Research Papers: Design Automation

Understanding Design Decisions Under Competition Using Games With Information Acquisition and a Behavioral Experiment

[+] Author and Article Information
Jitesh H. Panchal

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: panchal@purdue.edu

Zhenghui Sha

Department of Mechanical Engineering,
University of Arkansas,
Fayetteville, AR 72701
e-mail: zsha@uark.edu

Karthik N. Kannan

Krannert School of Management,
Purdue University,
West Lafayette, IN 47907
e-mail: kkarthik@purdue.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 14, 2017; final manuscript received June 23, 2017; published online July 26, 2017. Assoc. Editor: Christina Bloebaum.

J. Mech. Des 139(9), 091402 (Jul 26, 2017) (12 pages) Paper No: MD-17-1215; doi: 10.1115/1.4037253 History: Received March 14, 2017; Revised June 23, 2017

The primary motivation in this paper is to understand decision-making in design under competition from both prescriptive and descriptive perspectives. Engineering design is often carried out under competition from other designers or firms, where each competitor invests effort with the hope of getting a contract, attracting customers, or winning a prize. One such scenario of design under competition is crowdsourcing where designers compete for monetary prizes. Within existing literature, such competitive scenarios have been studied using models from contest theory, which are based on assumptions of rationality and equilibrium. Although these models are general enough for different types of contests, they do not address the unique characteristics of design decision-making, e.g., strategies related to the design process, the sequential nature of design decisions, the evolution of strategies, and heterogeneity among designers. In this paper, we address these gaps by developing an analytical model for design under competition, and using it in conjunction with a behavioral experiment to gain insights about how individuals actually make decisions in such scenarios. The contributions of the paper are two-fold. First, a game-theoretic model is presented for sequential design decisions considering the decisions made by other players. Second, an approach for synergistic integration of analytical models with data from behavioral experiments is presented. The proposed approach provides insights such as shift in participants' strategies from exploration to exploitation as they acquire more information, and how they develop beliefs about the quality of their opponents' solutions.

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Fig. 1

Classification of experiments with human subjects (based on Ref. [28])

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Fig. 2

Sequence of decisions made by participants in the game

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Fig. 3

Illustration of the Wiener process model with input points (0, 1) and (1, 0), σ = 1.0

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Fig. 4

Effect of σ on the next point for xj−1=0, xt*=xj=1,x̂j=0.5

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Fig. 5

Cumulative distribution of normalized xt+1 with exponential fit

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Fig. 6

Cumulative distribution of σ with exponential fit

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Fig. 7

Box plot of σ for different tries

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Fig. 8

Box plot of σ for different cost levels

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Fig. 9

Cumulative distribution of −f̂−i* with exponential fit

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Fig. 10

Box plot of f̂−i* for different periods




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