The Prophet’s Paradox: When Predictions Change the Future

Lately, I’ve been diving deep into the world of predictions and uncertainty — an interest sparked by my work at nPlan, where we build AI to forecast the outcomes of megaprojects. This experience has given me a unique perspective on how our forecasts can sometimes change the very events we’re trying to predict. Today, I want to explore what I call the prophet’s paradox, a paradox that challenges the very nature of prediction when human behavior comes into play.

Defining the Prophet’s Paradox

Imagine two roles in a prediction scenario: the predictor and the receiver. The predictor offers a forecast about the future. Now, consider a situation where the predictor announces a negative outcome — say, “You’re going to fail your exam.” Instead of accepting this as inevitable, the receiver might work extra hard to avoid that fate. In doing so, the prediction is rendered false.

This leads us to the essence of the prophet’s paradox:

A prediction can only be considered truly correct if it’s ignored — and even then, only if natural uncertainty allows the predicted event to occur.

In many cases, especially with negative predictions, the very act of forecasting prompts actions that defy the prediction.

Real-World Examples and Applications

The prophet’s paradox isn’t just a theoretical concept — it has practical implications in various fields:

• Academic and Personal Life: Imagine a teacher predicts that a student will struggle in class. The student, determined not to confirm this negative forecast, might invest extra effort and ultimately excel, effectively defeating the prophecy.
• Economic Forecasts: Consider an analyst who predicts that a particular stock is doomed to crash. Investors, acting on that forecast, might sell their shares. Their collective action could either trigger the crash (a self-fulfilling prophecy) or, in some cases, lead to market overreactions that later stabilize, contradicting the initial prediction.
• Behavioral Predictions: In politics or social dynamics, if a pundit forecasts violent reactions to a new policy, community leaders might take preemptive measures to ease tensions. By doing so, they avert the predicted outcome, thereby invalidating the original forecast.
• Megaproject Risk Mitigation at nPlan: At nPlan, our AI is designed to forecast potential risks in large-scale projects — such as delays caused by supply chain issues or unexpected cost overruns — by analyzing historical data and project-specific parameters. When our model signals a high risk (for example, a significant schedule delay), the project team is alerted and takes proactive measures: they might adjust timelines, secure additional resources, or find alternative suppliers. These actions often prevent the predicted issue from materializing. Consequently, the forecast, although statistically sound, appears less accurate because the very prediction spurred behavior that averted the problem. This is a classic example of the prophet’s paradox, where the predictor ends up being more right than chance would suggest when ignored, or more wrong than expected when the prediction influences behavior.

The Underlying Mechanism: Feedback Loops and Self-Reference

The key to understanding the prophet’s paradox lies in the concept of feedback loops. Predictions are not made in a vacuum — they interact with the behavior of those who hear them. This interaction creates a self-referential loop:

1. The Prediction Is Made: A negative forecast is announced.
2. Behavioral Response: The receiver changes their behavior in response.
3. Altered Outcome: The change in behavior modifies the conditions that would have led to the predicted outcome.

Only if the prediction is ignored does it stand a chance of being “verified.” Even then, the inherent uncertainty of future events means it might still turn out to be wrong.

The Accuracy Paradox: Being More Right or More Wrong

One of the most intriguing implications of the prophet’s paradox is how it affects the predictor’s accuracy. In traditional forecasting, you might expect that any error in your prediction reflects a straightforward miscalculation of probabilities. However, in a system influenced by the very act of prediction, the predictor ends up in a sort of double bind:

If the Prediction Is Heeded When a forecast — especially a negative one — is taken seriously, the receiver often alters their behavior to avoid the outcome. In this case, the prediction ends up being more wrong than it should be, not because the predictor misjudged the initial probability, but because their forecast actively prompted a change that prevented the outcome.

If the Prediction Is Ignored On the other hand, if the forecast is largely ignored and the outcome still occurs, then the prediction might be more right than chance would suggest. Here, the forecast stands as a rare instance where, despite being public knowledge, the outcome unfolds exactly as predicted.

This asymmetry means that the predictor is forced into a situation where their accuracy is skewed by the very influence of their prediction. They are either penalized by a self-defeating prophecy — making them more wrong than expected — or they achieve an extraordinary level of accuracy when their forecast goes unheeded.

Measuring Prediction Accuracy and the Role of Superforecasting

In many fields, especially those dealing with uncertainty, we rely on quantitative measures like the Brier Score or log scoring rules to assess prediction accuracy. These metrics help us gauge how closely a forecast aligns with the eventual outcome — a critical tool in both human judgment and AI-driven predictions (like those at nPlan).

This is where the concept of Superforecasting enters the discussion. In Superforecasting: The Art and Science of Prediction by Philip Tetlock and Dan Gardner, we learn about forecasters who consistently achieve remarkable accuracy. Their success isn’t just a matter of intuition; it’s built on systematic methods, continuous belief updating in the face of new evidence, and rigorous probabilistic reasoning.

However, the prophet’s paradox adds a twist to this narrative. Superforecasters typically excel in environments where their predictions don’t significantly alter the outcome. In many real-world scenarios — such as forecasting megaproject outcomes or economic shifts — the very act of making a prediction can influence the situation. If a forecast becomes widely known, it may trigger actions that either mitigate or completely negate the predicted result.

Thus, while traditional scoring methods like the Brier Score are incredibly valuable, they can be challenged in dynamic settings where predictions themselves impact behavior.

Implications for Forecasting in Dynamic Environments

Understanding the interplay between prediction and behavioral response has profound implications:

• Economic and Political Forecasting: When forecasters predict significant economic shifts or political upheavals, their forecasts can sometimes act as catalysts for change. For example, if an AI predicts delays in a megaproject, the forecast might prompt project managers to adjust resource allocation or implement new strategies to avoid the predicted issues.
• The Challenge of Dynamic Systems: In these dynamic, human-influenced systems, the relationship between forecast and outcome isn’t straightforward. While rigorous forecasting methods remain essential, we must also account for the fact that our predictions can change the very environment they aim to predict.

Superforecasting teaches us that forecasts should be seen as evolving tools rather than fixed endpoints. By continuously updating our beliefs and planning for multiple scenarios, we can navigate the inherent uncertainty — even when our predictions are part of the feedback loop.

Conclusion

The prophet’s paradox challenges us to reconsider the nature of predictions in a world where human behavior plays a critical role. It underscores the idea that our forecasts, especially negative ones, can prompt actions that avert the very outcomes they predict.

Moreover, this paradox forces predictors into an accuracy paradox: if their forecast influences behavior, they risk being more wrong than they should be, yet if it is ignored and the outcome occurs, they might be more right than chance would allow. This inherent asymmetry highlights the complex dance between expectation, action, and chance in dynamic systems.

I’d love to hear your thoughts — have you ever seen a prediction influence behavior in unexpected ways? Share your insights and experiences in the comments below!

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Happy forecasting!