The potential of agent-based modelling (ABM) for developing theory has been recognized, yet methodologies are lacking. Building theories of social-ecological systems is challenging because of complex causality, context-dependence, and social-ecological interdependencies. We propose an approach that addresses these challenges through combining case-based empirical research with ABM in a collaborative modelling process. In-depth empirical research is essential for identifying a puzzle and potential explanations thereof, and for recognizing context and social-ecological interdependencies. Collaborative model building and analysis enables careful abstraction and reflection, and allows further exploring and testing the emerging theory in dynamic contexts, leading to better-grounded and transparent assumptions and theories. We call this approach BIM (Being In the Middle) and articulate it through three features: contextually embedded, collaboratively abductive and empirically stylized. We highlight how BIM facilitates new interdisciplinary avenues for discovering social-ecological interdependencies, discuss how it can be applied and what challenges and frontiers lie ahead.

This open access book is about causal thinking and the use of causal language, with a focus on introducing philosophical ideas about causation to students and researchers of Social-Ecological Systems (SES). It takes a systematic approach to three central topics: the meanings of different causal expressions, sufficiency of evidence for inferences from observations to causal relations, and how to handle the complexity of causal relations in social-ecological systems. Consequently, the book is divided into three parts. In the first part the authors analyse and discuss the use of causal idiom in ordinary language, and in the second part they scrutinise the use of causal concepts and causal inference in science. Finally, the authors discuss causal reasoning about social-ecological systems in multi- and interdisciplinary contexts.

Understanding causation in social-ecological systems (SES) is indispensable for promoting sustainable outcomes. However, the study of such causal relations is challenging because they are often complex and intertwined, and their analysis involves diverse disciplines. Although there is agreement that no single research approach (RA) can comprehensively explain SES phenomena, there is a lack of ability to deal with this diversity. Underlying this diversity and the challenge of dealing with it are different causal reasonings that are rarely explicit. Awareness of hidden assumptions is essential for understanding how the causal reasoning of an RA is constituted, and for promoting the integration, translation, or juxtaposition of different RAs. We identify the following elements as particularly relevant for understanding causal reasoning: methods, frameworks and theories, accounts of causation, analytical focus, and causal notions. We begin with the idea that one of these elements typically figures as an entry point to an RA. This entry point is particularly important because it generates a path dependence that orients causal reasoning. In a subsequent step, when an approach is applied, causal reasoning concretizes as a result of a particular constellation of the remaining elements. We come to these insights by studying the application of four different RAs to the same social-ecological case (the collapse of Baltic cod stocks in the 1980s). On the basis of our findings we developed a guide for the analysis of causal reasoning by raising awareness of the assumptions, key elements, and the relations between these key elements for a given RA. The guide can be used to elicit the causal reasoning of RAs, facilitate interdisciplinary collaboration, and support disclosure of ethical/political dimensions that underlie management/governance interventions that are formulated on the basis of causal findings of research studies.

In this paper we extend the use of a relational approach to simulation modelling, a widely used knowledge practice in sustainability science. Among modellers, there is awareness that model results can only be interpreted in view of the assumptions that inform model construction and analysis, but less systematic questioning of those assumptions. Moreover, current methodological discussions tend to focus on integrating social and ecological dynamics or diverse knowledges and data within a model. Yet choices regarding types of modelling, model structure, data handling, interpretation of results and model validation are not purely epistemic. They are entangled with values, contexts of production and use, power relations, and pragmatic considerations. Situated Modelling extends a relational understanding of the world to scientific knowledge production and with that to modelling itself in order to enable a systematic interrogation of these choices and to research social-ecological transformations relationally. To make tangible the situatedness of simulation modelling, we build on existing practices and describe the situatedness of three distinct modelling approaches. We then suggest four guiding principles for Situated Modelling: 1. attending to the apparatus of knowledge production that is socially and materially embedded and produced by e.g. research infrastructures, power relations, and ways of thinking; 2. considering how agency is distributed between model, world, data, modeller in model construction; 3. creating heterogenous collectives which together occupy the formerly individualised subject position; and 4. using agonism as an epistemic virtue to retain and work with significant differentiations of social-ecological dynamics throughout the modelling process.

