Current research challenges in sustainability science require us to consider nonlinear changes e.g. shifts that do not happen gradually but can be sudden and difficult to predict. Central questions are therefore how we can prevent harmful shifts, promote desirable ones, and better anticipate both. The regime shifts and transformations literature is well-equipped to address these questions. Yet, even though both research streams stem from the same intellectual roots, they have developed along different paths, with limited exchange between the two, missing opportunities for cross- fertilisation. We here review the definitions and history of both research streams to disentangle common grounds and differences. We propose avenues for future research and highlight how stronger integration of both research streams could support the development of more powerful approaches to help us navigate toward safe and just futures.

This paper draws on process-relational perspectives to present an understanding of socio-environmental problems not as obstacles but as modes of existence. We argue that engaging with problems in this way unlocks their potential to be transformative, rather than merely resolving them. But how can those who set up and engage with processes of transformative change unlock this potential? This brings us to the topic of theorizing, as we argue that theorizing is an integral and fundamental part of engaging with problems. In this paper we develop an approach to theorizing for transformative collective action based on two key aspects: “Time”, building on Gilles Deleuze, and the “Complex We”, building on Marisol de La Cadena. As to the first, we explore how the past and future condition our understanding of problems, contrasting a linear, sequential view of time with a process-relational one where past and future are contracted in the present and are continuously reconfigured via processes of difference and repetition. As to the second, we introduce the "complex we", an emergent subjectivity as the agent of theorizing as that which supports reconfiguration of the past and the future. Together, these two aspects open up the transformative potential of socio-environmental problems beyond what could be conceptualized on the basis of linear time. We apply our approach to theorizing to a research project in Southern France, where problems such as tensions and conflicts over water availability are explored collaboratively. The paper concludes by discussing the strengths and limitations of our approach.

Understanding causal relations for sustainability scientists means studying phenomena that involve complex causality, e.g. multiple and heterogeneous relations and entities, context-sensitivity, and multi-scalar phenomena. To cope with this, sustainability scientists have borrowed concepts from neighboring disciplines, used causal expressions that have confusing meaning, or abstained from using causal language altogether. We argue for using causal language as it is useful for prediction, manipulation, explanatory understanding and responsibility attribution. However, traditional views on causality have limitations dealing with causal complexity. We spell out the challenge of formulating useful concepts. We argue that it is important to recognize the role of everyday causal cognition and its limitations, to distinguish the different ways in which sustainability scholars talk about complexity and to clarify the causal meaning of complexity concepts, like non-linearity, adaptive capacity, and feedback. Finally, we propose the concept of causal configuration to make explicit the causal meaning of complexity-related concepts.

CONTEXT Poverty can result from complex social-ecological interactions where persistent feedback loops create resistant, unsustainable states. In dryland regions, agricultural innovations intended to break poverty traps can often neglect long-term environmental consequences, leading to a reinforcing cycle of degradation and poverty. OBJECTIVE This study investigates how cross-level dynamics in agricultural innovation systems generate and sustain poverty traps. We ask: (i) How do poverty traps emerge in agricultural innovation systems? (ii) What characterizes agents experiencing these traps? (iii) How can traps be avoided or overcome? METHODS We combine dynamical systems modeling (DSM) and agent-based modeling (ABM) to analyze poverty trap emergence. DSM uses bifurcation analysis to reveal system-level dynamics under two innovation scenarios: low-impact (“gentle”) and high-impact (“strong”). ABM simulates these scenarios, tracking agent attributes across runs and mapping them onto DSM parameter space to identify producers and innovators in poor or non-poor states. Comparing agent outcomes with DSM parameter space identifies characteristics of poor and non-poor states. Together, DSM captures system dynamics while ABM reflects heterogeneity, enabling targeted interventions to escape poverty traps. RESULTS AND CONCLUSIONS Under gentle innovations, poverty and well-being depend on thresholds in innovation efficiency, funding, and desire: below thresholds, poverty is inevitable, at intermediate levels, outcomes depend on farmers' initial conditions and above thresholds, all reach well-being. Strong innovations carry higher ecological risks, with traps arising whenever thresholds are unmet. Low efficiency traps all farmers with fragile bistability and oscillating well-being at higher efficiencies. Low innovation funding and desire creates poor equilibria with stable well-being at higher levels. Improving innovation efficiency, through stronger knowledge efficiency (understanding producers' needs), greater innovation demand, and higher capital efficiency (better use of resources), increases the effectiveness of innovations and enables producers to escape poverty traps. Similarly, increasing innovation funding and demand for low-environmental-impact agricultural technologies supports pathways out of poverty by simultaneously improving income, ecological indicators, and crop production. SIGNIFICANCE This study highlights the critical role of agricultural innovation in shaping poverty trap dynamics and environmental outcomes. By focusing on cross-level interactions between micro-level producers and meso-level innovators, the study demonstrates how these interactions can create or sustain poverty traps. It emphasizes the importance of ecological feedback for understanding the long-term effects of interventions aimed at reducing poverty. Finally, it identifies pathways for breaking poverty traps that go beyond low-impact innovations, highlighting the need for systemic, coordinated interventions to achieve sustainable and resilient agricultural development.

