Framework 80 Handling Uncertainty in Change


Handling uncertainty is one of the biggest challenges of change.

The longer the time frame, the more unknown the future is, eg more uncertainty that any predictions are correct, ie
"...where time periods are long, complexity and uncertainty are high, and the consequences of error are substantial..."
Charles Conn et al 2018

The more variables that need to be understood, the greater the uncertainty of the outcomes. Some examples of issues with many variables revolve around societal and global scales; this can include obesity, terrorism, environmental degradation, poverty, climate change, refugees, homelessness, etc. These are complex issues with no single or simple solution. For example
"...Obesity has dozens of contributing causes......with its complexity and interdependencies. These are genetic, environmental, behavioural, cultural, societal, and income and educational dimensions......can cleave the problem around incidents and severity, behavioural and clinical perspectives, even financial and non-financial considerations..."
Charles Conn et al 2018

"...often that leverage on these more difficult problems comes from systems rather than partial solutions, making tonalities endogenous to the problem, and from novel ways of clearing the problem..."
Charles Conn et al 2018

Need to understand the type and level of uncertainty plus your risk tolerance.

Levels of uncertainty (5 level scheme for processing uncertainty)

Uncertainty level
How uncertainty is defined
5. Unknown unknowns Unexpected or unforeseen conditions (events that are unpredictable with current knowledge and technology) Meteorite hitting the earth; virus pandemic
4. True ambiguity, (impossible to predict or conceive potential outcomes) Future sea levels; bio-tech knowledge
3. A range of futures (not necessarily clear which one is most likely to succeed) Role of fossil fuel in energy mix; future jobs with advances in AI, machine learning, etc; booms and busts like China boom in the 1990s and the GFC starting in 2007
2. Alternative futures (legislation or technological changes, ie winners or losers) Driverless cars; mobile phones
1. Known unknowns Reasonably predictable futures (simple predictions and short-term forecasts are reasonably predictable) Product sales; tomorrow's weather; lifespan

NB There are statistical measures of variability and conditional probabilities that can be used to help understand and evaluate uncertainty.

Need to recognise and quantify the level and type of uncertainty and then develop approaches to handling them.

Some ways to address uncertainty

- do nothing (wait for more information; hope problem will solve itself, etc)

- collect information (obtaining and analysing data to clarify the sources of uncertainty; use modelling, etc)

- hedge (making a reasonable cost move or investment that counters down-side events, eg fossil fuel companies investing in renewable energy; buying water rights in areas expecting to have low rainfall, etc

- insure against (pay a premium to cover the negatives of an event happening like a weather disaster, eg flood, hurricanes/typhoons, droughts, fire, etc; annuities)

- low-cost strategic options (using a portfolio of initiatives like betting on multiple horses in a race, eg with major financial institutions investing some equity in the fintech start-ups so they develop an understanding of the new technology and can capitalise on it. An example where this was not followed is IBM with Microsoft
"...IBM had an opportunity to buy 30% of Microsoft for under $300 million in the mid-1986. By the end of 1996 that......would have been worth $33 billion..."
Charles Conn et al 2018
- no regret moves (when comfortable with the level of uncertainty; involves capacity building irrespective of potential outcomes)

- big bets (refers to a level of confidence about an outcome not shared by others; mathematical models can be used)

- flexibility (being diversified to reduce vulnerability of an adverse event having a critical impact, eg have a wide range of clients/customers in different industries so that if one industry or group of clients/customers experience adversity, the impact is minimised)

- scenario planning (explore a range of options by understanding the key drivers of the uncertanties of each option; ideally choose around 5 options like current situation, pragmatic/realistic, worst-case, best-case, left field)

- financial and other frameworks (like discounted cash flow, net present value, Monte Carlo simulations, Black-Scholes valuation, best guess range, sensitivity analysis, Porter's five force map, structure-conduct-performance framework, mapping competitive game evolution, growth staircases, etc; assumptions used are important, like cost of capital, duration of project, timing, exchange rates, price, volume, etc)

The social and environmental areas provide the longest term and most uncertainty. For example, Pacific Salmon case covers many countries and the entire North Pacific Ocean, timeline is decades and it impacts the whole ecosystem of which the Pacific salmon is a foundation species, and it affects a large number of stakeholders' livelihoods. This can be shown diagrammatically (see 2 diagrams below).

Diagram 1


NB "...this map summarises the team's overall strategy for preserving salmon and Salmon eco-system function.......1) preserving habitat integrity,  2) neutralising the threat from open-net aquaculture, 3) mitigating the impact of hatchery propagation, and 4) ensuring sustainable fisheries management..."
Charles Conn et al 2018

Diagram 2


NB Using 3 sequences around stages of development

- seed, ie strategies to create the early conditions change

- cultivate, ie bringing different stakeholders to work together

- harvest, ie cement gains and solidify stakeholders' support)

and linking this with aspiration via transformational change v incremental improvement)
" effective picture of trading off the risk of individual strategies against others, and for communicating the overall risk return profile..."
Charles Conn et al 2018

Some of the dimensions that need to be handled and can appear around uncertainty are:

a) where problems change shape a result of an intervention (eg changing government benefits like welfare payments, etc)

b) no such thing as a single right answer (eg, the role of nuclear power or fossil fuels in energy supply, etc)

c) where values are important (eg gun control in USA, death penalties, euthanasia, gay marriage, etc)

d) where the real problem is hidden in another more obvious problem(s) (eg homelessness which is more than just providing shelter as there are underlying social, financial and mental health concerns like domestic violence

NB Most managers are good at understanding single, simple risk and at mitigating against it. However many struggle to understand multi-risks and their relationships to each other plus causality chains.

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