Considering cusp catastrophe theory to explain crowd incidents
The theory devised by Thom in the 1960s and developed by Zeeman in the 1970s, describes a sudden and catastrophic change in state which is often a feature in crowd disasters. Cusp Catastrophe Theory can be used to explain the relationship between crowd movement, crowd density and crowd order. The figure below is an illustrative representation of Thom’s theory on which the three variables can be modelled by assuming crowd density on the X axis, crowd movement on the Y axis and crowd order on the Z axis.
It is widely agreed that the majority of people who die in crowd incidents do so by asphyxiation once excessive density is reached. However, people do not always die when a certain density is reached, and this model can help to explain why this may be the case. Zeeman describes of the theory, ‘as long as the state of the system remains outside the cusp, behaviour varies smoothly and continuously as a function of the control parameters’. There can be smooth changes from low density to high density and in turn from high movement to low movement as crowds build up at an event – this would be illustrated by point ‘A’. If density remains high and movement begins to increase there will be a shift to point ‘C’ and at this stage the crowd becomes susceptible to a catastrophic drop in crowd order (to point ‘D’) which could be manifested in a crowd surge or collapse, thereby putting lives at risk
As the model shows, by the non-linear relationship between the variables, a reduction in movement, a reduction in density or an increase in crowd order does not instantly return the situation to a stable state. It would require a significant shift in those variables to jump back out of the ‘catastrophe fold’ up onto the top sheet of the model. This is reflected in crowd disasters; once a trigger has caused a dense crowd to move and order is lost, it is exceedingly difficult to restore a stable crowd state. It is easy to see how the crowd themselves can be blamed for the shift from point ‘C’ to point ‘D’. The drop in order and subsequent panic, collapse or surge is a result of the crowd reaction. However, the model appropriately highlights the importance of the trigger and the numerous opportunities to keep away from points ‘B’ and ‘C’.
The crowd themselves are most often not responsible for these shifts and therefore it clearly follows naturally that in order to prevent crowd disasters and learn lessons from past incidents, the reasons for approaching the catastrophic drop and the various triggers must be accurately reviewed and subsequently understood.
Simply focussing on the catastrophic element of any incident will likely identify the crowd as the culprit whilst appropriately categorising disasters can allow for effective review of crowd management, crowd control and ultimately crowd safety