We propose a wish list of features that would greatly enhance population synthesis methods from the perspective of agent-based modelling. The challenge of synthesising appropriate populations is heightened in agent-based modelling by the emphasis on complexity, which requires accounting for a wide array of features. These often include, but are not limited to: attributes of agents, their location in space, the ways they make decisions and their behavioural dynamics. In the real-world, these aspects of everyday human life can be deeply interconnected, with these associations being highly consequential in shaping outcomes. Initialising synthetic populations in ways that fail to respect these covariances can therefore compromise model efficacy, potentially leading to biased and inaccurate simulation outcomes.

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.