Estimating Deprivation Cost Functions for Power Outages During Disasters: A Discrete Choice Modeling Approach

2025-06-20

AI Summary

This study addresses the lack of systematic quantification of deprivation cost functions under disaster-induced power outages. Leveraging stated-preference survey data from Harris County, Texas, we estimate discrete choice models—including multinomial and mixed logit specifications—to empirically derive, for the first time, a strictly increasing and convex deprivation cost function, revealing the nonlinear relationship between outage duration and willingness-to-pay. Methodologically, we innovatively incorporate Box-Cox and exponential utility transformations, and identify systematic preference heterogeneity and random utility variation through interactions with sociodemographic variables (e.g., income) and random-parameter estimation. Results demonstrate that deprivation costs accelerate with outage duration and exhibit significant intergroup valuation disparities. The findings provide a rigorous, quantifiable cost basis for infrastructure resilience investment, equity-oriented risk assessment, and humanitarian logistics decision-making.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Knowledge Representation and Reasoning: Preferences
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Security and Privacy: Large-scale security measurements

Abstract

Systems for the generation and distribution of electrical power represents critical infrastructure and, when extreme weather events disrupt such systems, this imposes substantial costs on consumers. These costs can be conceptualized as deprivation costs, an increasing function of time without service, quantifiable through individuals'willingness to pay for power restoration. Despite widespread recognition of outage impacts, a gap in the research literature exists regarding the systematic measurement of deprivation costs. This study addresses this deficiency by developing and implementing a methodology to estimate deprivation cost functions for electricity outages, using stated preference survey data collected from Harris County, Texas. This study compares multiple discrete choice model architectures, including multinomial logit and mixed logit specifications, as well as models incorporating BoxCox and exponential utility transformations for the deprivation time attribute. The analysis examines heterogeneity in deprivation valuation through sociodemographic interactions, particularly across income groups. Results confirm that power outage deprivation cost functions are convex and strictly increasing with time. Additionally, the study reveals both systematic and random taste variation in how individuals value power loss, highlighting the need for flexible modeling approaches. By providing both methodological and empirical foundations for incorporating deprivation costs into infrastructure risk assessments and humanitarian logistics, this research enables policymakers to better quantify service disruption costs and develop more equitable resilience strategies.

Problem

• Estimating deprivation costs during power outages.
• Comparing discrete choice models for cost functions.
• Analyzing sociodemographic impacts on outage valuation.

Innovation

• Uses discrete choice modeling for deprivation costs
• Compares multinomial and mixed logit model architectures
• Incorporates BoxCox and exponential utility transformations