Search Results (12)

In recent years, the frequent occurrence of public emergencies has often triggered the rapid spread and amplification of social emotions. The accumulation and intensification of negative emotions can lead to collective behaviors and even pose a threat to social stability. To better understand the formation and evolution of social emotions in such contexts, this study constructs a theoretical framework and simulation approach that combines opinion dynamics with emotional and trust interactions. First, we propose a clustering method that incorporates emotional similarity and trust relationships among users to delineate group structures involved in social emotion formation. Second, a dynamic trust adjustment mechanism is also proposed to capture how trust evolves as individuals interact emotionally. Third, a large-scale group emotional consensus decision-making approach, based on the DeGroot model, is developed to simulate how emotional exchanges and resonance drive groups toward consensus in public emergencies. Additionally, we present a strategy for guiding emotional interactions to reach a desired consensus that ensures minimal modifications to collective preference values while achieving an acceptable consensus level, helping to manage emotional escalation. To validate the proposed model, we conduct simulations using the “Fat Cat” incident as a case study. The results reveal key mechanisms underlying social emotion formation during public emergencies and highlight critical influencing factors, including user participation, opinion leader influence, and trust relationships. This study provides a clear understanding of how social emotions are generated and offers practical insights for managing emotional dynamics and improving group decision-making during crises. Full article

In the field of education, investigating the relationships between different majors in universities is an important topic in current educational research. The application of social networks from informatics provides new opportunities and potentials for the field of education. Due to the complexity of social interactions, the social network connections surrounding individuals exert a significant influence on their daily decision-making processes. This paper aims to introduce the social network and influence analysis theories from informatics into the field of education, regarding major as a variable, and comparing and analyzing the influence relationships between majors. An empirical study was conducted, involving the collection of questionnaire data on graduates’ evaluations of various aspects of their university experiences across different majors. The evolution of this model follows the DeGroot opinion dynamics with the inclusion of stubborn nodes. By defining leader majors and general majors based on the data and modeling the questionnaire data as the outcome of a discrete random process, an influence matrix is ultimately generated through the opinion dynamic model. Through this modeling approach, we revealed the underlying influence relationships between different disciplines (majors). These findings provide schools with insights to adjust the directions of discipline cultivation, and offer new perspectives and methods for the study of majors in higher education. Full article

Non-consensus phenomena are widely observed in human society, but more attention is paid to consensus phenomena. One famous consensus model is the DeGroot model, and there are a series of outstanding works derived from it. By introducing the cognition bias, resulting in over-confidence and under-confidence in the DeGroot model, we propose a non-consensus model, namely the DeGroot-Non-Consensus model. It bridges consensus phenomena and non-consensus phenomena. While different in meaning, the new opinion model can reproduce the DeGroot model’s behaviors and supply a series of interesting non-consensus states. We find frozen fragment states for the over-confident population and time-dependent states for strong interaction strength. In frozen fragment states, the population is polarized into opinion clusters formed by extremists. In time-dependent states, agents jump between two opinions that only differ in the sign, which provides a possible explanation for the swing in opinions in elections and the fluctuations in open questions in the absence of external information. All of these states are summarized in the phase diagrams of the self-confidence and the interaction strength plane. Moreover, the transition scenarios along different parameter paths are studied. Meanwhile, the influence of the nodes’ degree is illustrated in the phase diagrams and the relationship is given. The finite size effect is found in the not quite over-confident population. An interesting phenomenon for small population sizes is that neutral populations with large opinion variance are robust to the fluctuations induced by a finite population size. Full article

