Here we focus on the description of the mechanisms behind the process of information aggregation and decision making, a basic step to understand emergent phenomena in society, such as trends, information spreading or the wisdom of crowds. In many situations, agents choose between discrete options. We analyze experimental data on binary opinion choices in humans. The data consists of two separate experiments in which humans answer questions with a binary response, where one is correct and the other is incorrect. The questions are answered without and with information on the answers of some previous participants. We find that a Bayesian approach captures the probability of choosing one of the answers. The influence of peers is uncorrelated with the difficulty of the question. The data is inconsistent with Weber's law, which states that the probability of choosing an option depends on the proportion of previous answers choosing that option and not on the total number of those answers. Last, the present Bayesian model fits reasonably well to the data as compared to some other previously proposed functions although the latter sometime perform slightly better than the Bayesian model. The asset of the present model is the simplicity and mechanistic explanation of the behavior.
A collective opinion formation model under Bayesian updating and confirmation bias
我们提出了一个集体意见形成模型与所谓的确认偏见。确认偏误是一种心理效应，在意见形成的背景下，赞成某个意见的个人容易将新的信息误解为支持个人当前的信念。我们的模型修改了单个个体的贝叶斯决策模型[M.拉宾和J. L. Schrag，Q. J. Econ. 114，37（1999）]的情况下，相互作用的个体的良好混合的人口在没有外部输入。我们数值模拟的模型表明，所有的代理人最终同意的两个意见之一，只有当确认偏差是弱的。否则，随机种群动力学最终会产生不一致的配置（也称为极化），特别是对于大系统尺寸。强烈的确认偏差允许不同部分的个体最终形成不同的不同意配置，以支持相反的意见。
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）
Collective Adoption of Max-Min Strategy in an Information Cascade Voting Experiment
我们考虑一种情况，其中一个人必须选择一个乘数为m的选项。乘数与选择该选项的人数成反比，如果正确，则与回报成正比。如果一个人不知道正确的选择，我们称他为牧人，然后在牧人和其他设定乘数的人之间进行零和游戏。从期望收益最大化的角度来看，最大-最小策略是最优的，其中一个人的选择与m成反比。我们称最优放牧者为模拟放牧者。如果知情人选择的准确度q为1，则在热力学极限中，对于任何牧民比率值p<1，模拟牧民系统将正确选择的概率取为1。我们通过投票实验来研究牧民如何选择，其中50到60名受试者依次回答两个选择题测验。我们证明了当4/3 < m < 4时，牧民选择一个选择的概率与m成反比，并且在这个范围内，他们集体采取最大-最小策略。
Avalanches of choice: how stranger-to-stranger interactions shape crowd dynamics
Eindhoven University of Technology（埃因霍温理工大学）；University of Ferrara（费拉拉大学）；INFN Ferrara（意大利国家核物理研究所费拉拉分部）；Consiglio Nazionale delle Ricerche - Istituto per le Applicazioni del Calcolo（意大利国家研究委员会计算应用研究所）
