Promoting a shift away from meat consumption towards an increased share of plant-based alternatives is a promising strategy for addressing environmental challenges while also improving population health. Many consumers already express a strong interest in adopting more sustainable and healthy diets, however, empirical evidence suggests that there is only a weak link between these intentions and actual dietary changes. To unpack this intention-behavior gap this study explores three research questions 1) What factors explain intentions to reduce meat consumption among meat eaters? 2) How much meat do individuals with reduction intentions consume, compared to those without such intentions? and 3) Among participants with reduction intentions — what factors drive their continued meat consumption? To answer these questions, we developed a survey and recruited a nationally representative sample of Swedish consumers (n = 998). A backwards stepwise regression, including 14 theoretically informed variables, revealed that attitudes— towards meat (β = −0.32) and plant-based proteins (β = 0.35) both — were the strongest predictors of intentions to reduce meat consumption. Individual factors like environmental self-identity (β = 0.13) and gender (β = 0.08) played smaller but meaningful roles as did practical considerations such as the perceived convenience of cooking meat compared to plant-based foods (β = 0.09). Furthermore, although the result showed a significant difference in self-reported meat consumption between individuals with high stated intentions to reduce meat intake and those with low or no intention, the size of the difference was small only (d = 0.15), bordering negligible, reaffirming the suspected intention-behavior gap. Among those with intentions to lower their meat consumption, only two key variables emerged as driving continued meat eating — meat purchasing habits (β = 0.33) and a positive attitude towards mea t(β = 0.17). These results underscore the challenges of translating intentions into action and highlight how different variables are important for a) shaping intentions and b) driving these into actions. While intentions are a needed prerequisite for voluntary behavior change, they sometimes prove insufficient on their own, especially when the aim is to change behaviors heavily governed by habits. Practical implications suggest that focusing on breaking habits and fostering positive attitudes towards plant-based alternatives are key in bridging the gap between intentions and actual diet changes.

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.

Generative AI is all over the place, mainly in the form of large language models (LLMs). Recently, LLM researchers moved into the world of replacing human participants in social and behavioural science research and even social simulation. In this paper we want to make our community aware of these developments, discuss our worries but also the potential for using LLMs productively with agent-based social simulation, and pose some questions to the LLM approach to social simulation.

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.

Agent-based models have continuously increased regarding the implementation of the complexity of agent decision making. This poses questions for the ontological foundation of ABM as well as having implications for research design and data collections. This paper situates the meta-modelling framework CAFCA within other contextual and multidimensional agent-architectures, in particular focusing on its contributions to ontology and research design. We also present the challenges for data collection as a consequence of this increased representational complexity.

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.

Agent-based models of human behaviors in emergencies are extremely important in prevention, preparedness, response, and mitigation of crises. However, there is huge variation in the modeling of human cognitions and actions, with varying degrees of realism and even more diverse definitions of how realism should be implemented in the models. The aim of this Rapid Literature Review is to identify existing patterns in modelling realistic behaviours in simulation models of disasters and crises, but also to identify gaps in the literature on the basis of a qualitative assessment of review papers on the topic. We analyze eight papers (identified through a search of 68 papers in the Scopus database) that review ABM models designed either to investigate general behaviors in emergencies (i.e., evacuations), or reactions to specific crises (i.e., COVID-19). Our analysis shows that while all the papers agree in advocating for more realism in modeling human behavior, very little has been done in terms of designing agents, interactions and environments that can be considered realistic.

Social simulations can be a powerful tool for policy makers and other decision makers to support them in their decision making process. To be a powerful tool, it is not only important that the agents in the simulation exhibit realistic–human like—behavior, but also that the simulation is empowering the policy maker to use it in a—for them—meaningful way. To tackle this problem, we require interaction tools and visualization capabilities necessary to support non-expert users (such as policy makers). To understand what these non-expert users need, we performed a focus group study to gain insights into the potential requirements of such an interaction tool with respect to norms. This resulted in a list of requirements to support non-expert users in the analysis of norm conflicts after they happened during the simulation. But more importantly we are calling for a new research agenda in the field of social simulation to support non-expert users with the abstract analysis of norms before conflicts happen in the simulation run, as the results showed that the participants were not able to do that kind of analysis.

Despite the increasing use of standards for documenting and testing agent -based models (ABMs) and sharing of open access code, most ABMs are still developed from scratch. This is not only inefficient, but also leads to ad hoc and often inconsistent implementations of the same theories in computational code and delays progress in the exploration of the functioning of complex social -ecological systems (SES). We argue that reusable building blocks (RBBs) known from professional software development can mitigate these issues. An RBB is a submodel that represents a particular mechanism or process that is relevant across many ABMs in an application domain, such as plant competition in vegetation models, or reinforcement learning in a behavioural model. RBBs need to be distinguished from modules, which represent entire subsystems and include more than one mechanism and process. While linking modules faces the same challenges as integrating different models in general, RBBs are atomic enough to be more easily re -used in different contexts. We describe and provide examples from different domains for how and why building blocks are used in software development, and the benefits of doing so for the ABM community and to individual modellers. We propose a template to guide the development and publication of RBBs and provide example RBBs that use this template. Most importantly, we propose and initiate a strategy for community -based development, sharing and use of RBBs. Individual modellers can have a much greater impact in their field with an RBB than with a single paper, while the community will benefit from increased coherence, facilitating the development of theory for both the behaviour of agents and the systems they form. We invite peers to upload and share their RBBs via our website - preferably referenced by a DOI (digital object

Agent-based models are particularly suitable to reflect the dynamics of humans, nature, and their interactions, making them a crucial approach for understanding social-ecological systems. The formalisations of human decision-making are central to resulting model behaviours. Despite awareness of the complexity of human behaviour in social-ecological systems research, scholars tend to represent human decision-makers as simplified, perfectly informed rational optimisers, without explicitly considering the fit with decision context. Key reasons are a lacking uptake of social theories and insights. To advance, we need a practice of reflecting, sharing, and inquiring on the justification of the decision model fit with its context. This paper stimulates this practice by 1) supporting the justification of decision model (DM) fit by describing the DM landscape and providing guiding questions; and 2) by supporting researchers in considering alternative DMs through a survey-based impression of modeller practices, and through highlighting DM frontiers as inspiration for future research.