Product optimisation is one of the most crucial phases in the new product development or launch process. This work proposes applying penalty analysis to investigate the impact of not just-about-right (JAR) sensorial aspects on willingness to pay (WTP) and an overall liking for a local Italian ancient pear variety and to verify the mediating role of pleasantness in the relationship between not-JAR sensory attributes and consumers’ WTP using structural equation model (SEM). One hundred and twelve non-expert participants recruited during an in-store experiment evaluated overall liking and JAR attributes and were involved in an in-field experimental auction based on the non-hypothetical Becker–DeGroot–Marshak (BDM) mechanism. The participants’ average WTP for the sample was EUR 3.18 per kilogramme. Only juiciness and sourness significantly impact consumers’ overall liking but not on consumers’ WTP. Moreover, pleasantness did not mediate the relationship between non-balanced sensorial aspects and WTP. In conclusion, the penalty analysis for attributes not being JAR in monetary and hedonic terms is a beneficial research approach for a deep-inside evaluation of the potentiality of the product in the marketplace, providing helpful directions for product optimisation. Results show market potential for the local ancient pear variety ‘Angelica’. Full article

The last decade has witnessed a great number of opinion formation models that depict the evolution of opinions within a social group and make predictions about the evolution process. In the traditional formulation of opinion evolution such as the DeGroot model, an agent’s opinion is represented as a real number and updated by taking a weighted average of its neighbour’s opinions. In this paper, we adopt a hybrid representation of opinions that integrate both the discrete and continuous nature of an opinion. Basically, an agent has a ‘Yes’, ‘Neutral’ or ‘No’ opinion on some issues of interest and associates with its Yes opinion a support degree which captures how strongly it supports the opinion. With such a rich representation, not only can we study the evolution of opinion but also that of support degree. After all, an agent’s opinion can stay the same but become more or less supportive of it. Changes in the support degree are progressive in nature and only a sufficient accumulation of such a progressive change will result in a change of opinion say from Yes to No. Hence, in our formulation, after an agent interacts with another, its support degree is either strengthened or weakened by a predefined amount and a change of opinion may occur as a consequence of such progressive changes. We carry out simulations to evaluate the impacts of key model parameters including (1) the number of agents, (2) the distribution of initial support degrees and (3) the amount of change of support degree changes in a single interaction. Last but not least, we present several extensions to the hybrid and progressive model which lead to opinion polarization. Full article

Moving from the study of plasmonic materials with relaxation, in this work we propose a fractional Abraham–Lorentz-like model of the complex permittivity of conductor media. This model extends the Ciancio–Kluitenberg, based on the Mazur–de Groot non-equilibrium thermodynamics theory (NET). The approach based on NET allows us to link the phenomenological function of internal variables and electrodynamics variables for a large range of frequencies. This allows us to closer reproduce experimental data for some key metals, such as Cu, Au and Ag. Particularly, our fitting significantly improves those obtained by Rakic and coworkers and we were able to operate in a larger range of energy values. Moreover, in this work we also provide a definition of a substantial fractional derivative, and we extend the fractional model proposed by Flora et al. Full article

Genetic algorithms mimic the process of natural selection in order to solve optimization problems with minimal assumptions and perform well when the objective function has local optima on the search space. These algorithms treat potential solutions to the optimization problem as chromosomes, consisting of genes which undergo biologically-inspired operators to identify a better solution. Hyperparameters or control parameters determine the way these operators are implemented. We created a genetic algorithm in order to fit a DeGroot opinion diffusion model using limited data, making use of selection, blending, crossover, mutation, and survival operators. We adapted the algorithm from a genetic algorithm for design of mixture experiments, but the new algorithm required substantial changes due to model assumptions and the large parameter space relative to the design space. In addition to introducing new hyperparameters, these changes mean the hyperparameter values suggested for the original algorithm cannot be expected to result in optimal performance. To make the algorithm for modeling opinion diffusion more accessible to researchers, we conduct a simulation study investigating hyperparameter values. We find the algorithm is robust to the values selected for most hyperparameters and provide suggestions for initial, if not default, values and recommendations for adjustments based on algorithm output. Full article

Leveraging social influence is an increasingly common strategy to change population behavior or acceptance of public health policies and interventions; however, assessing the effectiveness of these social network interventions and projecting their performance at scale requires modeling of the opinion diffusion process. We previously developed a genetic algorithm to fit the DeGroot opinion diffusion model in settings with small social networks and limited follow-up of opinion change. Here, we present an assessment of the algorithm performance under the less-than-ideal conditions likely to arise in practical applications. We perform a simulation study to assess the performance of the algorithm in the presence of ordinal (rather than continuous) opinion measurements, network sampling, and model misspecification. We found that the method handles alternate models well, performance depends on the precision of the ordinal scale, and sampling the full network is not necessary to use this method. We also apply insights from the simulation study to investigate notable features of opinion diffusion models for a social network intervention to increase uptake of pre-exposure prophylaxis (PrEP) among Black men who have sex with men (BMSM). Full article

