DrAris Filos-Ratsikas

My research lies at the intersection of theoretical computer science, artificial intelligence, and economics. I study the foundations of collective decision-making, resource allocation, and other fundamental economic mechanisms: how can we make good decisions when people have different preferences, competing interests, and limited information? Drawing on algorithms, computational complexity, and game theory, I explore both the design of procedures with desirable guarantees and the fundamental limits of what they can achieve.

One strand of my research examines the computational foundations of economic problems. Mathematical arguments often guarantee that an equilibrium or a fair allocation exists, but finding such an outcome can be a much harder task. I investigate what makes these problems computationally difficult, when efficient algorithms are possible, and how their complexity connects to the mathematical principles underlying their existence. These questions arise across games, auctions, and competitive markets, where computation is essential to understanding how participants interact and which outcomes we can expect.

A second strand focuses on fair division: how should we distribute resources among people who value them differently? Fairness can take several forms, and its requirements become particularly challenging when resources cannot be divided arbitrarily. I study how to translate intuitive ideas of fairness into precise mathematical guarantees, when these guarantees can be satisfied, and how to design algorithms that achieve them. More broadly, I am interested in how fairness interacts with efficiency and with the information available about participants’ preferences.

A third strand concerns collective decisions made with limited or imperfect information. Voting rules and allocation procedures often rely on simplified expressions of preferences, such as rankings, which reveal what people prefer but not how strongly they feel. I study how this loss of information affects the quality of decisions, and how to design procedures that achieve strong guarantees without requiring participants to provide extensive information. This also leads to questions about how to use additional information or predictions effectively while remaining robust to errors and uncertainty.

I am also quite interested in the theoretical foundations of blockchain systems. In particular, I am currently working on the design of stablecoin mechanisms, aiming to understand the theoretical (im)possibilities of stablecoin design.