System dynamics methodology and research: opportunities for transitions research

Our society is on an unsustainable pathway exemplified by anthropogenic climate change, deforestation, human displacements and species extinction (IPCC 2018). Altering this pathway requires a combination of technical, organisational, economic, institutional, social-cultural and political efforts that are increasingly referred to as socio-technical transitions to an environmentally sustainable economy (Van den Bergh, Truffer, and Kallis 2011). Transitions research has developed frameworks and methods (e.g. Geels, Berkhout, and van Vuuren 2016; Papachristos 2018a) for understanding historical and contemporary transitions and for exploring system interventions related to governance and sustainability transitions ( Smith, Voß, and Grin 2010 ). While qualitative, narrative-based case studies dominated early research, modelling and simulation approaches have recently gained ground. These latter approaches are particularly suited to explore interdependencies across different system elements (Bergman et al. 2008; Papachristos 2011, 2014a; Holtz et al. 2015; Walrave and Raven 2016; Köhler et al. 2018) and to perform counter-factual analysis
(Sterman 2002) that allows posing ‘what-if’ questions and avoiding potential
selection biases that limit the power of pure empirical analysis. In this chapter we discuss how system dynamics (SD) modelling and simulation (Sterman 2000) can contribute to and complement – rather than substitute for – other methods in transitions research. SD is an established modelling and simulation research methodology that spans several subject areas (Sterman 2018). The phenomena discussed in the transitions literature are aligned with the foundational ideas of SD – a problem orientation, broad model boundaries across multiple realms and an ‘endogenous point of view’ towards processes of system change that are driven by actor decisions (Richardson 2011). Thus, SD is well suited to addressing transitions research questions that relate to high-leverage policies (Van den Bergh, Truffer, and Kallis 2011). As is the case with any modelling and simulation methodology considered for transition research, there are certain weaknesses that are worth bearing in mind (Turnheim et al. 2015) and the methodological approaches proposed to overcome them (e.g. Geels, Berkhout, and van Vuuren 2016; Papachristos 2018a). The chapter is structured as follows. Section 2 briefly overviews transitions research and some critical contemporary research issues. Section 3 briefly summarises SD history and foundations. Section 4 discusses the core SD practices and how they can provide entry points for transitions research. Section 5 discusses exemplary SD work relevant to transitions research across three levels of analysis: macro, meso and micro. Emphasis is on the meso level, as this level is also central in transitions research (Geels 2002; Geels and Schot 2007). In Section 6 we discuss some key opportunities and challenges for applying SD methods in transitions research.