Initial Opinion Research Articles

Group Polarization Based on Agent Emotional Characteristics and Credibility

The frequent occurrence of group polarization in many internet mass incidents produces adverse impacts on the social stability and poses a significant challenge for social management. We construct an opinion evolution model with agent emotional characteristics and credibility based on the Deffuant model to study the evolutionary mechanism of group polarization, which connects the agent’s firmness with the agent’s opinion, taking a full account of the agent heterogeneity. We analyze the impacts of initial opinion distribution, network structure, and opinion leader on the group opinion evolution. The results show that group polarization is easier to form when the initial opinions are in a normal distribution, and group polarization will also form under the impact of initial minority agents with extreme opinions. Different network structures will pose different effects on the group opinion evolution, and group polarization is easier to form in the small-world network and BA scale-free network. In addition, opinion leader also affects the group opinion evolution, and it will hinder the generation of group polarization.

Identifying Appropriate Delivery of and Referral to Pulmonary Rehabilitation in Uganda: A Survey Study of People Living with Chronic Respiratory Disease and Health Care Workers.

IntroductionPulmonary rehabilitation (PR) is a low cost, high impact intervention that ameliorates the disability associated with chronic respiratory diseases (CRD). PR is becoming increasingly recognized in low resource settings where the burden of CRD is rapidly increasing. To aid the implementation of PR in Uganda, we conducted a study to assess the attitudes and opinions towards PR among patients with CRD in Uganda and explore barriers faced by health care workers (HCWs) in referring to PR.MethodsA cross-sectional study comprising two survey populations: people living with CRD and HCWs regarded as potential PR referrers and PR deliverers. This exploratory study sought initial opinions and thoughts regarding PR, as well as baseline knowledge and potential barriers faced in the referral process.ResultsOverall, 30 HCWs (53% female, 43% doctors) and 51 adults with CRD (63% female) participated in the survey. Among those with CRD, the majority reported breathlessness as a major problem (86%) and breathlessness affected their ability to do paid and unpaid work (70%). Interest in PR was high amongst adults with CRD (92%) with preference for a hospital-based programme (67%) as opposed to community-based (16%) or home-based (17%). All HCWs considered PR important in lung disease management, but 77% do not refer patients due to a lack of information about PR. HCWs’ free-text responses identified the need for training in PR, patient education and streamlining the referral process as key elements to develop successful PR referral services.ConclusionTo successfully set up a PR service for people with CRD in Uganda, there is a great need for appropriately tailored training and education of prospective referrers about CRD and PR programs. Educating patients about the benefits of PR as well as streamlining the referral process is critical in expanding PR services across Uganda to fulfill this unmet need.

EFFECTS OF HIDDEN OPINION MANIPULATION IN MICROBLOGGING PLATFORMS

This paper studies a model for describing opinions’ evolution in a community linked by microblogging directed network systems. The network can evolve in time by means of the creation and the deletion of some connections. When a connexion is created, the individuals have access to the whole history of posts written by the corresponding author and, when a connexion is destroyed, all the posts written by the corresponding author become invisible. The agents’ opinions are described by a set of continuous opinion variables in the closed interval [Formula: see text]. They represent the agreement (or disagreement) of the corresponding agent with respect to a binary question (such as a referendum or an election with two candidates). The model takes into account the effects on public opinion caused by the sign and the intensity of the initial opinions of the agents, their activity in microblogging platforms the presence of leaders and the possible manipulations of the visibility of the posts by the microblogging platform owner. The model is written as a system of integro-differential equations and simulated by using a Runge–Kutta method. We show that hidden manipulation can have an important impact on the public opinion formation and that very mild interventions of the network owner may induce major effects on the population. In our simulations, the effect of hidden manipulation is shown to be more efficient in driving the public opinion than the action of a leader, in the case of bounded confidence models and for short-time intervals.

