A network of agents is considered whose decision processes are described by the quantum decision theory previously advanced by the authors. Decision making is done by evaluating the utility of alternatives, their attractiveness, and the available information, whose combinations form the probabilities to choose a given alternative. As a result of the interplay between these three contributions, the process of choice between several alternatives is multimodal. The agents interact by exchanging information, which can take two forms: (i) information that an agent can directly receive from another agent and (ii) information collectively created by the members of the society. The information field common to all agents tends to smooth out sharp variations in the temporal behaviour of the probabilities and can even remove them. For agents with short-term memory, the probabilities often tend to their limiting values through strong oscillations and, for a range of parameters, these oscillations last for ever, representing an ever lasting hesitation of the decision makers. Switching on the information field makes the amplitude of the oscillations smaller and even can halt the everlasting oscillations forcing the probabilities to converge to fixed limits. The dynamic disjunction effect is described.
Shared and structured inputs undermine collective random choice by reasoning AI agents
Research Center for Advanced Science and Technology, The University of Tokyo（东京大学先端科学技术研究中心）；Department of Aeronautics and Astronautics, School of Engineering, The University of Tokyo（东京大学工学部航空宇宙工学科）
Group size effects and collective misalignment in LLM multi-agent systems
Department of Mathematics, City St George’s University of London（伦敦城市圣乔治大学数学系）；IT University of Copenhagen（哥本哈根IT大学）；Pioneer Centre for AI, Copenhagen（哥本哈根先锋人工智能中心）；Departament de Física, Universitat Politècnica de Catalunya（加泰罗尼亚理工大学物理系）
Conformity Generates Collective Misalignment in AI Agents Societies
University of Konstanz（康斯坦茨大学）；Sony Computer Science Laboratories - Rome（索尼计算机科学实验室-罗马）；Sapienza University of Rome（罗马大学）；Max Planck Institute for Dynamics and Self-Organization（马克斯·普朗克动态与自组织研究所）；Institute for the Dynamics of Complex Systems, University of Gottingen（复杂系统动力学研究所，哥廷根大学）
When Is Collective Intelligence a Lottery? Multi-Agent Scaling Laws for Memetic Drift in LLMs
CBS–NTT Program in Physics of Intelligence（物理智能中心-NTT项目）；Center for Brain Science（脑科学中心）；Harvard University（哈佛大学）；Physics of Artificial Intelligence Group（人工智能物理研究组）；NTT Research, Inc.（NTT研究公司）
