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Emperors without clothes

  1. 1. Emperors without clothes Failures of management orthodoxies in the face of complex change Dr. Johannes Meier
  2. 2. Modern leadership context characterized by increasing volatility, uncertainty, ambiguity Complex Modern Leadership Context Global context Fragmented societies Techno- logical progress Regulation and policy risk Sustain- ability Reflexivity
  3. 3. 4Source: https://www.genome.gov/27565109/the-cost-of-sequencing-a-human-genome/
  4. 4. Risks are interconnected and impacts non-linear 5Source: http://reports.weforum.org/global-risks-2018/global-risks-landscape-2018/
  5. 5. The future is open, ambiguous, dialectical Fundamental trends Future space Age of Age of Large information and communication streams … of freedom and ideas … of confusion and propaganda … collective intelligence cooperative solutions … of individualism … of joint and civil engagement … of trivial pursuits Compression of time, distance and access … of global reach … of global disruptions Increasing access of poor nations and in parallel increasing asymmetries … of global well-being … of self-interest and protectionism Inadequate ethical and spiritual codes in the face of global risks … of higher awareness … of ideological battles Source : based on Ketan Patel „The Master Strategist: Power, Purpose and Principle“ 2005 Source: Ketan Patel, Master Strategist 6
  6. 6. The methodological challenge • Interdependencies between trends, risks, interventions, orthodoxies • Decoupling of societal action from space and time restrictions • Reflexivity of life in modern societies: social practices are constantly being evaluated and adapted in the face of new information which is changing their character fundamentally (Anthony Giddens) • Divergent interpretations and fundamental assumptions across cultural and semiotic contexts Process of production of shared meaning and values is critical This process is inherently non-linear 7
  7. 7. Conventional management practice: Four emperors without clothes (all related) 1. Emperor of command and control 2. Emperor of you-get- what-you-measure 3. Emperor of extrapolated forecasts 4. Emperor of reductionism My central thesis: All these emperors have no clothes and lead to fragile systems 8
  8. 8. Conventional management practice: Four emperors without clothes (all related) 1. Emperor of command and control 2. Emperor of you-get- what-you-measure 3. Emperor of extrapolated forecasts 4. Emperor of reductionism My central thesis: All these emperors have no clothes and lead to fragile systems 9
  9. 9. Command-and-Control Thinking • Perspective Top-down, hierarchy • Design Functional specialization • Decision-making Separated from work • Measurement Productivity output, targets • Attitude to customers Contractual • Attitude to suppliers Contractual • Role of management Manage people and budgets • Ethos Control • Change Reactive, projects, linear • Motivation Extrinsic Source: John Seddon 2008 10
  10. 10. Traditional governance (1.0) Goals: • Balanced Budgets • Financial Compliance KFS: • Regularity • Standardized accounting • Clear leadership hierarchy Perspective: • Internal only timabbott Issues: • Backward orientation • Year-end „games“ • No accounting for contingencies 11
  11. 11. Conventional management practice: Four emperors without clothes (all related) 1. Emperor of command and control 2. Emperor of you-get- what-you-measure 3. Emperor of extrapolated forecasts 4. Emperor of reductionism My central thesis: All these emperors have no clothes and lead to fragile systems 12
  12. 12. Traditional governance (1.1) Graphics www.lumaxart.com/ Goals: • Performance indicators • Balanced scorecards KFS: • Modelling dependencies • Mapping actions to indicators • Clear leadership hierarchy Perspective: • Internal processes with external performance indicators Issues: • Incomplete models of dependencies • Measuring output, not outcome • Still many „gaming“ opportunities • Little accounting for contingencies • Doing less of the wrong thing is not doing the right thing 13
  13. 13. Common observations WaveCult (luis.m.justino) • Targets drive people to use their ingenuity to meet the target, not improve performance. • End-to-end measures show huge variance, dysfunctional performance despite star-ratings for all departments. 14
  14. 14. Why do most call centers meet their targets? 15
  15. 15. Conventional management practice: Four emperors without clothes (all related) 1. Emperor of command and control 2. Emperor of you-get- what-you-measure 3. Emperor of extrapolated forecasts 4. Emperor of reductionism My central thesis: All these emperors have no clothes and lead to fragile systems 16
  16. 16. 17 Example complex system: Butterfly effect • Weather forecasts for just a few days work pretty well. • But the “flap of the butterfly’s wings” (the 0.000001 difference) is enough to separate the long-term fates of the system. • Note we can still predict climate change over longer time scales, but not the exact long-term behavior r = 1.8
  17. 17. 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 Example Thanksgiving : A Turkey‘s Tipping point Weight Turkey Surprise!! Source: N. Taleb, The Black Swan 18
  18. 18. 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 occurence. Foto: Baden de 19
  19. 19. Tipping point Financial Crisis: Managerial failure to deal with complexity? Failure of regulation? Limitation of linear thinking? 20
  20. 20. Tipping point German reunification: Unforeseen even two weeks before the end of the wall 21Foto Andreas Krüger,
  21. 21. Tipping point end of Easter Island civilization: The power of linear thinking? von vtveen 2222
  22. 22. The risk of tipping points from climate change 23Source: PIK
  23. 23. Learning from tipping points in 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. • This suggests that strategies for sustainable management of such ecosystems should focus on maintaining resilience. 24 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
  24. 24. Hysteresis and path dependency 25 Path 1: Start looking at image from the upper left corner proceeding to the lower right corner. Path 2: Start looking in inverse direction.  Perception of image changes at different points. Source: H. Haken (1983), Synergetik, Springer-Verlag.
  25. 25. Greenland Ice Sheet tipping point model 26Source: Nordhaus, William (2013). The Climate Casino – Risk, Uncertainty, and Economics for a Warming World. Yale UP
  26. 26. Different types of risk • Manageable Risks vs. Unmanageable Risks (W. Nordhaus – 2018 Nobel laureate) • Problem of long tails and discounting of long-term uncertainty (N. Taleb) • Relevance and the law of declining love (D. Kahneman, O. Hondrich) 27
  27. 27. Risk domains Interesting, normal life Black Swan Domain Classical management domain Insurance and Casino domain High exposure to rare, tail events Low exposure to rare, tail events Incomputable events Computable events „Robustification“ 28
