Latest revision as of 14:42, 12 February 2021

Abstract

italic>Background</jats:italic>. 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.<jats:italic> Objective</jats:italic>. 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.<jats:italic> Methods</jats:italic>. 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.<jats:italic> Results</jats:italic>. 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.<jats:italic> Applications and Recommendations</jats:italic>. 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.

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• http://dx.doi.org/10.1155/2019/2380348 under the license http://creativecommons.org/licenses/by/4.0/

• https://doi.org/10.1155/2019/2380348 under the license http://creativecommons.org/licenses/by/4.0

• http://downloads.hindawi.com/journals/jat/2019/2380348.pdf,

http://downloads.hindawi.com/journals/jat/2019/2380348.xml,

http://dx.doi.org/10.1155/2019/2380348 under the license cc-by

• http://dx.doi.org/10.1155/2019/2380348,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2019/2380348,

http://downloads.hindawi.com/journals/jat/2019/2380348.pdf,

https://findanexpert.unimelb.edu.au/scholarlywork/1415582-%27rationality%27-in-collective-escape-behaviour--identifying-reference-points-of-measurement-at-micro-and-macro-levels,

https://academic.microsoft.com/#/detail/2969905411