When reasoning about causes of sustainability problems and possible solutions, sustainability scientists rely on disciplinary-based understanding of cause-effect relations. These disciplinary assumptions enable and constrain how causal knowledge is generated, yet they are rarely made explicit. In a multidisciplinary field like sustainability science, lack of understanding differences in causal reasoning impedes our ability to address complex sustainability problems. To support navigating the diversity of causal reasoning, we articulate when and how during a research process researchers engage in causal reasoning and discuss four common ideas about causation that direct it. This articulation provides guidance for researchers to make their own assumptions and choices transparent and to interpret other researchers' approaches. Understanding how causal claims are made and justified enables sustainability researchers to evaluate the diversity of causal claims, to build collaborations across disciplines, and to assess whether proposed solutions are suitable for a given problem.

Dynamical systems modeling (DSM) explores how a system evolves in time when its elements and the relationships between them are known. The basic idea is that the structure of a dynamical system, expressed by coupled differential or difference equations, determines attractors of the system and, in turn, its behavior. This leads to structural understanding that can provide insights into qualitative properties of real systems, including ecological and social-ecological systems (SES). DSM generally does not aim to make specific quantitative predictions or explain singular events, but to investigate consequences of different assumptions about a system's structure. SES dynamics and possible causal relationships in SES get revealed through manipulation of individual interactions and observation of their consequences. Structural understanding is therefore particularly valuable for assessing and anticipating the consequences of interventions or shocks and managing transformation toward sustainability. Taking into account social and ecological dynamics, recognizing that SES may operate on different time scales simultaneously and that achieving an attractor might not be possible or relevant, opens up possibilities for DSM setup and analysis. This also highlights the importance of assumptions and research questions for model results and calls for closer connection between modeling and empirics. Understanding the potential and limitations of DSM in SES research is important because the well-developed and established framework of DSM provides a common language and helps break down barriers to shared understanding and dialog within multidisciplinary teams. In this primer we introduce the basic concepts, methods, and possible insights from DSM. Our target audience are both beginners in DSM and modelers who use other model types, both in ecology and SES research.

Generalization from a case study is a perennial issue in the methodology of the social sciences. The case study is one of the most important research designs in many social scientific fields, but no shared understanding exists of the epistemic import of case studies. This article suggests that the idea of mechanism-based theorizing provides a fruitful basis for understanding how case studies contribute to a general understanding of social phenomena. This approach is illustrated with a reconstruction of Espeland and Sauder's case study of the effects of rankings on US legal education. On the basis of the reconstruction, it is argued that, at least with respect to sociology, the idea of mechanism-based theorizing captures many of the generalizable elements of case studies.

Ideas about social scientic explanation lie at the core of debates about methodological individualism (MI). The spirit of MI is captured in a denition by Jon Elster:

[A] ll social phenomena-their structure and their change-are in principle explicable in ways that only involve individuals-their properties, their goals, their beliefs and their actions.

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Rationality and self-interest are routinely attributed an explanatory priority as an inherently understandable basis - as an ideal of natural order - for all social scientific explanation. We argue that this is not consistent with a causal-mechanistic understanding of science and that using self-interest and rationality heuristically as a default baseline biases social scientific research. From a naturalist perspective, both rationality and self-interest are empirical objects of explanation. We discuss one such explanatory hypothesis, according to which consistent self-interested behavior is sustained by a social norm.

The psychiatric category of addiction has recently been broadened to include new behaviors. This has prompted critical discussion about the value of a concept that covers so many different substances and activities. Many of the debates surrounding the notion of addiction stem from different views concerning what kind of a thing addiction fundamentally is. In this essay, we put forward an account that conceptualizes different addictions as sharing a cluster of relevant properties (the syndrome) that is supported by a matrix of causal mechanisms. According to this “addiction-as-a-kind” hypothesis, several different kinds of substance and behavioral addictions can be thought of as instantiations of the same thing—addiction. We show how a clearly articulated account of addiction can facilitate empirical research and the theoretical integration of different perspectives on addiction. The causal matrix approach provides a promising alternative to existing accounts of the nature of psychiatric disorders, the traditional disease model, and its competitors. It is a positive addition to discussions about diagnostic criteria, and sheds light on how psychiatric classification may be integrated with research done in other scientific fields. We argue that it also provides a plausible approach to understanding comorbidity. 

The core idea of analytical sociology is the importance of mechanism-based understanding of social processes. Sociological theories should provide clear and precise accounts of the social mechanisms by which the intentional activities of social agents bring about social phenomena. Theories about social mechanisms can be characterized as theories of middle range as they provide clear, precise, and simple explanations for specified aspects of range of different phenomena, without pretense of being able to explain all social phenomena. Intentional action plays an important role in social mechanisms, but the analytical sociology perspective suggests that our account of human agency should be based on findings and theories of psychological and cognitive sciences rather than on some preconceived ideas about human motivation or cognitive processing. Much of the development of mechanism-based knowledge consists of developing how-possibly explanation schemes. Agent-based computer simulations can be very useful for this kind of endeavor.

This comment discusses Kaidesoja (2013) and raises the issue whether his analysis justifies stronger conclusions than he presents in the book. My com- ments focus on four issues. First, I argue that his naturalistic reconstruction of critical realist transcendental arguments shows that transcendental arguments should be treated as a rare curiosity rather than a general argumentative strategy. Second, I suggest that Kaidesoja’s analysis does not really justify his optimism about the usefulness of causal powers ontology in the social sciences. Third, I raise some doubts about the heuristic value of Mario Bunge’s social ontology that Kaidesoja presents as a replacement for critical realist ontology. Finally, I propose an alternative way to analyze failures of aggregativity that might better serve Kaidesoja’s purposes than the Wimsattian scheme he employs in the book. 

This paper provides an inferentialist account of model-based understanding by combining a counterfactual account of explanation and an inferentialist account of representation with a view of modeling as extended cognition. This account makes it understandable how the manipulation of surrogate systems like models can provide genuinely new empirical understanding about the world. Similarly, the account pro- vides an answer to the question how models, that always incorporate assumptions that are literally untrue of the model target, can still provide factive explanations. Finally, the paper shows how the contrastive counterfactual theory of explanation can provide tools for assessing the explanatory power of models.