System Thinking talk for meetup

1. Applied Systems/Complexity Science
for the Data Science Professional
Rich Jolly
VP, Analytics & Strategy, Avamere Health Services
Adjunct Professor, Portland State University
@rich_jolly
I’ll post a link to the slides on Twitter

2. Motivation & Plan
• Why care?
– Some problems appear to be resistant to ‘accepted’
solutions
– In fact, these ‘accepted’ solutions may actually make things
worse (unintended consequences)
– In these cases, systems thinking may present new
approaches and insights
• Agenda:
– Background on complexity (systems) science
– Implications for Modeling
• Thinking about models, linearity, complexity, causality and
prediction
• Some frameworks for consideration

3. My Background
• Day job: VP Analytics & Strategy
– Over 10 years in analytics and data science
• Side gig: Systems Scientist
– Adjunct Professor, PSU Systems Science Graduate
department
– Author: Systems Thinking for Business: Capitalize on
Structures Hidden in Plain Sight (only $6.99 on
Amazon!)
• PhD: Systems Science (PSU) (+ MS-EECS, MBA)
@rich_jolly

4. Avamere Family of Companies
• Spectrum of post-acute care
– Comprehensive clinical resources
such as skilled nursing facilities,
therapy services, home healthcare,
as well as independent and assisted
living
– Avamere Health Services, Infinity
Rehab, Signature Healthcare and
CareConnect
– 246 locations in 18 states with over
8,800 employees
Likely hiring in H2’19 so let me know if you might be interested

5. Portland State University
Systems Science Program
• Graduate Certificates,
MS & PhD
• Online classes available
(quick entry form)
• Systems theory, System
dynamics, Game
Theory, Agent based,
Information theory,
neural networks and
much more

6. There was a mystery
Systems Thinking for Business 6

7. Similarity
• Netlogo model
Red
Blue
Vacant
Blue
Red
Red
Blue
Red
Blue
Percent Similar = 3/7 = 43%
7

8. Netlogo Demonstration
http://www.netlogoweb.org/launch#http://www.netlogoweb.org/assets/modelslib/IABM%20Textbook/chapter%203/Segregation%20Extensions/Segregation%20Simple%20Extension%201.nlogo

9. Takeaways
• What
– Processes can be decentralized and self organized
– Interactions aggregate – creates emergent effects
in time or space
• So what?
– Emergent effects from interactions may explain
current situations (like segregation)
– Emergent effects may provide a useful tool –
crowd sourcing, prediction markets, etc.
© Rich Jolly 2013 Systems Thinking for Business 9

10. Interactions in time – Feedback loops
Copyright © 2018 by Rich Jolly
Reinforcing (or Positive) Feedback Loop
Balancing (or Negative) Feedback Loop

11. Linked reinforcing and balancing loops
Overestimation
Time
System
Variable
Underestimation
Initial
Overestimation or
Underestimation
Copyright © 2018 by Rich Jolly

12. Normal Human
Mental Model
Typical Complex
System Characteristic
Trend
Spatial
Characteristics
Time Time
Linear Nonlinear
Simple Geometric
Fractal
mentalModels.pptx

13. Systems Thinking Overview
Dynamics
Interactions
Ecosystems & Evolution Networks
Strategic Behavior
Perspectives
Copyright © 2018 by Rich Jolly

14. What/So What: Systems (Complexity) Theory?
• Interactions
• Feedback
➜ Emergence
Me
14
Or, sometimes you can’t understand a system by
studying each of it’s components (reductionism)
Results in:
• Unexpected results
and
• Unanticipated solutions
Copyright © 2018 by Rich Jolly

15. Real Science: Not just consulting analysis
15
PSU Systems Science
Level of
abstraction
The Sciences Biology Psychology Computer Science
Math Philosophy
Deductive
Inductive
Systems Science
Reasoning
Tools
“An exact, and scientific,
metaphysics”
Mario Bunge
– Feedback and control
– Game Theory
– Information Theory
– Evolutionary systems
– Network theory
– Nonlinear dynamics
– Taleb’s work (e.g. Black
Swan and Antifragility)
Copyright © 2018 by Rich Jolly

