no code implementations • 29 Jun 2023 • Aggelos Kiayias, Philip Lazos, Jan Christoph Schlegel
Cryptocurrencies come with a variety of tokenomic policies as well as aspirations of desirable monetary characteristics that have been described by proponents as 'sound money' or even 'ultra sound money.'
no code implementations • 17 Sep 2021 • Georgios Amanatidis, Georgios Birmpas, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser
For Round-Robin we show that all of its pure Nash equilibria induce allocations that are EF1 with respect to the underlying true values, while for the algorithm of Plaut and Roughgarden we show that the corresponding allocations not only are EFX but also satisfy maximin share fairness, something that is not true for this algorithm in the non-strategic setting!
no code implementations • 4 Mar 2021 • Aris Filos-Ratsikas, Yiannis Giannakopoulos, Alexandros Hollender, Philip Lazos, Diogo Poças
We consider the problem of computing a (pure) Bayes-Nash equilibrium in the first-price auction with continuous value distributions and discrete bidding space.
Computer Science and Game Theory Computational Complexity
no code implementations • 16 Feb 2021 • Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Alberto Marchetti Spaccamela, Rebecca Reiffenhäuser
Submodular maximization is a classic algorithmic problem with multiple applications in data mining and machine learning; there, the growing need to deal with massive instances motivates the design of algorithms balancing the quality of the solution with applicability.
no code implementations • 15 Feb 2021 • Jonathan Katz, Philip Lazos, Francisco J. Marmolejo-Cossío, Xinyu Zhou
"Pay-per-last-$N$-shares" (PPLNS) is one of the most common payout strategies used by mining pools in Proof-of-Work (PoW) cryptocurrencies.
Fairness Computer Science and Game Theory Cryptography and Security
no code implementations • NeurIPS 2020 • Georgios Amanatidis, Federico Fusco, Philip Lazos, Stefano Leonardi, Rebecca Reiffenhäuser
Constrained submodular maximization problems encompass a wide variety of applications, including personalized recommendation, team formation, and revenue maximization via viral marketing.