Abstract

Existing literature on information sharing in contests has established that sharing contest-specific information influences contestant behaviors, and thereby, the outcomes of a contest. However, in the context of engineering design contests, there is a gap in knowledge about how contest-specific information such as competitors’ historical performance influences designers’ actions and the resulting design outcomes. To address this gap, the objective of this study is to quantify the influence of information about competitors’ past performance on designers’ belief about the outcomes of a contest, which influences their design decisions, and the resulting design outcomes. We focus on a single-stage design competition where an objective figure of merit is available to the contestants for assessing the performance of their design. Our approach includes (i) developing a behavioral model of sequential decision making that accounts for information about competitors’ historical performance and (ii) using the model in conjunction with a human-subject experiment where participants make design decisions given controlled strong or weak performance records of past competitors. Our results indicate that participants spend greater efforts when they know that the contest history reflects that past competitors had a strong performance record than when it reflects a weak performance record. Moreover, we quantify cognitive underpinnings of such informational influence via our model parameters. Based on the parametric inferences about participants’ cognition, we suggest that contest designers are better off not providing historical performance records if past contest outcomes do not match their expectations setup for a given design contest.

References

1.
Sha
,
Z.
,
Kannan
,
K. N.
, and
Panchal
,
J. H.
,
2015
, “
Behavioral Experimentation and Game Theory in Engineering Systems Design
,”
ASME J. Mech. Des.
,
137
(
5
), p.
051405
.
2.
Panchal
,
J. H.
,
Sha
,
Z.
, and
Kannan
,
K. N.
,
2017
, “
Understanding Design Decisions Under Competition Using Games With Information Acquisition and a Behavioral Experiment
,”
ASME J. Mech. Des.
,
139
(
9
), p.
091402
.
3.
Bayrak
,
A. E.
, and
Sha
,
Z.
,
2021
, “
Integrating Sequence Learning and Game Theory to Predict Design Decisions Under Competition
,”
ASME J. Mech. Des.
,
143
(
5
), p.
051401
.
4.
Che
,
Y.-K.
,
1993
, “
Design Competition Through Multidimensional Auctions
,”
RAND J. Econ.
,
24
(
4
), pp.
668
680
.
5.
Shiau
,
C.-S. N.
, and
Michalek
,
J. J.
,
2009
, “
Optimal Product Design Under Price Competition
,”
ASME J. Mech. Des.
,
131
(
7
), p.
071003
.
6.
Panchal
,
J. H.
,
2015
, “
Using Crowds in Engineering Design—Towards a Holistic Framework
,”
Proceedings of the 20th International Conference on Engineering Design
,
Milan, Italy
,
July 27–30
,
The Design Society
, pp.
041
050
.
7.
Shergadwala
,
M.
,
Forbes
,
H.
,
Schaefer
,
D.
, and
Panchal
,
J. H.
,
2020
, “
Challenges and Research Directions in Crowdsourcing for Engineering Design: An Interview Study With Industry Professionals
,”
IEEE Trans. Eng. Manage.
, pp.
1
13
.
8.
Dixit
,
A.
,
1987
, “
Strategic Behavior in Contests
,”
Am. Econ. Rev.
,
77
(
5
), pp.
891
898
.
9.
Deck
,
C.
, and
Sheremeta
,
R. M.
,
2012
, “
Fight Or Flight? Defending Against Sequential Attacks in the Game of Siege
,”
J. Conflict Resol.
,
56
(
6
), pp.
1069
1088
.
10.
Mago
,
S. D.
, and
Sheremeta
,
R. M.
,
2017
, “
Multi-Battle Contests: An Experimental Study
,”
South. Econ. J.
,
84
(
2
), pp.
407
425
.
11.
Gelder
,
A.
,
2014
, “
From Custer to Thermopylae: Last Stand Behavior in Multi-Stage Contests
,”
Games Econ. Behav.
,
87
, pp.
442
466
.
12.
Nalebuff
,
B. J.
, and
Stiglitz
,
J. E.
,
1983
, “
Prizes and Incentives: Towards a General Theory of Compensation and Competition
,”
Bell J. Econ.
,
14
(
1
), pp.
21
43
.
13.
O’Keeffe
,
M.
,
Viscusi
,
W. K.
, and
Zeckhauser
,
R. J.
,
1984
, “
Economic Contests: Comparative Reward Schemes
,”
J. Labor Econ.
,
2
(
1
), pp.
27
56
.
14.
Moldovanu
,
B.
, and
Sela
,
A.
,
2001
, “
The Optimal Allocation of Prizes in Contests
,”
Am. Econ. Rev.
,
91
(
3
), pp.
542
558
.
15.
