My office at the Technion:
423, Cooper building, dept. IE&M
Hello, and welcome to my homepage!
I am Fedor Sandomirskiy, a postdoctoral researcher at Technion Game Theory group and a member of Mechanism Design for Data Science group. I am also affiliated with the Game Theory Lab at HSE in St.Petersburg, which I was heading prior to joining Technion.
In Spring 2021, I am joining Caltech HSS group as a postdoc.
My research is on game theory and its applications to economic design.
I am especially interested in
I enjoy problems whose practical applications required deep math. On the economic side, I am using both classical microeconomic approach together with the novel ideas of algorithmic game theory, and, on the math side, I like the interplay of convex analysis, probability, and functional analysis.
My research statement tells more about my research and CV about my experience. Check also my papers, slides, and talks.
News:
Our paper has got the EC2020 Best Paper Award among approximately 500 submissions!
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 population of voters must elect representatives among themselves to decide on a sequence of possibly unforeseen binary issues. Voters care only about the final decision, not the elected representatives. While an issue-by-issue vote by all voters would maximize social welfare, we are interested in how well the preferences of the population can be approximated by a small committee.
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.
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 a 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.
Siblings who inherited several apartments would not be satisfied by an allocation resulting in an apartment with 50% probability or envy-free up to one apartment. We suggest a new approach to fair division of 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.
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.
May 2019: Tutorial
"Appeal and challenges of competitive approach to fair resource allocation"
Vasilis Gkatzelis and I created a tutorial at the “De Aequa Divisione” workshop at LUISS (Rome). Slides are available on the web-page of the workshop.
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).
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.