activity
20172026
most citedMulti-Agent Systems for Computational Economics and Finance

4 citations · 7 across the 9 of their papers we have counts for

collaborators

16 papers

cs.GT2026

Constant Inapproximability for Fisher Markets

Argyrios Deligkas, John Fearnley, Alexandros Hollender +1

We study the problem of computing approximate market equilibria in Fisher markets with separable piecewise-linear concave (SPLC) utility functions. In this setting, the problem was…

cs.GT2026

Fisher Markets with Approximately Optimal Bundles and the Need for a PCP Theorem for PPAD

Argyrios Deligkas, John Fearnley, Alexandros Hollender +1

We study the problem of computing a competitive equilibrium with approximately optimal bundles in Fisher markets with separable piecewise-linear concave (SPLC) utility functions, m…

cs.GT2025

Online EFX Allocations with Predictions

Themistoklis Melissourgos, Nicos Protopapas

We study an online fair division problem where a fixed number of goods arrive sequentially and must be allocated to a given set of agents. Once a good arrives, its true value for e…

cs.CC2022

On the Smoothed Complexity of Combinatorial Local Search

Yiannis Giannakopoulos, Alexander Grosz, Themistoklis Melissourgos

We propose a unifying framework for smoothed analysis of combinatorial local optimization problems, and show how a diverse selection of problems within the complexity class PLS can…

cs.GT2022★ 4 cited

Multi-Agent Systems for Computational Economics and Finance

Michael Kampouridis, Panagiotis Kanellopoulos, Maria Kyropoulou +2

In this article we survey the main research topics of our group at the University of Essex. Our research interests lie at the intersection of theoretical computer science, artifici…

cs.GT2022

Tight Inapproximability for Graphical Games

Argyrios Deligkas, John Fearnley, Alexandros Hollender +1

We provide a complete characterization for the computational complexity of finding approximate equilibria in two-action graphical games. We consider the two most well-studied appro…