The Bad Thing About Good Advice: Understanding When and How Advice Exacerbates Overconfidence

Much research on advice taking examines how people revise point estimates given input from others. This work has established that people often egocentrically discount advice. If they were to place more weight on advice, their point estimates would be more accurate. Yet the focus on point estimates and accuracy has resulted in a narrow conception of what it means to heed advice. We distinguish between revisions of point estimates and revisions of attendant probability distributions. Point estimates represent a single best guess; distributions represent the probabilities that people assign to all possible answers. A more complete picture of advice taking is provided by considering revisions of distributions, which reflect changes in both confidence and best guesses. We capture this using a new measure of advice utilization: the influence of advice. We observe that, when input from a high-quality advisor largely agrees with a person’s initial opinion, it engenders little change in one’s point estimate and, hence, little change in accuracy yet significantly increases confidence. This pattern suggests more advice taking than generally suspected. However, it is not necessarily beneficial. Because people are typically overconfident to begin with, receiving advice that agrees with their initial opinion can exacerbate overconfidence. In several experiments, we manipulate advisor quality and measure the extent to which advice agrees with a person’s initial opinion. The results allow us to pinpoint circumstances in which heeding advice is beneficial, improving accuracy or reducing overconfidence, as well as circumstances in which it is harmful, hurting accuracy or exacerbating overconfidence. This paper was accepted by Yuval Rottenstreich, judgment and decision making.

Novel Network Public Opinion Prediction and Guidance Model Based on “S-Curve”: Taking the Loss of Contact with “Malaysia Airlines”

It is of great significance for the government to control the network public opinion in time and maintain social stability to predict the network public opinion in emergency. This paper proposes a novel improvement method to “S-curve” theory in the context of big data and establishes three novel network public opinion prediction models. These models take into account the proliferation trend of initial and follow-up network public opinion over a long period of time when emergencies are formed and the objective environment suddenly changes, based on the information diffusion model conforming to the traditional “S-curve” theory. The novel improvement and establishment allow our model to have more accurate predictions than other scholars’ models that mainly study the first network public opinion in a shorter period of time. And it is more applicable to real social conditions, in line with the public’s cognition of reality, and provides more reference for the government to take preventive and corresponding positive guiding measures in advance. To better establish the model, we obtained the 24-day Weibo data associated with the incident of “Malaysia Airlines” loss of contact from big data for model establishment, public opinion prediction, and comprehensive evaluation. The result innovatively shows that, in addition to the initial public opinion that is worthy of attention, the follow-up public opinion is also noteworthy and proves that our model has more practical value.

Predicting the need for massive transfusion: Prospective validation of a smartphone-based clinical decision support tool

BackgroundImproper or delayed activation of a massive transfusion protocol may have consequences to individuals and institutions. We designed a complex predictive algorithm that was packaged within a smartphone application. We hypothesized it would accurately assess the need for massive transfusion protocol activation and assist clinicians in that decision. MethodsWe prospectively enrolled patients at an urban, level I trauma center. The application recorded the surgeon’s initial opinion for activation and then prompted inputs for the model. The application provided a prediction and recorded the surgeon’s final decision on activation. ResultsThree hundred and twenty-one patients were enrolled (83% male; 59% penetrating; median Injury Severity Score 9; mean base deficit –4.11). Of 36 massive transfusion protocol activations, 26 had an app prediction of “high” or “moderate” probability. Of these, 4 (15%) patients received <10 u blood as a result of early hemorrhage control. Two hundred and eighty-five patients did not have massive transfusion protocol activated by the surgeon with 27 (9%) patients having “moderate” or “high” likelihood predicted by the application. Twenty-four of these did not require massive transfusion, and all patients had acidosis that unrelated to hemorrhagic shock. For 13 (50%) of the patients with “high” probability, the surgeon correctly altered their initial decision based on this information. The algorithm demonstrated an adjusted accuracy of 0.96 (95% confidence interval [0.93–0.98); P ≤ .001]), sensitivity = 0.99, specificity 0.72, positive predictive value 0.96, negative predictive value 0.99, and area under the receiver operating curve = 0.86. ConclusionA smartphone-based clinical decision tools can aid surgeons in the decision to active massive transfusion protocol in real time, although it does not completely replace clinician judgment.

