Mann Richard P, Garnett Roman
Professorship of Computational Social Science, ETH Zurich, Zurich, Switzerland Department of Mathematics, Uppsala University, Uppsala, Sweden
Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO, USA.
J R Soc Interface. 2015 May 6;12(106). doi: 10.1098/rsif.2015.0037.
We identify a unique viewpoint on the collective behaviour of intelligent agents. We first develop a highly general abstract model for the possible future lives these agents may encounter as a result of their decisions. In the context of these possibilities, we show that the causal entropic principle, whereby agents follow behavioural rules that maximize their entropy over all paths through the future, predicts many of the observed features of social interactions among both human and animal groups. Our results indicate that agents are often able to maximize their future path entropy by remaining cohesive as a group and that this cohesion leads to collectively intelligent outcomes that depend strongly on the distribution of the number of possible future paths. We derive social interaction rules that are consistent with maximum entropy group behaviour for both discrete and continuous decision spaces. Our analysis further predicts that social interactions are likely to be fundamentally based on Weber's law of response to proportional stimuli, supporting many studies that find a neurological basis for this stimulus-response mechanism and providing a novel basis for the common assumption of linearly additive 'social forces' in simulation studies of collective behaviour.
我们确定了关于智能主体集体行为的独特观点。我们首先为这些主体因其决策可能遭遇的未来生活构建了一个高度通用的抽象模型。在这些可能性的背景下，我们表明因果熵原理，即主体遵循行为规则以使它们在未来所有路径上的熵最大化，预测了人类和动物群体中许多观察到的社会互动特征。我们的结果表明，主体通常能够通过作为一个群体保持凝聚力来最大化其未来路径熵，并且这种凝聚力会导致集体智能结果，而这强烈依赖于可能的未来路径数量的分布。我们推导了与离散和连续决策空间中最大熵群体行为相一致的社会互动规则。我们的分析进一步预测，社会互动可能从根本上基于韦伯对比例刺激的反应定律，这支持了许多发现这种刺激 - 反应机制存在神经学基础的研究，并为集体行为模拟研究中线性加性“社会力”这一常见假设提供了新的依据。
