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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.20250 |
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| _version_ | 1866916866986868736 |
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| author | Zhong, Tianyi Angeli, David |
| author_facet | Zhong, Tianyi Angeli, David |
| contents | Enhancing resilience in multi-agent systems in the face of selfish agents is an important problem that requires further characterisation. This work develops a truthful mechanism that avoids self-interested and strategic agents maliciously manipulating the algorithm. We prove theoretically that the proposed mechanism incentivises self-interested agents to participate and follow the provided algorithm faithfully. Additionally, the mechanism is compatible with any distributed optimisation algorithm that can calculate at least one subgradient at a given point. Finally, we present an illustrative example that shows the effectiveness of the mechanism. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20250 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Truthful Mechanism Design for Distributed Optimisation Algorithms in Networks with Self-interested Agents Zhong, Tianyi Angeli, David Systems and Control Enhancing resilience in multi-agent systems in the face of selfish agents is an important problem that requires further characterisation. This work develops a truthful mechanism that avoids self-interested and strategic agents maliciously manipulating the algorithm. We prove theoretically that the proposed mechanism incentivises self-interested agents to participate and follow the provided algorithm faithfully. Additionally, the mechanism is compatible with any distributed optimisation algorithm that can calculate at least one subgradient at a given point. Finally, we present an illustrative example that shows the effectiveness of the mechanism. |
| title | A Truthful Mechanism Design for Distributed Optimisation Algorithms in Networks with Self-interested Agents |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.20250 |