Sequential Information Acquisition and Decision Making in Design Contests: Theoretical and Experimental Studies
Abstract
Products are typically designed by accounting for competition. For example, Apple and Samsung competing to design better products. Such competition influences strategic design decisions which, in turn, influence the product design outcomes. Existing literature in Design for Market Systems utilizes behavioral Game Theory to investigate product design competitions to maximize a firm’s profit. However, in engineering design contexts, there is a lack of understanding of the influence of competition on designer behaviors and, thereby, the design outcomes. Consider an example of a design contest such as DARPA’s Robotics Challenge. In such a contest, the contest organizer (DARPA) possesses greater freedom as compared to free-market product development competitions because they get to design the contest environment. The organizers need to make contest-design decisions such as, what problem-specific and contest-specific information to share with the contestants. Moreover, the contestants are the designers who solve the design problem. Thus, the contest-design decisions influence how the contestants evaluate the competition as well as make the design decisions such as what information to acquire about the problem and when to stop acquiring information. Information acquisition decisions, in turn, influence decisions about the design artifact and, thereby, the contest outcomes. Such nuances of engineering design behaviors are unaccounted in existing literature on contests. Thus, there is a lack of theoretical foundations to understand how competition influences the decision-making behaviors of designers in engineering design contexts. Establishing such foundations would enable predictions about product design outcomes as well as aid organizers of design contests to better design competitive environments. The primary research question of this dissertation is, 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 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 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.
Degree
Ph.D.
Advisors
Panchal, Purdue University.
Subject Area
Design
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