Social Influence and the Collective Dynamics of Opinion Formation

Social influence is the process by which individuals adapt their opinion, revise their beliefs, or change their behavior as a result of social interactions with other people. In our strongly interconnected society, social influence plays a prominent role in many self-organized phenomena such as herding in cultural markets, the spread of ideas and innovations, and the amplification of fears during epidemics. Yet, the mechanisms of opinion formation remain poorly understood, and existing physics-based models lack systematic empirical validation. Here, we report two controlled experiments showing how participants answering factual questions revise their initial judgments after being exposed to the opinion and confidence level of others. Based on the observation of 59 experimental subjects exposed to peer-opinion for 15 different items, we draw an influence map that describes the strength of peer influence during interactions. A simple process model derived from our observations demonstrates how opinions in a group of interacting people can converge or split over repeated interactions. In particular, we identify two major attractors of opinion: (i) the expert effect, induced by the presence of a highly confident individual in the group, and (ii) the majority effect, caused by the presence of a critical mass of laypeople sharing similar opinions. Additional simulations reveal the existence of a tipping point at which one attractor will dominate over the other, driving collective opinion in a given direction. These findings have implications for understanding the mechanisms of public opinion formation and managing conflicting situations in which self-confident and better informed minorities challenge the views of a large uninformed majority.     

Multi-Level Opinion Dynamics under Bounded Confidence

Opinion dynamics focuses on the opinion evolution in a social community. Recently, some models of continuous opinion dynamics under bounded confidence were proposed by Deffuant and Krause, et al. In the literature, agents were generally assumed to have a homogeneous confidence level. This paper proposes an extended model for a group of agents with heterogeneous confidence levels. First, a social differentiation theory is introduced and a social group is divided into opinion subgroups with distinct confidence levels. Second, a multi-level heterogeneous opinion formation model is formulated under the framework of bounded confidence. Finally, computer simulations are conducted to study the collective opinion evolution, focusing on three key factors: the fractions of heterogeneous agents, the initial opinions, and the group size. The simulation results demonstrate that the number of final opinions depends on the fraction of close-minded agents when the group size and the initial opinions are fixed; the final opinions converge more easily when the initial opinions are closer; and the number of final opinions can be approximately modeled by a linear increasing function of the group size and the increasing rate is the fraction of close-minded agents.     

Opinion Formation Models on a Gradient

Statistical physicists have become interested in models of collective social behavior such as opinion formation, where individuals change their inherently preferred opinion if their friends disagree. Real preferences often depend on regional cultural differences, which we model here as a spatial gradient g in the initial opinion. The gradient does not only add reality to the model. It can also reveal that opinion clusters in two dimensions are typically in the standard (i.e., independent) percolation universality class, thus settling a recent controversy about a non-consensus model. However, using analytical and numerical tools, we also present a model where the width of the transition between opinions scales , not as in independent percolation, and the cluster size distribution is consistent with first-order percolation.     

Individualization as driving force of clustering phenomena in humans

One of the most intriguing dynamics in biological systems is the emergence of clustering, in the sense that individuals self-organize into separate agglomerations in physical or behavioral space. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is the clustering of opinions in human populations, particularly when opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing continuous opinion formation models predict "monoculture" in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness has not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution to the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct computer simulation experiments demonstrating that with this kind of noise a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting. In summary, the new model can explain cultural clustering in human societies. Strikingly, model predictions are not only robust to "noise"-randomness is actually the central mechanism that sustains pluralism and clustering.     

Influence of Opinion Dynamics on the Evolution of Games

Under certain circumstances such as lack of information or bounded rationality, human players can take decisions on which strategy to choose in a game on the basis of simple opinions. These opinions can be modified after each round by observing own or others payoff results but can be also modified after interchanging impressions with other players. In this way, the update of the strategies can become a question that goes beyond simple evolutionary rules based on fitness and become a social issue. In this work, we explore this scenario by coupling a game with an opinion dynamics model. The opinion is represented by a continuous variable that corresponds to the certainty of the agents respect to which strategy is best. The opinions transform into actions by making the selection of an strategy a stochastic event with a probability regulated by the opinion. A certain regard for the previous round payoff is included but the main update rules of the opinion are given by a model inspired in social interchanges. We find that the fixed points of the dynamics of the coupled model are different from those of the evolutionary game or the opinion models alone. Furthermore, new features emerge such as the independence of the fraction of cooperators with respect to the topology of the social interaction network or the presence of a small fraction of extremist players.     

