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BMAN31772 Towards 2050: Management and Managing in an Age of
Transitions
Scopes of Forecasting9221074 Veronika Bugaychuk10086908 Salomé Delay-Goyet
16th of February, 2017Seminar Thursday 4:00-5:00 pm
Forecasting - last lecture
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● TETLOCK P. (2005): People who make predictions regarding their business are no better than the rest of us → experts with great knowledge are likely to be less reliable in terms of forecasts than non-specialists
● Cynefin approach (SNOWDEN, BOONE 2007): a sense-making model organized around 4 systems (simple, complicated, complex, chaotic) enhancing every situation requires a specific reaction
● Adaptive Management (ALLEN et al. 2013) has similarities with the Cynefin model and
highlights the relevance of the amount of control on the situation (Uncorroborated, Trial and error, Step-wise, Horse race). People must admit their lack of knowledge and recognize that much of what they know is wrong
Seminar structure The Black Swan of Taleb (2007): The substantial role of randomness in
predictions - Critical discussion
“The 4 Quadrants ”: a Concrete Application of Taleb’s vision to Risk Management - Critical discussion
Impacts of scopes of forecasting on future managers’ behaviour
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The Black Swan by Taleb – Overview
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When all swans were white - common pattern - extraordinary becomes common
The Narrative Fallacy - “ confirmation bias” - “tunneling” - past creates the present
“Mediocristan” vs “Extremistan” - smooth away the rough features of reality
Phony Forecasting (Nerds and Herds) - “secret” of predicting outliers
N.N. Taleb (2007) The Black Swan, The Impact of the Highly Improbable
Impact on forecasting in management
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Black Swans are unpredictable. People continue see patterns in misleading data.
Positive Black Swans Negative Black Swans
Do not look/ignore “Experts” tend to have bias
Underappreciated Hedge against them
Know in many cases you cannot be sure
Avoid dogmatism - avoid nerds and herds (phony forecasting)
Look for nonobvious
Understand where you can be fooled
N.N. Taleb (2007) The Black Swan, The Impact of the Highly Improbable
Example: Are you good or bad forecasters? Bazerman (1984)
A firm expects to lay-off 6,000 employees in one year. Which plan would you choose as main manager of the department?
1st scenario
Gain scenario (opportunity) → risk aversion
Plan A: This plan will save 2,000 jobs
Plan B: This plan has a ⅓ probability of saving all 6,000 jobs, but a ⅔ probability of saving no jobs
2nd scenario
Loss scenario (threat) → risk seeking
Plan A: This plan will loose 4,000 jobs
Plan B: This plan has a ⅓ probability of no-one losing their job, but a ⅔ probability that 6,000 employees will lose their jobs
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>80%
<20%
<20%
>80%
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Scenario 1 Scenario 2
Plan A Save 2,000 jobs = Lose 4,000 jobs
Plan B ⅓ save 6,000 jobs⅔ save no jobs
= ⅓ lose no jobs= ⅔ lose 6,000 jobs
The firms expects a loss of 6,000 jobs. Problem simplified
The outcomes of both Plan A are the sameThe outcomes of both Plan B are the same
Relevant implications on forecasting Bazerman, 1984
The 4 Quadrants by Zeisberger & Munro - Overview
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A decision-making model applied to risk management. It classifies risks regarding the nature of the risks (normal or indeterminate) and their potential payoffs (simple or complex) in order to better understand risks and better manage it (=better forecast it).
Zeisberger C., Munro D. (2010), “The 4 Quadrants”: A World of Risk and a Road Map to Understand It
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Payoff
Distribution Simple (win/loose) Complex (almost anything)
Normal (Bell curve)Q1
Coin toss; Height Q3
Fat-tailed or Indeterminate Q2 Q4
Zeisberger C., Munro D. (2010), “The 4 Quadrants”: A World of Risk and a Road Map to Understand It
Predictable Gaussian world
• Very predictable• Risks are easily
manageable• 2 distributions:
binary / small range• No big surprises
Cynefin ModelSimple (Best practice)
Evident to everyoneClear cause-and-effect
relationship
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Payoff
Distribution Simple (win/loose) Complex (almost anything)
Normal (Bell curve) Q1 Q3
Fat-tailed or Indeterminate Q2Coconuts
Q4
Zeisberger C., Munro D. (2010), “The 4 Quadrants”: A World of Risk and a Road Map to Understand It
Risk Models CAN Work
• Predictable• Risks are
manageable• Irregular
distributions• One change can be
dramatic
Cynefin ModelComplicated (Good
practice)Not immediately evident to
everyoneCause-and-effect
relationship discoverable
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Payoff
Distribution Simple (win/loose) Complex (almost anything)
Normal (Bell curve) Q1 Q3Moon landing
Fat-tailed or Indeterminate Q2 Q4
Zeisberger C., Munro D. (2010), “The 4 Quadrants”: A World of Risk and a Road Map to Understand It
Think Engineering• Predictable but tricky• Risks can be
managed• High certainty • An error has
extreme implications
Cynefin ModelComplex (Emergent
practice)No right answers
Flux and unpredictability
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Payoff
Distribution Simple (win/lose) Complex (almost anything)
Normal (Bell curve) Q1 Q3
Fat-tailed or Indeterminate Q2 Q4Leveraged Finance
Zeisberger C., Munro D. (2010), “The 4 Quadrants”: A World of Risk and a Road Map to Understand It
Risk Model doesn’t Apply
• Unpredictable• Risks cannot be
managed• Extreme risks but
infrequent • Complexities and
interconnectednessCynefin ModelChaotic (Novel
practice)No clear cause-and-
effect relationshipHigh turbulence
Impact on forecasting in management
13Zeisberger C., Munro D. (2010), “The 4 Quadrants”: A World of Risk and a Road Map to Understand It
The 4 Quadrants The Cynefin Model (SNOWDEN, BOONE 2007)
Q1 No outliers or surprises - No need of risk managers in that environment - Forecasts are easy
Q2 Define the risks and raise awareness Rules-based solutions (planning)Insure risks
Q3 Introducing redundancy and fail-safe mechanismsResilience and the many R’s
Q4 Must stay out of it Not relying on statistics
Simple Create communication channelsStay connectedDon’t assume things are simple
Complicated Use external & internal opinions Use experiments to think out-of-the box
Complex Be patient and allow time for reflectionEncourage interaction
Chaotic Set up mechanisms to take advantage of opportunitiesEncourage debate Work to shift the content to complex
To which quadrant does the situation belong?
