managing in an age of transitions - scopes of forecasting

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


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


Normal (Bell curve) Q1 Q3

Fat-tailed or Indeterminate Q2Coconuts

Q4


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


Normal (Bell curve) Q1 Q3Moon landing

Fat-tailed or Indeterminate Q2 Q4


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


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].

managing in an age of transitions - scopes of forecasting

Economy & Finance