Canadian Melanoma Conference Recommendations on High-Risk Melanoma Surveillance: A Report from the 14th Annual Canadian Melanoma Conference; Banff, Alberta; 20-22 February 2020.

Introduction: There are a lack of established guidelines for the surveillance of high-risk cutaneous melanoma patients following initial therapy. We describe a novel approach to the development of a national expert recommendation statement on high-risk melanoma surveillance (HRS). Methods: A consensus-based, live, online voting process was undertaken at the 13th and 14th annual Canadian Melanoma Conferences (CMC) to collect expert opinions relating to “who, what, where, and when” HRS should be conducted. Initial opinions were gathered via audience participation software and used as the basis for a second iterative questionnaire distributed online to attendees from the 13th CMC and to identified melanoma specialists from across Canada. A third questionnaire was disseminated in a similar fashion to conduct a final vote on HRS that could be implemented. Results: The majority of respondents from the first two iterative surveys agreed on stages IIB to IV as high risk. Surveillance should be conducted by an appropriate specialist, irrespective of association to a cancer centre. Frequency and modality of surveillance favoured biannual visits and Positron Emission Tomography Computed Tomography (PET/CT) with brain magnetic resonance imaging (MRI) among the systemic imaging modalities available. No consensus was initially reached regarding the frequency of systemic imaging and ultrasound of nodal basins (US). The third iterative survey resolved major areas of disagreement. A 5-year surveillance schedule was voted on with 92% of conference members in agreement. Conclusion: This final recommendation was established following 92% overall agreement among the 2020 CMC attendees.

The Influence of Memory in Multi-Agent Consensus

Multi-agent consensus problems can often be seen as a sequence of autonomous and independent local choices between a finite set of decision options, with each local choice undertaken simultaneously, and with a shared goal of achieving a global consensus state. Being able to estimate probabilities for the different outcomes and to predict how long it takes for a consensus to be formed, if ever, are core issues for such protocols. Little attention has been given to protocols in which agents can remember past or outdated states. In this paper, we propose a framework to study what we call `memory consensus protocol'. We show that the employment of memory allows such processes to always converge, as well as, in some scenarios, such as cycles, converge faster. We provide a theoretical analysis of the probability of each option eventually winning such processes based on the initial opinions expressed by agents. Further, we perform experiments to investigate network topologies in which agents benefit from memory on the expected time needed for consensus.

The fragility of opinion formation in a complex world

How does the complexity of the world around us affect the reliability of our opinions? Motivated by this question, we quantitatively study an opinion formation mechanism whereby an uninformed observer gradually forms opinions about a world composed of subjects interrelated by a signed network of mutual trust and distrust. We show numerically and analytically that the observer’s resulting opinions are highly inconsistent (they tend to be independent of the observer’s initial opinions) and unstable (they exhibit wide stochastic variations). Opinion inconsistency and instability increase with the world’s complexity, intended as the number of subjects and their interactions. This increase can be prevented by suitably expanding the observer’s initial amount of information. Our findings imply that an individual who initially trusts a few credible information sources may end up trusting the deceptive ones even if only a small number of trust relations exist between the credible and deceptive sources.

Opinion dynamics in modified expressed and private model with bounded confidence

In a social network, an individual may express an opinion against his/her own private opinion. Moreover, during the evolution of opinion dynamics, an individual may only accept parts of opinions that are similar to his/her own opinion. These phenomenons bring new challenges to the study of the opinion dynamics. In this paper, we propose a modified expressed-private-opinion (MEPO) model with bounded confidence. In the MEPO model, two categories of neighborhood relationships, namely, the communication neighbor and the opinion neighbor, are proposed. More specifically, the communication neighbor represents the information flow among individuals while the opinion neighbor, which is a subset of the communication neighbor, implies whether or not the corresponding opinions will be accepted to be incorporated into the individual’s opinion updating process. Furthermore, the scope of the opinion neighbor is determined by the confidence level such that the whole group is divided into open-minded, moderate-minded, and closed-minded individuals accordingly. As a result, an individual’s private opinion in the proposed MEPO model evolves by his/her own private opinion and the stubbornness value, and also the impacts from the expressed opinions of the opinion neighbors.Specifically, we focus on the design of the stubbornness parameter λ, i.e., one’s stubbornness to his/her initial private opinion. By different scenarios, three expression formats are taken into consideration including (1) λ with constant value 1; (2) λ monotonously increasing to 1; and (3) λ determined by the rate of opinion neighbor number over communication neighbor number. A series of experiments are designed to analyze the relation between the stubbornness parameter λ and the final opinion cluster number. The experimental results show that the final cluster number is mainly determined by both of the rate of closed-minded individuals and λ: it is inversely proportional to the rate of closed-minded individuals as well as the growth rate of λ.

