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Crossing the Glass Bridge
What Squid Game can Teach us about
Systems Thinking, Data Science & UX
CanUX Conference
November 2022
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Summary
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OVERVIEW
Synopsis & Opportunity Framing
Let’s “Math” it
Enter UX
Insights

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The Opportunity

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THE PLOT

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THE OPPORTUNITY
Image source: CNET
Image source: CNET
THE PLOT

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THE PLOT
Glass
panels

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THE OPPORTUNITY
Nefarious Consulting Agency

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THE OPPORTUNITY
Image source: CNET
Image source: CNET
THE OPPORTUNITY

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01
Business
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THE OPPORTUNITY
What kind of problem is this?

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01
Business
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Math
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THE OPPORTUNITY
What kind of problem is this?

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01
Business
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Math
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Psychology
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THE OPPORTUNITY
What kind of problem is this?

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Business
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Math
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Psychology
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UX
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THE OPPORTUNITY
What kind of problem is this?

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01
Business
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Math
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Psychology
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UX
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THE OPPORTUNITY
What kind of problem is this?

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DATA MODEL
REAL WORLD
(SYSTEM)
Predict , Insights
Analyze and Synthesize
Choose and Extract
THE OPPORTUNITY
What does it mean to understand a problem?

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THE OPPORTUNITY
Image source: CNET
Image source: CNET
THE OPPORTUNITY

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Let’s “Math” it!

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THE APPROACH
Image source: LinkedIn
Start with a simple model
Image source: LinkedIn
Start with a simple quant model
QUANT

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QUANT
Probability Distribution*
*based on a computer simulation of 10,000 repetitions
Number of Survivors
Number
of
Crossings
1750
1500
1250
1000
750
500
250
0
0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0

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What might “business” think about that?
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Image source: The Guardian
QUANT

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What might “business” think about that?
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Image source: The Guardian
6-14 survivors...
Let’s build that
bridge!
QUANT

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QUANT
Probability Distribution*
*based on a computer simulation of 10,000 repetitions
Number of Survivors
Number
of
Crossings
1750
1500
1250
1000
750
500
250
0
0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0

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QUANT
Probability distributions tell us, quantitatively, something about how a
system is behaving, or will help you predict how it might behave.
But, as soon as you change something in the system, that will cause
perturbations in the system that might affect that distribution.

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QUANT
Probability Distribution*
*for a “fair” coin tossed 20 times
0.25
0.20
0.15
0.10
0.05
0.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Number of Heads from Twenty Tosses
Probability

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QUANT
Probability Distribution*
0.25
0.20
0.15
0.10
0.05
0.00
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Binomial Distribution (n = 20, p = .25)
Number of Heads from Twenty Tosses
Probability
*for an “unfair” coin tossed 20 times

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QUANT
Probability Distribution*
*based on a computer simulation of 10,000 repetitions
Number of Survivors
Number
of
Crossings
1750
1500
1250
1000
750
500
250
0
0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0

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QUANT
Assumptions
What assumptions did we make about the people crossing the bridge in
order to create our simple model?
Image source: Telecoms

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QUANT
Assumptions
What assumptions did we make about the people crossing the bridge in
order to create our simple model?
Image source: Telecoms

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Enter UX

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ENTER UX
People are not robots.
In our design for the game, we need to remember that these are terrified
people crossing the bridge, not robots.
What are some of the “people” factors that could impact the result?
Image source: CNET

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ENTER UX
People are not robots.
Forgetting (imperfect memories)
Stopping or pausing
Teamwork (people jump as pairs)
The “Ringer”
Combinations of the above
Image source: CNET

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ENTER UX
All those factors could affect the distribution
Number of Survivors
Number
of
Crossings
1750
1500
1250
1000
750
500
250
0
0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0

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ENTER UX
All those factors could affect the distribution
The mean?
The shape?
The variance?
And by how much?
Number of Survivors
Number
of
Crossings
1750
1500
1250
1000
750
500
250
0
0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0

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ENTER UX
UX can help!
Using UX methods of reviewing existing data, conducting qualitative
research, and reviewing “game films” we can help refine the model,
and therefore the quantitative estimates.
Image source: POPSUGAR

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ENTER UX
4 Profiles
Research indicates that there are four profiles we need to consider
when designing the game:
The Ringer
The Selfish Person
The Altruist
The Unpredictable
Image source: UNILAD

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ENTER UX
The Ringer
Image source: INEWS

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Quant UX

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So let’s redo the estimate with a “ringer” included

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THE RINGER
Audience participation – The Ringer
Put the ringer where you want in the line of participants and see result
Enter the location in line (between 1st and 20th place) for the ringer
Tap here to
input a
number for the
ringer’s place
in line
Tap refresh to input a
different number for the
ringer’s place in line to see
the impact it has on the
mean of casualties