Studies on social-ecological transformations have explained change and no-change at systemic level through and overlooked the contribution of agents that keep the system from changing and how their actions respond to their position, which constitutes a critical analytical gap due to the limited success of societies at transforming social-ecological systems. However, when it comes to analysing underlying dynamics, frameworks focus on key agents of change, such as institutional entrepreneurs. This approach overlooks the contribution of agents that keep the system from changing, which constitutes a critical analytical gap due to the limited success of societies at transforming social-ecological systems. We advance an analytical strategy that tackles this gap by explicitly accounting for both causes of change and no-change. This view links agent’s motivations and courses of action to their positionality. We draw from social realism’s morphogenetic approach to analytically distinguish between material and ideational drivers of action, identify situations that bring them about, and account for their relations. To operationalize this view, we trace actors’ interaction and how they lead to outcomes. We rely on the social-ecological action situations framework. This combined approach renders explicit: a) the constitution of material interests by both ecosystemic processes and social structures; b) the relations of compatibility/incompatibility between groups’ material interest; and c) the role of social-ecological interaction in shaping discursive relations of compatibility/incompatibility among actor groups. We illustrate the usefulness of this approach by applying it to the well-documented case of governance transformation of Chilean small-scale fisheries

Process-relational perspectives (PRP) have been put forward as a crucial contribution for conceptualizing radical transformations towards sustainability. This is because PRP conceptualize transformations as open processes. This openness is attributed, first, to processes and relations having performative power, which means that processes and relations are constitutive of elements. Second, PRP take processes and relations as continuously unfolding which means that elements taking part in transformations continuously change. Therefore, transformations are conceptualized beyond what elements are and do at a particular moment, setting PRP apart from other ways of conceptualizing transformations that don’t. This has an impact on transformative potential which for PRP is thus different (and perhaps more radical) than for the more conventional counterparts. Inquiring into the implications of tapping into this potential brings us to the topic of causation. Fostering transformation requires an understanding of the causal workings of systems. However, establishing causal links is difficult and for many, speaking of causation entails the risk of conveying a deterministic perspective inadequate for such a task. To avoid such risk, process-relational scholars urge us to rethink the concept of causation so that it can be mobilized to support a PRP on transformations. This paper takes the reader through a conceptual deep dive into process-relational understandings of transformation and causation. It encourages the reader to question conventional views of causation and ends by offering a process-relational take on theories of change (ToCs) that are often mobilized to foster transformations towards sustainability.

Process-relational perspectives have been proposed as new ways of conceptualising, analysing and engaging with social–ecological systems (SES) that are capable of dealing with intertwinedness and complexity. The application of PR perspectives in SES research, however, remains challenging and largely conceptual. We explore the possibilities of combining process-relational thought with agent-based modelling as a methodology for thinking with and exploring the becoming/emergence of SES. We call it relation-based modelling (RBM) and develop it through modelling the emergence/becoming of a virtual small-scale fishery. RBM focuses attention towards the apparatus, i.e. the material and discursive practices that shape the model structure which then provides the conditions for the emergence of fishery assemblages in a virtual, simulated world. Our attempt to produce a model from a process-relational perspective supported critical reflection of our assumptions about fisheries and agent-based modelling, particularly with respect to questioning common ways of dissecting the world that hinder understanding their intertwinedness and dynamism. Analysis of simulation results and our reflections about the apparatus together reveal how organisation at different levels, from the arrangement of practices that shape the design of the model to the arrangements of elements in the virtual world of the simulation influence the emergence of a virtual fishery. We reflect on the tensions we encountered when disentangling the entangled and formalising process-relational ideas and conceptualisations in the model and the learning and transformations that occurred through this process. A process-relational practice of modelling can open up possibilities to think differently about SES and change the way we theorise and act within them.

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, contextdependence, 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.

