Abstract
The aim of this study is to evaluate the reliability of a crowd simulation model developed by the authors by reproducing Dyer et al.'s experiments (published in Philosophical Transactions in 2009) on human leadership and consensus decision making in a computer-based environment. The theoretical crowd model of the simulation environment is presented, and its results are compared and analysed against Dyer et al.'s original experiments. It is concluded that the simulation results are largely consistent with the experiments, which demonstrates the reliability of the crowd model. Furthermore, the simulation data also reveals several additional new findings, namely: 1) the phenomena of sacrificing accuracy to reach a quicker consensus decision found in ants colonies was also discovered in the simulation; 2) the ability of reaching consensus in groups has a direct impact on the time and accuracy of arriving at the target position; 3) the positions of the informed individuals or leaders in the crowd could have significant impact on the overall crowd movement; and 4) the simulation also confirmed Dyer et al.'s anecdotal evidence of the proportion of the leadership in large crowds and its effect on crowd movement. The potential applications of these findings are highlighted in the final discussion of this paper.
Highlights
Collective movement and consensus decision making have been found in many animal groups, such as honey bees [1,2,3], fishes [4,5,6], and monkeys [7]
Dyer et al have performed a series of experiments [8], [9] on consensus decision making on human groups
As agents can be attributed, individual behaviours have been considered in many agent-based models and the results suggest that individual behaviours could affect crowd behaviours
Summary
Collective movement and consensus decision making have been found in many animal groups, such as honey bees [1,2,3], fishes [4,5,6], and monkeys [7]. Dyer et al have performed a series of experiments [8], [9] on consensus decision making on human groups. Their studies showed similar findings to animal groups, such as the minority can lead the group effectively and the importance of the positions of the informed individuals in small size human groups. (the word ‘‘experiments’’ refers to the experiments in Dyer et al.’s study (2009), if no explicit reference has been made) To overcome such ‘‘logistical difficulties’’ for larger groups, one possible solution is to employ crowd simulation technology which utilises a computer programme to simulate crowd behaviour. A number of models [11] have been developed to represent some typical crowd phenomena (e.g. clogging, pushing, unadventurous exiting and faster-is-slower) and other crowd models [12,13,14,15,16] have been developed to simulate the counter-flow of crowd movement
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