Modeling Decision-Making Learning Process Under Crisis Situation

The purpose of this paper is to study the decision-making learning processes under crisis conditions with particular emphasis on the impact of training, or lack thereof, and the response to the crisis. Crisis decision-making is very important since in many cases its consequences are irreversible and even fatal. Sufficient amount of the right training can make response instinctive or intuitive. However, how to train a person to handle crisis decision-making tasks is rarely studied, partially due to the difficulty in replication of crisis. In this paper, we utilized the power law of learning to construct the learning curve for decision-making tasks under time stress and uncertain information. A microworld simulation (FireChief) was used to conduct a case study. Results show the human decision-making learning process under crisis follows a power law manner

Effect of HFACS and non-HFACS-related factors on fatalities in general aviation accidents using neural networks.

This study applied a backpropagation artificial neural network approach to investigate both the Human Factors Analysis and Classification System (HFACS)-related unsafe act tiers of factors and other non-HFACS factors in an attempt to recognize patterns for general aviation accident fatalities. Data were obtained from the HFACS database and extracted from the National Transportation Safety Board database from 1990 to 2002. Multiple neural network models were created and the best fit model was selected based on a sequence of criteria. A sensitivity analysis was performed on the validated model to rank the factors that lead to general aviation fatalities. Results are discussed and practical implications are given