Sheremeta
,
R. M.
,
2010
, “
Experimental Comparison of Multi-Stage and One-Stage Contests
,”
Games Econ. Behav.
,
68
(
2
), pp.
731
747
.
16.
Parco
,
J. E.
,
Rapoport
,
A.
, and
Amaldoss
,
W.
,
2005
, “
Two-Stage Contests With Budget Constraints: An Experimental Study
,”
J. Math. Psychol.
,
49
(
4
), pp.
320
338
.
17.
Schmitt
,
P.
,
Shupp
,
R.
,
Swope
,
K.
, and
Cadigan
,
J.
,
2004
, “
Multi-Period Rent-Seeking Contests With Carryover: Theory and Experimental Evidence
,”
Econ. Governance
,
5
(
3
), pp.
187
211
.
18.
Mago
,
S. D.
,
Samak
,
A. C.
, and
Sheremeta
,
R. M.
,
2016
, “
Facing Your Opponents: Social Identification and Information Feedback in Contests
,”
J. Conflict Resol.
,
60
(
3
), pp.
459
481
.
19.
Vrolijk
,
A.
, and
Szajnfarber
,
Z.
,
2015
, “
When Policy Structures Technology: Balancing Upfront Decomposition and In-Process Coordination in Europe's Decentralized Space Technology Ecosystem
,”
Acta Astronautica
,
106
, pp.
33
46
.
20.
Szajnfarber
,
Z.
,
Zhang
,
L.
,
Mukherjee
,
S.
,
Crusan
,
J.
,
Hennig
,
A.
, and
Vrolijk
,
A.
,
2020
, “
Who Is in the Crowd? Characterizing the Capabilities of Prize Competition Competitors
,”
IEEE Trans. Eng. Manage.
, pp.
1
15
.
21.
Simon
,
H. A.
,
1979
, “
Information Processing Models of Cognition
,”
Annu. Rev. Psychol.
,
30
(
1
), pp.
363
396
.
22.
Kreuzbauer
,
R.
, and
Malter
,
A. J.
,
2005
, “
Embodied Cognition and New Product Design: Changing Product Form to Influence Brand Categorization
,”
J. Product Innov. Manage.
,
22
(
2
), pp.
165
176
.
23.
Shergadwala
,
M.
,
Bilionis
,
I.
,
Kannan
,
K. N.
, and
Panchal
,
J. H.
,
2018
, “
Quantifying the Impact of Domain Knowledge and Problem Framing on Sequential Decisions in Engineering Design
,”
ASME J. Mech. Des.
,
140
(
10
), p.
101402
.
24.
Cash
,
P.
, and
Gonçalves
,
M.
,
2017
, “Information-Triggered Co-Evolution: A Combined Process Perspective,”
Analysing Design Thinking: Studies of Cross-Cultural Co-Creation
,
B. T.
Christensen
,
L. J.
Ball
, and
K.
Halskov
, eds.
CRC Press
,
London, UK
, pp.
501
520
.
25.
Gao
,
S.
, and
Kvan
,
T.
,
2004
,
“An Analysis of Problem Framing in Multiple Settings
,”
Design Computing and Cognition ’04
,
J. S.
Gero
, ed.,
Springer
,
The Netherlands
, pp.
117
134
.
26.
Schön
,
D. A.
,
1987
,
Educating the Reflective Practitioner: Toward a New Design for Teaching and Learning in the Professions
,
Jossey-Bass
,
San Francisco, CA
.
27.
Schön
,
D. A.
,
1984
, “
Problems, Frames and Perspectives on Designing
,”
Des. Stud.
,
5
(
3
), pp.
132
136
.
28.
Cardoso
,
C.
,
Badke-Schaub
,
P.
, and
Eris
,
O.
,
2016
, “
Inflection Moments in Design Discourse: How Questions Drive Problem Framing During Idea Generation
,”
Des. Stud.
,
46
, pp.
59
78
.
29.
Zheng
,
H.
,
Li
,
D.
, and
Hou
,
W.
,
2011
, “
Task Design, Motivation, and Participation in Crowdsourcing Contests
,”
Int. J. Electron. Commer.
,
15
(
4
), pp.
57
88
.
30.
Chaudhari
,
A. M.
,
Sha
,
Z.
, and
Panchal
,
J. H.
,
2018
, “
Analyzing Participant Behaviors in Design Crowdsourcing Contests Using Causal Inference on Field Data
,”
ASME J. Mech. Des.
,
140
(
9
), p.
091401
.
31.
GrabCAD, 2019, https://grabcad.com/challenges/finished, Accessed September 10, 2019.
32.
Aydinliyim
,
T.
, and
Murthy
,
N. N.
,
2016
, “
Managing Engineering Design for Competitive Sourcing in Closed-Loop Supply Chains
,”
Decis. Sci.
