Formulating an emergency plan based on expectation-maximization is one thing, but applying it to a single case is another

Abstract

This research extends the exploration of single-play/multiple-play distinctions from monetary gambling paradigm to emergency management situation. We conducted three studies (two survey studies and one eye tracking study) to test whether an emergency plan we formulated in advance based on expectation-maximization would be likely to be applied in a single case. In the first two survey studies we found that the plan with the higher EV was more likely to be preferred when the plan was applied 100 times or to 100 areas than when the plan was applied only once or to only one area. We also found significant framing and reflection effects, both of which violated the invariance principle in the single-application condition, but not in the multiple-application condition. Furthermore, in the eye tracking study, we found distinctly different eye movement patterns in the single-application condition and the multiple-application condition. The eye movement patterns in the multiple-application condition are more consistent with the predictions deduced from expectation computation. The overall results suggest that a gap exists between the formulation and the implementation of an emergency plan. Formulating an emergency plan based on expectation-maximization is doable, but applying it to a single case may be more challenging.

Keywords:

• emergency plan
• expectation-maximization
• single-application
• multiple-application
• eye movement patterns

Acknowledgments

This research was partially supported by the National Basic Research Program of China (973 Program, No. 2011CB711000), the Knowledge Innovation Project of the Chinese Academy of Sciences (No.KSCX2-EW-J-8), and the National Natural Science Foundation of China (No. 70871110; 31170976; 31200793). The authors thank Juan Li and Cheng-Ming Jiang for their assistance with data collection and Rhoda E. and Edmund F. Perozzi for very extensive English and content editing assistance.