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THE RINGER
How he affects the distribution of fatalities
Number of Fatalities
Number
of
Crossings
Number of Fatalities
Number of Fatalities
In 4th place In 13th place
In 20th place
0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0
10000
8000
6000
4000
2000
0
Number
of
Crossings
Number
of
Crossings
0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0
10000
8000
6000
4000
2000
0
0.0 2.5 5.0 7.5 10.0 12.5 15.0 17.5 20.0
10000
8000
6000
4000
2000
0

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And that was just looking at one human behaviour that could impact
the system.
What else could impact the number of people crossing?
Image source: CNET
QUANT UX

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There are many others that could impact the number of people crossing
Forgetting (imperfect memories)
Stopping or pausing
Teamwork (people jump as pairs)
“Imperfect” ringer
Combinations of the above
Image source: CNET
QUANT UX

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Insights

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Where does that leave us?
It turns out that controlling the outcome ahead of time within the 6-14
range might be harder than anticipated.
Image source: CNET
INSIGHTS

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INSIGHTS
If I start with “N” people, what fraction will survive per game and overall?
Each game has a different level of variance.
Image sources: Hypebae; Food52; LinkedIn; Distractify; UNILAD; TECHTOSEE

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INSIGHTS
System values also change in-between games
Image sources: Hypebae; Food52; LinkedIn; Distractify; UNILAD; TECHTOSEE

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Where does that leave us?
It turns out that controlling the outcome ahead of time within the 6-14
range might be harder than anticipated.
What to do?
Image source: CNET
INSIGHTS

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Where does that leave us?
INSIGHTS
No single model will work.
However, there is a design strategy you can infer.

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THE CURRENT DESIGN IS A FIXED SYSTEM
Glass
panels

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SYSTEMS THINKING
System thinking helps
WHERE CAN WE
IMPACT/INTERVENE?

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SYSTEMS THINKING
System thinking helps
WHERE CAN WE
IMPACT/INTERVENE?

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SYSTEMS THINKING
System thinking helps
WHERE CAN WE
IMPACT/INTERVENE?

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This is a complex problem
You need a flexible/adaptable system design to account for high
variance instances in the journey if you want to ensure getting to a final
game within your targeted range.
When precision is next to impossible in prediction, then adaptiveness
in design is needed - and that adaptiveness needs to act in real time
UX DESIGN

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BRIDGE GAME ACROSS GAMES
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UX DESIGN
What are possible levers in system design for these scales of intervention?
Modify the panel probability “P” (currently ½) in real time
Change the differential of panels to participants (ie: add or subtract panels)
Include the ability to change the position of the “Ringer”
Build in a Gamemaster or “AI” to continuously monitor the probability state and
trends as people are crossing the bridge
Include a way to adjust the time (ie: a way to increase or decrease the remaining
time for crossing the bridge
Create an adaptable physical environment (ie: adjust the lighting, make the
surface wet so it becomes slippery, add gusts of wind)
If someone stops and threatens to not let anyone cross the bridge, have the
ability to remove someone from the bridge
If many people are forgetting which panels are safe, include an ability to indicate
which panels have been safely crossed
Ability to change the order of games
Modify the “in between games” environment
Be able to “swap out” high variance games for low variance games depending
on the game outcomes
Gather empirical data on all games to see incorporate into models

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Summary

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We took an approach that
often will work in complex systems
Identify the system (and/or the subsystem[s]) goals and metrics.
Create a baseline, in our case simplified human behaviour, and construct
a quantitative model tied to a key metric.
Investigate the factors that might perturb the model from its assumed
state.
Image source: CNET
SUMMARY

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What else did we learn?
Through research, investigate a design strategy that could
stabilize/optimize the business metrics and identify the “levers” in the
system.
Look at the implications for the future state ecosystem with this new
design strategy.
Work with a cross-functional team to go from strategy to solution.
Image source: CNET
SUMMARY

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DATA MODEL
REAL WORLD
(SYSTEM)
Predict , Insights
Analyze and Synthesize
Choose and Extract
And just keep cycling through observing, analyzing and modeling
SUMMARY

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SUMMARY
Remember, always, that everything you know, and everything
everyone knows, is only a model.
Get your model out there where it can be viewed.
Invite others to challenge your assumptions and
add their own.
DONELLA MEADOWS
“

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REFERENCE BOOKS
Calling Bullshit - The Art of Skepticism in a Data-Driven World
Carl Bergstrom and Jevin West
Weapons of Math Destruction
Cathy O’Neil
Noise a Flaw in Human Judgement
Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein
Thinking in Systems
Donella Meadows
Rebooting AI - Building Artificial Intelligence We Can Trust
Gary Marcus and Ernest Davis
Once Upon a Number - The Hidden Mathematical Logic of Stories
John Allen Paulos
Humble Pi - When Math Goes Wrong in the Real World
Matt Parker
The Signal and Noise
Nate Silver
The Model Thinker
Scott Page

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Thank you.
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Jennifer Fraser
jennifer.fraser@macadamian.com