CONTEXT Food insecurity remains a global challenge, with differing narratives shaping interventions in sub-Saharan Africa. The “crisis narrative,” favored by aid agencies, links insecurity to production issues, advocating agricultural innovations. Meanwhile, the “chronic poverty narrative,” reflected in African policy, ties insecurity to farmer poverty, emphasizing livelihood and economic solutions. Narrative subjectivity can lead to uncritical privileging of certain understandings and solutions, necessitating a critical exploration of contexts, causes, and solutions to food insecurity in the region. Our research addresses the need to understand and illustrate the complex problem of food insecurity in the region. OBJECTIVE This study employs a mixed-method approach, combining collaborative storytelling, model exploration, and scenario analysis, to investigate food security, agricultural innovation, and climate adaptation in Mali, West Africa. METHODS We developed a three-stage methodology represented as a story arc: beginning (exposition and problem statement), development (action), and completion (solution), providing a cohesive narrative framework. The arc unfolds with the story exposition introducing characters, plot, and problem statement. The story development includes participant-led model simulations and modeler-led scenario analysis. The story completion integrates insights from model simulations and scenario analysis to develop the collective understanding of the narratives surrounding food (in)security. RESULTS AND CONCLUSIONS This study generates several insights that highlight the inherent complexities within agricultural innovation systems that emerge from the non-linear dynamic interaction of actors operating across scales that contribute to food insecurity. We redirect the focus of narratives of causes (and subsequent solutions) of food insecurity from solely climate-driven production losses and poverty to the complex interplay of climate, agroecology, innovation networks, risk perception, innovation beliefs, desires, and knowledge transmission. A shared narrative emerges, characterizing food security as a complex adaptive system influenced by factors such as climate-induced production variability, agroecological heterogeneity, network structures and climate risk perception. The study underscores the methodological value of collaborative storytelling and model simulation to enable a structured and reflective exploration of these complex systems. By transforming participants into co-creators of knowledge, this methodology fosters systems thinking, turning abstract systemic relationships into tangible, actionable insights. SIGNIFICANCE Our study demonstrates the need to critically reevaluate the role of narratives in shaping agricultural innovation systems and their capacity to transform food systems toward enhanced sustainability and food security. Our participatory and systems-driven approach offers a pathway to more adaptive and effective interventions in the face of complex, dynamic challenges.

Social-ecological systems increasingly face polarization dynamics that challenge environmental governance. Such polarization implies the development of opposing narratives with limited interaction, each framing environmental problems and solutions in distinct ways. In this study, we analyze a case of narrative polarization around the eutrophication crises of the Mar Menor lagoon in Spain, focusing on how proposed solutions are narrated to address this complex environmental puzzle. We use a mixed-method approach that combines social network analysis and an analysis of narrative practices in interview situations, to investigate whether and how potential solutions to eutrophication in the Mar Menor can be understood as bridging spaces that create opportunities for interaction between divergent societal narratives. Our three-step analysis includes: (a) a network analysis of reports proposing solutions to identify solutions with a bridging role (i.e., those linking reports that otherwise have little overlap in the solutions proposed); (b) a thematic narrative analysis to investigate the solutions proposed by diverse actors; and (c) an analysis of narrative practices around selected bridging solutions to explore if they constitute new spaces where narratives can interact and confront positions - what we call bridging spaces. We suggest this mixed methods approach allows for the identification of potential bridging spaces to mediate polarization and outline directions for future research on both the case study specifically, and on polarization in environmental governance more generally.

Common pool resources, like fish, timber, water, are essential in providing food, income and raw material. However, maintaining sustainable practices for common pool resources is a collective challenge due to the social and ecological uncertainties. Climate impacts only further complicates the collective governance of these resources, as resource availability will substantially change and reduce. To understand how do resource users actually deal with these changes in resource availability is a central to our understanding the sustainable collective use of natural resources. From the few empirical studies available we learned that fishers make differently sense of ecological change, which should be taken into account when studying how collective resource use behaviour changes in dynamic social (what others may do) and ecological settings. Yet, very few studies of collective sustainable resource use, if any, investigate the role of heterogeneous attribution of ecological change mechanisms at the individual level for collective resource use. Our project thus seeks understanding of the role of how individual's attribution of ecological change may influence sustainable collective resource use with agent-based modelling (ABM).

Small-scale fisheries are likely to experience a higher frequency and magnitude of environmental and socioeconomicchange because of increasing climate shocks and pressures that result from them, as well as because of the influence of global marketdynamics. Fisheries' responses to the impacts of global change are often influenced by relations between fishers and traders. Suchrelations constitute a link between markets, fishers, and the marine ecosystems. However, the ways that fisher-trader relations respondto global change, influencing the adaptive capacities of small-scale fisheries are poorly understood. Addressing this gap in this paper,we explore how fisher-trader relations, embedded within other social, ecological, and social-ecological relations, mediate change, suchas disasters, new policies, or market demand. We do this by mapping the interactions that shape the mediating role of the fisher-traderrelations in five case studies of small-scale fisheries. Synthesizing among the case studies we develop a typology of combinations ofrelations, their roles, and characteristics that influence the capacity of small-scale fisheries to respond to abrupt, slow, and cyclicalchange, resulting in absorbing or reinforcing its effects. Particularly we show how fisher-trader relations can generate the capacity tomaintain livelihoods and form new relations when exposed to disruptive change and the capacity to increase supply in response to newmarket opportunities. The findings highlight the importance of studying responses to change in small-scale fisheries through the lensof relations and combinations of relations rather than individual behaviors. Future research on this topic could explore how theidentified patterns of relations, including fisher-trader relations, may mediate change in other socio-cultural and social-ecologicalcontexts, and when exposed to different types of disturbances.

