Many decisions within engineering systems design are typically made by humans. These decisions significantly affect the design outcomes and the resources used within design processes. While decision theory is increasingly being used from a normative standpoint to develop computational methods for engineering design, there is still a significant gap in our understanding of how humans make decisions within the design process. Particularly, there is lack of knowledge about how an individual's domain knowledge and framing of the design problem affect information acquisition decisions. To address this gap, the objective of this paper is to quantify the impact of a designer's domain knowledge and problem framing on their information acquisition decisions and the corresponding design outcomes. The objective is achieved by (i) developing a descriptive model of information acquisition decisions, based on an optimal one-step look ahead sequential strategy, utilizing expected improvement maximization, and (ii) using the model in conjunction with a controlled behavioral experiment. The domain knowledge of an individual is measured in the experiment using a concept inventory, whereas the problem framing is controlled as a treatment variable in the experiment. A design optimization problem is framed in two different ways: a domain-specific track design problem and a domain-independent function optimization problem (FOP). The results indicate that when the problem is framed as a domain-specific design task, the design solutions are better and individuals have a better state of knowledge about the problem, as compared to the domain-independent task. The design solutions are found to be better when individuals have a higher knowledge of the domain and they follow the modeled strategy closely.

References

References
1.
Hazelrigg
,
G. A.
,
1998
, “
A Framework for Decision-Based Engineering Design
,”
ASME J. Mech. Des.
,
120
(
4
), pp.
653
658
.
2.
Marston
,
M.
, and
Mistree
,
F.
,
1997
, “
A Decision-Based Foundation for Systems Design: A Conceptual Exposition
,”
International Design Seminar Proceedings on Multimedia Technologies for Collaborative Design and Manufacturing
(
CIRP 1997
), pp.
1
11
.
3.
Lewis, K.
,
Chen, W.
,
Schmidt, L.
, and
Chen, W.
, eds., 2006,
Decision Making in Engineering Design
, ASME Press, New York.
4.
Fischer
,
G. W.
,
1979
, “
Utility Models for Multiple Objective Decisions: Do They Accurately Represent Human Preferences?
,”
Decis. Sci.
,
10
(
3
), pp.
451
479
.
5.
Belton
,
V.
,
1986
, “
A Comparison of the Analytic Hierarchy Process and a Simple Multi-Attribute Value Function
,”
Eur. J. Oper. Res.
,
26
(
1
), pp.
7
21
.
6.
Gurnani
,
A.
, and
Lewis
,
K.
,
2008
, “
Collaborative, Decentralized Engineering Design at the Edge of Rationality
,”
ASME J. Mech. Des.
,
130
(
12
), p.
121101
.
7.
See
,
T.-K.
, and
Lewis
,
K.
,
2006
, “
A Formal Approach to Handling Conflicts in Multiattribute Group Decision Making
,”
ASME J. Mech. Des.
,
128
(
4
), pp.
678
688
.
8.
Wassenaar
,
H. J.
, and
Chen
,
W.
,
2003
, “
An Approach to Decision-Based Design With Discrete Choice Analysis for Demand Modeling
,”
ASME J. Mech. Des.
,
125
(
3
), pp.
490
497
.
9.
Thompson
,
S. C.
,
2011
, “
Rational Design Theory: A Decision-Based Foundation for Studying Design Methods
,”
Ph.D. thesis
, Georgia Institute of Technology, Atlanta, GA.https://smartech.gatech.edu/handle/1853/39490
10.
Moore
,
J. C.
, and
Whinston
,
A. B.
,
1986
, “
A Model of Decision-Making With Sequential Information-Acquisition (Part 1)
,”
Decis. Support Syst.
,
2
(
4
), pp.
285
307
.
11.
Campanella
,
G.
, and
Ribeiro
,
R. A.
,
2011
, “
A Framework for Dynamic Multiple-Criteria Decision Making
,”
Decis. Support Syst.
,
52
(
1
), pp.
52
60
.
12.
Antle
,
J. M.
,
1983
, “
Sequential Decision Making in Production Models
,”
Am. J. Agric. Econ.
,
65
(
2
), pp.
282
290
.
13.
Chiesi
,
H. L.
,
Spilich
,
G. J.
, and
Voss
,
J. F.
,
1979
, “
Acquisition of Domain-Related Information in Relation to High and Low Domain Knowledge
,”
J. Verbal Learn. Verbal Behav.
,
18
(
3
), pp.
257
273
.
14.
Gao
,
S.
, and
Kvan
,
T.
,
2004
, “
An Analysis of Problem Framing in Multiple Settings
,”
Design Computing and Cognition '04
,
J. S.
Gero
, ed.,
Springer
,
Dordrecht, The Netherlands
, pp.
117
134
.
15.
Schön
,
D. A.
,
1987
,
Educating the Reflective Practitioner: Toward a New Design for Teaching and Learning in the Professions
,
Jossey-Bass
,
San Francisco, CA
.
16.
Schön
,
D. A.
,
1984
, “
Problems, Frames and Perspectives on Designing
,”
Des. Stud.
,
5
(
3
), pp.
132
136
.
17.
Hestenes, D.
,
Wells, M.