  28. 28. The moral dimension of climate risk At what probability of getting hit by a car would you still let your child play in the street? Muhammad Yunus 29
  29. 29. Precautionary principle in the face of tipping points 1992 United Nations‘ Rio Declaration on Environment and Development: „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.“ Game-theory minimax strategy: In the absence of scientific certainty, society should make policies that prevent the worst outcome. Nordhaus‘ cost-benefit perspective: If the damages are uncertain, highly nonlinear, and clifflike in the Climate Casino, then a cost- benefit analysis will generally lower the optimal target to provide insurance against the worst-case outcomes. As complex systems do not have obvious one-dimensional cause- and-effect mechanisms, you should not mess with such systems under opacity. 30
  30. 30. Conventional management practice: Four emperors without clothes (all related) 1. Emperor of command and control 2. Emperor you-get- what-you-measure 3. Emperor of extrapolated forecasts 4. Emperor of reductionism My central thesis: All these emperors have no clothes and lead to fragile systems 31
  31. 31. Real world, science, and consultants 32Source: 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.
  32. 32. Counterintuitive observations about the collective • The average behavior of the market participants will not allow us to understand the general behavior of the market • The psychological experiments on individuals showing „biases“ do not allow us to automatically understand aggregates or collective • The higher the dimension, i.e. the number of possible interactions, the more disproportionally difficult it is to understand the macro from the micro, the general from the simple units Fundamental limitation of behavioral economics on how to play the market or generate policy Mathematically speaking, the mean-field approach, where one generalizes from the average interaction to the group is only possible if there are no asymmetries 33Source: Taleb, Skin in the game, appendix to book 3
  33. 33. Homo sapiens vs. Homo economicus Most social processes are not neatly decomposable into separate subprocesses – economic, demographic, cultural spatial. Yet, this compartmentalization in insular departments and journals is how we tend to organize the study of social systems (e.g., finance vs. strategy vs. marketing departments in B-schools) In order to develop realistic and relevant models it is essential to begin with solid foundations regarding individual behavior and spatial structures. Current laboratory social science (e.g. Tversky and Kahneman) are giving us an ever-clearer picture how homo sapiens – as against homo economicus – actually makes decisions. Agent-based modeling offers an integrative, inherently multi- dimensional alternative. 34Joshua M. Epstein (2006). Generative Social Science: Studies in Agent-based Computational Modeling. Princeton UP
  34. 34. The concave nature of travel 35 • A small disturbance in a congested traffic system can lead to major disruptions/delays. • Systems like traffic rarely experience positive disturbances. • Traffic is a system with „one-sided disturbances“ which leads to underestimation of randomness and harm. • Buffers are important to avoid major failures. The average does not matter. Foto basykes
  35. 35. Fragility – The concave Example: Driving a car against an obstacle 36 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 Reference: Taleb, Antifragility
  36. 36. Case: Fannie Mae 2003 internal risk report of Fannie Mae showed • Move upward in an economic variable led to massive losses • Move downward in same variable led to small profits • Acceleration of harm in concave curve visible as simple fragility detection heuristic High fragility indication possible even without good model for risk measurement 37 Fragility is quite measurable, risk in real life not so at all, particularly risk associated with rare events. Not seeing a tsunami or an economic event coming is excusable; building something fragile to them is not. Reference: Taleb, Antifragility, Chapter 19
  37. 37. Antifragility – The convex Antifragility: Some things benefit from shocks; they thrive and grow when exposed to volatility, randomness, disorder, and stressors and love adventure, risk, and uncertainty. Detecting antifragility: For the antifragile, shocks bring higher benefits as their intensity increases 38 Variable Gains Convex curve: For a set deviation in a variable the convex gains more than it loses Reference: Taleb, Antifragility
  38. 38. Different types of exposures of systems Fragile Robust/Resilient Antifragile Errors Hates mistakes Irreversible, large (but rare) errors, blowups Mistakes are just information Loves mistakes (since they are small) Produces reversible, small errors Biological & economic systems Efficiency, optimized Redundancy Degeneracy (functional redundancy) Science & technology Directed research Opportunistic research Stochastic tinkering, bricolage Regulation Rules Principles Virtue Political systems Nation-state, centralized Collection of city-states, decentralized Economic life Bureaucrats, Agency problem Entrepreneurs, Principal operated Physical training Organized sports, gym machines Street fights 39Reference: Taleb, Antifragility
  39. 39. Solutions to antifragility You are antifragile for a source of volatility if potential gains exceed potential losses (and vice versa) • Simple test: If I have „nothing to lose“ then it is all gain and I am antifragile. • Recognize path dependence: No upside without survival • First step towards antifragility: Decrease downside, protect yourself from extreme harm (negative Black Swan) and let the upside (positive Black Swan) take care of itself Solution to antifragility is combination of aggressiveness and paranoia Solution takes form of a barbell (bimodal strategy) „Provide for the worst; the best can take care of itself.“ (Yiddish proverb) Yet, there is ample evidence from insurance that people are averse to small losses, but so much toward very large Black Swan risks (which they underestimate) 40
  40. 40. Examples of barbell strategies • Ray Dalio on speculative bets: „Make sure that the probability of the unacceptable (i.e. the risk of ruin) is nil.“ • Montaigne‘s life strategy as a serial barbell: First „doer“ then „thinker“ • In social policy, protect the very weak and let the strong do their job, rather than helping the middle class to consolidate ist privileges • In publishing, avoid having 100% of the people finding your mission acceptable, but instead aim for high percentage of people disliking you and your message combined with a low percentage of extremely loyal and enthusiastic supporters 41
  41. 41. 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 42
  42. 42. Outlook: 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