Managing Consensus With Minimum Adjustments in Group Decision Making With Opinions Evolution

Nowadays, online social networks, such as “Facebook” and “WeChat,” facilitate the expression, diffusion, and interactions of individuals' opinions regarding various issues. In this environment, individuals' opinions are liable to be influenced by others and then evolve over the time. In this paper, we propose an approach based on minimum adjustments to manage the consensus in the group decision making (GDM) with opinions evolution. First, inspired by the idea proposed in DeGroot model, we establish a novel GDM model with opinions evolution, and then discuss its consensus conditions. Based on this, we propose an algorithm to achieve the network partition, and then provide a consensus model with minimum adjustments to obtain the optimal adjusted initial opinions and collective consensus opinion. Finally, we provide a numerical example to demonstrate the feasibility and effectiveness of the proposed theoretical results, and design comparative simulations to explore the effects of the opinions evolution on the final consensus solution.

Modeling Confirmation Bias and Peer Pressure in Opinion Dynamics

Confirmation bias and peer pressure are regarded as the main psychology origins of personal opinion adjustment. Each show 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 on large-scale social networks. In this paper, we propose an agent-based model of opinion dynamics which incorporates the conjugate effect of confirmation bias (characterized by the population identity scope and initiative adaptation speed) and peer pressure (described by a susceptibility threshold and passive adaptation speed). First, a counterintuitive non-monotonous phenomenon arises in the homogeneous population: the number of opinion clusters first increases and then decreases to one as the population identity scope becomes larger. We then consider heterogeneous populations where “impressionable” individuals with large susceptibility to peer pressure and “confident” individuals with small susceptibility coexist. We find that even a small fraction of impressionable individuals could help eliminate public polarization when population identity scope is relatively large. In particular, the impact of impressionable agents would be greater if these agents are hubs. More intriguingly, while impressionable individuals have randomly distributed initial opinions, most of them would finally evolve to moderates. We highlight the emergence of these “impressionable moderates” who are easily influenced, yet are important in public opinion competition, which may inspire efficient strategies in winning competitive campaigns.

Optimal majority dynamics for the diffusion of an opinion when multiple alternatives are available

We consider opinion diffusion on social graphs where agents hold opinions and where social pressure leads them to conform to the opinion manifested by the majority of their neighbors. Within this setting, we look for dynamics that allows us to maximize the diffusion of a target opinion given the initial opinions of all agents. In particular, we focus on the setting where more than two opinions are available to the agents, and we show that the properties of this setting are entirely different from those characterizing the setting where agents hold binary opinions only. Indeed, while it is well-known that greedy dynamics are always optimal ones in the binary case, this is no longer true in our more general setting and—rather surprisingly—even if just three opinions are available. Moreover, while it is possible to decide in polynomial time if a dynamics leading to consensus exists when agents have two available opinions, the problem becomes computationally intractable with three opinions, regardless of the fraction of agents that have the target opinion as their initial opinion.

Distribution of Consensus in a Broadcasting-based Consensus-forming Algorithm

The consensus achieved in the consensus-forming algorithm is not generally a constant but rather a random variable, even if the initial opinions are the same. In the present paper, we investigate the statistical properties of the consensus in a broadcasting-based consensus-forming algorithm. We focus on two extreme cases: consensus forming by two agents and consensus forming by an infinite number of agents. In the two-agent case, we derive several properties of the distribution function of the consensus. In the infinite-numberof- agents case, we show that if the initial opinions follow a stable distribution, then the consensus also follows a stable distribution. In addition, we derive a closed-form expression of the probability density function of the consensus when the initial opinions follow a Gaussian distribution, a Cauchy distribution, or a L´evy distribution.

