To address a crucial point immediately: I agree with the professor that the future is not merely uncertain but inherently unknowable. However, some situations are more unknowable than others, and prediction need not achieve perfect accuracy. Being somewhat less wrong can still provide value. Even so, the potential pitfalls of prediction are numerous and serious.

These problems fall into three distinct categories: limitations in the forecasting methods and models themselves, human behavioral biases interfering with the prediction process, and communication breakdowns between forecasters and decision-makers.

To start with the methods: most predictions heavily rely on past data and historical patterns, assuming that previous relationships will persist into the future. This assumption frequently fails precisely when accurate forecasting becomes most critical: during periods of rapid change, structural breaks, or unprecedented circumstances. Forecasting exercises typically concentrate on specific, preselected variables while neglecting broader environmental contexts. This myopic approach blinds forecasters to crucial interconnected factors, emerging developments, or systemic changes that can render specific predictions meaningless. Especially when the consequences of these relatively unlikely events are disporportional, the prediction failure is huge. A generational example is the Great Financial Crisis of 2008, which Nassim Nicolas Taleb famously chronicled and documented in his landmark book “The Black Swan”.

Whether it’s cause or effect is unclear, but because of this dynamic most forecasting efforts focus on relatively short time horizons, failing to account for longer-term structural changes or rare but high-impact events that can fundamentally alter the predictive landscape. And even well built models can be built on incorrect assumptions about causal relationships and key variables. Reality tends to be far more complex than the models, and complex systems are rife with non-linear effects, emerging properties and all kinds of other curveballs.

As if the models weren’t bad enough, they are typically wielded by human beings, opening up an entire second category of trouble. Human judgment suffers from predictable cognitive limitations including confirmation bias, overconfidence, groupthink, reliance on mental shortcuts, hasty generalizations from small samples, and emotional interference with rational analysis. Expert overconfidence proves particularly problematic, as domain knowledge can increase certainty without improving accuracy.

Accountability Evasion: Many forecasters employ deliberately vague language that makes post-hoc accuracy assessment difficult or impossible. This ambiguity allows poor forecasters to claim vindication regardless of outcomes—a problem especially acute among television pundits and public commentators who benefit from maintaining credibility without accepting responsibility.

Institutional Gaming: When forecasting becomes institutionalized, perverse incentives emerge. Forecasters may cherry-pick favorable data, adjust methodologies to produce desired results, or engage in statistical manipulation. Organizations often prefer conventional failures over original mistakes, creating pressure to conform rather than innovate.

Anchoring and Path Dependence: Initial forecasts unduly influence subsequent predictions and decisions, creating path dependence that prevents adaptive responses to changing circumstances.

Category 3: Communication and Decision-Making Breakdowns

Technical Translation Failures: The handoff between forecasters and decision-makers introduces critical communication gaps. Leaders often receive predictions without understanding underlying reasoning, methodology, or confidence intervals, leading to misplaced confidence in uncertain projections.

Leadership-Forecasting Cultural Clash: Qualities valued in organizational leaders—confidence, decisiveness, clear vision—directly conflict with intellectual habits that produce accurate forecasts: probabilistic thinking, intellectual humility, willingness to change positions, and comfort with admitting error.

Inadequate Uncertainty Communication: Few forecasters excel at communicating the inherent uncertainty in their predictions. Decision-makers rarely conduct sensitivity analyses or Monte Carlo simulations to understand the range of possible outcomes, instead treating point estimates as gospel.

Oversimplification Pressure: Organizational and political pressures favor simple, definitive answers over nuanced, probabilistic assessments. This dynamic incentivizes forecasters to present false precision and decision-makers to ignore uncertainty.

Question Framing Problems: Both forecasters and decision-makers may fail to ask the right questions initially, focusing on easily quantifiable metrics while ignoring more fundamental but harder-to-measure factors.

Additional Arguments Against Prediction

Resource Opportunity Cost: Extensive forecasting efforts consume significant time, money, and cognitive resources that might generate greater value when deployed elsewhere, particularly when accuracy improvements are marginal.

False Security: Predictions can create dangerous illusions of control and certainty, leading to inadequate contingency planning and reduced organizational adaptability.

Complexity Underestimation: Many systems exhibit emergent properties, non-linear behaviors, and feedback loops that make them fundamentally unpredictable regardless of analytical sophistication.

This framework suggests that prediction failures occur not merely due to inherent uncertainty, but through systematic breakdowns across methodology, human psychology, and organizational communication—making the case for scenario planning’s emphasis on preparation over prediction increasingly compelling.