,
47
(
2
), pp.
257
297
.
33.
Milgrom
,
P.
, and
Roberts
,
J.
,
1986
, “
Relying on the Information of Interested Parties
,”
RAND J. Econ.
,
17
(
1
), pp.
18
32
.
34.
Toma
,
C.
, and
Butera
,
F.
,
2015
, “
Cooperation Versus Competition Effects on Information Sharing and Use in Group Decision-Making
,”
Soc. Pers. Psychol. Compass
,
9
(
9
), pp.
455
467
.
35.
Li
,
J.
,
Sikora
,
R.
,
Shaw
,
M. J.
, and
Tan
,
G. W.
,
2006
, “
A Strategic Analysis of Inter Organizational Information Sharing
,”
Decis. Support Syst.
,
42
(
1
), pp.
251
266
.
36.
Folgado
,
H.
,
Duarte
,
R.
,
Fernandes
,
O.
, and
Sampaio
,
J.
,
2014
, “
Competing With Lower Level Opponents Decreases Intra-Team Movement Synchronization and Time-Motion Demands During Pre-season Soccer Matches
,”
PLoS One
,
9
(
5
), p.
e97145
.
37.
Epstein
,
J. A.
, and
Harackiewicz
,
J. M.
,
1992
, “
Winning Is Not Enough: The Effects of Competition and Achievement Orientation on Intrinsic Interest
,”
Pers. Soc. Psychol. Bull.
,
18
(
2
), pp.
128
138
.
38.
Corchón
,
L. C.
,
2007
, “
The Theory of Contests: A Survey
,”
Rev. Econ. Des.
,
11
(
2
), pp.
69
100
.
39.
Dorst
,
K.
,
2004
, “
On the Problem of Design Problems—Problem Solving and Design Expertise
,”
J. Des. Res.
,
4
(
2
), pp.
185
196
.
40.
Whitney
,
D. E.
,
1990
, “
Designing the Design Process
,”
Res. Eng. Des.
,
2
(
1
), pp.
3
13
.
41.
Roozenburg
,
N. F.
, and
Cross
,
N.
,
1991
, “
Models of the Design Process: Integrating Across the Disciplines
,”
Des. Stud.
,
12
(
4
), pp.
215
220
.
42.
Hoogveld
,
A. W.
,
Paas
,
F.
, and
Jochems
,
W. M.
,
2003
, “
Application of an Instructional Systems Design Approach by Teachers in Higher Education: Individual Versus Team Design
,”
Teach. Teach. Educ.
,
19
(
6
), pp.
581
590
.
43.
Cross
,
N.
,
2001
, “Design Cognition: Results From Protocol and Other Empirical Studies of Design Activity.”
Design Knowing and Learning: Cognition Design Education
,
C.
Eastman
,
W.
Newstetter
, and
M.
McCracken
, eds.,
Elsevier
,
Oxford, UK
, pp.
79
103
.
44.
Lu
,
C.-C.
,
2015
, “
The Relationship Between Student Design Cognition Types and Creative Design Outcomes
,”
Des. Stud.
,
36
, pp.
59
76
.
45.
Papalambros
,
P. Y.
, and
Wilde
,
D. J.
,
2000
,
Principles of Optimal Design: Modeling and Computation
,
Cambridge University Press
,
Cambridge, UK
.
46.
Sarkar
,
P.
, and
Chakrabarti
,
A.
,
2011
, “
Assessing Design Creativity
,”
Des. Stud.
,
32
(
4
), pp.
348
383
.
47.
Shergadwala
,
M. N.
, and
El-Nasr
,
M. S.
,
2021
, “
Esports Agents With a Theory of Mind: Towards Better Engagement, Education, and Engineering
,” arXiv preprint arXiv:2103.04940.
48.
Shergadwala
,
M. N.
,
Teng
,
Z.
, and
El-Nasr
,
M. S.
,
2021
, “
Can We Infer Player Behavior Tendencies From a Player’s Decision-Making Data? Integrating Theory of Mind to Player Modeling
,”
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
, Virtual Conference,
Oct. 11–15
, Vol.
17
, pp.
195
202
.
49.
Premack
,
D.
, and
Woodruff
,
G.
,
1978
, “
Does the Chimpanzee Have a Theory of Mind?
,”
Behav. Brain Sci.
,
1
(
4
), pp.
515
526
.
50.
Cash
,
P. J.
,
Hartlev
,
C. G.
, and
Durazo
,
C. B.
,
2017
, “
Behavioural Design: A Process for Integrating Behaviour Change and Design
,”
Des. Stud.
,
48
, pp.
96
128
.
51.
