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.

References

1.
Vincent
,
T. L.
,
1983
, “
Game Theory as a Design Tool
,”
J. Mech. Transm. Autom. Des.
,
105
(
2
), pp.
165
170
.10.1115/1.3258503
2.
Lewis
,
K.
, and
Mistree
,
F.
,
1998
, “
Collaborative, Sequential and Isolated Decisions in Design
,”
ASME J. Mech. Des.
,
120
(
4
), pp.
643
652
.10.1115/1.2829327
3.
Xiao
,
A.
,
Zeng
,
S.
,
Allen
,
J. K.
,
Rosen
,
D. W.
, and
Mistree
,
F.
,
2005
, “
Collaborative Multidisciplinary Decision Making Using Game Theory and Design Capability Indices
,”
Res. Eng. Des.
,
16
(
1–2
), pp.
57
72
.10.1007/s00163-005-0007-x
4.
Chanron
,
V.
, and
Lewis
,
K.
,
2005
, “
A Study of Convergence in Decentralized Design Processes
,”
Res. Eng. Des.
,
16
(
3
), pp.
133
145
.10.1007/s00163-005-0009-8
5.
Devendorf
,
E.
, and
Lewis
,
K.
,
2011
, “
The Impact of Process Architecture on Equilibrium Stability in Distributed Design
,”
ASME J. Mech. Des.
,
133
(
10
), p.
101001
.10.1115/1.4004463
6.
Ciucci
,
F.
,
Honda
,
T.
, and
Yang
,
M. C.
,
2011
, “
An Information-Passing Strategy for Achieving Pareto Optimality in the Design of Complex Systems
,”
Res. Eng. Des.
,
23
(
1
), pp.
71
83
.10.1007/s00163-011-0115-8
7.
Fernández
,
M. G.
,
Panchal
,
J. H.
,
Allen
,
J. K.
, and
Mistree
,
F.
,
2005
, “
Concise Interactions and Effective Management of Shared Design Spaces: Moving Beyond Strategic Collaboration Towards Co-Design
,”
ASME
Paper No. DETC2005-85381.10.1115/DETC2005-85381
8.
Rao
,
S. S.
, and
Freiheit
,
T. I.
,
1991
, “
A Modified Game Theory Approach to Multiobjective Optimization
,”
ASME J. Mech. Des.
,
113
(
3
), pp.
286
291
.10.1115/1.2912781
9.
Takai
,
S.
,
2010
, “
A Game-Theoretic Model of Collaboration in Engineering Design
,”
ASME J. Mech. Des.
,
132
(
5
), p.
051005
.10.1115/1.4001205
10.
Frischknecht
,
B. D.
,
Whitefoot
,
K.
, and
Papalambros
,
P. Y.
,
2010
, “
On the Suitability of Econometric Demand Models in Design for Market Systems
,”
ASME J. Mech. Des.
,
132
(
12
), p.
121007
.10.1115/1.4002941
11.
Taha
,
A. F.
, and
Panchal
,
J. H.
,
2014
, “
Multilevel Decision-Making in Decentralized Energy Systems With Multiple Technologies and Uncertainty
,”
IEEE Trans. Syst. Man Cybern.
,
44
(
7
), pp.
894
907
.10.1109/TSMC.2013.2284578
12.
Taha
,
A. F.
,
Hachem
,
N. A.
, and
Panchal
,
J. H.
,
2014
, “
A Quasi-Feed-In-Tariff Policy Formulation in Micro-Grids: A Bi-Level Multi-Period Approach
,”
Energy Policy
,
71
, pp.
63
75
.10.1016/j.enpol.2014.04.014
13.
Kahneman
,
D.
,
Slovic
,
P.
, and
Tversky
,
A.
,
1982
,
Judgment Under Uncertainty: Heuristics and Biases
,
Cambridge University Press
,
New York
.10.1017/CBO9780511809477
14.
Simon
,
H.
,
1996
,
The Sciences of the Artificial
,
The MIT Press
,
Cambridge, MA
.
15.
Papalambros
,
P. Y.
,
2010
, “
The Human Dimension
,”
ASME J. Mech. Des.
,
132
(
5
), p.
050201
.10.1115/1.4001602
16.
