21.12.2021

Three papers accepted at AAMAS

The International Conference on Autonomous Agents and Multiagent Systems (AAMAS) is the most influential venue for research on behavior that emerges from the interaction between multiple agents. Authors of the Algorithm Engineering group contribute three papers to the 2022 edition, which will be held completely virtually on May 9-13, hosted by the University of Auckland. The first two papers both originate from Jannik Peters' master thesis on the complexity of popular matchings, which he wrote under the supervision of Ágnes Cseh. The third paper is the result of a collaboration within our research unit on Algorithms, Dynamics, and Information Flow in Networks with colleagues at Hamburg University, Goethe University Frankfurt, and the University of Bremen. It discusses the convergence time of opinion dynamics on social networks with asynchronous agent activations.

Clean Citation Style 005

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Cseh, Ágnes; Peters, Jannik Three-Dimensional Popular Matching with Cyclic PreferencesAutonomous Agents and Multi-Agent Systems (AAMAS) 2022: 77–87

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Cseh, Ágnes; Friedrich, Tobias; Peters, Jannik Pareto Optimal and Popular House Allocation with Lower and Upper QuotasAutonomous Agents and Multi-Agent Systems (AAMAS) 2022: 300–308

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Berenbrink, Petra; Hoefer, Martin; Kaaser, Dominik; Lenzner, Pascal; Rau, Malin; Schmand, Daniel Asynchronous Opinion Dynamics in Social NetworksAutonomous Agents and Multi-Agent Systems (AAMAS) 2022: 109–117

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@inproceedings{berenbrink2022asynchronous,
  abstract = {Opinion spreading in a society decides the fate of elections, the success of products, and the impact of political or social movements. The model by Hegselmann and Krause is a well-known theoretical model to study such opinion formation processes in social networks. In contrast to many other theoretical models, it does not converge towards a situation where all agents agree on the same opinion. Instead, it assumes that people find an opinion reasonable if and only if it is close to their own. The system converges towards a stable situation where agents sharing the same opinion form a cluster, and agents in different clusters do not influence each other. We focus on the social variant of the Hegselmann-Krause model where agents are connected by a social network and their opinions evolve in an iterative process. When activated, an agent adopts the average of the opinions of its neighbors having a similar opinion. By this, the set of influencing neighbors of an agent may change over time. To the best of our knowledge, social Hegselmann-Krause systems with asynchronous opinion updates have only been studied with the complete graph as social network. We show that such opinion dynamics with random agent activation are guaranteed to converge for any social network. We provide an upper bound of \(\mathcal{O}(n |E|^2 (\varepsilon/\delta)^2)\) on the expected number of opinion updates until convergence, where |E| is the number of edges of the social network. For the complete social network we show a bound of \( \mathcal{O}(n^3(n^2 + (\varepsilon/\delta)^2)) \) that represents a major improvement over the previously best upper bound of \( \mathcal{O}(n^9(\varepsilon/\delta)^2) \). Our bounds are complemented by simulations that indicate asymptotically matching lower bounds.},
  author = {Berenbrink, Petra and Hoefer, Martin and Kaaser, Dominik and Lenzner, Pascal and Rau, Malin and Schmand, Daniel},
  booktitle = {Autonomous Agents and Multi-Agent Systems (AAMAS)},
  keywords = {aamas year2022 pascallenzner},
  pages = {109&ndash;117},
  title = {Asynchronous Opinion Dynamics in Social Networks},
  year = 2022
}