Uncertainty Management: a Negative Capability?

By Jack Springman

“…and at once it struck me what quality went to form a Man of Achievement … I mean Negative Capability, viagra order that is, when a man is capable of being in uncertainties, mysteries, doubts, without any irritable reaching after fact and reason.” – John Keats

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A 19th Century romantic poet would seem an unlikely source of wisdom for a 21st Century business manager, but Keats insight into human nature – particularly how individually we are discomforted by uncertainty (and collectively we are panicked by it, as stock market reaction often shows) – is timeless. As the previous post (Uncertainty – the Final Frontier) describes, ‘irritable reaching after fact and reason’ in the face of uncertainty is counterproductive. Results appear reassuringly concrete but confidence is increased more than accuracy – a delusion that

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is psychologically appealing but economically damaging.

At first blush, uncertainty would appear to be the enemy of good decision-making in business. But uncertainty exists for everyone. Rather than downplaying its presence, why not revel in it and see it as an opportunity for advantage? Better management of uncertainty – shrinking both its dimensions and scale – will inevitably result in better strategies. Embracing it is the first step towards profiting from it.

Keats negative capability celebrates the search for beauty and those antithetical skills to analysis: creativity and imagination. Both are important in discovering – uncovering, if you prefer – the world of possibilities inherent in uncertainty.

Uncertainty management involves identifying the areas of uncertainty that most critically impact business strategy, defining the range of possibilities for each one, then systematically shrinking it through the targeted accumulation of knowledge.

The process starts well before such insights become critical, e.g. for a business case. Rather than a quick binge of fact-collection to support a particular decision, strategy-relevant insights are gathered an ongoing basis in preparation for such eventualities.

The process starts with defining the areas where greater insight will improve the quality of strategy development. This requires constructing scenarios for how key external factors (e.g. technology trends, market opportunities, competitor activity) may evolve, then attributing likelihood and impact scores to each scenario. As well as identifying where reducing uncertainty provides the greatest pay-off, this also frames the questions to be answered. And once the insights sought are specified, their acquisition can be planned. Just communicating the questions (to employees, partners, etc.) starts this process, but the wisdom of employees can also be tapped in more fun ways using prediction markets – the aggregation of opinions producing a better forecast than that of almost any individual.

Linking resource allocation to uncertainty reduction requires discovery-driven planning, whereby increased funding is directly linked to increased knowledge. An assumption checklist is maintained and reviewed at key milestones with further financial commitment postponed until there is sufficient evidence to justify taking the next step. The starting point for entering a new geography might be creating a trading relationship with an agent in that country. Should demand be proven, the next stage would be a more formal partnership or buying a small existing player. Since learning is the objective, the best decision is the one that yields the most knowledge per dollar spent. What is learnt determines whether the company scales up its investment or exits.

Each stage gate should involve Monte Carlo simulations – running the financial model thousands of times with the value for each variable in each iteration driven by its defined probability distribution. At each milestone, the range of possible values for each assumption should shrink due to the further knowledge gained; and that will generate a more concentrated distribution of financial outcomes. This distribution more accurately reflects the compound probabilities of multiple variables than static single-point models, even when sensitivities are included.

The disadvantage of such distributions is that they often provide a less clear cut case to proceed. But the track record of corporate acquisitions suggests that may be no bad thing. More importantly the existence of uncertainty is fairly represented. That does not make decision-making easier, it probably makes it harder. In the end, it boils down to judgment, but at least that judgment will be better informed, the inherent uncertainty recognized rather than concealed.

Elusive Growth: Why prevailing practices in strategy, marketing and management education are the problem, not the solution, by Jack Springman is now available on Amazon

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About Jack Springman

I am a consultant with experience in business strategy and customer strategy development, customer management and customer service transformation.