# The Problem of Algorithmic Collisions: Mitigating Unforeseen Risks in a Connected World

arXiv: https://arxiv.org/abs/2505.20181 · HTML: https://mlsanity.com/paper/2505.20181/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 10 of 10)

- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024)
- [Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents](https://mlsanity.com/paper/2505.02077/) (2025)
- [Escalation Risks from Language Models in Military and Diplomatic Decision-Making](https://mlsanity.com/paper/2401.03408/) (2024)
- [Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Media](https://mlsanity.com/paper/2305.16941/) (2023)
- [The AI Agent Index](https://mlsanity.com/paper/2502.01635/) (2025)
- [Formalising Human-in-the-Loop: Computational Reductions, Failure Modes, and Legal-Moral Responsibility](https://mlsanity.com/paper/2505.10426/) (2025)
- [An FDA for AI? Pitfalls and Plausibility of Approval Regulation for Frontier Artificial Intelligence](https://mlsanity.com/paper/2408.00821/) (2024)
- [Integrators at War: Mediating in AI-assisted Resort-to-Force Decisions](https://mlsanity.com/paper/2501.06861/) (2025)
- [Pay Attention: a Call to Regulate the Attention Market and Prevent Algorithmic Emotional Governance](https://mlsanity.com/paper/2402.16670/) (2024)

## Built on by (most cited first, 2 of 2)

- [Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks](https://mlsanity.com/paper/2508.06411/) (2025)
- [Reframing AI Loss of Control: What Control Is, How to Have It, How to Lose It](https://mlsanity.com/paper/2606.12442/) (2026)

## Abstract (arXiv)

The increasing deployment of Artificial Intelligence (AI) and other autonomous algorithmic systems presents the world with new systemic risks. While focus often lies on the function of individual algorithms, a critical and underestimated danger arises from their interactions, particularly when algorithmic systems operate without awareness of each other, or when those deploying them are unaware of the full algorithmic ecosystem deployment is occurring in. These interactions can lead to unforeseen, rapidly escalating negative outcomes - from market crashes and energy supply disruptions to potential physical accidents and erosion of public trust - often exceeding the human capacity for effective monitoring and the legal capacities for proper intervention. Current governance frameworks are inadequate as they lack visibility into this complex ecosystem of interactions. This paper outlines the nature of this challenge and proposes some initial policy suggestions centered on increasing transparency and accountability through phased system registration, a licensing framework for deployment, and enhanced monitoring capabilities.