Notas do Editor

  • Multiple, interacting stresses

    Global change does not operate in isolation but rather interacts with an almost bewildering array of natural variability modes and also with other human-driven effects at many scales. Especially important are those cases where interacting stresses cause a threshold to be crossed and a rapid change in state or functioning to occur.

    Adapted from Steffen et al, Global Change and the Earth System, 2004 (pdf, 4.2 MB)
  • The sensitivity of chaotic systems to initial conditions is particularly well known under the moniker of the “butterfly effect,” which is a metaphorical illustration of the chaotic nature of the weather system in which “a flap of a butterfly’s wings in Brazil could set off a tornado in Texas.” The meaning of this expression is that, in a chaotic system, a small perturbation could eventually cause very large-scale difference in the long run. Figure 9.2 shows two simulation runs of Eq. (8.37) with r = 1.8 and two slightly different initial conditions, x0 = 0.1 and x0 = 0.100001. The two 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). But 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. Such extreme sensitivity of chaotic systems makes it practically impossible for us to predict exactly their long-term behaviors (this is why there are no two-month weather forecasts).

    But this doesn’t necessarily mean we can’t predict climate change over longer time scales. What is not possible with a chaotic system is the prediction of the exact long-term behavior, e.g., when, where, and how much it will rain over the next 12 months. It is possible, though, to model and predict long-term changes of a system’s statistical properties, e.g., the average temperature of the global climate, because it can be described well in a much simpler, non-chaotic model. We shouldn’t use chaos as an excuse to avoid making predictions for our future!
  • Völker, die nicht die Gabe der Voraussicht haben, sind dem Untergang geweiht.
    Jean Monnet (1888-1979)
  • With temperature increases come the risks of tipping points in nature. Tipping points tend to be irreversible, dramatic system changes that start slowly but then built up a terrible momentum.
  • To show the moral dimension of these probabilities let me repeat a question that Muhammad Yunus once asked me to illustrate the deeper nature of the challenge: At what probability of getting seriously hurt would you let your child still play out on the street?
  • There is convincing evidence that a Ph.D. in finance or economics causes people to build vastly more fragile portfolios. (e.g. Long Term Capital Managment with Robert Merton, Myron Scholes, Chi-Fu Huang)