260 research outputs found
A relative approach to opinion formation
Formal models of opinion formation commonly represent an individual’s opinion by a value on a fixed opinion interval. We propose an alternative modeling method wherein interpretation is only provided to the relative positions of opinions vis-à -vis each other. This method is then considered in a similar setting as the discrete-time Altafini model (an extension of the well-known DeGroot model), but with more general influence weights. Even in a linear framework, the model can describe, in the long run, polarization, dynamics with a periodic pattern, and (modulus) consensus formation. In addition, in our alternative approach key characteristics of the opinion dynamic can be derived from real-valued square matrices of influence weights, which immediately allows one to transfer matrix theory insights to the field of opinion formation dynamics under more relaxed conditions than in the DeGroot or discrete-time Altafini models. A few specific themes are covered: (i) We demonstrate how stable patterns in relative opinion dynamics are identified which are hidden when opinions are considered in an absolute opinion framework. (ii) For the two-agent case, we provide an exhaustive closed-form description of the relative opinion model’s dynamic in the long run. (iii) We explore group dynamics analytically, in particular providing a non-trivial condition under which a subgroup’s asymptotic behavior carries over to the entire population
A Bad Barrel Spoils a Good Apple:How Uncertainty and Networks Affect Whether Matching Rules Can Foster Cooperation
Meritocratic matching solves the problem of cooperation by ensuring that only prosocial agents group together while excluding proselfs who are less inclined to cooperate. However, matching is less effective when estimations of individual merit rely on group-level outcomes. Prosocials in uncooperative groups are unable to change the nature of the group and are themselves forced to defect to avoid exploitation. They are then indistinguishable from proselfs, preventing them from accessing cooperative groups. We investigate informal social networks as a potential solution. Interactions in dyadic network relations provide signals of individual cooperativeness which are easier to interpret. Network relations can thus help prosocials to escape from uncooperative groups. To test our intuitions, we develop an ABM modeling cooperative behavior based on a stochastic learning model with adaptive thresholds. We investigate both randomly and homophilously formed networks. We find that homophilous networks create conditions under which meritocratic matching can function as intended. Simulation experiments identify two underlying reasons. First, dyadic network interactions in homophilous networks differentiate more between prosocials and proselfs. Second, homophilous networks create groups of prosocial agents who are aware of each other’s behavior. The stronger this prosociality segregation is, the more easily prosocials cooperate in the group context. Further analyses also highlight a downside of homophilous networks. When prosocials successfully escape from uncooperative groups, non-cooperatives have fewer encounters with prosocials, diminishing their chances to learn to cooperate through those encounters
The Role of Community in Understanding Involvement in Community Energy Initiatives
Community energy initiatives are set up by volunteers in local communities to promote sustainable energy behaviors and help to facilitate a sustainable energy transition. A key question is what motivates people to be involved in such initiatives. We propose that next to a stronger personal motivation for sustainable energy, people’s perception that their community is motivated to engage in sustainable energy and their involvement in the community (i.e., community identification and interpersonal contact) may affect their initiative involvement. We tested this proposition with a questionnaire study among inhabitants of seven local communities (N = 439). Results suggested that community factors are uniquely related to initiative involvement (willingness to actively participate and attendance of an initiative meeting) next to personal sustainable energy motivations. In particular, stronger community identification and more interpersonal contact with other community members increased the likelihood that people become involved in a community initiative, but the perception of the sustainable energy motivation of one’s community was not uniquely related to initiative involvement. We discuss theoretical and practical implications of these findings
Communication in Online Social Networks Fosters Cultural Isolation
Online social networks play an increasingly important role in communication between friends, colleagues, business partners, and family members. This development sparked public and scholarly debate about how these new platforms affect dynamics of cultural diversity. Formal models of cultural dissemination are powerful tools to study dynamics of cultural diversity but they are based on assumptions that represent traditional dyadic, face-To-face communication, rather than communication in online social networks. Unlike in models of face-To-face communication, where actors update their cultural traits after being influenced by one of their network contacts, communication in online social networks is often characterized by a one-To-many structure, in that users emit messages directly to a large number of network contacts. Using analytical tools and agent-based simulation, we show that this seemingly subtle difference can have profound implications for emergent dynamics of cultural dissemination. In particular, we show that within the framework of our model online communication fosters cultural diversity to a larger degree than offline communication and it increases chances that individuals and subgroups become culturally isolated from their network contacts
An Agent-Based Model of Collective Emotions in Online Communities
We develop a agent-based framework to model the emergence of collective
emotions, which is applied to online communities. Agents individual emotions
are described by their valence and arousal. Using the concept of Brownian
agents, these variables change according to a stochastic dynamics, which also
considers the feedback from online communication. Agents generate emotional
information, which is stored and distributed in a field modeling the online
medium. This field affects the emotional states of agents in a non-linear
manner. We derive conditions for the emergence of collective emotions,
observable in a bimodal valence distribution. Dependent on a saturated or a
superlinear feedback between the information field and the agent's arousal, we
further identify scenarios where collective emotions only appear once or in a
repeated manner. The analytical results are illustrated by agent-based computer
simulations. Our framework provides testable hypotheses about the emergence of
collective emotions, which can be verified by data from online communities.Comment: European Physical Journal B (in press), version 2 with extended
introduction, clarification
Well begun is half done:The importance of small group teaching for interaction, self-efficacy and study success in the first semester
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