The purpose of this study is to analyze the impact of homophily on diffusion over social networks. An agent-based simulation model is developed to serve as the experimental ground for this analysis. Diffusion dynamics of a nonsticky innovation is investigated by varying homophily levels in the social network depicted in the model as the primary control variable. First of all, the results show that homophily is self-reinforcing. Second, starting from a nonhomophilous network, early increases in the level of homophily have a positive effect on the extent of diffusion, whereas further increases have a negative impact. Finally, several local minima and maxima are observed in the relation between the homophily level and the extent of diffusion. Our analysis focuses on node properties such as connectedness and average degrees in order to explain the observed regular relationship between homophily and diffusion. We argue that (i) homophily increases the connectedness of different status groups separately and (ii) increasing levels of homophily decreases the marginal importance of a single homophilous tie by increasing the sources of valuable information. Future research involves investigating the coevolution of social behavior and networks by allowing the adopted innovation to lead to value homophily, exploration of different diffusion initiation types, and different adoption heuristics.

In this article, we take stock of the findings from conceptual and empirical work on the role of transition initiatives for accelerating transitions as input for modeling acceleration dynamics. We applied the qualitative modeling approach of causal loop diagrams to capture the dynamics of a single transition initiative evolving within its regional context. In doing so, we aim to address two key challenges in transition modeling, namely conceptualization, and the framing of empirical insights obtained for various case study regions in a consistent modeling framework. Our results show that through this systematic approach one can translate conceptual and qualitative empirical work into a transition model design. Moreover, the causal loop diagrams can be used as discussion tools to support dialogue among researchers and stakeholders, and may support a comparison of transition dynamics across case-study regions. We reflect on main limitations related to empirical model validation (lack of data) and to model structure (high level of aggregation), and describe next steps for moving from a qualitative single transition initiative to a quantitative multiple transition initiatives model.

The emerging need for societal transitions raises the need for a better understanding of the dynamic nature of large scale societal systems, and therefore the development of an analytical approach for drawing dynamic conclusions based on system’s dynamic mechanisms, feedback relationships and interacting components. The objective of this study is to explore the degree to which System Dynamics as an approach enhances the process of understanding transition dynamics in socio-technical systems. In other words, it is aimed to reveal the type of insights that can be developed about such systems and their dynamic behaviour using the approach, as well as the shortcomings of the approach in this challenging task. In order to do so, a modeling study aiming to understand the underlying mechanisms of the waste management transition in the Netherlands is conducted. The quantitative model developed is based on the historical case of the waste management transition of the Netherlands, and it portrays issues as the dynamics of actors’ preferences, development of infrastructure and environmental consequences of dominant mode of functioning and provides an instance for demonstrating and evaluating the feedback-focused perspective discussed in this paper. Finally, the paper discusses a set of points regarding the utilized approach, System Dynamics, observed during this study both in general and in the specific context of transitions. In short, System Dynamics stands as a promising approach mainly due to its strength in explaining the source of complex dynamics based on interacting feedback loops, but it also has certain drawbacks in the context of transitions.