Gurnani
,
A.
, and
Lewis
,
K.
,
2008
, “
Collaborative, Decentralized Engineering Design at the Edge of Rationality
,”
ASME J. Mech. Des.
,
130
(
12
), p.
121101
.10.1115/1.2988479
17.
Franssen
,
M.
, and
Bucciarelli
,
L. L.
,
2005
, “
On Rationality in Engineering Design
,”
ASME J. Mech. Des.
,
126
(
6
), pp.
945
949
.10.1115/1.1803850
18.
Boudreau
,
K. J.
,
Lacetera
,
N.
, and
Lakhani
,
K. R.
,
2011
, “
Incentives and Problem Uncertainty in Innovation Contests: An Empirical Analysis
,”
Manage. Sci.
,
57
(
5
), pp.
843
863
.10.1287/mnsc.1110.1322
19.
Seda Ertaç
,
S. M.
,
2010
,
Experimental Economics
,
SAGE Publications
,
Thousand Oaks, CA
, pp.
873
883
.
20.
Ariely
,
D.
, and
Norton
,
M. I.
,
2007
, “
Psychology and Experimental Economics
,”
Curr. Dir. Psychol. Sci.
,
16
(
6
), pp.
336
339
.10.1111/j.1467-8721.2007.00531.x
21.
Simon
,
H. A.
,
1956
, “
Rational Choice and the Structure of the Environment
,”
Psychol. Rev.
,
63
(
2
), pp.
129
138
.10.1037/h0042769
22.
Holt
,
C. A.
,
2007
,
Markets, Games, and Strategic Behavior
,
Addison Wesley
,
Boston
, MA.
23.
Howe
,
J.
,
2008
,
Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business
,
Crown Business
,
New York
.
24.
LocalMotors
,
2014
, “
Local Motors Challenges
.” Available at: https://localmotors.com/challenges/
25.
Innocentive
,
2014
, “
Innocentive
.” Available at: http://www.innocentive.com/
26.
Quirky
,
2014
, “
Quirky
.” Available at: https://www.quirky.com/
27.
TopCoder
,
2014
, “
Topcoder
.” Available at: http://www.topcoder.com/
28.
Salek
,
M.
,
Bachrach
,
Y.
, and
Key
,
P.
,
2013
, “
Hotspotting: A Probabilistic Graphical Model for Image Object Localization Through Crowdsourcing
,”
Twenty-Seventh AAAI Conference on Artificial Intelligence
,
Bellevue
,
WA
, July 14–18, pp. 1156–1162.
29.
Lin
,
C. H.
,
Mausam
, and
Weld
,
D. S.
,
2012
, “
Crowdsourcing Control: Moving Beyond Multiple Choice
,”
Twenty-Eighth Conference on Uncertainty in Artificial Intelligence
,
Catalina Island
,
CA
, Aug. 14–18, pp.
491
500
.
30.
Raykar
,
V. C.
, and
Yu
,
S.
,
2012
, “
Eliminating Spammers and Ranking Annotators for Crowdsourced Labeling Tasks
,”
J. Mach. Learn. Res.
,
13
(
1
), pp.
491
518
.
31.
Baba
,
Y.
, and
Kashima
,
H.
,
2013
, “
Statistical Quality Estimation for General Crowdsourcing Tasks
,”
19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
,
KDD’13
, ACM, pp.
554
562
.10.1145/2487575.2487600
32.
Green
,
M.
,
Seepersad
,
C. C.
, and
Holtta-Otto
,
K.
,
2014
, “
Crowd-Sourcing the Evaluation of Creativity in Conceptual Design: A Pilot Study
,”
ASME
Paper No. DETC2014–34434. 10.1115/DETC2014-34434
33.
Gerth
,
R. J.
,
Burnap
,
A.
, and
Papalambros
,
P.
,
2012
, “
Crowdsourcing: A Primer and Its Implications for Systems Engineering
,”
2012 NDIA Ground Vehicle Systems Engineering and Technology Symposium
, Troy, MI, Aug. 14–16.
34.
Kudrowitz
,
B. M.
, and
Wallace
,
D.
,
2013
, “
Assessing the Quality of Ideas From Prolific, Early-Stage Product Ideation
,”
J. Eng. Des.
,
24
(
2
), pp.
