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Improving Group Decision Making for Techno-Economic Analysis

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thesis
posted on 16.11.2022, 22:52 authored by Niles Xunan GuoNiles Xunan Guo

Group decision-making for techno-economic assessment often involves coordination between multiple stakeholders who have their own perspectives, motivations, objectives, and expertise. This increases the complexity of the decision-making process, requiring interventions that can help groups tradeoff multiple objectives and avoid suboptimal decisions. I focus on two critical problems faced by groups of system designers that have been incompletely explored in behavioral and decision science domain: generating efficient designs and reaching group consensus. I develop and test two classes of behavioral interventions to address these problems: real-time feedback and consensus-driven group recommender systems. Although these have the potential to help groups generate better designs and reach consensus, they lack quantitative evidence of their effectiveness and feasibility to improve group decision-making processes in techno-economic analyses.

The objective of this thesis is to provide quantitative evidence of the feasibility and effectiveness of those interventions using behavioral experiments in a laboratory setting. I achieved these objectives by recruiting a wide range of participants (students, laypeople in the general public, experts in an emerging technology), organizing them into groups, and evaluated the feasibility and effectiveness of the interventions on helping them to improve their decisionmaking in three different techno-economic areas. I first evaluated the effectiveness of providing real-time feedback to groups of students tasked with designing a complex wastewater management system with multiple objectives. Later, I extend the behavioral experiment framework to build and evaluate the feasibility and effectiveness of using a consensus-driven recommender system to help groups of laypeople to come to a consensus for climate change policy. Finally, I apply the model developed previously to help experts in metal additive manufacturing (MAM) domain to determine part and subassembly suitability for MAM in an U.S. Army context. 

History

Date

25/02/2022

Degree Type

Dissertation

Department

Engineering and Public Policy

Degree Name

  • Doctor of Philosophy (PhD)

Advisor(s)

Alex Davis, Meagan Mauter