Fedor Sandomirskiy

Working papers

Feasible joint posterior beliefs (with Itai Arieli, Yakov Babichenko, and Omer Tamuz)

EC2020 Best Paper Award

February 2020, arXiv:2002.11362

slides (Caltech, December 2019)

We study the set of possible joint posterior belief distributions of a group of agents who share a common prior regarding a binary state and who observe some information structure. Our main result is that, for the two-agent case, a quantitative version of Aumann's Agreement Theorem provides a necessary and sufficient condition for feasibility. We use our characterization to construct joint belief distributions in which agents are informed regarding the state, and yet receive no information regarding the other's posterior. We also study a related class of Bayesian persuasion problems with a single sender and multiple receivers, and explore the extreme points of the set of feasible distributions.

Can society learn without opinion leaders? (with Itai Arieli and Rann Smorodinsky)

June 2020

slides, video (CMID20, June 2020)

We are interested in the existence of social-network topologies that aggregate information well even if 95% of agents are inactive and do not take part in the learning process. Agents are Bayesian-rational, they take irrevocable decisions as in models of herding, and the arrival order is random. We derive new sufficient conditions for good information aggregation in terms of the topological properties of the network and find connections to the theory of expander graphs.

A polynomial-time algorithm for computing a Pareto optimal and almost proportional allocation (a short note with Haris Aziz and Herve Moulin)

R&R in Operations Research Letters

September 2019, arXiv:1909.00740

In this note, we show that Pareto-optimal and almost-fair (Proportional up to 1 item) allocations of a mixture of indivisible goods and bads always exist and can be computed in strongly-polynomial time.

The technique is based on the trading-cycle algorithm for finding divisible Pareto-improvements from the recent working paper "Fair division with minimal sharing" with Erel Segal-Halevi and on an extension of Barman-Krishnamurthy rounding.

Fair Division with minimal sharing (with Erel Segal-Halevi)

August 2019, arXiv:1908.01669

slides (De Aequa Divisione workshop on Fair Division, LUISS, Rome, May 2019)

Siblings inherited several apartments would not be satisfied by an allocation giving them an apartment with probability 50% or envy-free up to one apartment. We suggest a new approach to fair division with valuable items, which bridges the modern "divisible" and "indivisible" literature: sharing minimization. The problem of sharing-minimization among fair Pareto-optimal allocations turns out to be algorithmically-tractable for almost all instances.

Algorithms for Competitive Division of Chores (with Simina Branzei)

R&R in Mathematics of Operations Research

July 2019, arXiv:1907.01766

slides (Algorithms Seminar, TAU, March 2019)

This is the first explicit algorithm for computing market equilibria of "non-convex" exchange economies that have disconnected equilibrium set. We avoid the "black-box" of Cell-Enumeration technique, used in the literature, by the novel approach based on enumeration of all the faces of the Pareto frontier via a simple 2-agent reduction.

The results are applied to approximately fair division of indivisible chores.

Protecting the Protected Group: Circumventing Harmful Fairness (with Omer Ben-Porat and Moshe Tennenholtz)

May 2019, arXiv:1905.10546

Fairness constraints used for Fair Machine Learning may harm the protected group because the "price of fairness" is reallocated to this group by self-interested decision makers (e.g., a bank giving loans).

A simple online fair division problem (with Herve Moulin and Anna Bogomolnaia)

R&R in Management Science

March 2019, arXiv:1903.10361

slides with a different set of results including exact Price of Fairness for general bargaining (CompEcon seminar at HUJI, May 2019)

We propose a new family of allocation rules that, using minimal statistical information about the environment, achieve fairness and welfare guarantees. These are the first results on dynamic resource allocation that combine both fairness and efficiency design objectives.