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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.

The agricultural sector of the Western Cape is highly affected by climate change and thus requires fundamental change to move to a more resilient path. We analyze the development of a climate change plan to obtain a better understanding of what is needed to make such a strategic intervention meaningful in terms facilitating a sustainability transition. We use the concept of transition arena to understand how group composition and processes facilitated or hindered structural change, and apply social learning theory to uncover learning processes and outcomes for whether capacity and momentum for moving towards a more climate resilient sector were created. Commitment on the part of project members created trust in the project and vice versa, while the ongoing involvement of governmental actors was key for creating capacities and commitment for change. The limited participation of niche actors may have hampered the ability to identify more innovative development pathways.

A key challenge in social simulation is how to represent human behavior, specifically in its social context. The Social Identity approach (SIA) reflects a promising potential as it describes how people behave while being part of a group, how groups interact and how these interactions and ‘appropriate group behaviors’ can change over time. SIA is used in a variety of fields and increasingly implemented in agent-based models. A systematic review and comparison of SIA formalizations and implementations is so far missing. We present our impressions from a pre-review of the current state of SIA models, such as what key SIA concepts have been formalized and how their formalization compares. We found a diversity of application areas of models that use (parts of) SIA. We further noted differences in how parts of SIA have been formalized and used, e.g. the widespread use of the emergence of group norms and behavior, while other SIA aspects such as different strategies to positive self-esteem received little attention.

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.

Social learning in collaborative processes is considered important for addressing complex natural resource dilemmas and for supporting multi-actor interactions in joint problem framing, and the co-construction of solutions. While social learning is often presented as a normative approach for stimulating shared understanding among multiple actors, little is known about how long-term emergent social learning processes occur. In this paper, we analyse a long-term emergent social learning process within transboundary river basin management. To this end, we apply the concept of the learning space - meaning arenas for interaction, deliberation and re-framing - as a lens for evaluating emergent social learning processes. Results show that in the Zambezi Basin, social learning in emergent processes occurs in stages and is triggered by various factors that result in collective outcomes throughout the process. Emergent social learning processes were triggered by structural reorganization processes, natural calamities, the influences of cooperating partners and international frameworks. Over time, the spaces for interaction and deliberation led to the reframing of basin management practices and the subsequent development of new institutions. In addition, social learning outcomes included increased relational capacities of national and regional actors and trust, but also mistrust and power issues. While emergent long-term processes are difficult to evaluate, our findings reveal that the concept of the learning space provides a structure for assessing long-term emergent social learning process outcomes, and thus, can provide a broader understanding of processes needed in designing long-term management practices or institutions.

We discuss our experiences with the energy transition game (ETG) in Groningen, Tokyo, and Osnabruck, all in educational settings. The ETG is an agent-based game in which roles that can be played are energy companies and political parties. A unique aspect is the inclusion of an artificial population of simulated people. In the ETG the simulated consumers choose an energy provider based on price, safety, and greenness, and they vote for a political party in the government. In this paper, we share the experiences we had with playing ETG with first-year bachelor students at the University of Groningen (The Netherlands), with young professionals from the energy sector at the University of Tsukuba (Japan), and in a mixed seminar for master and bachelor students with diverse interdisciplinary backgrounds at the University of Osnabruck (Germany). The experiences we share with the game suggest that the ETG can be used and developed in different directions: a short introduction of the complexities of the energy transition, courses or projects in which students become codevelopers and try to improve the ETG, or as basis for further extensions and testing of specific hypotheses. We consider a common platform for different versions a sensible next step, including a version control, description of different implementations, and game dynamics.

We introduce a systematic method to compare externalized mental models with a group model resulting from a group model building (GMB) process. To this end, we categorize the various concepts included in the models, and propose Venn diagrams and matrices to demonstrate the composition of the group model and identify the overlapping categories. We then suggest a set of metrics to analyze the matrices. We apply our method to a dataset of three GMB processes, finding the suggested method to be helpful for evaluating the extent to which all participants were able to include their perspectives, and whether new ideas emerged from the process, thus constituting innovation. We conclude that our method may be a valuable contribution to the evaluation of GMB processes. Furthermore, we demonstrate that the categorization of the concepts is a delicate process and has a significant influence on the results. Copyright © 2015 System Dynamics Society

Raising awareness through social learning is one important strategy to deal with today’s challenges. Social learning is a complex and dynamic process, thus modeling may help to explore key dynamics. In this chapter I present a model of a Social Learning Community (SLC) which is not based on a network approach, but rather connects actors to different group settings where they interact. Within these social interaction rooms where actors meet and exchange they construct their own social reality, resulting in group phenomena. The social interaction contexts may span from such informal but long-lasting settings as a family to loose groups. Knowledge learned in one interaction setting can be brought into another one via the participating actors. The SLC is able to link social-psychological findings of group interaction (e.g., conformity) to social learning in wider social units.

Social learning among different stakeholders is often a goal in problem solving contexts such as environmental management. Participatory methods (e.g., group model-building and role playing games) are frequently assumed to stimulate social learning. Yet understanding if and why this assumption is justified is quite limited. Difficulties arise from the complexity and context-dependence of processes influencing social learning. Furthermore, continuing discussion of the exact meaning and theoretical basis of social learning result in a limited capacity to assess and evaluate whether social learning has occurred. In this paper we introduce an analytical framework to develop an in depth understanding of essential processes underlying social learning facilitated by participatory methods. Concepts from different fields of science are discussed and integrated, including resource management, small group research and learning research. The individual and group perspectives are brought together via mental models and emergent roles. We added the direction of learning, being either convergent or divergent, to be able to explore if and when personal views on a problem converge into a shared understanding of a problem. The analysis of convergence and divergence of learning is facilitated through the use of the mental model concept. Methods for measurement of proposed indicators for social learning are also discussed. The framework developed provides a conceptual basis for the analysis of social learning facilitated by participatory methods and an operationalization for application in empirical research. Keywords Social learning – Resources management – Mental model – Role – Participatory method – Shared understanding

Social learning in natural resource management is considered important for addressing complex problems by supporting multi-stakeholder interactions in problem framing and co-construction of solutions. Despite the considerable progress in the social learning discourse, few scholars have empirically examined relational features in social learning interactions. Relational features such as trust and shared group identities are important for supporting engagement and interaction among actors. This study analyzed emergent social learning processes in transboundary river basin cooperation processes in the Zambezi basin. To do this, data was conducted through in-depth interviews with diverse actors, observations of participatory workshops, and review of documents on transboundary cooperation processes in the Zambezi basin. The study evaluated how trust and shared group identities shaped learning spaces (opportunities for interaction, deliberation and reframing) and in turn impacted transboundary river basin cooperation. The study found that trust and shared group identities had a crucial impact on learning spaces and in turn impacted transboundary river basin cooperation in the Zambezi basin. The results suggest that leveraging on trust and shared group identities can play a critical role in stimulating cooperation processes. However, it is not a guarantee for cooperation. This study highlights that structural-learning spaces such as institutions support the development of binding commitments and enduring shared practices. However, success of such institutionalization is strongly influenced by the prior development of trust and a shared social identity.