Analysing the Unequal Effects of Positive and Negative Information on the Behaviour of Users of a Taiwanese On-Line Bulletin Board

The impact of social influence causes people to adopt the behaviour of others when interacting with other individuals. The effects of social influence can be direct or indirect. Direct social influence is the result of an individual directly influencing the opinion of another, while indirect social influence is a process taking place when an individual’s opinion and behaviour is affected by the availability of information about others’ actions. Such indirect effect may exhibit a more significant impact in the on-line community because the internet records not only positive but also negative information, for example on-line written text comments. This study focuses on indirect social influence and examines the effect of preceding information on subsequent users’ opinions by fitting statistical models to data collected from an on-line bulletin board. Specifically, the different impacts of information on approval and disapproval comments on subsequent opinions were investigated. Although in an anonymous situation where social influence is assumed to be at minimum, our results demonstrate the tendency of on-line users to adopt both positive and negative information to conform to the neighbouring trend when expressing opinions. Moreover, our results suggest unequal effects of the local approval and disapproval comments in affecting the likelihood of expressing opinions. The impact of neighbouring disapproval densities was stronger than that of neighbouring approval densities on inducing subsequent disapproval relative to approval comments. However, our results suggest no effects of global social influence on subsequent opinion expression.     

Abandonment of Dogs and Cats: Public Opinions as Population Management Indicators

This study involved characterizing public opinions on selected issues related to the abandonment of dogs and cats in Votorantim, São Paulo, Brazil. This goal was achieved by applying multiple correspondence analysis to answers recorded in questionnaires of a population-based survey. The results suggest that the tolerance of potentially problematic behaviors did not depend on the specific kind of behavior, at least among the behaviors evaluated. The opinions given for the most frequent fate of the companion animals did not depend on the species, and those who considered abandonment to be one of the most frequent fates tended to also consider abandonment as a solution to problematic behaviors. The provision of reasons for abandonment was more frequent among interviewees who considered abandonment as one of the more common fates of nonhuman animals and as a solution to problematic behaviors. Population management interventions should be reflected in public opinion, so the public opinion characteristics that were found can be used as indicators of changes generated by such interventions.     

Resource Construction and Evaluation for Indirect Opinion Mining of Drug Reviews

Opinion mining is a well-known problem in natural language processing that has attracted increasing attention in recent years. Existing approaches are mainly limited to the identification of direct opinions and are mostly dedicated to explicit opinions. However, in some domains such as medical, the opinions about an entity are not usually expressed by opinion words directly, but they are expressed indirectly by describing the effect of that entity on other ones. Therefore, ignoring indirect opinions can lead to the loss of valuable information and noticeable decline in overall accuracy of opinion mining systems. In this paper, we first introduce the task of indirect opinion mining. Then, we present a novel approach to construct a knowledge base of indirect opinions, called OpinionKB, which aims to be a resource for automatically classifying people’s opinions about drugs. Using our approach, we have extracted 896 quadruples of indirect opinions at a precision of 88.08 percent. Furthermore, experiments on drug reviews demonstrate that our approach can achieve 85.25 percent precision in polarity detection task, and outperforms the state-of-the-art opinion mining methods. We also build a corpus of indirect opinions about drugs, which can be used as a basis for supervised indirect opinion mining. The proposed approach for corpus construction achieves the precision of 88.42 percent.     

The Undecided Have the Key: Interaction-Driven Opinion Dynamics in a Three State Model

The effects of interpersonal interactions on individual’s agreements result in a social aggregation process which is reflected in the formation of collective states, as for instance, groups of individuals with a similar opinion about a given issue. This field, which has been a longstanding concern of sociologists and psychologists, has been extended into an area of experimental social psychology, and even has attracted the attention of physicists and mathematicians. In this article, we present a novel model of opinion formation in which agents may either have a strict preference for a choice, or be undecided. The opinion shift emerges, in a threshold process, as a consequence of a cumulative persuasion for either one of the two opinions in repeated interactions. There are two main ingredients which play key roles in determining the steady states: the initial fraction of undecided agents and the change in agents’ persuasion after each interaction. As a function of these two parameters, the model presents a wide range of solutions, among which there are consensus of each opinion and bi-polarization. We found that a minimum fraction of undecided agents is not crucial for reaching consensus only, but also to determine a dominant opinion in a polarized situation. In order to gain a deeper comprehension of the dynamics, we also present the theoretical framework of the model. The master equations are of special interest for their nontrivial properties and difficulties in being solved analytically.     