The role of decision confidence in advice-taking and trust formation.

In a world where ideas flow freely across multiple platforms, people must often rely on others' advice and opinions without an objective standard to judge whether this information is accurate. The present study explores the hypothesis that an individual's internal decision confidence can be used as a signal to learn the accuracy of others' advice, even in the absence of feedback. According to this "agreement-in-confidence" hypothesis, people can learn about an advisor's accuracy across multiple interactions according to whether the advice offered agrees with their own initial opinions, weighted by the confidence with which these initial opinions are held. We test this hypothesis using a judge-advisor system paradigm to precisely manipulate the profiles of virtual advisors in a perceptual decision-making task. We find that when advisors' and participants' judgments are independent, people can correctly learn advisors' features, like their accuracy and calibration, whether or not objective feedback is available. However, when their judgments (and thus errors) are correlated-as is the case in many real social contexts-predictable distortions in trust can be observed between feedback and feedback-free scenarios. Using agent-based simulations, we explore implications of these individual-level heuristics for network-level patterns of trust and belief formation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Ideology, communication and polarization.

Ideologically committed minds form the basis of political polarization, but ideologically guided communication can further entrench and exacerbate polarization depending on the structures of ideologies and social network dynamics on which cognition and communication operate. Combining a well-established connectionist model of cognition and a well-validated computational model of social influence dynamics on social networks, we develop a new model of ideological cognition and communication on dynamic social networks and explore its implications for ideological political discourse. In particular, we explicitly model ideologically filtered interpretation of social information, ideological commitment to initial opinion, and communication on dynamically evolving social networks, and examine how these factors combine to generate ideologically divergent and polarized political discourse. The results show that ideological interpretation and commitment tend towards polarized discourse. Nonetheless, communication and social network dynamics accelerate and amplify polarization. Furthermore, when agents sever social ties with those that disagree with them (i.e. structure their social networks by homophily), even non-ideological agents may form an echo chamber and form a cluster of opinions that resemble an ideological group. This article is part of the theme issue 'The political brain: neurocognitive and computational mechanisms'.

Nontraumatic resuscitation room management of critically ill patients

Kritisch kranke Patienten werden regelmäßig in der prähospitalen Notfallmedizin versorgt. Gerade an der Nahtstelle zwischen prähospitaler und innerklinischer Versorgung besteht eine große Herausforderung darin, die Patientenbehandlung ohne Zeitverlust und auf hohem Niveau fortzuführen. Hierzu gehören die Stabilisierung der Vitalfunktionen (inkl. Atemwegsicherung, nichtinvasive/invasive Beatmung, Kreislaufstabilisierung) und Umsetzung einer geeigneten Diagnostik- und Therapiestrategie (inkl. Laboruntersuchungen, Sonographie, radiologischer Bildgebung). In den letzten Jahren hat sich daher ein Interessens- und Forschungsschwerpunkt zum Thema „nichttraumatologische Schockraumversorgung“ entwickelt. Mittlerweile liegen erste monozentrische Daten vor und es wird kontinuierlich an der Entwicklung eines nichttraumatologischen Schockraummanagements zur optimalen Versorgung von kritisch kranken Patienten in der zentralen Notaufnahme gearbeitet. Der vorliegende Beitrag beschreibt auf der Basis von ersten Studien, Erfahrungen und Expertenmeinungen eine strukturierte Vorgehensweise im nichttraumatologischen Schockraummanagement.