Wendel
,
S.
,
2020
,
Designing for Behavior Change: Applying Psychology and Behavioral Economics
,
O’Reilly Media
,
Sebastopol, Canada
.
52.
Shaughnessy
,
J. J.
, and
Zechmeister
,
E. B.
,
1985
,
Research Methods in Psychology
,
Alfred A. Knopf
,
New York, NY
.
53.
Faul
,
F.
,
Erdfelder
,
E.
,
Buchner
,
A.
, and
Lang
,
A.-G.
,
2009
, “
Statistical Power Analyses Using G* Power 3.1: Tests for Correlation and Regression Analyses
,”
Behav. Res. Methods
,
41
(
4
), pp.
1149
1160
.
54.
Eatwell
,
J.
,
Milgate
,
M.
, and
Newman
,
P.
,
1987
,
The New Palgrave: A Dictionary of Economics
,
Macmillan
,
London
.
55.
Konings
,
M. J.
,
Schoenmakers
,
P. P.
,
Walker
,
A. J.
, and
Hettinga
,
F. J.
,
2016
, “
The Behavior of an Opponent Alters Pacing Decisions in 4-km Cycling Time Trials
,”
Physiol. Behav.
,
158
, pp.
1
5
.
56.
Hettinga
,
F.
,
Konings
,
M.
, and
Pepping
,
G.-J.
,
2017
, “
The Science of Racing Against Opponents: Affordance Competition and the Regulation of Exercise Intensity in Head-to-Head Competition
,”
Front. Physiol.
,
8
, p.
118
.
57.
Sheremeta
,
R.
,
2013
, “
Overbidding and Heterogeneous Behavior in Contest Experiments
,”
J. Econ. Surv.
,
27
(
3
), pp.
491
514
.
58.
Fallucchi
,
F.
,
Renner
,
E.
, and
Sefton
,
M.
,
2013
, “
Information Feedback and Contest Structure in Rent-Seeking Games
,”
Eur. Econ. Rev.
,
64
, pp.
223
240
.
59.
Von Neumann
,
J.
,
Morgenstern
,
O.
, and
Kuhn
,
H. W.
,
2007
,
Theory of Games and Economic Behavior (Commemorative Edition)
,
Princeton University Press
,
Princeton, NJ
.
60.
Loch
,
C. H.
,
Terwiesch
,
C.
, and
Thomke
,
S.
,
2001
, “
Parallel and Sequential Testing of Design Alternatives
,”
Manage. Sci.
,
47
(
5
), pp.
663
678
.
61.
Rasmussen
,
C. E.
, and
Williams
,
C. K.
,
2006
,
Gaussian Processes for Machine Learning
,
MIT Press
,
Cambridge, MA
.
62.
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, Inc.
, pp.
55
63
.
63.
Szajnfarber
,
Z.
, and
Vrolijk
,
A.
,
2018
, “
A Facilitated Expert-Based Approach to Architecting ‘Openable’ Complex Systems
,”
Syst. Eng.
,
21
(
1
), pp.
47
58
.
64.
Shergadwala
,
M.
,
Bilionis
,
I.
, and
Panchal
,
J. H.
,
2018
, “
Students As Sequential Decision-Makers: Quantifying the Impact of Problem Knowledge and Process Deviation on the Achievement of Their Design Problem Objective
,”
International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
, Vol.
51784
,
American Society of Mechanical Engineers
, Paper No. V003T04A011.
65.
Loch
,
C. H.
,
DeMeyer
,
A.
, and
Pich
,
M.
,
2011
,
Managing the Unknown: A New Approach to Managing High Uncertainty and Risk in Projects
,
John Wiley & Sons
,
Hoboken, NJ
.
66.
Terwiesch
,
C.
, and
Xu
,
Y.
,
2008
, “
Innovation Contests, Open Innovation, and Multiagent Problem Solving
,”
Manage. Sci.
,
54
(
9
), pp.
1529
1543
.
67.
Hoffman
,
M. D.
, and
Gelman
,
A.
,
2014
, “
The No-u-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo
,”
J. Mach. Learn. Res.
,
15
(
1
), pp.
1593
1623
.
68.
Duane
,
S.
,
Kennedy
,
A. D.
,
Pendleton
,
B. J.
, and
Roweth
,
D.
,
1987
, “
Hybrid Monte Carlo
,”
Phys. Lett. B
,
195
(
2
), pp.
216
222
.
69.
Salvatier
,
J.
,
Wiecki
,
T. V.
, and
Fonnesbeck
,
C.
,
2016
, “
Probabilistic Programming in Python Using PyMC3
,”
PeerJ Comput. Sci.
,
2
, p.
e55
.
You do not currently have access to this content.