‘Rationality’ in Collective Escape Behaviour: Identifying Reference Points of Measurement at Micro and Macro Levels

Abstract

Background. Evacuation behaviour of human crowds is often characterised by the notion of ‘irrational behaviour’. While the term has been frequently used in the literature, clear definitions and methods for measuring rationality do not exist. Objective. Here, we suggest that rationality, in this context, can alternatively and more effectively be formulated as a question of ‘optimal behaviour’. Decision optimality can potentially be measured and quantified. The main challenges, however, include (i) distinctly identifying the level at which we measure optimality, and (ii) identifying proper reference points at each level. Methods. We differentiate between optimality at the individual (i.e., micro) and the system (i.e., macro/aggregate) levels and illustrate how certain reference points can be established at each level. We suggest that, at the micro level, optimality of individual decisions can be quantified by comparing the outcome of each individual’s decision to those of their ‘nearly equal peers’. At the macro level, optimality can be measured by simulating the system using parametric numerical models and measuring the system performance while altering the behavioural parameters compared to their empirical estimates. Results. Having applied these methods, we observed that variation in micro level decision optimality rises rapidly as the space becomes more heavily crowded. As crowdedness increases in the environment, the difference between ‘good’ and ‘bad’ decisions becomes more distinct; and suboptimal decisions become more frequent. In other words, optimality at individual level seems to be moderated by the level of crowdedness. At the macro level, numerical simulations showed that, for certain exit attributes (like exit congestion), extreme marginal valuations (or preferences) were optimal, whereas for certain other attributes (like exit visibility), intermediate levels of valuation were closer to the optimal. In most cases, the natural observed (or estimated) tendency of evacuees (at the aggregate level) was not quite at the optimum level, meaning that the system could improve by modifying individuals’ marginal valuations of exit attributes. Applications and Recommendations. These results highlight the importance of guiding evacuation decisions particularly in heavily crowded spaces. They also theoretically illustrate the potential benefit of influencing/modifying people’s evacuation strategies, so they make decisions that are collectively more efficient. A crucial step to this end, however, is to identify what optimum strategy is and under what circumstances people are likelier to make suboptimal decisions.

Belief Factors Associated With Employees’ Intention to Evacuate During a Fire Alarm

“…Even though the benefits of evacuation during a fire alarm are widely known (Kuligowski, 2013; Pillemer et al, 1994; Proulx & Pineau, 1996), many people still fail to evacuate during fire alarms especially in the workplace (Gwynne et al, 2016; Jelenewicz, 2008). While existing literature examines different facets of fire alarm evacuation (e.g., demographics, personality traits, knowledge and experience with fires, judgment, herd behavior, stress resistance; Gershon et al, 2012; Haghani et al, 2019; Haghani & Sarvi, 2019; Kinateder et al, 2015; Kobes et al, 2010; Nguyen et al, 2019; Proulx & Reid, 2006; Tsurushima, 2019; USFA, 2019), little research has been done on theory-based belief factors underlying employees’ decision to evacuate. This formative study, using the RAA, provides us the perspective of the worker and identifies the belief factors associated with intention to leave the office building immediately.…”

Measuring Dynamics in Evacuation Behaviour with Deep Learning

“…In Figure 7, we validate our approach on these two cases. In Figure 7a, we use the same deep CNN model to learn the initial population densities from a series of intermediate processes which are (24,24,8) images. With the same size of data set, testing performance is MSE = 6.73 × 10 −4 and R 2 = 0.906.…”

“…To achieve this, many researchers have been investigating collective behaviours in simulations and experiments for decades [ 1 , 2 , 3 , 4 , 5 ]. In the evacuation scenario, irrational behaviours are inevitable in decision-making [ 6 , 7 , 8 ], in which diverse behaviour patterns emerge [ 9 ]. In principle, “rationality” could describe an optimal strategy that will bring a maximum payoff at both individual and whole levels in game theory [ 10 , 11 ], which can be quantified by, e.g., minimizing escape time in evacuation behaviour [ 8 , 12 ].…”

“…In the evacuation scenario, irrational behaviours are inevitable in decision-making [ 6 , 7 , 8 ], in which diverse behaviour patterns emerge [ 9 ]. In principle, “rationality” could describe an optimal strategy that will bring a maximum payoff at both individual and whole levels in game theory [ 10 , 11 ], which can be quantified by, e.g., minimizing escape time in evacuation behaviour [ 8 , 12 ]. Henceforth in the paper, we try to measure the optimality of exit decisions related to escape time in evacuation.…”

Panic, Irrationality, and Herding: Three Ambiguous Terms in Crowd Dynamics Research

“…Therefore, it is not sufficient to merely talk about the rationality of human responses without measuring the effectiveness of the response relative to a proper reference point and that is an element that is often missing from the discussions on this topic. How such a reference point can be set and how the efficiency or rationality or optimality of behaviour can be measured against it is certainly a matter of research in this area [32], but its necessity seems to be indisputable. Further on that issue, a considerable number of studies that were reviewed pointed out that what seems an irrational act may be an individual's best perceived course of action.…”