We study a model of binary decision making when a certain population of agents is initially seeded with two different opinions, `$+$' and `$-$', with fractions $p_1$ and $p_2$ respectively, $p_1+p_2=1$. Individuals can reverse their initial opinion only once based on this information exchange. We study this model on a completely connected network, where any pair of agents can exchange information, and a two-dimensional square lattice with periodic boundary conditions, where information exchange is possible only between the nearest neighbors. We propose a model in which each agent maintains two counters of opposite opinions and accepts opinions of other agents with a power law bias until a threshold is reached, when they fix their final opinion. Our model is inspired by the study of negativity bias and positive-negative asymmetry known in the psychology literature for a long time. Our model can achieve stable intermediate mix of positive and negative opinions in a population. In particular, we show that it is possible to achieve close to any fraction $p_3, 0\leq p_3\leq 1$, of `$-$' opinion starting from an initial fraction $p_1$ of `$-$' opinion by applying a bias through adjusting the power law exponent of $p_3$.
Kinetic exchange opinion model: solution in the single parameter map limit
我们研究了最近提出的动力学交换意见模型（Lallouache et.例如，Rev E 82：056112，2010）在单个参数图的限制中。虽然它不包括多智能体版本的本质上复杂的行为，但它为我们提供了关于系统序参量的选择以及其他一些动力学性质的见解。我们还研究了广义双参数模型，并给出了精确的相图.沿着这个相边界的适当定义的序参量的普遍行为。
Confirmation bias and peer pressure both have substantial impacts on the formation of collective decisions. Nevertheless, few attempts have been made to study how the interplay between these two mechanisms affects public opinion evolution. Here we propose an agent-based model of opinion dynamics which incorporates the conjugate effect of confirmation bias (characterized by the population identity scope and individual stubbornness) and peer pressure (described by a susceptibility threshold). We show that the number of opinion fragments first increases and then decreases to one as population ide…
A model needs to make verifiable predictions to have any scientific value. In opinion dynamics, the study of how individuals exchange opinions with one another, there are many theoretical models which attempt to model opinion exchange, one of which is the Martins model, which differs from other models by using a parameter that is easier to control for in an experiment. In this paper, we have designed an experiment to verify the Martins model and contribute to the experimental design in opinion dynamic with our novel method.
We study the changes of opinions about vaccination together with the evolution of a disease. In our model we consider a multiplex network consisting of two layers. One of the layers corresponds to a social network where people share their opinions and influence others opinions. The social model that rules the dynamic is the M-model, which takes into account two different processes that occurs in a society: persuasion and compromise. This two processes are related through a parameter $r$, $r<1$ describes a moderate and committed society, for $r>1$ the society tends to have extremist opinions, w…
We show using scaling arguments and Monte Carlo simulations that a class of binary interacting models of opinion evolution belong to the Ising universality class in presence of an annealed noise term of finite amplitude. While the zero noise limit is known to show an active-absorbing transition, addition of annealed noise induces a continuous order-disorder transition with Ising universality class in the infinite-range (mean field) limit of the models.
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。
本文研究了正方格子LCCC模型（Lallouache et al. in Phys. Rev. E 82 056112（2010）中提出的一种观点交换动力学模型）中几何团簇随信念和影响参数变化的逾渗转变.所述聚类包括具有大于或等于意见的预定阈值（Ω）的意见值的相邻站点。该转变点与Lallouache等人发现的序参量（平均意见值）的转变点不同。虽然转变点随意见阈值的变化而变化，但从最大团簇尺寸、团簇尺寸分布和Binder累积量的数据崩溃中获得的逾渗转变的临界指数保持不变。的指数也是独立的信念和影响参数的值，表明这种过渡的鲁棒性。这些指数与任何其他已知的渗透指数（例如静态伊辛、动态伊辛、标准渗透）不匹配，因此表征了LCCC模型属于单独的普适类。
Individuals of modern societies share ideas and participate in collective processes within a pervasive, variable, and mostly hidden ecosystem of content filtering technologies that determine what information we see online. Despite the impact of these algorithms on daily life and society, little is known about their effect on information transfer and opinion formation. It is thus unclear to what extent algorithmic bias has a harmful influence on collective decision-making, such as a tendency to polarize debate. Here we introduce a general theoretical framework to systematically link models of o…
It has been observed people tend to have opinions that are far more internally consistent than it would be reasonable to expect. Here, we study how that observation might emerge from changing how agents trust the opinions of their peers in a model for opinion dynamics with multiple issues. A previous Bayesian inspired opinion model for continuous opinions is extended to include multiple issues. In the original model, agents tended to trust less opinions that were too different from their own. We investigate the properties of the extended model in its natural form. And we also introduce the pos…
Mean field solutions of kinetic exchange opinion models
在这里，我们提出了一个无限范围，离散，意见形成模型的精确解。该模型显示出主动吸收相变，类似于在其最近提出的连续版本（Lallouache等人，Rev E 82，056112（2010））。除了两个代理人的相互作用，我们还报告了有三个代理人的相互作用的影响。该相图具有由三临界点分开的连续过渡线（两种试剂相互作用为主）和不连续过渡线（三种试剂相互作用为主）。
Probabilistic neighbors' selection competes with confirmation bias in a bounded confidence model
CNRS, Complex Systems Institute of Paris Île-de-France (ISC-PIF)（法国国家科学研究中心，巴黎法兰西岛复杂系统研究所）；Laboratoire de Physique Théorique et Modélisation, CNRS-CY Cergy Paris Université（理论与建模物理实验室，CNRS-CY塞尔吉巴黎大学）；EHESS, Centre d’Analyse et de Mathématique Sociales (CAMS)（高等社会科学学院，社会分析与数学中心）；Cergy Paris Université（塞尔吉巴黎大学）
Disentangling Interaction and Bias Effects in Opinion Dynamics of Large Language Models