,
Swackhamer, G.
,
Halloun, I.
,
Hake, R.
, and
Mosca, E.
, 1995, “
Revised Force Concept Inventory
,” PhysPort, accessed June 26, 2018, https://www.physport.org/assessments/assessment.cfm?I=5&A=FCI
18.
Loch
,
C. H.
,
Terwiesch
,
C.
, and
Thomke
,
S.
,
2001
, “
Parallel and Sequential Testing of Design Alternatives
,”
Manage. Sci.
,
47
(
5
), pp.
663
678
.
19.
Simon
,
H. A.
,
1990
,
Bounded Rationality
,
Palgrave Macmillan
,
London
.
20.
Rasmussen
,
C. E.
, and
Williams
,
C. K.
,
2006
,
Gaussian Processes for Machine Learning
,
MIT Press
,
Cambridge, UK
.
21.
Lucas
,
C. G.
,
Griffiths
,
T. L.
,
Williams
,
J. J.
, and
Kalish
,
M. L.
,
2015
, “
A Rational Model of Function Learning
,”
Psychon. Bull. Rev.
,
22
(
5
), pp.
1193
1215
.
22.
Wilson
,
A. G.
,
Dann
,
C.
,
Lucas
,
C.
, and
Xing
,
E. P.
,
2015
, “
The Human Kernel
,”
Advances in Neural Information Processing Systems 28
,
C.
Cortes
,
N. D.
Lawrence
,
D. D.
Lee
,
M.
Sugiyama
, and
R.
Garnett
, eds.,
Curran Associates
, Red Hook, NY, pp.
2854
2862
.
23.
Borji
,
A.
, and
Itti
,
L.
,
2013
, “
Bayesian Optimization Explains Human Active Search
,”
Advances in Neural Information Processing Systems 26
,
C. J. C.
Burges
,
L.
Bottou
,
M.
Welling
,
Z.
Ghahramani
, and
K. Q.
Weinberger
, eds.,
Curran Associates
, Red Hook, NY, pp.
55
63
.
24.
Hinson
,
J. M.
,
Jameson
,
T. L.
, and
Whitney
,
P.
,
2003
, “
Impulsive Decision Making and Working Memory
,”
J. Exp. Psychology: Learn., Memory, Cognit.
,
29
(
2
), pp.
298
306
.
25.
Bernardo
,
J.
,
Bayarri
,
M.
,
Berger
,
J.
,
Dawid
,
A.
,
Heckerman
,
D.
,
Smith
,
A.
, and
West
,
M.
,
2011
, “
Optimization Under Unknown Constraints
,”
Bayesian Statistics 9
, Oxford University Press, Oxford, UK, pp.
229
256
.
26.
Metropolis
,
N.
,
Rosenbluth
,
A. W.
,
Rosenbluth
,
M. N.
,
Teller
,
A. H.
, and
Teller
,
E.
,
1953
, “
Equation of State Calculations by Fast Computing Machines
,”
J. Chem. Phys.
,
21
(
6
), pp.
1087
1092
.
27.
Patil
,
A.
,
Huard
,
D.
, and
Fonnesbeck
,
C. J.
,
2010
, “
PYMC: Bayesian Stochastic Modelling in Python
,”
J. Stat. Software
,
35
(
4
), pp.
1
81
.
28.
Chi
,
M. T.
,
Feltovich
,
P. J.
, and
Glaser
,
R.
,
1981
, “
Categorization and Representation of Physics Problems by Experts and Novices
,”
Cognit. Sci.
,
5
(
2
), pp.
121
152
.
29.
Valerij
,
D.
,
2013
, “
Relationship Between Learning, Knowledge Creation and Organisational Performance
,”
Ann. Alexandru Ioan Cuza Univ.-Econ.
,
60
(
1
), pp.
79
93
.
30.
Shaughnessy
,
J. J.
, and
Zechmeister
,
E. B.
,
1985
,
Research Methods in Psychology
,
Alfred A. Knopf
,
New York
.
31.
Eatwell
,
J.
,
Milgate
,
M.
, and
Newman
,
P.
,
1987
,
The New Palgrave: A Dictionary of Economics
,
Macmillan
,
London
.
32.
Wang
,
J.
, and
Bao
,
L.
,
2010
, “
Analyzing Force Concept Inventory With Item Response Theory
,”
Am. J. Phys.
,
78
(
10
), pp.
1064
1070
.
33.
Shergadwala
,
M.
,
Kannan
,
K. N.
, and
Panchal
,
J. H.
,
2016
, “
Understanding the Impact of Expertise on Design Outcome: An Approach Based on Concept Inventories and Item Response Theory
,”
ASME
Paper No. DETC2016-59038.
34.
Panchal
,
J. H.
, and
Szajnfarber
,
Z.
, “
Experiments in Systems Engineering and Design Research
,”
Syst. Eng.
,
20
(
6
), pp.
529
541
.
35.
Shadish, W.
,
Cook, T. D.
, and
Campbell, D. T.
, 2002,
Experimental and Quasi-Experimental Designs for Generalized Causal Inference
, Wadsworth Cengage Learning, Belmont, CA.
You do not currently have access to this content.