Decision-Making in Uncertain Times. Leveraging Scenario Planning and Probabilistic Thinking

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In the past, leaders made choices relying on straightforward predictions, like estimating steady 5% growth each year. But when unexpected events occurred, reality diverged drastically from predictions. For example, a company might reject the uncertainty of a shifting market landscape, confidently projecting stable demand. Yet disruptive new competitors and technologies then emerge, upending their forecasts and plans.

Organizations need to accept uncertainty as normal when making decisions today. Rather than rejecting uncertainty or failing to account for it in singular predictions, they should incorporate potential variability into their planning.

Why Uncertainty Matters Today

Uncertainty refers to difficulty in assigning probabilities to future outcomes. It is distinct from risk, which assumes event likelihoods are quantifiable. For example, risk would be estimating a 60% chance of rain based on weather data. Uncertainty would be difficulty predicting weather far in the future due to climate change.

There is more uncertainty today than ever before because of:

• The fast pace of change in technology, markets, regulations, and society
• Increasing connections between systems that make effects less predictable
• The past being less useful for predicting unprecedented futures
• More unexpected events disrupting existing patterns
• Growing complexity in the economy, politics, and the environment

Old ways of making decisions don't work as well anymore. In the past, leaders made choices based on experience and single predictions of the future. But the world is changing faster than ever before. Unexpected events are happening more often. This makes the old approaches unreliable.

Leaders need new tools to make good decisions when the future is unclear. Two useful frameworks are Scenario Planning and Probabilistic Thinking. Using these can help organizations succeed even when things are unpredictable.

Exploring Possibilities with Scenario Planning

Scenario planning is a technique for decision-making under uncertainty. It evaluates a range of plausible future scenarios and assesses options under each.

Unlike forecasting’s focus on the single most likely future state, scenarios explore multiple divergent but still probable futures. First, driving forces like technology change or consumer shifts are identified based on rigorously researched trends.

Next, critical uncertainties that will shape the future landscape are highlighted, such as regulation, innovations, or shocks. Scenarios are then constructed capturing different manifestations of the critical uncertainties.

For example, a hospitality firm may identify autonomous mobility and climate change policies as critical uncertainties. This could produce four scenarios: rapid adoption with strict regulations; rapid adoption with lax regulations; slow adoption with strict regulations; and slow adoption with lax regulations.

In another example, a software firm wants to understand self-driving car impacts. Using scenario planning, they identify key driving forces like regulation and technology advancement. They then develop three scenarios: rapid adoption with lenient oversight, moderate progress with strict rules, and limited viability with tech barriers. The scenarios explore different plausible futures to stress test strategies.

Each scenario offers a distinct picture of the future context. Strategic options are then stress tested across the scenarios to evaluate resilience. For instance, hotel locations optimized for eco-tourism may fare well in strict climate regulation scenarios.

The key is avoiding anchoring on a single future while exploring possibilities systemically. Scenario planning provides the methodology to inform strategy in uncertainty.

Quantifying Subjective Uncertainty with Probabilistic Thinking

Probabilistic thinking provides a complementary approach to quantify the relative likelihood of different scenarios unfolding and sensitivities around projections. Mathematical probabilities are assigned to capture subjective uncertainty levels.

For example, the hospitality firm may assess a 40% probability of rapid mobility tech adoption but only 30% chance of highly strict climate regulation based on leading indicators in each space. These probabilities provide additional context for evaluating options.

In another example, a retail company is considering expanding their digital sales channels. Using probabilistic thinking, they identify uncertainties like how quickly customers will adopt online shopping. They assign a 70% probability that online revenue will reach $1M in the first year. But they also calculate a range, finding a 50% chance revenue reaches between $500K and $1.5M. This quantifies the potential variability.

Next, potential error ranges around forecasts are modeled using techniques like Monte Carlo simulation which runs thousands of variations. For a projected 10% rise in eco-tourism, modeling may show a 70% likelihood the actual increase falls between 5-15%.

Probabilistic thinking provides the rigor to make uncertainty quantifiable in a nuanced way. The probabilities convey the breadth of possibilities beyond selective point forecasts. This enriches scenario planning.

Integrating Scenario Planning and Probabilistic Thinking

Combining scenario planning and probabilistic thinking provides a robust framework for decision-making amidst uncertainty. Scenarios explore divergent futures informed by meticulous research into driving forces and critical uncertainties.

Probabilistic modeling layers in quantifiable likelihood estimates across the scenarios and around specific projections. This enables a nuanced interpretation of the possibility space.

Leaders can then stress test strategic options across scenarios and probabilities to evaluate resilience. For example, identify portfolios hedging different climate regulation scenarios based on their probabilities.

The fusion of qualitative scenario exploration and quantitative probabilistic enrichment provides a structured approach to navigating uncertainty’s challenges and seizing its opportunities.

Implementation Best Practices

Organizations should consider the following best practices when implementing robust decision-making frameworks under uncertainty:

1. Make uncertainty quantification mandatory in planning processes rather than nice-to-have. Frame it as a key strategic capability.
2. Select a small set of base scenarios. Avoid overwhelming decision-makers with too many options.
3. Ensure diverse input into driving forces and critical uncertainties framing. Leverage experts, unlikely voices, and broader ecosystem perspectives.
4. Enrich scenarios and probabilities iteratively over time as new data emerges rather than static one-time views. Continually update.
5. Provide interactive tools for manipulating assumptions and assessing implications in real time. Quantify sensitivities.
6. Focus on resilience across scenarios versus over-optimizing for one future. Seek hedge and adapt options.
7. Distill insights into simple memorable narratives, not just data. Tell compelling stories to inspire action in the face of uncertainty.
8. Allow probabilities to enrich not replace judgment. Quantification should sharpen instincts, not impose automation.
9. Balance probabilities with preparedness. Unlikely but high-impact events still warrant mitigation and contingency efforts.
10. Monitor weak signals and adjust scenarios, plans, and resource allocation accordingly. Continuously align strategy to emerging realities.

With the right mix of art and science, organizations can make robust strategic decisions despite growing uncertainty, volatility, and complexity. The winners will be those who embrace possibility, adopt sophisticated frameworks, and continuously align actions with emerging realities.