Structural inequalities and identity processes are pivotal to understanding public response to COVID‐19. We discuss how identity processes can be used to promote community‐level support, safe normative behaviour, and increase compliance with guidance. However, we caution how government failure to account for structural inequalities can alienate vulnerable groups, inhibit groups from being able to follow guidance, and lead to the creation of new groups in response to illegitimate treatment. Moreover, we look ahead to the longitudinal impacts of inequalities during pandemics and advise government bodies should address identity‐based inequalities to mitigate negative relations with the public and subsequent collective protest.

We present the first experimental evidence to our knowledge that ingroup relations attenuate core disgust and that this helps explain the ability of groups to coact. In study 1, 45 student participants smelled a sweaty t-shirt bearing the logo of another university, with either their student identity (ingroup condition), their specific university identity (outgroup condition), or their personal identity (interpersonal condition) made salient. Self-reported disgust was lower in the ingroup condition than in the other conditions, and disgust mediated the relationship between condition and willingness to interact with target. In study 2, 90 student participants smelled a sweaty target t-shirt bearing either the logo of their own university, another university, or no logo, with either their student identity or their specific university identity made salient. Walking time to wash hands and pumps of soap indicated that disgust was lower where the relationship between participant and target was ingroup rather than outgroup or ambivalent (no logo).disgust | social identity | groups | group processes | coaction

Computer simulations are increasingly used to monitor and predict behavior at large crowd events, such as mass gatherings, festivals and evacuations. We critically examine the crowd modeling literature and call for future simulations of crowd behavior to be based more closely on findings from current social psychological research. A systematic review was conducted on the crowd modeling literature (N = 140 articles) to identify the assumptions about crowd behavior that modelers use in their simulations. Articles were coded according to the way in which crowd structure was modeled. It was found that 2 broad types are used: mass approaches and small group approaches. However, neither the mass nor the small group approaches can accurately simulate the large collective behavior that has been found in extensive empirical research on crowd events. We argue that to model crowd behavior realistically, simulations must use methods which allow crowd members to identify with each other, as suggested by self-categorization theory.

Research in crowd psychology has demonstrated key differences between the behaviour of physical crowds where members are in the same place at the same time, and the collective behaviour of psychological crowds where the entire crowd perceive themselves to be part of the same group through a shared social identity. As yet, no research has investigated the behavioural effects that a shared social identity has on crowd movement at a pedestrian level. To investigate the direction and extent to which social identity influences the movement of crowds, 280 trajectories were tracked as participants walked in one of two conditions: (1) a psychological crowd primed to share a social identity; (2) a naturally occurring physical crowd. Behaviour was compared both within and between the conditions. In comparison to the physical crowd, members of the psychological crowd (i) walked slower, (ii) walked further, and (iii) maintained closer proximity. In addition, pedestrians who had to manoeuvre around the psychological crowd behaved differently to pedestrians who had to manoeuvre past the naturally occurring crowd. We conclude that the behavioural differences between physical and psychological crowds must be taken into account when considering crowd behaviour in event safety management and computer models of crowds.

Social scientists have criticised computer models of pedestrian streams for their treatment of psychological crowds as mere aggregations of individuals. Indeed most models for evacuation dynamics use analogies from physics where pedestrians are considered as particles. Although this ensures that the results of the simulation match important physical phenomena, such as the deceleration of the crowd with increasing density, social phenomena such as group processes are ignored. In particular, people in a crowd have social identities and share those social identities with the others in the crowd. The process of self categorisation determines norms within the crowd and influences how people will behave in evacuation situations. We formulate the application of social identity in pedestrian simulation algorithmically. The goal is to examine whether it is possible to carry over the psychological model to computer models of pedestrian motion so that simulation results correspond to observations from crowd psychology. That is, we quantify and formalise empirical research on and verbal descriptions of the effect of group identity on behaviour. We use uncertainty quantification to analyse the model's behaviour when we vary crucial model parameters. In this first approach we restrict ourselves to a specific scenario that was thoroughly investigated by crowd psychologists and where some quantitative data is available: the bombing and subsequent evacuation of a London underground tube carriage on July 7th 2005.Comment: accepted by Safety Science, 34 pages (incl. bibliography