The document discusses various types of non-linear change including exponential growth, logarithmic decline, critical transitions, crises/breakdowns/disruptions, and uncertain vacillations. It provides examples of each type of change and discusses complications from psychological biases and governance. The document concludes by outlining heuristics for mastering different types of non-linear change such as understanding limits, identifying early warning signs, strengthening resilience, and avoiding pseudo-stabilization.

1. Mastering non-linear change
Butterfly effect
Future intelligencePublic value
Generative social science
Types of risk
Sustainability
Networktheory
Systems
architecting
Antifragility
Holistic leadership
Adaptiveleaders
Personal mastery
Tipping
points
Viral growth
Blackswans
Hysteresis
Resilience
Transition
managementIntegral
governance
Intangibles
Network externalities
Agent-based modeling
Systems thinking
Heuristics
Complexity
science
Reflexivity
Restructuring Skin in the game

2. Two observations as
starting point
1. Fallacy of linear
extrapolation
2. Simple rules leading to
highly complex behavior
2

3. 0 kg
1 kg
2 kg
3 kg
4 kg
5 kg
6 kg
Jan Feb Mrz Apr Mai Jun Jul Aug Sep Okt Nov Dez
We mostly assume incremental, linear change –
in leadership and management practices and tools
Weight
Surprise!!
Source: N. Taleb, The Black Swan 3

4. Thirty Years ago: German reunification
4Foto Andreas Krüger,

5. 300 Iterations
Simple rules can lead to highly complex behaviour
Source: S. Wolfram, A New Kind of Science 5
Example: 2-dimensional Cellular Automaton with 2 colors (Rule 30)
25 Iterations

6. Simple rules can lead to highly complex behaviour
Source: S. Wolfram, A New Kind of Science 6
Example: 2-dimensional Cellular Automaton with 3 colors (Rule 2049)

7. The beauty of fractals
– The Mandelbrot set
The Mandelbrot set is the set of complex
numbers c for which the function fc(z) = z2 + c
does not diverge when iterated from z = 0, i.e.
for which the sequence fc(0), fc( fc(0)), etc.
remains bounded in absolute value.
7Reference: https://www.ibm.com/developerworks/community/blogs/jfp/entry/My_Christmas_Gift?lang=en

8. Zoom 1
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9. Zoom 2
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10. Zoom 3
10

11. Zoom 4
11

12. Agenda
12
Important types of
non-linear change
Psychological and
governance
complications
Heuristics for mastering
different types of
non-linear change

13. Important types of
non-linear change
13
Exponential growth
Logarithmic decline
Critical transitions
Crisis/break-down/disruption
Uncertain vacillations, turbulence and
Black Swans

14. The power of geometric progression
Exercise:
Fold a – very large piece of –
paper 50 times.
How high is the resulting stack?
14
1,4 mm * 250 ~ 157.600.000 km
Foto: NASA Creative Commons Attribution 2.0 Generic License.
Distanz Erde – Sonne
~ 149.600.000 km

15. 15Source: https://www.genome.gov/27565109/the-cost-of-sequencing-a-human-genome/
Beyond Moore‘s Law

17. 17Source: Rogers, 2003
Logarithmic decline and inflection points
Logarithmic decline typical for
end of life-cycle
Strategic inflection points deadly
when attended to
To find inflection point look for
shares of early vs. late adopters

18. 18
Example: Divestment from coal

19. 19
Example: Divestment from coal
Source: Lazard, Levelized Cost of Energy Analysis - Version 13.0
Levelized Cost of Energy as early warning indicator for inflection point

20. Critical transitions: Learning from nature
• All ecosystems are exposed to gradual changes in climate,
nutrient loading, habitat fragmentation or biotic exploitation.
• Nature is usually assumed to respond to gradual change in a
smooth way. However, studies on lakes, coral reefs, oceans,
forests and arid lands have shown that smooth change can be
interrupted by sudden drastic switches to a contrasting state.
• Although diverse events can trigger such shifts, recent studies
show that a loss of resilience usually paves the way for a switch to
an alternative state.
• It is important to understand the path dependency in the
transition.
20
Source: Marten Scheffer, Steve Carpenter, Jonathan A. Foley, Carl Folkes & Brian Walkerk, Catastrophic shifts in ecosystems, NATURE, VOL 413, 11
OCTOBER 2001, p. 591-596

21. Example: Equilibrium states in a lake
21
Source: Scheffer, Marten (2009). Critical Transitions in Nature and Society. Princeton Studies in Complexity. Princeton UP
https://books.google.de/books/about/Critical_Transitions_in_Nature_and_Socie.html?id=jYSZgaaxRv0C&redir_esc=y
Positive feedback loops often at the center of
hysteresis phenomena
Example: Simple/idealized lake model
• turbidity increases with the nutrient level
(phytoplankton growth)
• vegetation reduces turbidity
• vegetation disappeares when a critical
turbidity is exceeded
The arrows indicate the direction of change
when the lake is not in one of the two
alternative stable states

22. Hysteresis and path dependency
22
Path 1: Start looking at image
from the upper left corner
proceeding to the lower right
corner.
Path 2: Start looking in inverse
direction.
à Perception of image changes
at different points.
Source: H. Haken (1983), Synergetik, Springer-Verlag.

