Comparative Decision Making: From Playgrounds to CEOS Abstract

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Comparative Decision Making:
From Playgrounds to CEOS
Angele Yazbec, Krysta Rydecki, and Mario Fific
Grand Valley State University, Michigan
Procedures
This study compares decision making strategies
among children and adults via a computerized, deferred
decision making task. The objective was to make a
decision to buy or not to buy a product based on the
recommendations consulted. The first goal is to investigate
how source reliability affects the number of
recommendations consulted and the accuracy of the
decision. The second goal is to observe how the dynamics
of stopping rule selection changes across age groups. The
third goal was is to see whether or not participants operate
as optimal decision makers. Results showed the striking
differences in a number of reviews consulted and accuracy
as result of both reliability of the source and subject age.
• In a differed decision making task a subject has to decide
either to buy or not to buy a product of unknown quality.
They were to base their decision on reviews selected.
• The reliability of the reviews varied block to block and
were indicated by different video game characters.
• On a correct decision the subjects received 1 token, on
an incorrect decision the subjects lost 1 token. The
display of amount earned was intended to create a real
life buying scenario.
Introduction
• Previous studies have revealed obvious disparities
between children and adults in terms of accurate
decision making. The precise strategies between the
two age groups, however, are not very clear and
warranted investigation through these studies.
• Hypothesis 1: The quality of decision making changes
with age based mental capacities (memory, attention
span, mathematical ability) and with the development
of complex decision making strategies
• Hypothesis 2: Neither children nor adults will operate
as optimal decision makers.
• The Stopping Rule Selection (SRS) theory
hypothesizes that decision makers select different
strategies and stopping rules specific to the decision at
hand.
Comparison to Optimal
Decision Maker
Low Reliability Recommendations
Children
Adults
34
32
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
Optimal
Observed
0
Experimental Design
Time Condition (timed, untimed) X Source Reliability
(high, medium, low, mixed) X Source knowledge
(informed, uninformed) X Type of Response (buy, don’t
buy) X Real Value of the product (good, bad)
2
3
4
5
6
7
Optimal
Observed
0
8
1
2
3
4
5
6
7
8
Difference Recommendation
High Reliability Recommendations
Children
Adults
Results
34
32
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
Optimal
Observed
1
2
3
4
5
6
7
Difference Recommendations
8
# Recommendations
p=0.9
Average # of Recommendations
Average # Recommendations value of nt
p=0.9
Ages 19-32
Untimed
Ages 7-10
Ages 11-14
Ages 15-18
6
4
•
•
2
•
0.60
0.75
•
0.90 0.75mix
Reliability
•
1.1
1.0
Ages 19-32
Untimed
Ages 7-10
Ages 11-14
Ages 15-18
0.9
0.8
0.7
0.6
0.5
0.4
0.60
0.75
0.90 0.75mix
Reliability
E-mail: [email protected], [email protected], [email protected]
8
34
32
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
Optimal
Observed
0
1
2
3
4
5
6
7
8
Difference Recommendations
Conclusions
0
Accuracy P(C)
• Deferred Decision Task: a subject chooses to open an
optional number of reviews before making a decision.
• SRS Theory: utilizes multiple simple decision rules in
real time. In different environments, a decision maker
acts adaptively, constantly looking for the best decision
strategies, stopping rules, and critical values
• Stopping Rule: a decision rule used to decide when to
stop with evidence collection and for making final
decisions.
• Critical Difference: stop when a total sum of bipolar
evidence reaches a critical value of (d)
• Optimal Decision Rule: use a difference between
evidence as a stopping rule and possess a perfect
knowledge of all aspects of the environment.
• One Reason Decision Making: a decision rule that uses
only one evidence for making final decision.
1
34
32
30
28
26
24
22
20
18
16
14
12
10
8
6
4
2
0
Difference Recommendations
0
Definitions
p=0.6
p=0.6
Average # of Recommendations
Abstract
Average # Recommendations
Supported by NSF (SES-1156681) PI: Mario Fific,
"Stopping Rule Selection Theory”
The number of recommendations consulted and the
overall accuracy of decisions increase with age
Neither adults nor children perform as optimal decision
makers under time pressure
Strikingly similar patterns observed among children and
adults for the difference stopping rule
Factors such as memory, attention, and mathematical
ability may account for differences in accuracy (Baron,
Granato, Spranca, & Teubal, 1993)
SRS Theory needed to explain these discrepancies in
terms of multiple stopping rules
Recommended Readings
• Baron, J., Granato, L., Spranca, M., & Teubal, E. (1993). Decision- making
biases in children and early adolescents: exploratory studies.
• Busemeyer, J. R., & Rapoport, A. (1988). Psychological models of
deferred decision making.
• Fific M., & Buckmann M. (2013). Stopping Rule Selection (SRS) Theory
Applied to Deferred Decision Making.
• Garon, N. & Moore, C. (2004). Complex decision-making in early childhood.
• Pitz, G. F. (1968). Information seeking when available information
is limited.
• Pitz, G. F., Reinhold, H., & Geller, E. S. (1969). Strategies of
information seeking in deferred decision making.
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