Science,Politics and Policy: How Michiganders think about the Risks Facing the Great Lakes

The Great Lakes region is an important ecological asset for the United States, yet studies show that several environmental risks threaten its viability. As a result, it is important to respond to these risks with effective policies. When and how policy is implemented often depends on public opinion and perceptions; yet, we understand little about how individuals from the Great Lakes region construct opinions about the threats facing the lakes. We seek to understand how individuals from the state of Michigan form opinions on three risks to the lakes: invasive Asian carp, climate change, and offshore drilling. To do this, we evaluate the utility of two dominant models of environmental opinion formation: trust and deference to scientific authority, and partisan bias and motivated reasoning. We find that when issues have been politicized, opinion is greatly influenced by political factors like partisanship but that trust and deference as well as underlying environmental attitudes play a more important role for issues that have not been politicized. We discuss the implications of these findings in terms of policy and communication in Michigan, arguing that if we want Michiganders to support policy consistent with science, they must view risk in ways that are consistent with scientific consensus. For that to happen, advocates and policy makers must focus on reducing the political rhetoric around these threats, developing communication that taps into underlying trust and deference to science, and using underlying attitudes about the role of government in environmental protection to promote environmental policy.     

An evaluation of the extent to which young people are informed about the acquired immunodeficiency syndrome

The data on the evaluation of the volume of information on the transmission routes of HIV infection, risk groups and prophylactic measures, acquired by young people aged 14-18 years, their attitude to different sources of information on AIDS, as well as the evaluation of the influence of lectures on the formation of their opinion of these problems, are presented. As found by questioning, 84.4-98.7% of adolescents who answered the questionnaires correctly indicated the routes of the transmission of HIV infection, prophylactic measures were known to 73.1-90.3%, risk groups were correctly defined by 80.6-98.0%, more than 70% of adolescents covered by questioning believed that mass media provided insufficient information on AIDS. In a group of adolescents given a lecture an increase in the number of correct answers and a decrease in the number of wrong answers on the transmission routes of HIV infection and on risk groups were registered.     

火干扰与生态系统的碳循环

火干扰是陆地生态系统碳循环的重要影响因子。它改变着整个系统的碳循环过程与碳分布格局。正确评估火干扰在碳循环过程中的作用,对推进全球碳循环研究有着重要的意义。从4个方面系统的回顾了火干扰对碳循环的影响过程及其研究方法:(1)火烧过程中含碳痕量气体排放的估算;(2)火烧迹地恢复过程中净第一性生产力(NPP)与土壤呼吸的变化;(3)火干扰对生态系统碳源/汇的影响;(4)模型方法在火干扰与生态系统碳循环研究中的应用。目前火灾碳排量的估算方法业已成熟,但进行更精确的估算必须基于对受干扰生态系统的性质以及火势的时空变异性质的准确理解;相比之下,对于间接的、更为重要的影响,即对火烧迹地恢复过程中碳循环变化的研究则显不足。由于数据缺乏,现有研究大多限于对碳循环某一方面的观测与定量描述,缺乏全面的机理性分析。对此,实地观测、模型模拟与遥感观测的跨尺度集成将成为未来火干扰研究的一个主要方向。     

The Value of Opinion in Science and the Forest Service Research Organization

ABSTRACT There is confusion about conflicts of interest between sources of funding and the extent to which Forest Service researchers are free to publish their findings. Forest Service Research is an independent entity with no administrative accountability to policy makers up to the office of the Chief of the Forest Service. Congressional mandate ensures that research will be free from the influence of politics that land management necessarily entails. Because politics involves opinions, it is important to note that opinions per se are not scientific and must be appropriately compared with empirical data before they can be considered so. It is the quantitative test of an opinion that renders it scientific, not the opinion itself.     

Integrated approaches to climate-crop modelling: needs and challenges

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate-vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO2), ozone (03) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate-chemistry-crop-hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models.     

Lifting the Veil of Morality: Choice Blindness and Attitude Reversals on a Self-Transforming Survey

Every day, thousands of polls, surveys, and rating scales are employed to elicit the attitudes of humankind. Given the ubiquitous use of these instruments, it seems we ought to have firm answers to what is measured by them, but unfortunately we do not. To help remedy this situation, we present a novel approach to investigate the nature of attitudes. We created a self-transforming paper survey of moral opinions, covering both foundational principles, and current dilemmas hotly debated in the media. This survey used a magic trick to expose participants to a reversal of their previously stated attitudes, allowing us to record whether they were prepared to endorse and argue for the opposite view of what they had stated only moments ago. The result showed that the majority of the reversals remained undetected, and a full 69% of the participants failed to detect at least one of two changes. In addition, participants often constructed coherent and unequivocal arguments supporting the opposite of their original position. These results suggest a dramatic potential for flexibility in our moral attitudes, and indicates a clear role for self-attribution and post-hoc rationalization in attitude formation and change.     