23. Ecosystem equilibrium states vary with conditions
23
Source: Marten Scheffer, Steve Carpenter, Jonathan A. Foley, Carl Folkes & Brian Walker, Catastrophic shifts in ecosystems, NATURE, VOL 413, 11
OCTOBER 2001, p. 591-596

24. Ecosystem equilibrium states vary with conditions
24
Source: Marten Scheffer, Steve Carpenter, Jonathan A. Foley, Carl Folkes & Brian Walker, Catastrophic shifts in ecosystems, NATURE, VOL 413, 11
OCTOBER 2001, p. 591-596
One equilibrium state
per set of conditions
Up to 3 equilibrium states
per set of conditions
• For certain environmental conditions, the ecosystem
has two alternative stable states, separated by an
unstable equilibrium that marks the border between
the basins of attraction of the states.
• This pattern, in which the forward and backward
switches occur at different critical conditions, is
known as hysteresis.

25. Crisis/break-down/disruption
True crisis: a critical transition that threatens survival (of product, of business, of
species)
Important distinction between biological and social survival: biological survival
does not necessarily depend on civilizational values
Drivers of collapse of societies according to Jared Diamond:
• Natural changes in the climate
• Environmental damage caused by people themselves (inadvertently or not)
• A decline in support from neighbors or trading partners
• Hostile neighbors
• How a society anticipates and reacts to its problems
25
Source: J. Diamond, Collapse

26. Uncertain vacillations, turbulence and Black Swans
Typical examples found in complex environments with a multitude of feedback
mechanisms
• Stock markets
• Pandemics
• Panic propagation
Key question:
Does the uncertainty stem from a fundamental randomness
or from a degree of complexity that obscures patterns at a deeper level?
26
Source: J. Diamond, Collapse

27. Two curves show timeseries for
with r = 1.8
and x0 = 0.1 and x0 = 0.100001
27
Butterfly effect: Small changes in initial state leading to
vastly diverging pathways
• Simulations are fairly similar for the first several steps, because the system is fully
deterministic (this is why weather forecasts for just a few days work pretty well).
• The “flap of the butterfly’s wings” (the 0.000001 difference) grows eventually so big that it
separates the long-term fates of the two simulation runs.

28. Black Swans
Black Swans are large-scale unpredictable
and irregular events of massive
consequence.
Man-made complex systems tend to develop
cascades and runaway chains of reactions
that decrease, even eliminate, predictability
and cause outsized events.
So the modern world may be increasing in
technological knowledge, but, paradoxically,
it is making things a lot more unpredictable.
The rarer the event, the less tractable, and
the less we know about how frequent its
occurrence.
Foto: Baden de
28

29. Agenda
29
Important types of
non-linear change
Psychological and
governance
complications
Heuristics for mastering
different types of
non-linear change

30. 30
“Never change a
winning team”
Inertia in economic
orthodoxy
Bias/constraints in
mental models
Laziness and lack of
imagination

31. Looking forward with
anxiety
Vicious Circle
Turning
inwards
Preserving the
present reality
Using force to
prevent change
No energy for
planning and creating
a future
Shlomo Shoham’s vicious circle
Source: Shlomo Shoham (1. Commissioner for the Future Generations of the Knesset) 31

32. Complex reflexivity of interpretation economics
Source: WEF Global Risk map 2020
https://reports.weforum.org/global-risks-report-2020/survey-results/the-global-risks-interconnections-map-2020/
32

33. Dangerous simplifications and reductionisms
33Source: Real World vs. Science according to N. Taleb, Skin in the game
Consultants
Consultants often reduce a
complex reality to a problem
that can be solved with a given
methodology.
Powerpoints are not very
precise forms of
communication when it comes
to qualifying solution claims.
In a complex system this often
leads to unforeseen higher-
order effects and non-
sustainable impacts.

