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. (shrink)

I reflect on challenges to the validation of theoretical models from the perspective of a critical rationalist seeking to develop true Explanation of empirical phenomena. I illustrate my arguments with examples from the rich literature on social-influence models, a field that has profited from contributions from various disciplines such as physics, and mathematics. While this field is characterized by a large number of competing formal models, it has been criticized for having failed to generate reliable explanations and predictions, because of (...) a lack of empirical research validating models. I list five challenges to model validation in the social sciences: First, social-scientific theories are based on many obscure concepts. Second, many social-scientific concepts are latent. Third, the Representation of time is unclear in many models. Forth, in most social settings, various processes influence dynamics in parallel. Fifth, context dependencies limit the development of general models. (shrink)

In groups where members deliberate with limited information, consensus can emerge where, under complete information, fundamental disagreement would prevail. Using an agent-based model, we explore the factors contributing to group consensus by comparing argumentation styles in two types of groups: agents in groups of advocates communicate arguments for options perceived as personally beneficial. Agents in groups of diplomats do the same but avoid disagreement in that they bring up arguments supporting a second-best option whenever their interaction partner perceives to benefit (...) the least from what the sender finds best. Results show that consensus depends on argumentation style, but also on what members initially perceive as preferred. Diplomats are more likely to form consensus when initial perceptions accurately align with full information preferences, which diverge within the group. Conversely, and perhaps counterintuitively, in the presence of inaccurate initial perceptions, groups of advocates converge while diplomats part in disagreement. Our results imply that the ideal argumentation style must be considered carefully in light of both the desired outcome and the initial information distribution: when conflicting stakes produce a trade-off between consensus and truthful perceptions, polite versus selfish ways of deliberation may produce one or the other outcome, depending on the initial information members are equipped with. (shrink)

‘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 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. (shrink)