011

Sequential Multilateral Search for a Common Goal

Igor Rochlin; David Sarne; Gil Zussman


Web Intelligence and Agent Systems: An International Journal | 2013

Sequential multi-agent exploration for a common goal

Igor Rochlin; David Sarne; Gil Zussman


adaptive agents and multi agents systems | 2014

Constraining information sharing to improve cooperative information gathering

Igor Rochlin; David Sarne


Multiagent and Grid Systems | 2014

Utilizing costly coordination in multi-agent joint exploration

Igor Rochlin; David Sarne

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