Comparison of initial and second opinion reads of multiparametric magnetic resonance imaging of the prostate for transperineal template-guided biopsies with MRI-Ultrasound fusion

PurposeTo investigate the value of second-opinion evaluation of multiparametric prostate magnetic resonance imaging (MRI) by subspecialised uroradiologists for the detection of significant cancer in transperineal fusion prostate biopsy. MethodsThe evaluated data included age, PSA (ng/ml), PSA density, Gleason score, digital rectal examination (DRE), prostate volume of 149 patients. Twenty-seven patients (18%) had no previous prostate biopsy, 114 patients (77%) had a previous negative biopsy, and 8 patients (5%) were on active surveillance. Using PI-RADS v2 scores for mpMRI a second report was performed by a specialist uroradiologist. In all cases a subsequent transperineal biopsy was performed with at least 2 cores per target and additional background systemic cores. Initial and second-opinion radiology reports were evaluated for detection of any cancer and Gleason score (GS) 7-10 cancer, including positive predictive value and negative (NPV) and compared by Fisher's exact test. ResultsAt transperineal biopsy, 51 % (76/149) of patients had a GS 6-10 prostate cancer (PCa), 27 % (40/149) of patients had a GS 3 + 3 PCa and 12 % (18/149) a GS 3 + 4 and 12 % (18/149) had a GS ≥4 + 3 PCa. Agreement between initial and second-opinion reads was observed in 57.7% (86/149; kappa value = 0.32). The detection of clinically significant cancers with second-opinion reads was significantly higher (0.61; 17/28) compared to initial reads (0.35; 17/49); P = 0.034. ConclusionsSecond reading of prostate mpMRIs by subspecialised uroradiologists significantly improved the positive predictive value for detection of clinically significant prostate cancer and showed a trend towards improved NPV for MRI-negative cases where biopsy could be safely avoided. Urologists should be aware that the experience of the reporter will affect the report when making decisions if and how to obtain biopsies. Reporter experience may help to reduce overcalling and avoid over-targeting of lesions.

Pengaruh Leverage, Opini Audit Tahun Sebelumnya, Pertumbuhan Perusahaan, dan Ukuran Perusahaan terhadap Opini Audit Going Concern

Audit reports are used by auditors to inform the accuracy of the information in the financial statements. The auditor as an independent party has the competence to provide an opinion on the client's financial condition. If it is estimated that the company cannot continue its activities, it is likely that it will get a going concern audit opinion. Audit opinion can help investors and other stakeholders in assessing the status of the company's business continuity. The important thing from a going concern audit opinion is to provide additional information for investors in making investment decisions. This study analyzes the factors that determine going concern audit opinion on manufacturing companies listed on the Indonesia Stock Exchange. The factors tested in this study are leverage, initial opinion, company growth and company size. The study used a sample of 125 companies for the 2014-2018 period. The results of data analysis were obtained from logistic regression tests. The empirical results show that prior opinion and leverage increase the likelihood of receiving a going concern opinion. Meanwhile, company growth and company size have no effect on going concern audit opinion. Nagelkerke's R Square test shows the ability of the factors in this study to explain 63.1% of going-concern audit opinion, while 36.9% is explained by other factors outside the research model. The findings from this study are expected to help investors and other stakeholders to prevent losses if they invest in companies that have the potential to go bankrupt.

The Analysis of Opinion Evolution and Control Based on the Prisoner’s Dilemma Game in Social Networks

In a social network, a user is greatly influenced by their neighbors’ opinions, and the user’s opinion updating can be regarded as the prisoner’s dilemma game. In view of such considerations, this paper proposes an opinion evolution and control model based on the prisoner’s dilemma game and gives the corresponding opinion evolution and control algorithm. Under different initial positive opinion proportions, different opinion control levels, and the same control threshold value and under different initial positive opinion proportions, different opinion control levels, and different opinion control threshold values in a scale-free network, the experiments illustrate the opinion evolution trends and control strategies according to the measures of changing the opinion control levels and opinion control threshold values for network regulators. The experiments show that the lower the initial positive opinion proportion is and the smaller (resp., larger) the control opinion threshold value chosen by the network regulators is, the lower (resp., higher) the opinion control level is; the larger the initial positive opinion proportion is and the larger the control opinion threshold value chosen by the network regulators is, the lower the opinion control level is.