Kudos Column: Richard Philpot
I am a PhD candidate in social psychology at the University of Exeter, United Kingdom. In my work, I examine the role of groups and third-parties, and how they can shape the trajectory and severity of violence in British public spaces. To this end, I conduct behavioural micro-analyses of CCTV footage and examine what people in and around the situation actually do during violent events. I also test social-psychological models of aggression against alternative models in agent-based modelling environments.
Aggression and violence can result in physical, psychological and societal harm, all of which burden public services and concern communities. Given these considerations, there is an inherent need for research to better understand, predict, and prevent violence in public spaces; however, our methods are limited. Police-recorded crime data offers a wealth of information on levels of violence, and the geographical hotspots in which this harm is most prevalent, but it fails to provide a sufficient level of detail on how individuals behave as an aggressive episode unfolds. Interviews and verbal accounts complement these ‘harder’ statistics by offering a greater description of the real experiences and behaviours during aggressive episodes. However, these methods have limitations associated with participant response bias, self-deception and memory failure.
Hoping to eliminate and control for these unmeasurable (and often confounding) variables, many investigators of violence have advocated the study of aggression paradigms in stringent laboratory settings (see Anderson & Bushman, 1997). These experiments typically provoke a participant under varying conditions and measure the subsequent aggressive behavioural responses. The resultant General Aggression Model (Anderson & Bushman, 2002) and I3 Theory (Finkel, 2014) have identified an impressive list of ‘personal’ and ‘situational’ risk factors relevant in determining the likelihood and strength of aggressive impulses. However, these now dominant paradigms fundamentally model mild laboratory aggression and are less effective in describing or predicting real-world violence (Ferguson & Dyck, 2012). The key criticism of these approaches for understanding public space violence is the overemphasis on the properties of the aggressive dyad (protagonist-target) at the expense of the wider dynamic social context.
Several alternative strands of research stress the importance of this wider social context in understanding and better predicting violence. For example, ethnographic work has shown that violence is not mindless, but rather governed by social values and implicit rules that determine which behaviours are acceptable and which targets legitimate (Drury & Reicher, 1999; Marsh, Rosser & Harré, 1978; Reicher, 1984). Meanwhile, micro-sociologists have postulated that violence cannot be well understood by the background factors of an aggressor (Collins, 2008; Nassauer, 2011). Rather violence is a social interaction that emerges from a series of micro-interactions between all relevant actors in the immediate situation. In a similar fashion, literature on school violence has shifted its focus away from the characteristics of an aggressor or victim, towards an analysis of how those around (typically bystanders and peers) may maintain a bullying ‘architecture’ (Polanin, Espelage & Pigott, 2012). With this in mind, it is inadequate just to consider the dyadic interaction between the aggressor and the victim – as models of aggression chiefly do. Rather, researchers need to look beyond the dyadic pair and understand the relative contributions and interactions of all actors and the importance of third parties.
Stimulated by these lines of research, Mark Levine and I (2016) analysed a corpus of public-space CCTV footage (video clips, N = 43; participants, N = 330) which captured real-life aggressive events. We showed how a detailed microanalysis of the actual behaviours captured on CCTV allows us to explore beyond the aggressive dyad, i.e., into how third-parties may shape the trajectory of violence. Specifically, a sequential analysis of the escalatory and conciliatory actions of those present revealed that the actions of third-parties - rather than the aggressive dyad – were best predictive of whether a conflict resulted in mild/moderate or severe violence (Levine et al., 2011; Philpot & Levine, 2016). Further examination of precisely who performed each de-escalatory act found that peaceful interventions were most effective when three (as opposed to two, or one) third-parties contributed the actions. These findings emphasise that successful violence inhibition comes from the cumulative de-escalatory response of third-parties; with an absence of de-escalatory actions, or the presence of third-party escalatory acts, best predictive of severe violence.
