Managing risk in infrastructure systems implies dealing with interdependent physical networks and their relationships with the natural and societal contexts. Computational tools are often used to support operational decisions aimed at improving resilience, whereas economics‐related tools tend to be used to address broader societal and policy issues in infrastructure management. We propose an optimization‐based framework for infrastructure resilience analysis that incorporates organizational and socioeconomic aspects into operational problems, allowing to understand relationships between decisions at the policy level (e.g., regulation) and the technical level (e.g., optimal infrastructure restoration). We focus on three issues that arise when integrating such levels. First, optimal restoration strategies driven by financial and operational factors evolve differently compared to those driven by socioeconomic and humanitarian factors. Second, regulatory aspects have a significant impact on recovery dynamics (e.g., effective recovery is most challenging in societies with weak institutions and regulation, where individual interests may compromise societal well‐being). And third, the decision space (i.e., available actions) in postdisaster phases is strongly determined by predisaster decisions (e.g., resource allocation). The proposed optimization framework addresses these issues by using: (1) parametric analyses to test the influence of operational and socioeconomic factors on optimization outcomes, (2) regulatory constraints to model and assess the cost and benefit (for a variety of actors) of enforcing specific policy‐related conditions for the recovery process, and (3) sensitivity analyses to capture the effect of predisaster decisions on recovery. We illustrate our methodology with an example regarding the recovery of interdependent water, power, and gas networks in Shelby County, TN (USA), with exposure to natural hazards.
A key to collaborative decision making is to aggregate individual evaluations into a group decision. One of its fundamental challenges lies in the difficulty in identifying and dealing with irregular or unfair ratings and reducing their impact on group decisions. Little research has attempted to identify irregular ratings in a collaborative assessment task, let alone develop effective approaches to reduce their negative impact on the final group judgment. In this article, based on the synergy theory, we propose a novel consensus‐based collaborative evaluation (CE) method called Collaborative Evaluation based on rating DIFFerence (CE‐DIFF) for identifying irregular ratings and mitigating their impact on collaborative decisions. CE‐DIFF determines and assigns different weights automatically to individual evaluators or ratings based on the level of consistency of one's ratings with the group assessment outcome through continuous iterations. We conducted two empirical experiments to evaluate the proposed method. The results show that CE‐DIFF has higher accuracy in dealing with irregular ratings than existing CE methods, such as arithmetic mean and trimmed mean. In addition, the effectiveness of CE‐DIFF is independent of group size. This study provides a new and more effective method for collaborative assessment, as well as novel theoretical insights and practical implications on how to improve collaborative assessment.
more » « less- NSF-PAR ID:
- 10361018
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Decision Sciences
- Volume:
- 52
- Issue:
- 6
- ISSN:
- 0011-7315
- Page Range / eLocation ID:
- p. 1432-1451
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract A fundamental challenge in emotion research is measuring feeling states with high granularity and temporal precision without disrupting the emotion generation process. Here we introduce and validate a new approach in which responses are sparsely sampled and the missing data are recovered using a computational technique known as
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