Crowd counting: a behavioural economics perspective

Abstract

Developments in technology have facilitated the emergence of new crowd counting organisations. Some of the organisations have established platforms to disseminate their data, making it available to researchers for the first time. These databases promise to increase the quality and quantity of research in various fields. In the late 2010s, specialist crowd counting organisations emerged with the sole purpose of counting crowds at protests and disseminating the results, sometimes in a purely partisan manner. Because of the contemporary relevance of protest behaviour, we frame our discussion within this context. For social scientists considering the utilisation of these new databases, it is essential that crowd numbers be linked to underlying human behaviour in a way that promises a chain of connections to investigate and explore. We use behavioural economics to show why relative crowd size may be important for human decision-makers. And we show how the significance of relative crowd size relates to other aspects of the human decision-making process, including risk preferences and probability assessments. Far from being a theory of protest behaviour, we present a behavioural economics-based primer for empirical researchers and social scientists engaging with newly available crowd counting data. The conclusions may apply in other contexts and might be extended to encompass specific types of behaviour, including aggression and violence. Indeed, the conclusions may guide the analysis of the emergence of the crowd counting organisations themselves.

Emergence of Protests During The COVID-19 Pandemic: Quantitative Models To Explore The Contributions of Societal Conditions

“…For example, better global estimates of the number of attendees of protests would enable to better fit the mobilisation process of our SDM. While development of such data sets are taking place at ACLED and CCC, the current progress, quality, or scope of such data sets is insufficient to optimise models 43,53 . Combining other modelling approaches could be another way to improve predictive power.…”