A finite representation model for an asynchronous culture of E. coli

A computer model is described which models an asynchronous population of E. coli by using a large, but finite number of representative single cells. Asynchrony generation and maintenance occurs at the single cell level by modulating the activity of an enzyme responsible for septum formation. Such modulation introduces cycle time imprecision and does not require the introduction of any new parameters into the single-cell model. Based on comparisons to experiment, reasonable predictions are possible for changes of cellular dry weight during exponential growth and turbidostat washout, and overall chemostat cell yields and changes in cell number, glucose concentration, and cell size distribution for a chemostat subject to a step change in dilution rate. Additionally, a correlation between cell RNA content and size is predicted as is an inertial effect when chemostat residence time is decreased under conditions of initially high glucose concentrations. Limitations imposed by the model's finite nature and their solutions are discussed.     

Indicators of environmentally sound land use in the humid tropics: The potential roles of expert opinion,knowledge engineering and knowledge discovery

Despite abundant literature on indicators for sustainable resource management, practical tools to help local users to apply its general concepts at a local to regional level are scarce. This means that decisions over land evaluation and land use at a local level are often not based on the formal application of indicators or decision support systems for environmentally sound management but instead on the opinion of local expertise, for instance forest managers, cattle breeders, farmers and/or academics. This is particularly seen to be the case in the tropics where access to modern communication and information technologies is restricted.As the opinions of experts are often based on and influenced by personal experience, intuition, heuristics and bias, their evaluations and decision are often unclear to the non-expert working at a local level. In order to make their reasoning more comprehensible to the non-expert, the ecological condition of 176 plots in the tropical Southeast of Mexico were evaluated by experts on soil fertility, forest management, cattle breeding and agriculture. With the assistance of a knowledge engineer (one who converts expert knowledge and reasoning into a model), these expert opinions and reasoning were then translated into a formal computer model.As an alternative approach we applied a knowledge discovery technique, namely the induction of regression trees and automatically developed models using the expert evaluations as training data. Where knowledge engineering was tedious and time consuming, regression models could be rapidly generated. Moreover, the correspondence between regression trees and expert opinions was considerably higher than the correspondence between expert opinion and their own models. The regression trees used less explicative variables than the models generated by the experts. The minimisation of sampling effort due to variable space reduction means that the application of regression tree induction has a high potential for a rapid development of indicators for narrowly defined ecological assessments, needed for decision making on a local or regional scale.     

Coupling microscale vegetation–soil water and macroscale vegetation–precipitation feedbacks in semiarid ecosystems

At macroscale, land–atmosphere exchange of energy and water in semiarid zones such as the Sahel constitutes a strong positive feedback between vegetation density and precipitation. At microscale, however, additional positive feedbacks between hydrology and vegetation such as increase of infiltration due to increase of vegetation, have been reported and have a large impact on vegetation distribution and spatial pattern formation. If both macroscale and microscale positive feedbacks are present in the same region, it is reasonable to assume that these feedback mechanisms are connected. In this study, we develop and analyse a soil‐vegetation‐atmosphere model coupling large‐scale evapotranspiration–precipitation feedback with a model of microscale vegetation–hydrology feedback to study the integration of these nonlinearities at disparate scales. From our results, two important conclusions can be drawn: (1) it is important to account for spatially explicit vegetation dynamics at the microscale in climate models (the strength of the precipitation feedback increased up to 35% by accounting for these microscale dynamics); (2) studies on resilience of ecosystems to climate change should always be cast within a framework of possible large‐scale atmospheric feedback mechanism (substantial changes in vegetation resilience resulted from incorporating macroscale precipitation feedback). Analysis of full‐coupled modelling shows that both type of feedbacks markedly influence each other and that they should both be accounted for in climate change models.     

What failure in collective decision-making tells us about metacognition

Condorcet (1785) proposed that a majority vote drawn from individual, independent and fallible (but not totally uninformed) opinions provides near-perfect accuracy if the number of voters is adequately large. Research in social psychology has since then repeatedly demonstrated that collectives can and do fail more often than expected by Condorcet. Since human collective decisions often follow from exchange of opinions, these failures provide an exquisite opportunity to understand human communication of metacognitive confidence. This question can be addressed by recasting collective decision-making as an information-integration problem similar to multisensory (cross-modal) perception. Previous research in systems neuroscience shows that one brain can integrate information from multiple senses nearly optimally. Inverting the question, we ask: under what conditions can two brains integrate information about one sensory modality optimally? We review recent work that has taken this approach and report discoveries about the quantitative limits of collective perceptual decision-making, and the role of the mode of communication and feedback in collective decision-making. We propose that shared metacognitive confidence conveys the strength of an individual's opinion and its reliability inseparably. We further suggest that a functional role of shared metacognition is to provide substitute signals in situations where outcome is necessary for learning but unavailable or impossible to establish.