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Mastering Nonlinear Change

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Presentation given at HHL Ph.D. Summer School 2020 on 6 July 2020

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Mastering Nonlinear Change

  1. 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. 2. Two observations as starting point 1. Fallacy of linear extrapolation 2. Simple rules leading to highly complex behavior 2
  3. 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. 4. Thirty Years ago: German reunification 4Foto Andreas Krüger,
  5. 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. 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. 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. 8. Zoom 1 8
  9. 9. Zoom 2 9
  10. 10. Zoom 3 10
  11. 11. Zoom 4 11
  12. 12. Agenda 12 Important types of non-linear change Psychological and governance complications Heuristics for mastering different types of non-linear change
  13. 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. 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. 15. 15Source: https://www.genome.gov/27565109/the-cost-of-sequencing-a-human-genome/ Beyond Moore‘s Law
  16. 16. 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
  17. 17. 18 Example: Divestment from coal
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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.
  22. 22. 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
  23. 23. 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.
  24. 24. 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
  25. 25. 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
  26. 26. 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.
  27. 27. 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
  28. 28. Agenda 29 Important types of non-linear change Psychological and governance complications Heuristics for mastering different types of non-linear change
  29. 29. 30 “Never change a winning team” Inertia in economic orthodoxy Bias/constraints in mental models Laziness and lack of imagination
  30. 30. 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
  31. 31. 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
  32. 32. 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.
  33. 33. 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
  34. 34. Agenda 35 Important types of non-linear change Psychological and governance complications Heuristics for mastering different types of non-linear change
  35. 35. 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
  36. 36. 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
  37. 37. 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
  38. 38. Topical example of agent-based modeling 39https://www.youtube.com/watch?v=gxAaO2rsdIs
  39. 39. 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
  40. 40. CO2, temperature and carbon budget 41
  41. 41. Understanding climate sensitivities - the human factor 42
  42. 42. The risk of tipping points from climate change 43Source: PIK
  43. 43. Greenland Ice Sheet tipping point model 44Source: Nordhaus, William (2013). The Climate Casino – Risk, Uncertainty, and Economics for a Warming World. Yale UP
  44. 44. 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
  45. 45. Uncertain vacillations, turbulence, Black Swans 1. Understand exposure of system • Fragile – Robust/Resilient - Antifragile 46
  46. 46. 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
  47. 47. 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
  48. 48. 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
  49. 49. 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
  50. 50. 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
  51. 51. 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
  52. 52. 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
  53. 53. 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
  54. 54. Thank you for your attention johannes.meier@xigmbh.de 55

Presentation given at HHL Ph.D. Summer School 2020 on 6 July 2020

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