120
139
.10.1080/09544828.2012.676633
35.
DARPA
,
2014
, “
DARPA Adaptive Vehicle Make (AVM)
.” Available at: http://www.darpa.mil/NewsEvents/Releases/2013/04/22.aspx
36.
Corchón
,
L. C.
,
2007
, “
The Theory of Contests: A Survey
,”
Rev. Econ. Des.
,
11
(
2
), pp.
69
100
.10.1007/s10058-007-0032-5
37.
Terwiesch
,
C.
, and
Xu
,
Y.
,
2008
, “
Innovation Contests, Open Innovation, and Multiagent Problem Solving
,”
Manage. Sci.
,
54
(
9
), pp.
1529
1543
.10.1287/mnsc.1080.0884
38.
Skaperdas
,
S.
,
1996
, “
Contest Success Functions
,”
Economic Theory
,
7
(
2
), pp.
283
290
.10.1007/BF01213906
39.
Chen
,
W.
,
Hoyle
,
C.
, and
Wassenaar
,
H. J.
,
2013
,
Decision-Based Design: Integrating Consumer Preferences into Engineering Design
,
Springer
,
New York
.
40.
Skaperdas
,
S.
,
1998
, “
On the Formation of Alliances in Conflict and Contests
,”
Public Choice
,
96
(
1/2
), pp.
25
42
.10.1023/A:1004912124496
41.
Baldwin
,
C. Y.
, and
Clark
,
K. B.
,
2000
,
Design Rules
(The Power of Modularity, Vol. 1),
The MIT Press
,
Cambridge, MA
.
42.
Papalambros
,
P. Y.
, and
Wilde
,
D. J.
,
2000
,
Principles of Optimal Design: Modeling and Computation
, 2nd ed.,
Cambridge University Press
,
New York
.10.1017/CBO9780511626418
43.
Andreoni
,
J.
, and
Croson
,
R.
,
2008
, “
Partners Versus Strangers: Random Rematching in Public Goods Experiments
,”
Handbook of Experimental Economics Results
, Vol.
1
,
Elsevier, Amsterdam
,
The Netherlands
, pp.
776
783
.10.1016/S1574-0722(07)00082-0
44.
Fischbacher
,
U.
,
2007
, “
z-Tree: Zurich Toolbox for Ready-Made Economic Experiments
,”
Exp. Econ.
,
10
(
2
), pp.
171
178
.10.1007/s10683-006-9159-4
45.
Tversky
,
A.
, and
Kahneman
,
D.
,
1974
, “
Judgment Under Uncertainty: Heuristics and Biases
,”
Science
,
185
(
4157
), pp.
1124
1131
.10.1126/science.185.4157.1124
46.
Simpson
,
T. W.
, and
Martins
,
J. R. R. A.
,
2011
, “
Multidisciplinary Design Optimization for Complex Engineered Systems: Report From a National Science Foundation Workshop
,”
ASME J. Mech. Des.
,
133
(
10
), p.
101002
.10.1115/1.4004465
47.
Camerer
,
C. F.
,
2003
,
Behavioral Game Theory: Experiments in Strategic Interaction
,
Princeton University Press
,
Princeton, NJ
.
48.
Loewenstein
,
G.
,
1999
, “
Experimental Economics From the Vantage-Point of Behavioural Economics
,”
Econ. J.
,
109
(
453
), pp.
F25
F34
.10.1111/1468-0297.00400
49.
Smith
, V
. L.
,
1976
, “
Experimental Economics: Induced Value Theory
,”
Am. Econ. Rev.
,
66
(
2
), pp.
274
279
.
50.
Lichtenstein
,
S.
, and
Slovic
,
P.
,
1973
, “
Response-Induced Reversals of Preference in Gambling: An Extended Replication in Las Vegas
,”
J. Exp. Psychol.
,
101
(
1
), pp.
16
20
.10.1037/h0035472
51.
Lichtenstein
,
S.
, and
Slovic
,
P.
,
1971
, “
Reversals of Preference Between Bids and Choices in Gambling Decisions
,”
J. Exp. Psychol.
,
89
(
1
), pp.
46
55
.10.1037/h0031207
You do not currently have access to this content.