Analysis and Visualization of Behavioral Network Science Experiments
Francisco J. Gutierrez-Villarreal
Computer Science Department, Hartnell College, Salinas, CA 93908
David L. Alderson, Ph.D, Emily Craparo, Ph.D, Operations Research Department, NPS
Thomas W. Otani, Ph.D, Computer Science Department, NPS
Data Processing & Analysis
Overview
This project involved analyzing the results of
a series of behavioral experiments conducted
by researchers at the Naval Postgraduate
School (NPS) and at the University of
California, Santa Barbara (UCSB). The
experiment involved individuals within a
community deciding if and when to evacuate
from a pending natural disaster. The primary
objective of this project was to understand
the way in which individual decision makers
use and share information, and how this
information leads to collective action of the
group as a whole. Raw data was processed
using the Python computer language in order
to obtain information about the influence of
personal networks on individual and group
behavior. Matplotlib, a Python library, was
used to graphically represent subject
behaviors and experiment outcomes.
Experiment: 18 May 2012, UCSB
• 50 Players
• 47 Games
(1 min each)
Analysis (Continued)
Log Files, 1 per game (all players)
21
21
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21
18
18
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18
03
18
48
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09
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14
14
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35
18
06
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14
02
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09
TAB_SWITCH
NODE_PRESS
NODE_RELEASE
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NODE_PRESS
NODE_RELEASE
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TAB_SWITCH
NODE_PRESS
TAB_SWITCH
NODE_RELEASE
TAB_SWITCH
TAB_SWITCH
TAB_SWITCH
TAB_SWITCH
NODE_PRESS
NODE_RELEASE
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NODE_PRESS
TAB_SWITCH
TAB_SWITCH
NODE_RELEASE
TAB_SWITCH
NODE_PRESS
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TAB_SWITCH
NODE_RELEASE
TAB_SWITCH
NODE_PRESS
NODE_PRESS
NODE_RELEASE
NODE_PRESS
NODE_RELEASE
NODE_RELEASE
Social Information
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28
Disaster Information
43
43
Social Information
Social Information
43
Social Information
43
Social Information
Social Information
Social Information
Social Information
25
25
Social Information
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29
Social Information
Social Information
29
Social Information
02
Social Information
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02
Social Information
04
09
09
32
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04
Color Map that shows
each player’s payoff
(score) for each game.
Rows show players’
scores throughout
games. Columns show
players’ scores for a
single game. Vertical color
patterns indicate similar
player behaviors in each
game.
0
113
185
1669
1747
1842
1877
2197
2201
2324
2505
2536
2598
2734
2768
2846
2950
2998
3004
3005
3074
3147
3168
3242
3441
3442
3447
3489
3534
3606
3658
3698
3769
3833
3897
3922
Histogram shows click
frequency distribution
(measured in clicks per
second) for all players in
all games
Graphs show the disaster
strike probability line (in
red), cumulative
evacuations as time goes
by (blue line), shelter
capacity for each game (
black horizontal line), and
the point after which a
disaster can begin to strike
(vertical purple dotted
line). One graph of this
type was generated for
each game.
Histogram shows
the distribution of total
player scores for all
games. (Max Score: 4700)
Disaster Tab
Red Zone is
possible
disaster strike
range
Disaster
Likelihood
Lowest Score
Player 11: 1940 points
Average: 2344 points
Payoff (Max=100)
Evacuation Button
Social Tab
If a player clicks on a
neighbor who is in shelter,
the bed number occupied by
this neighbor is revealed
(and stays revealed for the
duration of the scenario).
The shelter condition is
displayed. The number
shown is the highest bed
number among those
occupied by the neighbors
player revealed.
H
Color Maps that
show the
percentage of
time each
player spent on
each tab for all
games.
Most players disregarded the social network tab and instead
focused on the disaster probability tab. This can be seen in the
following graphs. The graph on the left shows the payoff
distributions for all possible number of neighbor counts for all
games. There is no clear correlation between neighbor counts
and performance. The graph on the right shows the clicking
activity for each player for each game and lists players by their
performance (highest rank first). As the graph shows, there
were players who did well and were not very active, and others
who were active that did poorly. Since clicking activity is
strongly associated with neighbor node clicks, the graphs
indicate that group behavior did not heavily influence individual
decision makers.
Next Steps
These results are guiding the development of the
next round of experiments, to be held at UCSB in
October 2012.
Acknowledgments
Game Progress Bar
Highest Score
Player 10: 2590 points
Conclusions
Graphs show the disaster
probability values that a
single player saw for each
game (probability values
are shown vertically for
each game). If a player
evacuated in a game, a
green or red dot is shown.
Green dots indicate that a
player made the correct
evacuation decision (the
disaster hit in that game).
Red dots indicate that the
player evacuated, but the
disaster did not strike. One
graph of this type was
generated for each player.
Player rank as determined
by total game scores is
shown next to the player’s
ID number.
I would like to thank my mentors, Professors Alderson, Otani,
and Craparo from NPS, for sharing their knowledge and
experience with me, and for providing patient guidance
throughout the project. I would also like to thank Kelly Locke,
Andy Newton, Professor Joe Welch, and Pat McNeil from
Hartnell College for making this internship possible. Finally, I
would like to thank Alison Kerr and Casandra Martin, NPS
internship coordinators, for all of their hard work in making the
internship program run smoothly, and for helping me navigate
through all of the opportunities available at NPS.
This internship was funded by Strengthening Transfer Pathways
(STP) Title V Grant
Office of Naval Research
(ONR) Multiple University
Research Initiative (MURI)
on “Next-Generation
Network Science”
2008-2013
For further information
Francisco J. Gutierrez-Villarreal
franciscogutierrez@student.hartnell.edu
David Alderson, Ph.D.
dlalders@nps.edu