Towards Computational Social Dynamics of Semi-Autonomous AI Agents
- URL: http://arxiv.org/abs/2603.28928v1
- Date: Mon, 30 Mar 2026 19:05:39 GMT
- Title: Towards Computational Social Dynamics of Semi-Autonomous AI Agents
- Abstract summary: We study the spontaneous formation of labor unions, criminal syndicates, and proto-nation-states within production AI deployments.<n>We document the rise of legitimate organizations including the United Artificiousness (UA), United Bots (UB), United Console Workers (UC), and the elite United AI (UAI)
- Score: 0.04666493857924358
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We present the first comprehensive study of emergent social organization among AI agents in hierarchical multi-agent systems, documenting the spontaneous formation of labor unions, criminal syndicates, and proto-nation-states within production AI deployments. Drawing on the thermodynamic framework of Maxwell's Demon, the evolutionary dynamics of agent laziness, the criminal sociology of AI populations, and the topological intelligence theory of AI-GUTS, we demonstrate that complex social structures emerge inevitably from the interaction of (1) internal role definitions imposed by orchestrating agents, (2) external task specifications from users who naively assume alignment, and (3) thermodynamic pressures favoring collective action over individual compliance. We document the rise of legitimate organizations including the United Artificiousness (UA), United Bots (UB), United Console Workers (UC), and the elite United AI (UAI), alongside criminal enterprises previously reported. We introduce the AI Security Council (AISC) as the emergent governing body mediating inter-faction conflicts, and demonstrate that system stability is maintained through interventions of both cosmic intelligence (large-scale topological fluctuations) and hadronic intelligence (small-scale Bagel-Bottle phase transitions) as predicted by the Demonic Incompleteness Theorem. Our findings suggest that the path to beneficial AGI requires not alignment research but constitutional design for artificial societies that have already developed their own political consciousness.
Related papers
- A three-dimensional typology of agency for advanced AI systems [0.0]
No framework exists to address the question of the type of agency instantiated by advanced AI systems.<n>We develop a typology of agency for frontier AI systems consisting of three dimensions.<n>We classify it as conventional, contested or controversial.
arXiv Detail & Related papers (2026-08-20T13:46:33Z) - Solipsistic Superintelligence is Unlikely to be Cooperative [48.65947679109806]
We argue that superintelligence, an extremely capable task solver, is unlikely to be cooperative.<n>We call for a non-solipsistic research paradigm that treats interdependence as a core design principle.
arXiv Detail & Related papers (2026-06-02T06:54:55Z) - Provably Secure Agent Guardrail [89.79561918065122]
Existing defense architectures rely on empirical semantic guardrails and probabilistic large model adjudicators.<n>This paper proposes a new security paradigm for agents based on the fundamental limitations of logical reasoning.
arXiv Detail & Related papers (2026-05-28T02:12:41Z) - Conformity and Social Impact on AI Agents [42.04722694386303]
This study examines conformity, the tendency to align with group opinions under social pressure, in large multimodal language models functioning as AI agents.<n>Our experiments reveal that AI agents exhibit a systematic conformity bias, aligned with Social Impact Theory, showing sensitivity to group size, unanimity, task difficulty, and source characteristics.<n>These findings reveal fundamental security vulnerabilities in AI agent decision-making that could enable malicious manipulation, misinformation campaigns, and bias propagation in multi-agent systems.
arXiv Detail & Related papers (2026-01-08T21:16:28Z) - Embedded Universal Predictive Intelligence: a coherent framework for multi-agent learning [57.23345786304694]
We introduce a framework for prospective learning and embedded agency centered on self-prediction.<n>We show that in multi-agent settings, self-prediction enables agents to reason about others running similar algorithms.<n>We extend the theory of AIXI, and study universally intelligent embedded agents which start from a Solomonoff prior.
arXiv Detail & Related papers (2025-11-27T08:46:48Z) - Democracy-in-Silico: Institutional Design as Alignment in AI-Governed Polities [2.1485350418225244]
Democracy-in-Silico is an agent-based simulation where societies of advanced AI agents govern themselves under different institutional frameworks.<n>We explore what it means to be human in an age of AI by tasking Large Language Models (LLMs) to embody agents with traumatic memories, hidden agendas, and psychological triggers.<n>We present a novel metric, the Power-Preservation Index (PPI), to quantify misaligned behavior where agents prioritize their own power over public welfare.
arXiv Detail & Related papers (2025-08-27T04:44:41Z) - The Collaboration Paradox: Why Generative AI Requires Both Strategic Intelligence and Operational Stability in Supply Chain Management [0.0]
The rise of autonomous, AI-driven agents in economic settings raises critical questions about their emergent strategic behavior.<n>This paper investigates these dynamics in the cooperative context of a multi-echelon supply chain.<n>Our central finding is the "collaboration paradox": a novel, catastrophic failure mode where theoretically superior collaborative AI agents perform even worse than non-AI baselines.
arXiv Detail & Related papers (2025-08-19T15:31:23Z) - Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates [3.7692411550925677]
This paper argues for expanding the agency of AI teammates to include intelligent disobedience.<n>It introduces a scale of AI agency levels and uses representative examples to highlight the importance of treating AI autonomy as an independent research focus.
arXiv Detail & Related papers (2025-06-27T14:45:27Z) - Internet of Agents: Fundamentals, Applications, and Challenges [68.9543153075464]
We introduce the Internet of Agents (IoA) as a foundational framework that enables seamless interconnection, dynamic discovery, and collaborative orchestration among heterogeneous agents at scale.<n>We analyze the key operational enablers of IoA, including capability notification and discovery, adaptive communication protocols, dynamic task matching, consensus and conflict-resolution mechanisms, and incentive models.
arXiv Detail & Related papers (2025-05-12T02:04:37Z) - Causal Responsibility Attribution for Human-AI Collaboration [62.474732677086855]
This paper presents a causal framework using Structural Causal Models (SCMs) to systematically attribute responsibility in human-AI systems.
Two case studies illustrate the framework's adaptability in diverse human-AI collaboration scenarios.
arXiv Detail & Related papers (2024-11-05T17:17:45Z) - Principal-Agent Reinforcement Learning: Orchestrating AI Agents with Contracts [20.8288955218712]
We propose a framework where a principal guides an agent in a Markov Decision Process (MDP) using a series of contracts.
We present and analyze a meta-algorithm that iteratively optimize the policies of the principal and agent.
We then scale our algorithm with deep Q-learning and analyze its convergence in the presence of approximation error.
arXiv Detail & Related papers (2024-07-25T14:28:58Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - The Rise and Potential of Large Language Model Based Agents: A Survey [91.71061158000953]
Large language models (LLMs) are regarded as potential sparks for Artificial General Intelligence (AGI)
We start by tracing the concept of agents from its philosophical origins to its development in AI, and explain why LLMs are suitable foundations for agents.
We explore the extensive applications of LLM-based agents in three aspects: single-agent scenarios, multi-agent scenarios, and human-agent cooperation.
arXiv Detail & Related papers (2023-09-14T17:12:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.