An agent-based model of consensus building

Our model, CollAct is built around the question how people gain a shared understanding and reach consensus in an interactive group setting. This is an important question which is rather difficult to analyze within case studies. We model agents in a cognitive way, including substantive and relational knowledge in mental models, which may change through learning. The agents in CollAct discuss with each other and produce a group model (consensus). Factors identified to have an important influence on the results of a group discussion include group size, the level of controversy within the discussion, cognitive diversity, social behavior in form of cognitive biases (Asch and halo effect), and, depending on group size, the existence of a leading role at the beginning. Furthermore, the integration of topics into the consensus follows a saturation curve, thus the ending time of discussions should be carefully chosen to avoid a loss of information

Social identity modelling

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.Energie and Industri

Social agents?:A systematic review of social identity formalizations

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

Sustainable WEF Nexus Management : A Conceptual Framework to Integrate Models of Social, Economic, Policy, and Institutional Developments

Funding Information: This work was supported by the Decision Analytic Framework to explore the water-energy-food nexus in complex transboundary water resource systems of fast developing countries (DAFNE) project, which has received funding from the European Union's Horizon 2020 research and innovation program under grant Agreement No. 690268.Peer reviewedPublisher PD

How participatory methods facilitate social learning in natural resource management. An exploration of group interaction using interdisciplinary syntheses and agent-based modeling

In this thesis, the central interest is to better understand how participatory methods applied during participatory processes in natural resources management can serve as nuclei for social learning. Thereby, the main focus is on learning via interaction in groups. My approach begins with the aim of developing an analytical framework which reflects the main processes that are effective within participatory methods. The framework presents an analytical tool, including proposed methods to monitor and compare the results of participatory approaches with respect to social learning. Building upon this framework, I develop an agent-based model to simulate and explore group dynamics. This model is intended to support a theoretical exploration of whether or not and if so, at what stage, personal views of a problem evolve into a shared understanding of a problem (which can be seen as a key element of social learning), and an assessment of how individual mental models and group properties relate to each other. Results of the model are interpreted to offer suggestions about factors hindering or fostering social learning during the application of participatory methods

An agent-based model of consensus building

Our model, CollAct is built around the question how people gain a shared understanding and reach consensus in an interactive group setting. This is an important question which is rather difficult to analyze within case studies. We model agents in a cognitive way, including substantive and relational knowledge in mental models, which may change through learning. The agents in CollAct discuss with each other and produce a group model (consensus). Factors identified to have an important influence on the results of a group discussion include group size, the level of controversy within the discussion, cognitive diversity, social behavior in form of cognitive biases (Asch and halo effect), and, depending on group size, the existence of a leading role at the beginning. Furthermore, the integration of topics into the consensus follows a saturation curve, thus the ending time of discussions should be carefully chosen to avoid a loss of information

Evaluating trust and shared group identities in emergent social learning processes in the Zambezi river basin

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

Simulate this! An Introduction to Agent-Based Models and their Power to Improve your Research Practice

The method of agent-based modeling is rarely used in social psychology, but has the potential to complement and improve traditional research practices. An agent-based model (ABM) consists of a number of virtual individuals – the “agents” – interacting in an arti cial, experimenter-controlled environment. In this article, we discuss several characteristics of ABMs that could prove particularly useful with respect to recent recommendations aimed at countering issues related to the current “replication crisis”. We address the potential synergies between planning and implementing an ABM on the one hand, and the endeavor of pre-registration on the other. We introduce ABMs as tools for both the generation and the improvement of theory, testing of hypotheses, and for extending traditional experimental approaches by facilitating the investigation of social processes from the intra-individual all the way up to the societal level. We describe examples of ABMs in social psychology, including a detailed description of the CollAct model of social learning. Finally, limitations and drawbacks of agent-based modeling are discussed. In annex 1 and 2, we provide literature and tool recommendations for getting started with an ABM.SCOPUS: ar.jinfo:eu-repo/semantics/publishe