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Research Papers: Design Automation

Behavioral Experimentation and Game Theory in Engineering Systems Design

[+] Author and Article Information
Zhenghui Sha

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907

Karthik N. Kannan

Associate Professor
Krannert School of Management,
Purdue University,
West Lafayette, IN 47907

Jitesh H. Panchal

Assistant Professor
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907

Since the number of tries has been scaled, one unit means 10 tries in low cost setting and 20 tries in high cost setting.

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received October 1, 2014; final manuscript received January 15, 2015; published online March 5, 2015. Assoc. Editor: Bernard Yannou.

J. Mech. Des 137(5), 051405 (May 01, 2015) (10 pages) Paper No: MD-14-1665; doi: 10.1115/1.4029767 History: Received October 01, 2014; Revised January 15, 2015; Online March 05, 2015

Game-theoretic models have been used to analyze design problems ranging from multi-objective design optimization to decentralized design and from design for market systems (DFMS) to policy design. However, existing studies are primarily analytical in nature, which start with a number of assumptions about the individual decisions, the information available to the players, and the solution concept (generally, the Nash equilibrium). There is a lack of studies related to engineering design, which rigorously evaluate the validity of these assumptions or that of the predictions from the models. Hence, the usefulness of these models to realistic engineering systems design has been severely limited. In this paper, we take a step toward addressing this gap. Using an example of crowdsourcing for engineering design, we illustrate how the analytical game-theoretic models and behavioral experimentation can be synergistically used to gain a better understanding of design situations. Analytical models describe what players with assumed behaviors and cognitive capabilities would do under specified conditions, and the behavioral experiments shed light on how individuals actually behave. The paper contributes to the design literature in multiple ways. First, to the best of our knowledge, it is a first attempt at integrated theoretical and experimental game-theoretic analysis in design. We illustrate how the analytical models can be used to design behavioral experiments, which, in turn, can be used to estimate parameters, refine models, and inform further development of the theory. Second, we present a simple experiment to understand behaviors of individuals in a design crowdsourcing problem. The results of the experiment show new insights on using crowdsourcing contests for design.

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Figures

Grahic Jump Location
Fig. 1

Relationship between parameters

Grahic Jump Location
Fig. 3

Relationship between number of tries and quality of solution for sessions 1 and 4 (low cost first)

Grahic Jump Location
Fig. 4

Relationship between number of tries and quality of solution for sessions 2 and 3 (high cost first)

Grahic Jump Location
Fig. 5

Predicted winning probability as a function of number of tries (e) for four scenarios

Grahic Jump Location
Fig. 2

Screenshot of the interface used by the participants

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