Measuring the rationality in evacuation behavior with deep learning

The bounded rationality is a crucial component in human behaviors. It plays a key role in the typical collective behavior of evacuation, in which the heterogeneous information leads to the deviation of rational choices. In this study, we propose a deep learning framework to extract the quantitative...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-06
Main Authors: Hou, Huaidian, Wang, Lingxiao
Format: Article
Language:English
Subjects:
Artificial neural networks
Cellular automata
Deep learning
Deviation
Evacuation
Human behavior
Playgrounds
Rationality
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The bounded rationality is a crucial component in human behaviors. It plays a key role in the typical collective behavior of evacuation, in which the heterogeneous information leads to the deviation of rational choices. In this study, we propose a deep learning framework to extract the quantitative deviation which emerges in a cellular automaton (CA) model describing the evacuation. The well-trained deep convolutional neural networks (CNNs) accurately predict the rational factors from multi-frame images generated by the CA model. In addition, it should be noted that the performance of this machine is robust to the incomplete images corresponding to global information loss. Moreover, this framework provides us with a playground in which the rationality is measured in evacuation and the scheme could also be generalized to other well-designed virtual experiments.
ISSN:2331-8422
DOI:10.48550/arxiv.2106.05636