# A biologically Inspired Trust Model for Open Multi-Agent Systems that is Resilient to Rapid Performance Fluctuations

arXiv: https://arxiv.org/abs/2504.15301 · HTML: https://mlsanity.com/paper/2504.15301/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 17 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Multi-Perspective Trust Management Framework for Crowdsourced IoT Services](https://mlsanity.com/paper/2101.04244/) (2021)
- [Towards a Machine Learning-driven Trust Evaluation Model for Social Internet of Things: A Time-aware Approach](https://mlsanity.com/paper/2102.10998/) (2021)
- [Trust Computational Heuristic for Social Internet of Things: A Machine Learning-based Approach](https://mlsanity.com/paper/2102.10997/) (2021)
- [A biologically inspired computational trust model for open multi-agent systems which is resilient to trustor population changes](https://mlsanity.com/paper/2404.10014/) (2024)
- [A Matrix Factorization Model for Hellinger-based Trust Management in Social Internet of Things](https://mlsanity.com/paper/1909.12432/) (2019)
- [Using Deep Q-Learning to Dynamically Toggle between Push/Pull Actions in Computational Trust Mechanisms](https://mlsanity.com/paper/2404.18296/) (2024)

## Abstract (arXiv)

Trust management provides an alternative solution for securing open, dynamic, and distributed multi-agent systems, where conventional cryptographic methods prove to be impractical. However, existing trust models face challenges related to agent mobility, changing behaviors, and the cold start problem. To address these issues we introduced a biologically inspired trust model in which trustees assess their own capabilities and store trust data locally. This design improves mobility support, reduces communication overhead, resists disinformation, and preserves privacy. Despite these advantages, prior evaluations revealed limitations of our model in adapting to provider population changes and continuous performance fluctuations. This study proposes a novel algorithm, incorporating a self-classification mechanism for providers to detect performance drops potentially harmful for the service consumers. Simulation results demonstrate that the new algorithm outperforms its original version and FIRE, a well-known trust and reputation model, particularly in handling dynamic trustee behavior. While FIRE remains competitive under extreme environmental changes, the proposed algorithm demonstrates greater adaptability across various conditions. In contrast to existing trust modeling research, this study conducts a comprehensive evaluation of our model using widely recognized trust model criteria, assessing its resilience against common trust-related attacks while identifying strengths, weaknesses, and potential countermeasures. Finally, several key directions for future research are proposed.