Toward a methodology for explaining and theorizing about social-ecological phenomena

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Highlights

  • Case comparison and synthesis methods are increasingly being used alongside in-depth case studies to investigate causal effects and causal mechanisms in social-ecological systems (SES).

  • The potential of agent-based modelling (ABM) for developing generative explanations and building theory has recently been highlighted across several natural and social sciences.

  • Combining generalizing from case studies with ABM allows building and testing empirically grounded, generative explanations of phenomena in social-ecological systems that take emergence, complex causation, and context into account.

  • We propose a four-step abductive, collaborative, and transdisciplinary methodology for explanation and middle-range theory building.

Explanations that account for complex causation, emergence, and social-ecological interdependence are necessary for building theories of social-ecological phenomena. Social-ecological systems (SES) research has accumulated rich empirical understanding of SES; however, integration of this knowledge toward contextualized generalizations, or middle-range theories, remains challenging. We discuss the potential of an iterative and collaborative process that combines generalizing from case studies with agent-based modelling as an abductive methodology to successively build and test explanations rooted in complexity thinking. Collaboration between empirical researchers, theoreticians, practitioners, and modellers is imperative to accommodate this process, which can be seen as a first step toward building middle-range theories.

Introduction

As social-ecological systems (SES) research is maturing, calls for moving beyond describing toward explaining and theorizing social-ecological phenomena are becoming more frequent [1, 2, 3]. In the last few decades, SES research has accumulated a rich body of empirical knowledge about how the interplay between human agency, social networks, and ecological dynamics shapes social and ecological outcomes [4,5]. This in-depth, mostly place-based knowledge, together with the increasing availability of generalized knowledge from synthesis studies [6], provides a valuable source for the development of explanations and theory. Generalization and theorizing in SES, however, face many challenges because of complex causation, emergent processes, social-ecological intertwinedness, and context dependence that characterize SES [7,8, 9, 10].

Recent advances in the use of agent-based modelling (ABM) for explanation and theory development in several natural and social sciences provide interesting opportunities for overcoming some of these challenges. ABM is grounded in complexity thinking, allows studying emergent processes and has proven to be a valuable tool for interdisciplinary and transdisciplinary collaboration [11,12]. While ABM is becoming increasingly popular in SES research it is mainly used for exploring social-ecological dynamics and global change or policy implications in particular places, to support participatory processes or as a conceptual tool [11]. We argue that ABM could also be a valuable tool for facilitating a transdisciplinary process of theory development that builds on existing empirical knowledge, particularly recent attempts to develop generalized knowledge claims [6,13••]. Yet, combining generalization from case studies with ABM to develop and test explanations of social-ecological phenomena is not often done (but see Ref. [14] for an example and Ref. [15] for a discussion of how meta-studies can support the development of process-based land change models).

For this purpose, we introduce here a methodology that combines case-study research with ABM to facilitate developing generative explanations, that is, explanations that specify the causal mechanisms and processes that bring about a phenomenon of interest [16]. Contrary to deductive approaches of theory building and testing, this methodology builds theory from empirical understanding in an abductive manner. The resulting theories are so-called middle-range theories [17], or ‘contextual generalizations’ [18] that apply to a delimited set of cases specified by the conditions under which the proposed mechanisms are effective (see Box 1 for a glossary of terms).

We aim to advance the development of middle-range theories of social-ecological phenomena by proposing a transdisciplinary process of theorizing that iterates between empirical knowledge integration and modelling. The approach is inspired by the mechanism-based approach from Analytical Sociology [16], but extends beyond it by incorporating diverse knowledge sources and ways of knowing through a collaborative process of developing, testing, and refining explanations. This methodology requires empiricists, theorists, practitioners, and modellers to work together across disciplines and methods. ABM can be used as a tool to collect, contest, compare, and integrate these various types of knowledge and understanding. In this article, we first discuss current trends in generalizing from case studies in SES research and review the potential of ABM for developing explanations of phenomena recently put forward in several natural and social sciences. We then propose four steps to combine empirical synthesis with ABM and discuss their potential for supporting an abductive and iterative process of theorizing that moves from empirics to theory and back.

Section snippets

Case study methods for finding commonalities and generalizing across cases

Synthesizing empirical evidence across cases to identify common patterns, mechanisms or drivers of SES outcomes has recently received increased attention, particularly in the subfield of land system science (see Figure 4 in Ref. [13••] for a review of studies in land system science from 1995–2012). These studies have applied a variety of qualitative and quantitative case study and synthesis methods. A review of a subset of the most commonly found methods in SES research provides insights into

Developing explanations of complex system phenomena through agent-based modelling

ABM is a computational method that represents different types of agents, their attributes, interaction structures and behaviors and simulates their interactions with each other and their environment over time. Agent-based models are used to study emergent, meso-level and macro-level outcomes and perform experiments to test whether changes in the action logics of agents, their relational structures or the environment are likely to change the outcome [39,40]. ABM provides generative explanations;

Combining empirical synthesis with agent-based modelling in an iterative, transdisciplinary process

We propose a four-step, iterative methodology for combining empirical synthesis with ABM to advance explanation and theorization of social-ecological phenomena (Figure 1). The methodology builds on the insights presented above and our experiences with combining empirical synthesis and ABM in collaborative research processes that involved empirical scientists, theoreticians, and modellers [14,59,60]. The steps are as follows:

  • Step 1: Developing possible explanations of the empirical phenomenon

Concluding reflections

Recent advances in generalizing from SES case studies and explaining complex phenomena through ABM provide novel opportunities for theorizing social-ecological phenomena. Combining generalized empirical knowledge with ABM in a transdisciplinary process allows building evidence and confidence of causal claims and provides an empirically grounded, complexity-based methodology for developing middle-range theory. The use of ABM is particularly interesting for SES research because it is one of the

Conflict of interest statement

Nothing declared.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

We thank Rodrigo Martinez Peña for help with reviewing the literature on ABM for explanation and theory building. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 682472 — MUSES).

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