AbstractIn this chapter we explore the connections between on the one hand causal relations and on the other hand strict and less strict laws, i.e., regularities, expressed as correlations and regressions.It is tempting to think that laws and regularities describe general causal relations. They do not. Neither laws nor regularities distinguish between cause and effect, they state relations between quantities only; the causal aspect is connected to the manipulation and this aspect is not represented in formulations of laws and regularities.Non-strict laws, often called ‘regularities’, differ from strict laws in that they are conditioned on ceteris paribus clauses, i.e., unspecified clauses of the form ‘all else the same’. This makes generalisations, i.e., inferences to unobserved situations, difficult.The main points of this chapter are: Laws, strict and non-strict, express relations between quantities, not cause-effect relations. Regularities are expressed by two measures, coefficient of correlation and regression. Inferring causal relations from laws and regularities requires additional information. Having such information one may represent causal relations using directed graphs and/or structural equations.

Process-relational perspectives have been proposed as new ways of conceptualising, analysing and engaging with social-ecological systems (SES) that are capable of dealing with intertwinedness and complexity. The application of PR perspectives in SES research, however, remains challenging and mostly conceptual. We explore the possibilities and limitations of combining process-relational thought with agent-based modelling as a methodology for thinking with and exploring the becoming/emergence of SES. We call it relation-based modelling (RBM) and develop it through modelling the emergence/becoming of a virtual fishery. The RBM focuses attention towards the apparatus, i.e. the material and discursive practices, that shape the model structure which then provides the conditions for the emergence of fishery assemblages in a virtual, simulated world. Our attempt to produce a model from a process-relational perspective supported critical reflection of our assumptions about fisheries and agent-based modelling, particularly with respect to questioning common ways of dissecting the world that hinder understanding their intertwinedness and dynamism. We highlight how organisation at different levels, from the arrangement of practices that shape the design of the model to the arrangements of elements in the virtual world of the simulation influence the emergence of a virtual fishery. We reflect on the tensions we encountered when disentangling the entangled, and formalising process-relational ideas and conceptualisations in the model and the learning and transformations that occurred through this process. A process-relational practice of modelling can open up possibilities to think differently about SES and change the way we theorise and act within them.

AbstractIn this chapter we start the discussion about causal idiom by giving excerpts from three papers, each discussing the dynamics of a social-ecological system. There is plenty of talk about causes in these papers, but, interestingly, the authors talk about causes and effects without much reflection on the criteria for something being a cause of something else, nor about the required evidence for such claims.

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.

AbstractWhen talking about causes we often think of an imagined contrast to the real sequence of events: we use a counterfactual, asking what would have happened if the cause had not occurred. But one might be doubtful about the explanatory force of this analysis. The basic problem is that the truth or falsity of a counterfactual statement cannot be determined by empirical means. In some cases, notably in physics, we can apply a strict law when justifying claims about alternative scenarios. In most cases, however, we have at best regularities and in such cases it is difficult to have any confidence in the corresponding counterfactual. An account of causation in terms of the more restricted concept of potential outcomes is much more useful. It is closer to empirical practice and is more reliable. The main points are: It is common to explain the meaning of A caused B’ as ‘If A had not occurred, then B would not have occurred.’ This is no step forward, since we do not in general know whether the counterfactual is true. Rubin’s more restricted notion of potential outcome is to be preferred, since it can be applied to empirical research.

Social-ecological systems research aims to understand the nature of social-ecological phenomena, to find ways to foster or manage conditions under which desired phenomena occur or to reduce the negative consequences of undesirable phenomena. Such challenges are often addressed using dynamical systems models (DSM) or agent-based models (ABM). Here we develop an iterative procedure for combining DSM and ABM to leverage their strengths and gain insights that surpass insights obtained by each approach separately. The procedure uses results of an ABM as inputs for a DSM development. In the following steps, results of the DSM analyses guide future analysis of the ABM and vice versa. This dialogue, more than having a tight connection between the models, enables pushing the research frontier, expanding the set of research questions and insights. We illustrate our method with the example of poverty traps and innovation in agricultural systems, but our conclusions are general and can be applied to other DSM-ABM combinations.