More recently, Levine, Koschate, Everson and I have been interested in the role of social group membership on rates of intervention and antagonistic behaviours in public space violence. We have evidenced that collective group self-regulation takes place both within and between groups. However, we have shown that intragroup aggression is biased towards subsequent de-escalation, while intergroup aggression has a tendency to be met with further inflammation (Philpot, Levine, Koschate & Everson, 2016). Additional examination of the relationship between the number of third-parties and levels of physical escalation, challenges theoretical preconceptions on the negative impact of audience-presence in street violence (Collins, 2008; Felson et al., 1984). Rather than audience-presence predicting increased violence, we found that as the size of the audience increased for intragroup fights, there was significantly less physical aggression (Philpot, Levine, Koschate & Everson, 2016). Interestingly, this effect was not evidenced during intergroup fights, where we found no changes in the levels of physical escalation or de-escalation as the number of third-parties increased. Such findings stress the appraisal of social relationships as explanatory components of public space violence.
Finally, collaborative work with criminologists at the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) and sociologists at the University of Copenhagen now examines the immediate aftermath of violent events. Beyond how third-parties shape outcomes during a conflict, we examined how post-conflict group interventions can alleviate and reassure victims of violence (Lindegaard et al., 2016). Although there is still much work to be completed, we hope that such findings may ensure that the importance of social context is not neglected; but alternatively included as an important facet of future violence research.
Anderson, C. A., & Bushman, B. J. (1997). External validity of" trivial" experiments: The case of laboratory aggression. Review of General Psychology, 1, 19.
Anderson, C. A., & Bushman, B. J. (2002). Human aggression. Annual Review of Psychology, 53, 27-51.
Collins, R. (2008). Violence: A micro-sociological theory. Woodstock, Oxford: Princeton University Press.
Drury, J., & Reicher, S. (1999). The intergroup dynamics of collective empowerment: Substantiating the social identity model of crowd behavior. Group Processes & Intergroup Relations, 2, 381-402.
Felson, R. B., Ribner, S. A., & Siegel, M. S. (1984). Age and the effect of third parties during criminal violence. Sociology & Social Research, 68, 452-62.
Ferguson, C. J., & Dyck, D. (2012). Paradigm change in aggression research: The time has come to retire the General Aggression Model. Aggression and Violent Behavior, 17, 220-228.
Finkel, E. J. (2014). The I3 model: Metatheory, theory, and evidence. Advances in Experimental Social Psychology, 49, 1-104.
Levine, M., Taylor, P. J., & Best, R. (2011). Third parties, violence, and conflict resolution the role of group size and collective action in the microregulation of violence. Psychological Science, 22, 406-412.
Lindegaard, M. R., Liebst, L. S., Bernasco, W., Heinskou, M. B., Philpot, R., Levine, M., & Verbeek, P. (2016). Consolation in the aftermath of commercial robberies resembles post-aggression consolation in chimpanzees. Manuscript submitted for publication.
Marsh, P., Rosser, E., & Harré, R. (1978). The rules of disorder. London: Routledge & Kegan Paul.
Nassauer, A. (2010). From hate to collective violence: Research and practical implications. Journal of Hate Studies, 9, 199-220.
Philpot, R., Levine, M. (2016). Street violence as a conversation: Using CCTV footage to explore the dynamics of violent episodes. Presentation given at the Annual Meeting of the American Society of Criminology, ASC 2016, New Orleans, USA. 15-19 November 2016.
Philpot R., Levine, M., Koschate, M., & Everson, R. (2016). Groups and violence: Distinctions in behavioural patterns of violence in intragroup and intergroup street fights. Manuscript submitted for publication.
Polanin, J. R., Espelage, D. L., & Pigott, T. D. (2012). A meta-analysis of school-based bullying prevention programs' effects on bystander intervention behavior. School Psychology Review, 41, 47-65.
Reicher, S. D. (1984). The St. Pauls' riot: An explanation of the limits of crowd action in terms of a social identity model. European Journal of Social Psychology, 14, 1-21.