Why do faultlines matter? A computational model of how strong demographic faultlines undermine team cohesion

Lau and Murnighan (LM) suggested that strong demographic faultlines threaten team cohesion and reduce consensus. However, it remains unclear which assumptions are exactly needed to derive faultline effects. We propose a formal computational model of the effects of faultlines that uses four elementary social mechanisms, social influence, rejection, homophily and heterophobia. We show that our model is consistent with the central hypotheses of LM's theory. We also find that negative effects of faultlines can be derived even when - unlike LM - we assume that initially there is no correlation between the demographic characteristics and the opinions of team members. (c) 2007 Elsevier B.V. All rights reserved

The strength of weak bots

Some fear that social bots, automated accounts on online social networks, propagate falsehoods that can harm public opinion formation and democratic decision-making. Empirical research, however, resulted in puzzling findings. On the one hand, the content emitted by bots tends to spread very quickly in the networks. On the other hand, it turned out that bots’ ability to contact human users tends to be very limited. Here we analyze an agent-based model of social influence in networks explaining this inconsistency. We show that bots may be successful in spreading falsehoods not despite their limited direct impact on human users, but because of this limitation. Our model suggests that bots with limited direct impact on humans may be more and not less effective in spreading their views in the social network, because their direct contacts keep exerting influence on users that the bot does not reach directly. Highly active and well-connected bots, in contrast, may have a strong impact on their direct contacts, but these contacts grow too dissimilar from their network neighbors to further spread the bot\u27s content. To demonstrate this effect, we included bots in Axelrod\u27s seminal model of the dissemination of cultures and conducted simulation experiments demonstrating the strength of weak bots. A series of sensitivity analyses show that the finding is robust, in particular when the model is tailored to the context of online social networks. We discuss implications for future empirical research and developers of approaches to detect bots and misinformatio

A behavioral study of “noise” in coordination games

‘Noise’ in this study, in the sense of evolutionary game theory, refers to deviations from prevailing behavioral rules. Analyzing data from a laboratory experiment on coordination in networks, we tested ‘what kind of noise’ is supported by behavioral evidence. This empirical analysis complements a growing theoretical literature on ‘how noise matters’ for equilibrium selection. We find that the vast majority of decisions (96%96%) constitute myopic best responses, but deviations continue to occur with probabilities that are sensitive to their costs, that is, less frequent when implying larger payoff losses relative to the myopic best response. In addition, deviation rates vary with patterns of realized payoffs that are related to trial-and-error behavior. While there is little evidence that deviations are clustered in time or space, there is evidence of individual heterogeneity

The strength of weak bots

Some fear that social bots, automated accounts on online social networks, propagate falsehoods that can harm public opinion formation and democratic decision-making. Empirical research, however, resulted in puzzling findings. On the one hand, the content emitted by bots tends to spread very quickly in the networks. On the other hand, it turned out that bots’ ability to contact human users tends to be very limited. Here we analyze an agent-based model of social influence in networks explaining this inconsistency. We show that bots may be successful in spreading falsehoods not despite their limited direct impact on human users, but because of this limitation. Our model suggests that bots with limited direct impact on humans may be more and not less effective in spreading their views in the social network, because their direct contacts keep exerting influence on users that the bot does not reach directly. Highly active and well-connected bots, in contrast, may have a strong impact on their direct contacts, but these contacts grow too dissimilar from their network neighbors to further spread the bot\u27s content. To demonstrate this effect, we included bots in Axelrod\u27s seminal model of the dissemination of cultures and conducted simulation experiments demonstrating the strength of weak bots. A series of sensitivity analyses show that the finding is robust, in particular when the model is tailored to the context of online social networks. We discuss implications for future empirical research and developers of approaches to detect bots and misinformatio

Persuasion without polarization? Modelling persuasive argument communication in teams with strong faultlines

Strong demographic faultlines are a potential source of conflict in teams. To study conditions under which faultlines can result in between-group bi-polarization of opinions, a computational model of persuasive argument communication has been proposed. We identify two hitherto overlooked degrees of freedom in how researchers formalized the theory. First, are arguments agents communicate influencing each other's opinions explicitly or implicitly represented in the model? Second, does similarity between agents increase chances of interaction or the persuasiveness of others' arguments? Here we examine these degrees of freedom in order to assess their effect on the model's predictions. We find that both degrees of freedom matter: in a team with strong demographic faultline, the model predicts more between-group bi-polarization when (1) arguments are represented explicitly, and (2) when homophily is modelled such that the interaction between similar agents are more likely (instead of more persuasive)

Аскаридоз – чи є вплив на непліддя? чи здатен викликати післяпологовий ендометрит? (випадок з практики)

Представлен случай из практики выявления Ascaris lumbricoides у пациентки после первичного бесплодия с осложнениями в послеродовом периоде. Приведена клиническая картина и фотографии аскариды длинной 10 см при кольпоскопии на 30 сутки послеродового периода.We present case of patient with I infertility 3 years, complication of pregnancy (anemia, cervical insufficiency), presence of Ascaris lumbricoides in the vagina in the postpartum period (with photos)

Job Done? New Modeling Challenges After 20 Years of Work on Bounded-Confidence Models

Since the first publication of the bounded-confidence models 20 years ago, hundreds of articles studying this class of social-influence models have been written. Bounded-confidence models proposed an intriguing so-lution to a pervasive research puzzle and have helped unveil and explain intriguing phenomena. Here, we re-flect about remaining research problems and future modeling challenges, arguing that there remain counter-intuitive model implications to be understood. To illustrate that there remain uncovered model challenges, we extend the bounded-confidence model. We assume assimilative influence when agents connected by positive relationships hold sufficiently similar opinions, adopting the core assumption of the bounded-confidence models. We combine this with another influential modeling approach, the notion that if agents connected by a negative social relationship disagree too much, opinion differences increase due to repulsive influence. This allows us to vary the relative strength of assimilation and repulsion in the influence dynamics, also allowing for the possibility that neither occurs in a particular interaction. Simulation experiments reveal three surpris-ing findings: Counter the intuition that stronger assimilation decreases opinion diversity, we show that in the presence of repulsion, intensifying the strength of assimilation can actually generate more opinion bipolarization. Second, we show that if repulsion becomes weaker this may still result in more bipolarization. Third, it turns out that more negative social relationships between or within subgroups can result in less bipolarization. We demonstrate these effects in very simple and highly stylized settings, in order to show that intuition fails to capture the complexity arising from the interplay of assimilative and repulsive influence even in these simple settings. We discuss implications of our findings for the ongoing debate about societal conditions fostering bipolarization, including in particular the design of personalized online social networks. Further, we address how our results may inform future work comparing and integrating alternative models of social-influence dy-namics.</p