34. Famous misjudgments in the face of non-linear change
• “The Americans have need of the telephone, but we do not. We have plenty of
messenger boys.” (Sir William Preece, Chief Engineer, British Post Office in
1878)
• “The horse is here to stay but the automobile is only a novelty—a fad.” (The
president of the Michigan Savings Bank, advising Henry Ford’s lawyer not to
invest in the Ford Motor Company in 1903)
• “Television won’t last because people will soon get tired of staring at a
plywood box every night.” (Darryl Zanuck, movie producer, 20th Century Fox in
1946)
• “We don’t like their sound, and guitar music is on the way out.” (Decca
Recording Company on declining to sign the Beatles in 1962)
• “There is no reason for any individual to have a computer in his home.” (Ken
Olson, president, chairman and founder of Digital Equipment Corporation at
the World Future Society meeting in 1977)
• “There’s no chance that the iPhone is going to get any significant market
share.” (Steve Ballmer in 2007)
34

35. Agenda
35
Important types of
non-linear change
Psychological and
governance
complications
Heuristics for mastering
different types of
non-linear change

36. Heuristics for exponential growth
1. Understand the limits of exponential growth
• # respirators in the Corona pandemic
• Carbon budget in atmosphere
2. Leverage cell divisions and self-management
• Dunbar‘s number
• Buurtzorg.com
3. Share protocols as integrative counterweight
• Shared conflict resolution protocols
• Shared problem-solving methodologies
• Communication protocols
• Shared accounting and IT systems
• Shared values
4. Use subsidiarity principle to get balance right
36

37. Heuristics for logarithmic decline
1. Understand inflection points
• Challenge bias rooted in old mental models of success
• Explore revitalization of declining product (new market niches, channels etc.
• Expect chaos or volatility when moving to new S-curve
• “Only the paranoid survive“
2. Anticipate people transition challenges
• Acceptable options depend on social security and corporate culture regimes
• Invest in life-long learning
• Establish social security instruments that encourage employee mobility
• Always treat people with respect in the transition
– starting with transparent communications
3. Manage profitability and exit cost
• Opportunities from installed customer base, maintenance contracts, spare parts,
transition support
37

38. Heuristics for critical transitions
1. Understand the nature of the system
• Extensively managed - Partially managed - Unmanageable systems
• Assess agency and sensitivities and risks
• Use agent-based models, cybernetics, systems thinking, stochastic modeling,
big data algorithms
2. Identify early warning signs of critical transitions
• Violent swings between extremes as warning sign
• e.g., # extreme weather events
3. Strengthen resilience
• Calculate insurance premiums
• Precautionary principle: „Where there are threats of serious or irreversible
damage, lack of full scientific certainty shall not be used as a reason for
postponing cost-effective measures to prevent environmental degradation.“
(1992 UN Rio Declaration)
38

39. Topical example of agent-based modeling
39https://www.youtube.com/watch?v=gxAaO2rsdIs

40. Heuristics for crisis/breakdowns/disruptions
1. Take quick and consequential actions to ensure survival
• In business crisis focus on cashflow and strength of balance sheet
• Depending on scope of crisis boundaries between state and businesses will
change
2. Develop stories of hope and a positive future
• Often helpful to “make the problem bigger“ when developing compelling story
3. Communicate relentlessly to rebuild trust and manage expectations
• Trust erodes fast in a crisis per default
4. Choose how to react in the face of adversity
• Viktor Frankl, Man‘s Search for Meaning
• Decency and transparent processes always an option
40

41. CO2, temperature and carbon budget
41

42. Understanding climate sensitivities - the human factor
42

43. The risk of tipping points from climate change
43Source: PIK

44. Greenland Ice Sheet tipping point model
44Source: Nordhaus, William (2013). The Climate Casino – Risk, Uncertainty, and Economics for a Warming World. Yale UP

45. The sustainability illusion
Consumption and footprint distributions
45
First World
Rest of
World
7,5 bn Consumption/Footprint
x 32 per-capita average
~80% of total footprint
Can the planet support
7,5 bn people with the
consumption patterns
and footprint of the
First World?
X
“We promise developing countries that, if they will only adopt good policies, like honest
government and free market economies, they too can become like the First World today.
That promise is utterly impossible, a cruel hoax.” Jared Diamond

46. Uncertain vacillations, turbulence, Black Swans
1. Understand exposure of system
• Fragile – Robust/Resilient - Antifragile
46

47. Two essential types of nonlinearities
The convex The concave
For a given variation,
more upside than downside
For a given variation,
more downside than upside 47

