Search Results for author: Giacomo Como

Found 7 papers, 0 papers with code

Multipolar opinion evolution in biased networks

no code implementations6 Mar 2024 Luka Baković, David Ohlin, Giacomo Como, Emma Tegling

Motivated by empirical research on bias and opinion formation, we formulate a multidimensional nonlinear opinion-dynamical model where agents have individual biases, which are fixed, as well as opinions, which evolve.

A Consensus-Based Generalized Multi-Population Aggregative Game with Application to Charging Coordination of Electric Vehicles

no code implementations18 Oct 2023 Mahsa Ghavami, Babak Ghaffarzadeh Bakhshayesh, Mohammad Haeri, Giacomo Como, Hamed Kebriaei

In the upper layer, population coordinators collaborate for a distributed estimation of the coupling aggregate term in the agents' cost function and the associated Lagrange multiplier of the coupling constraint, transmitting the latest updated values to their population's agents.

Nash equilibria of the pay-as-bid auction with K-Lipschitz supply functions

no code implementations13 Jun 2023 Martina Vanelli, Giacomo Como, Fabio Fagnani

We model a system of n asymmetric firms selling a homogeneous good in a common market through a pay-as-bid auction.

Can Competition Outperform Collaboration? The Role of Misbehaving Agents

no code implementations4 Jul 2022 Luca Ballotta, Giacomo Como, Jeff S. Shamma, Luca Schenato

We investigate a novel approach to resilient distributed optimization with quadratic costs in a multi-agent system prone to unexpected events that make some agents misbehave.

Distributed Optimization

Equilibria in Network Constrained Energy Markets

no code implementations15 Jun 2022 Leonardo Massai, Giacomo Como, Fabio Fagnani

We study an energy market composed of producers who compete to supply energy to different markets and want to maximize their profits.

Competition-Based Resilience in Distributed Quadratic Optimization

no code implementations26 Mar 2022 Luca Ballotta, Giacomo Como, Jeff S. Shamma, Luca Schenato

This paper proposes a novel approach to resilient distributed optimization with quadratic costs in a networked control system (e. g., wireless sensor network, power grid, robotic team) prone to external attacks (e. g., hacking, power outage) that cause agents to misbehave.

Distributed Optimization

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