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LOAN PAYMENT PROCESS
THE SEARCH FOR OIL
Mary E., B. and David J., S. (2007). A Leader’s Framework for Decision Making. [online] Harvard Business Review.
Q2Q3 Q4
Q1
Critically discuss Taleb’s
Methodology
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What impact the scopes of forecasting might have on
managers? Managers will need to develop interpersonal and multidisciplinary skills to better predict events and adapt to different situations
Leave place for randomness: learning to unlearn → great knowledge and preferences for data and information often lead to misinterpretation and engender adverse implications
Realise the cumulative effect of slow trends
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Summary The 4 Quadrants: a risk is either simple (well-defined and well-known) or complex (several variables to take into account). The risk is either normally distributed or fat-tailed (a small variable can have tremendous implications)
Black Swans are becoming more consequential and are unpredictable
People continue see patterns in misleading data Our minds focus on variability
Embrace randomness - The misuse of knowledge (information and data) and voluntary/involuntary ignorance about unpredictable events lead to false/unaccurate predictions
Methodologies categorizing risks give us advice to better address risk management but have to be critically discussed
Not reliable forecasting can influence firms success by missing growth opportunities.
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Concluding commentsForecasting is a tricky tasks and even more difficult over the long term. That is why companies have specialists (experts) that predict events regarding historical data and fact analysis. But they are considered to be wrong most of the time.
Our societies are mostly based on data, numbers, analysis and worship of knowledge. However, we realize that non-specialists can offer predictions as good as experts, which lead us to wonder whether one of our future challenges is going to be able to step back from knowledge and information, in order to improve the management of future societies.
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ANY QUESTIONs?19
ReferencesAldous, D. (2009). [online] Stat.berkeley.edu. Available at: https://www.stat.berkeley.edu/~aldous/157/Books/taleb.html [Accessed 15 Feb. 2017].
Allen G., Fontaine J.J., Pope K.L. , Garmestani A.S., (December 2010) Adaptive management for a turbulent future, Journal of Environmental Management, 92 (2011) 1339-1345 University of Nebraska-Lincoln
Bazerman, M. H. (1984). The relevance of Kahneman and Tversky's concept of framing to organizational behavior. Journal of Management, 10, 333-343
Economist.com. (2015). Predicting the future Unclouded vision Forecasting is a talent. Luckily it can be learned . [online] Available at: http://www.economist.com/news/books-and-arts/21666098-forecasting-talent-luckily-it-can-be-learned-unclouded-vision [Accessed 15 Feb. 2017].
Mary E., B. and David J., S. (2007). A Leader’s Framework for Decision Making. [online] Harvard Business Review. Available at: https://hbr.org/2007/11/a-leaders-framework-for-decision-making [Accessed 13 Feb. 2017].
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Menand L. (2005). Putting predictions to the test. Everybody’s and expert.
Snowden, D. J., & Boone, M. E. (2007). A leader's framework for decision making. Harvard Business Review, 85(11), 68.
Taleb N.N. (2007). The Black Swan, The impact of the Highly Improbable. Random House.
Taleb N.N (July 2010). Convexity, Robustness and Model Error Inside the Fourth Quadrant. Draft version related to the second edition of The Black Swan. Available at: http://www.fooledbyrandomness.com/OxfordBTLecture.pdf
Zeisberger C, Munro D. (2010) “The 4 Quadrants”, A World of Risk and a Road Map to Understand It, Risk Management Note, INSEAD Business School Review
Further readings:
Aldous, D. (2009). [online] Stat.berkeley.edu. Available at: https://www.stat.berkeley.edu/~aldous/157/Books/taleb.html [Accessed 15 Feb. 2017].
Mary E., B. and David J., S. (2007). A Leader’s Framework for Decision Making. [online] Harvard Business Review. Available at: https://hbr.org/2007/11/a-leaders-framework-for-decision-making [Accessed 13 Feb. 2017].