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Research Papers: Design Theory and Methodology

Collaborative, Decentralized Engineering Design at the Edge of Rationality

[+] Author and Article Information
Ashwin Gurnani

 Vanderplaats Research and Development, Inc., Novi, MI 48375agurnani@vrand.com

Kemper Lewis1

Department of Mechanical and Aerospace Engineering, University at Buffalo-SUNY, Buffalo, NY 14260kelewis@buffalo.edu

1

Corresponding author.

J. Mech. Des 130(12), 121101 (Oct 08, 2008) (9 pages) doi:10.1115/1.2988479 History: Received October 30, 2007; Revised July 30, 2008; Published October 08, 2008

One perspective of a design process in the engineering design community is that it is largely a process marked and defined by a series of decisions. The fundamental assumption in most developed design decision support methodologies is that decision makers make rational choices; that is, choices that maximize the payoff for the predicted outcome. Decisions that do not maximize the predicted payoff are termed as mistakes or irrational choices and discarded. However, research in behavioral economics, psychology, and cognitive science has studied the human mind and suggested the notion of “bounded rationality” to explain decision errors. Bounded rationality refers to the intrinsic inability of human beings to accurately choose “rational” options prescribed by decision models such as expected utility. This paper extends the notion of bounded rationality within engineering design. Specifically, this paper studies the design of complex systems that require interaction among several different subsystems contributing to the overall product design. For convergent decentralized design problems, rational designers converge to equilibrium solutions that lie at the intersection of their individual rational reaction sets. These equilibrium solutions are usually not Pareto optimal and due to the dynamics of the designers’ interaction in collaborative design, it is rarely possible for them to converge to Pareto optimal solutions. However, when models for bounded rationality are introduced into individual designer behavior, it is seen that the converged solutions can improve the resulting solution. Bounded rational decisions within decentralized design are modeled, and the effects of propagating such decisions within a design process are studied.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 1

Schematic of distributed design iterative process

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Figure 4

Iterative solution process for two design problem (divergent case)

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Figure 5

Modified iterative solution process for convergent problem

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Figure 6

Percentage expected improvement in F1 over Nash equilibrium for varied σ

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Figure 7

Percentage expected improvement in F2 over Nash equilibrium for varied σ

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Figure 8

Average number of iterations for convergence versus error levels (σ)

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Figure 9

Expected improvement in F1 versus average number of iterations

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Figure 10

Modified iterative solution process for divergent problem

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Figure 11

Cost benefit analysis for divergent problem

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Figure 2

Iterative solution process for two designer problem (convergent case)

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Figure 3

Convergent problem with solutions superior to the Nash equilibrium

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