The paper presents a survey on selected models of opinion dynamics. Both discrete (more precisely, binary) opinion models as well as continuous opinion models are discussed. We focus on frameworks that assume non-Bayesian updating of opinions. In the survey, a special attention is paid to modeling nonconformity (in particular, anticonformity) behavior. For the case of opinions represented by a binary variable, we recall the threshold model, the voter and q-voter models, the majority rule model, and the aggregation framework. For the case of continuous opinions, we present the DeGroot model and some of its variations, time-varying models, and bounded confidence models. Full article

We investigate a novel nonlinear consensus from the extreme points of doubly stochastic quadratic operators (EDSQO), based on majorization theory and Markov chains for time-varying multi-agent distributed systems. We describe a dynamic system that has a local interaction network among agents. EDSQO has been applied for distributed agent systems, on a finite dimensional stochastic matrix. We prove that multi-agent systems converge at a center (common value) via the extreme waited value of doubly stochastic quadratic operators (DSQO), which are only 1 or 0 or 1/2$\frac{1}{2}$ if the exchanges of each agent member has no selfish communication. Applying this rule means that the consensus is nonlinear and low-complexity computational for fast time convergence. The investigated nonlinear model of EDSQO follows the structure of the DeGroot linear (DGL) consensus model. However, EDSQO is nonlinear and faster convergent than the DGL model and is of lower complexity than DSQO and cubic stochastic quadratic operators (CSQO). The simulation result and theoretical proof are illustrated. Full article

Motor vehicle exhaust emissions have made air pollution increasingly serious in China, and advocating for the concept of green travel can help alleviate the air pollution caused by motor vehicle exhaust. Thus, the research on the green travel choice behavior of limited rational individuals in the complex social network and the evolution of group behavior is the focus of this paper. Based on the theory of planned behavior, this paper established the individual cognition-behavior model. Meanwhile, an interaction model of individuals in the network is constructed based on the DeGroot model and scale-free network. The simulation results of the model show that: (1) it is difficult to control the behavior of green travel: even if the knowledge level of green travel is high, the proportion of green travel individuals in the group is still very low; (2) the individual intention for green travel is dependent on behavioral attitude, which can effectively improve the proportion of green travel individuals; (3) if the individual intention is too dependent on the subjective norm and the perception of behavioral result, the proportion of green travel individuals would become lower; and (4) when the network is connected, the proportion of individuals who choose green travel will reach the peak through social interaction and learning. This study has a certain practical significance for the environmental protection work of relevant departments, which can guide the behavior of individuals through the design of government institutions, and enable the concept of green travel to form an ideology by means of education and knowledge dissemination, so as to generate some kind of consensual behavioral consciousness. Meanwhile, this study provides a new research perspective for behavioral research and extends the research scope of group behavior. Full article

In this study, traceability in pork profile information with ex-ante quality assurance and ex-post traceability are constructed. Consumers’ willingness to pay (WTP) for traceability information is investigated in Wuxi, China, by combining the Multiple Price Lists method and the Becker–DeGroot–Marschak (BDM) experimental auction. The main factors affecting consumers’ WTP are also analyzed using a Tobit model. The results demonstrate that consumers have higher WTP for ex-ante quality assurance than for ex-post traceability. The highest WTP is for the ex-ante quality assurance attribute of pork quality inspection. Consumers’ WTP for traceability information is influenced by their individual characteristics, including age, education and income, as well as their concern and satisfaction about food safety and confidence in food safety labeling. The contribution of this paper is that it improves the meaning of traceable food information attributes and measures the significance of attributes to consumers. Furthermore, this paper introduces a Becker–DeGroot–Marschak experimental auction method which amends the measurement deviation of hypothetical experiments. Full article