48. Fragility – The concave
Example: Driving a car against an obstacle
48
Speed
Harm
The nature of fragility:
• For the fragile, shocks bring higher harm as their intensity increases (up to a
certain level)
• For the fragile, the cumulative effect of small shocks is smaller than the single
effect of an equivalent single large shock.
• The more concave an exposure, the more harm from the unexpected, and
disproportionately so.
Concave or negative
convex curve
For a set deviation in a variable
the concave loses more than it gains
Source: Taleb, Antifragility

49. Uncertain vacillations, turbulence, Black Swans
1. Understand exposure of system
• Fragile – Robust/Resilient - Antifragile
2. Learn from nature
• The mechanical, noncomplex, nonbiological vs. The organic, complex, biological
• Nature likes diversity between organisms rather than diversity within an
immortal system
• Recognize path dependence: No upside without survival
3. Avoid pseudo-stabilization
• Volatility is information
49

50. Examples of pseudo-stabilization
• Micro-management of forests to avoid small
forest fires that would otherwise cleanse the
system of the most flammable material so that
it cannot accumulate
• Personal doctors (Michael Jackson, Prince)
• Helicopter parents removing every random
element from children‘s lives
• Greenspan‘s ironing out the “boom-bust cycle“
• US stabilization strategies in Middle East
(Egypt before the riots of 2011, Saudi Arabia)
• Reductions of humans to what appears to be
efficient and useful
• Avoiding fluctuations in the market via price
fixing or forbidding noise traders
(George Cooper The Origin of Financial Crises)
Foto: David Earle
50

51. Uncertain vacillations, turbulence, Black Swans
1. Understand exposure of system
• Fragile – Robust/Resilient - Antifragile
2. Learn from nature
• The mechanical, noncomplex, nonbiological vs. The organic, complex, biological
• Nature likes diversity between organisms rather than diversity within an
immortal system
• Recognize path dependence: No upside without survival
3. Avoid pseudo-stabilization
• Volatility is information
4. Build up antifragility
• Learn to profit from volatility
• Bimodal strategies: protect yourself from extreme harm, let the upside take
care of itself
51

52. Factors affecting the fragile-antifragile balance
Antifragility
Fragility
• Specialization
• Departmentalization
• Silo Thinking
• Bureaucracy
• Privatization of gains,
socialization of risk
• Numerical predictions
• Overconfidence of experts
• Success and fear of loss
• Narrative knowledge
• Curiosity
• Interdisciplinarity
• Systems Thinking
• Entrepreneurialism
• Skin in the game
• Large libraries
• Wisdom in decision-making
• Mentally adjusting to „the
worst“
• Optionality
52

53. Summary: Mastering non-linear change
Deconstruct the types
of non-linear change
53
Understand psychological
and governance barriers
Develop toolkit
of heuristics
• Mental models
• Reflexivity
• Interpretation
economics
• Biases
• Contingency theory
• Non-linear tools
• Interdisciplinarity
• Humility
• Empathy

54. Further Reading
1. Christensen, C.M. (1998), The Innovator’s Dilemma, HarperBusiness Essentials.
2. Drucker, P. (1993), Managing in Turbulent Times, Harper Paperbacks.
3. Gladwell, M. (2000), The Tipping Point: How Little Things Can Make a Big Difference, London. Abacus.
4. Grove, A.S. (1996), Only the paranoid survive: How to exploit the crisis points that challenge every company
and career, New York. Currency Doubleday.
5. Kaplan, S., Murray, F., and Henderson, R. (2003) ‘Discontinuities and senior management: assessing the role of
recognition in pharmaceutical firm response to biotechnology’, Industrial and Corporate Change, XII, 2, pp.
203-233. http://dx.doi.org/ 10.1093/icc/12.2.203.
6. Kay, J. (2011), Obliquity: Why Our Goals Are Best Achieved Indirectly, Penguin Press.
7. Mintzberg, H. (1994), The Rise and Fall of Strategic Planning, The Free Press.
8. Mandelbrot, B. (1997) The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin, and Reward, Profile Books.
9. Modis, T., and Debecker, A. (1992) ‘Chaos-like states can be expected before and after logistic growth’,
Technological Forecasting and Social Change, XLII, 2, pp. 111-120.
10. Nordhaus, W. (2015), The Climate Casino, Yale University Press.
11. Rogers, E. (2003) The Diffusion of Innovations. 5th edition. Free press.
12. Scheffer M. et al., (2001) Catastrophic shifts in ecosystems, Nature 413:591—596.
13. Taleb, N. N. (2012), Anti-Fragility, Random House.
14. Wolfram, S. (2002), A New Kind of Science (1st Ed.), Wolfram Media.
54

55. Thank you for your attention
johannes.meier@xigmbh.de
55