Purdue University Graduate School
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posted on 2020-07-30, 02:52 authored by Murtuza N ShergadwalaMurtuza N Shergadwala

The primary research question of this dissertation is, \textit{How do contestants make sequential design decisions under the influence of competition?} To address this question, I study the influence of three factors, that can be controlled by the contest organizers, on the contestants' sequential information acquisition and decision-making behaviors. These factors are (i) a contestant's domain knowledge, (ii) framing of a design problem, and (iii) information about historical contests. The \textit{central hypothesis} is that by conducting controlled behavioral experiments we can acquire data of contestant behaviors that can be used to calibrate computational models of contestants' sequential decision-making behaviors, thereby, enabling predictions about the design outcomes. The behavioral results suggest that (i) contestants better understand problem constraints and generate more feasible design solutions when a design problem is framed in a domain-specific context as compared to a domain-independent context, (ii) contestants' efforts to acquire information about a design artifact to make design improvements are significantly affected by the information provided to them about their opponent who is competing to achieve the same objectives, and (iii) contestants make information acquisition decisions such as when to stop acquiring information, based on various criteria such as the number of resources, the target objective value, and the observed amount of improvement in their design quality. Moreover, the threshold values of such criteria are influenced by the information the contestants have about their opponent. The results imply that (i) by understanding the influence of an individual's domain knowledge and framing of a problem we can provide decision-support tools to the contestants in engineering design contexts to better acquire problem-specific information (ii) we can enable contest designers to decide what information to share to improve the quality of the design outcomes of design contest, and (iii) from an educational standpoint, we can enable instructors to provide students with accurate assessments of their domain knowledge by understanding students' information acquisition and decision making behaviors in their design projects. The \textit{primary contribution} of this dissertation is the computational models of an individual's sequential decision-making process that incorporate the behavioral results discussed above in competitive design scenarios. Moreover, a framework to conduct factorial investigations of human decision making through a combination of theory and behavioral experimentation is illustrated.


Crowdsourcing for Engineering Systems Design: Theoretical and Experimental Studies

Directorate for Engineering

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Understanding Information Acquisition Decisions in Systems Design through Behavioral Experiments and Bayesian Analysis

Directorate for Engineering

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Degree Type

  • Doctor of Philosophy


  • Mechanical Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Jitesh Panchal

Additional Committee Member 2

Ilias Bilionis

Additional Committee Member 3

Karthik Ramani

Additional Committee Member 4

Karthik Kannan

Additional Committee Member 5

Tahira Reid