This systematic literature review synthesises the literature on socio-psychological drivers and barriers to heat pump adoption and efficient use in households, drawing from the 16 research articles available. The review reveals mixed findings: variables were found influential in some studies but not in others. In addition to financial considerations, negative expectations regarding comfort and performance also hinder adoption. The literature on user behaviours suggests that comfort, knowledge, and home characteristics influence how heat pumps are operated, including temperature settings, heating area, and ventilation behaviour. A key research gap is the insufficient study of variables relating to the individual, such as psychological and socio-demographic factors. Based on the findings, we recommend public awareness campaigns to emphasise non-financial benefits of heat pumps, particularly comfort, which users often experience as an advantage. To optimise user behaviour, we recommend offering technical support services, simplifying system interfaces, and providing actionable feedback information on energy consumption.

Households are crucial in the energy transition, accounting for over 25% of the European Union's energy consumption. To design effective policy measures that motivate households to change their behavior in favor of the energy transition, agent-based models (ABMs) are vital. For ABMs to reach their full potential in policy design, they must appropriately represent behavioral dynamics. One way to accomplish this is by strengthening the fit in ABMs between behavioral determinants (e.g., trust in energy companies) and the behavior of interest (e.g., adopting tariff structures). This study investigates whether a structured behavioral analysis improves this “determinants-behavior-fit.” A systematic review of 71 ABMs addressing household energy decisions reveals that models incorporating a behavioral analysis formalize nearly twice as many behavioral determinants, indicating a more systematic uptake. Subsequently, we find a difference between models focusing on investment-related behaviors (e.g., households buying solar panels) and those examining daily energy practices (e.g., households adjusting charging habits). Models in the first category integrate more social factors when incorporating behavioral analyses, corresponding with the influence of networks and peer effects on investment behaviors. Models in the second category emphasize individual and external factors in response to behavioral analyses, corresponding with the energy practices' habitual and contextual nature. Despite the benefits of a behavioral analysis for improving the determinants-behavior fit in ABMs, only one-third of the studies apply it partially. On top of that, almost half of the studies do not report a rationale for their choice of behavioral determinants. This suggests that many models may not fully capture the behavioral mechanisms underlying household energy decisions, limiting ABMs' potential to inform policymakers. Our findings highlight the need for systematic behavioral assessments in model development. We conclude that collaboration between behavioral scientists and modelers is crucial to accomplish such integration, and we emphasize the importance of allowing sufficient time and resources for meaningful exchange. Future research could further investigate empirical validation of behavioral insights in ABMs and explore how ABM results improve with a better determinants-behavior fit. By bridging behavioral science with computational modeling, ABMs' decision-support power to policymakers can be improved, ultimately accelerating the energy transition.

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.

Path-breaking transformative change is needed in human-environment relations to move towards more sustainable development trajectories at local, national and global scales. Crises may trigger transformative change and learning in the short and in the long term. However, in particular, a short-term response to crises may also be reactive, strengthening established unsustainable practices and further perpetuating vulnerability and inequality rather than supporting transformative change towards a more sustainable path. To understand the nature and response to a crisis in the context of sustainability transformations, this paper elaborates on the following aspects of a crisis: What are the characteristics of a crisis? What and who shapes the narrative(s) of a crisis? What and who shapes the nature of the response to a crisis? Do responses to crises trigger higher levels of learning? Conceptual synthesis is complemented with an exploratory comparative analysis of the Cape Town water crisis and of the COVID-19 pandemic in South Africa. To this end the paper analyzes the interplay between mobilizing individual, collective and relational agency and navigating and transforming power relations to challenge and profit from already weakened unsustainable structures. This approach proves to be promising to understand the role of crises in catalysing and supporting transformative learning to eventually replace unsustainable structures.

Simulating collective decision-making and behaviour is at the heart of many agent-based models (ABMs). However, the representation of social context and its influence on an agent’s behaviour remains challenging. Here, the Social Identity Approach (SIA) from social psychology, offers a promising explanation, as it describes how people behave while being part of a group, how groups interact and how these interactions and ingroup norms can change over time. SIA is valuable for various application domains while also being challenging to formalise. To address this challenge and enable modellers to learn from existing work, we took stock of ABM formalisations of SIA and present a systematic review of SIA in ABMs. Our results show a diversity of application areas and formalisations of (parts of) SIA without any converging practice towards a default formalisation. Models range from simple to (cognitively) rich, with a group of abstract models in the tradition of opinion dynamics employing SIA to specify group-based social influence. We also found some complex cognitive SIA formalisations incorporating contextual behaviour. When considering the function of SIA in the models, representing collectives, modelling group-based social influence and unpacking contextual behaviour all stood out. Our review was also an inventory of the formalisation challenge attached to using a very promising socialpsychological theory in ABMs, revealing a tendency for reference to domain-specific theories to remain vague.

This is an editorial to the special section on “Social Identity Modelling”, published in Volume 26, Issues 2 and 3, 2023 of the Journal of Artificial Societies and Social Simulation. It provides information on how the Social Identity Approach (SIA) and the research using its theoretical framework explains collective behaviour, tailored specifically for modellers. The discussion centres around describing and reflecting on the state of the art in modelling SIA. The editorial ends with looking ahead towards formalising SIA as a means to enable more collective behavioural realism in agent-based social simulations.

The COVID-19 pandemic is causing a dramatic loss of lives worldwide, challenging the sustainability of our health care systems, threatening economic meltdown, and putting pressure on the mental health of individuals (due to social distancing and lock-down measures). The pandemic is also posing severe challenges to the scientific community, with scholars under pressure to respond to policymakers’ demands for advice despite the absence of adequate, trusted data. Understanding the pandemic requires fine-grained data representing specific local conditions and the social reactions of individuals. While experts have built simulation models to estimate disease trajectories that may be enough to guide decision-makers to formulate policy measures to limit the epidemic, they do not cover the full behavioural and social complexity of societies under pandemic crisis. Modelling that has such a large potential impact upon people’s lives is a great responsibility. This paper calls on the scientific community to improve the transparency, access, and rigour of their models. It also calls on stakeholders to improve the rapidity with which data from trusted sources are released to the community (in a fully responsible manner). Responding to the pandemic is a stress test of our collaborative capacity and the social/economic value of research.