Social reinforcement with weighted interactions

Abstract

The speed and extent of diusion of behaviors in social networks depends on network structure and individual preferences. The contribution of the present study is twofold. First, we introduce weighted interactions between potential adopters that depend on the similarity in their preferences and moderate the strength of social reinforcement. The reason for the extension is the existence of a conrmation bias in the way agents treat information by prioritizing evidence conforming to their opinion. As a result, individuals become less likely to be inuenced by peers with relatively dierent preferences, reducing the overall diusion rate under clustered networks. Second, we enrich our analysis by also considering a scale free network topology with a high degree asymmetry, motivated by its pervasiveness in online social networks. This network performs consistently well in terms of diusion for dierent parameter combinations and clearly outperforms clustered networks under weighted interactions. Our results show that more realistic assumptions regarding agents' interactions shift the focus from clustering to degree distribution in the study of network structures allowing for fast and widespread behavior adoption.

Biased perceptions of other people's attitudes to carbon taxation

“…This can build on earlier work (Schuldt et al, 2019;Goldberg et al, 2019;Rohring and Akerlof, 2020). Furthermore, it might be worth investigating how social media use and social networks (Konc and Savin, 2019) shape a person's social perceptions. Many people nowadays get their news and information in general and specifically on climate change from like-minded peers in online environments (Williams et al, 2015), which may further amplify the identified biased perceptions.…”

The social multiplier of environmental policy: Application to carbon taxation

“…21 For more discussion of a social norm diusing through weighted social interactions see Konc and Savin (2019). at 20,000 and the maximum income at 1,000,000.…”

Seeing what can(not) be seen: Confirmation bias, employment dynamics and climate change

“…The importance of this issue to our understanding of different aspects of social cohesion has been recently highlighted in an agent-based framework by scholars such as Sobkowicz (2018) and Konc and Savin (2019) . Having greenhouse gas mitigation practices as general benchmark and applying tools from network theory, the latter modelled weighted interactions among agents that depend on the similarity in preferences.…”

“…The latter occurs when a slight change in a system's parameters causes a new periodic trajectory to emerge from an existing periodic trajectory, the new one having double the period of the original. With the doubled period, it takes twice as many iterations for the numerical values visited by the system to repeat themselves (for a rigorous assessment, see Kuznetsov, 2004 ).…”