Our study of periodic octopus closures helps to fill an empirical gap in community-based marine protected area (MPA) research on socially-diverse multidimensional wellbeing impacts. Human wellbeing provides a more meaningful and holistic measure of social impacts than previous economic measures while recognising equity- evidence that ultimately ensures support and enables long-term success in conservation. We trace the flow of benefits, costs and burdens from closures at three sites in Zanzibar and explore how different types of fishers and traders perceive impacts. This is done at a personal, livelihood group and village or community level, as well as in terms of ecosystem effects. Storytelling, photo-elicitation tasks and focused discussions prioritized participants’ emic descriptions and understandings of closures. We iteratively, qualitatively coded data using a three-dimensional (material, relational and subjective) social wellbeing approach. Despite different conditions and histories at the three sites, participants identified similar wellbeing attributes as affected by the closure. Themes included social conflict, non-compliance, income, education, food/nutrition, and communal benefits reflecting recent literature on MPAs and human wellbeing. Perceptions of inequity cross cut all three dimensions and gender was a strong dimension that emphasized procedural and distributional inequity between different types of livelihood groups e.g. small-scale traderwomen and male skindivers. Material wellbeing losses due to poor market environments highlighted how better alignment is needed between periodic closure activities and resulting value chain dynamics. Opening events intensely impacted wellbeing across all dimensions, suggesting that these moments are critical for creating positive perceptions or losing support for closures.

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.

Marine protected areas like periodic closures are increasingly used to improve both fisheries management and biodiversity conservation, and often secondarily, human well-being. Yet rule breaking, whether formal regulations, or community-agreed norms, continues to negate expected management and conservation outcomes, remaining a major challenge. Although compliance scholarship today is expanding beyond non-economic explanations of (non)-compliance behaviour, approaches and theory used fail to show the diversity of perceptions and dispositions that can underpin motivations. A deepening engagement with behavioural sciences like sociology, psychology or behavioural economics is increasingly recognized as key in tackling compliance issues in marine protected areas. In this paper, we contribute to such a strand of compliance scholarship by presenting the responses and positions to (non-) compliance, rules/regulations and authorities of different people, for example, fishers, traders and others involved in an area-based fishery management intervention in Zanzibar, Tanzania. We take a qualitative approach aligning with an interpretive research ethic to carry out story circles, photo-elicitation tasks and focus groups at three sites enacting periodic octopus closures. Theory drawn from sociology, anthropology and cognitive sciences was used to interpret how participants justified were motivated by or dispositioned towards rules, rule makers and rule breakers. Generally, the area-management intervention was supported by participants, signalling the potential for future compliance most commonly based on the recognition of a degraded marine ecosystem in need of protection. However, the diverse and dynamic responses by different livelihood groups (i.e. traders and fishers) indicated on which basis and through which logics compliance behaviour was condoned or legitimated. This type of insight can help managers gauge or anticipate the potential for non-compliance in participatory approaches that can impact the health of marine ecosystems. Recommendations include providing discussion spaces for conservation participants' (not just fishermen or fishery organizations) own meanings to be understood and not assumed. Meanings which create and shape relations, actions and concepts in marine protected area processes and thus directly impact the uptake and sustainability of conservation interventions. Read the free Plain Language Summary for this article on the Journal blog.

A key aim of sustainable development is the joint achievement of prosperity, equality, and environmental integrity: in other words, material living standards that are high, broadly -distributed, and low -impact. This has often been called the triple bottom line. But instead, what if there is a trilemmathat inhibits the simultaneous achievement of these three goals? We analysed international patterns and trends in the relationships between per -capita gross national income, the Gini coefficient for income distribution, and per -capita ecological footprint from 1995 to 2017, benchmarking them against thresholds from the existing literature. A dynamicanalysis of the trajectories of 59 countries and a staticanalysis of a larger sample of 140 countries found that none met the triple bottom line, and that instead there were widespread tradeoffs among the three indicators. These tradeoffs, leading to divergent national trajectories and country clusters, show that common pair -wise explanations such as Kuznets Curves do not adequately capture important development dynamics. In particular, while only a few countries simultaneously met the thresholds for prosperity and equality on the one hand and equality and environment on the other, none did for prosperity and environment. Moreover, inequality likely makes resolving this critical tradeoff more difficult. Our findings suggest that mitigating the sustainability trilemma may require countries - especially those that are already prosperous - to prioritize economic redistribution and environmental stewardship over further growth.

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.

Agent-based modeling (ABM) has been widely used in numerous disciplines and practice domains, subject to many eulogies and criticisms. This article presents key advances and challenges in agent-based modeling over the last two decades and shows that understanding agents' behaviors is a major priority for various research fields. We demonstrate that artificial intelligence and data science will likely generate revolutionary impacts for science and technology towards understanding agent decisions and behaviors in complex systems. We propose an innovative approach that leverages reinforcement learning and convolutional neural networks to equip agents with the intelligence of self-learning their behavior rules directly from data. We call for further developments of ABM, especially modeling agent behaviors, in the light of data science and artificial intelligence.