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（复杂系统动力学研究所）；University of Göttingen（哥廷根大学）；Campus Institute for Dynamics of Biological Networks（校园生物网络动力学研究所）
Biswas-Chatterjee-Sen (BChS) kinetic exchange opinion model on modular networks
School of Computational & Integrative Sciences, Jawaharlal Nehru University（计算与整合科学学院，贾瓦哈拉尔·尼赫鲁大学）；Department of Physics, SRM University - AP（物理系，SRM大学-安得拉邦分校）
In this work we study a model of opinion dynamics considering activation/deactivation of agents. In other words, individuals are not static and can become inactive and drop out from the discussion. A probability $w$ governs the deactivation dynamics, whereas social interactions are ruled by kinetic exchanges, considering competitive positive/negative interactions. Inactive agents can become active due to interactions with active agents. Our analytical and numerical results show the existence of two distinct nonequilibrium phase transitions, with the occurrence of three phases, namely ordered (…
In this study, I present a theoretical social learning model to investigate how confirmation bias affects opinions when agents exchange information over a social network. Hence, besides exchanging opinions with friends, agents observe a public sequence of potentially ambiguous signals and interpret it according to a rule that includes confirmation bias. First, this study shows that regardless of level of ambiguity both for people or networked society, only two types of opinions can be formed, and both are biased. However, one opinion type is less biased than the other depending on the state of…
Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarization/radicalization. Moreover, recommendation algorithms - intended to enhance platform usage - are likely to augment such phenomena, generating the so called Algorithmic Bias. In this work, we study the impact that different network topologies have on…
Trust assessment plays a key role in many online applications, such as online money lending, product reviewing and active friending. Trust models usually employ a group of parameters to represent the trust relation between a trustor-trustee pair. These parameters are originated from the trustor's bias and opinion on the trustee. Naturally, these parameters can be regarded as a vector. To address this problem, we propose a framework to accurately convert the single values to the parameters needed by 3VSL. The framework firstly employs a probabilistic graph model (PGM) to derive the trustor's op…
The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of …
Traditional models of opinion dynamics provide a simple approach to understanding human behavior in basic social scenarios. However, when it comes to issues such as polarization and extremism, we require a more nuanced understanding of human biases and cognitive tendencies. In this paper, we propose an approach to modeling opinion dynamics by integrating mental models and assumptions of individuals agents using Bayesian-inspired methods. By exploring the relationship between human rationality and Bayesian theory, we demonstrate the efficacy of these methods in describing how opinions evolve. O…
The effect of undecided agents is studied within populations in an opinion-forming dynamic, varying the number of undecided agents for different proportions of populations in a complete opinion-exchange network. The result is that the dynamic depends on the number of undecided agents, with 10\% of the undecided population potentially affecting the change in consensus and then becoming linear with a negative slope.
Recently a model for the interplay between homophily-based appraisal dynamics and influence-based opinion dynamics has been proposed. The model explores for the first time how the opinions of a group of agents on a certain number of issues/topics is influenced by the agents' mutual appraisal and, conversely, the agents' mutual appraisal is updated based on the agents' opinions on the various issues, according to a homophily model. In this paper we show that a simplified (and, in some situations, more feasible) version of the model, that accounts only for the signs of the agents' appraisals rat…
Modelling efforts in opinion dynamics have to a large extent ignored that opinion exchange between individuals can also have an effect on how willing they are to express their opinion publicly. Here, we introduce a model of public opinion expression. Two groups of agents with different opinion on an issue interact with each other, changing the willingness to express their opinion according to whether they perceive themselves as part of the majority or minority opinion. We formulate the model as a multi-group majority game and investigate the Nash equilibria. We also provide a dynamical systems…
Social media has emerged as a significant source of information for people. As agents interact with each other through social media platforms, they create numerous complex social networks. Within these networks, information spread among agents and their opinions may be altered by their neighbors' influence. This paper explores opinion dynamics on social networks, which are influenced by complex network structure, confirmation bias, and specific issues discussed. We propose a novel model based on previous models to simulate how public opinion evolves from consensus to radicalization and to pola…
Accurate modeling of opinion dynamics has the potential to help us understand polarization and what makes effective political discourse possible or impossible. Here, we use physics-based methods to model the evolution of political opinions within a continuously distributed population. We utilize a network-free system of determining political influence and a local-attraction, distal-repulsion dynamic for reaction to perceived content. Our approach allows for the incorporation of intergroup bias such that messages from trusted in-group sources enjoy greater leeway than out-group ones. We are abl…