16. On Models
Filter /
Transform
Model Audience
Data &
Knowledge
Information /
Conclusion
“All models are wrong, but some are useful”
George Box
“Art is a lie that makes us realize the truth”
Picasso

17. Layers of Models
© Rich Jolly 2013 Systems Thinking for Business 17
Reality

18. Support?
Knowledge (Explanations)
Data (Observations)
Data Analysis
Pattern
Recognized
Observation
Prediction Made
New Theory
Proposed
Deduction Induction
New Theory
Formed
Ways of Knowing

19. The Inference Problem
Self Organized Criticality
Outside variable not seen in training data
The model works until you need it the most!
Long nonlinear process

20. Distributions
Gaussian (normal) Long tail (power law)
Mediocristan Extremistan
Per Taleb in ‘The Black Swan’

21. Simple Causality in Complex Systems
Images courtesy Wikimedia commons Copyright © 2018 by Rich Jolly

22. Causality?
http://tylervigen.com/spurious-correlations

23. The role of randomness
• The role of randomness may not appreciated
in past events
– Psychology effect - Narrative Fallacy
• In aggregated interactions
– Randomness can radically change outcomes
– Randomness can help create order – The genetic
algorithm/evolution needs randomness!
Copyright © 2018 by Rich Jolly

24. Let’s Predict!
2019
1968

25. 1001 Reasons Your Model Might not
Predict Correctly
• Have all feedback loops which
are actually present been
modeled? Some may be
dormant.
• How well do decision models
represent reality?
• How sensitive are results to
parameter selection?
• Has the model been over-fit?
How much RBP data do you
have to test this?
• What about diversity? And
randomness?
• What about an exogenous
shock?
© Rich Jolly 2013 Systems Thinking for Business 25
• Have you modeled all aspects
of strategic behavior?
• Have you modeled
evolutionary effects?
• Is there a possibility of
punctuated equilibrium?
• Have the networks been
mapped onto the model?
• Have all three perspectives
(TOP) been considered?
• and so on …

26. Assessing Predictability
Low predictability
High predictability
Copyright © 2018 by Rich Jolly

27. Tailor actions to level of predictability
Courtney, Kirkland and Viguerie, ‘Strategy Under Uncertainty,’
Harvard Business Review, Nov-Dec 1997
Level 1: A clear-enough Future – use
‘traditional’ strategy toolkit
Level 2: Alternate Futures – Decision
analysis, Option valuation models,
game theory
Level 3: A Range of Futures – Latent-
demand research, Technology
forecasting, scenario planning
Level 4: True ambiguity – Analogies
and pattern recognition, nonlinear
dynamic models
Copyright © 2018 by Rich Jolly

28. Models as a tool (not the real thing)
© Rich Jolly 2013 Systems Thinking for Business 28
The slippery slope
Thinking
Models are
Reality
Ignore
models
Utility
Model Use

29. Systems Thinking Framework
The Tools of System Thinking
Understanding
the Agents –
and the Thinker
Using the Tools
Properly –
Modeling,
Predictions,
Causality
Copyright © 2018 by Rich Jolly

30. Systems approaches
Lean Agile
Scrum
Andon
Sydney Opera House:
Original Plan – Open in 1963, Cost of $AU7M
Actual – Open in 1973, Cost of $AU104M
How good are we at creating schedules?
Versus methods that encompass system effects.
Source: Taleb, “The Black Swan”
Elimination of Muda, Muri & Mura
(types of waste)
Iterations of working, customer
driven, products

31. Methods of Dealing with Complex
Data from Systems Projects
• Information Theory
– Developed by Claude Shannon at Bell Labs in 1948
– Information entropy, analogous to (but not the same as)
thermodynamic entropy
– Reconstructability Analysis – OCCAM at PSU
• http://dmm.sysc.pdx.edu
• Genetic Algorithm
– Optimization process analogous to evolutionary processes
– Able to deal with complex solution landscapes (multiple
local maximums)
– Surprisingly simple to code & use

32. The Goal
32

33. Thank you!
@rich_jolly
Feedback
Emergence
Interactions
Unexpected Results
Unanticipated Solutions
Copyright © 2018 by Rich Jolly