Wireless Emergency Alerts: Trust Model Simulations

Abstract

Trust is a key factor in the effectiveness of the Wireless Emergency Alerts (WEA) service. Alert originators must trust WEA to deliver alerts to the public in an accurate and timely manner. Members of the public must also trust the WEA service before they will act on the alerts that they receive. This research aimed to develop a trust model to enable the Federal Emergency Management Agency to maximize the effectiveness of WEA and provide guidance for alert originators that would support them in using WEA in a manner that maximizes public safety. This report overviews the public trust model and the alert originator trust model. The research method included Bayesian belief networks (BBNs) to model trust in WEA because they enable reasoning about and modeling of uncertainty. The report details the procedures used to run simulations on the trust models. For each trust model, single-factor, multifactor, random-input, and special-case simulations were run on each factor and group of factors investigated. The analysis of the simulations had two goals: to identify those simulations that predicted the highest levels of trust and those simulations that predicted the lowest levels of trust. This report includes the results for each trust model.

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Document Details

Document Type
Technical Report
Publication Date
Feb 01, 2014
Accession Number
ADA610096

Entities

People

  • Christopher Larkin
  • Joseph P. Elm
  • Robert W. Stoddard Ii
  • Timothy B. Morrow

Organizations

  • Carnegie Mellon University

Tags

DTIC Thesaurus Topics

  • Application Software
  • Department Of Homeland Security
  • Emergencies
  • Emergency Response
  • Factorial Design
  • Geographic Regions
  • Governments
  • Mobile Phones
  • Probability Distributions
  • Public Safety
  • Reasoning
  • Simulations
  • Social Media
  • Software Development
  • Statistical Inference
  • United States
  • United States Government

Readers

  • Computational Modeling and Simulation
  • Emergency Management and Homeland Security.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - DoD AI Strategy