On Forecasting

Despite the common cultural associations with the word, forecasting is much more than accurately predicting the weather, stock fluctuations, or huge geopolitical events. Whether we know it or not, we're making forecasts almost every day: we decide whether or not we should take on the additional weight of an umbrella leaving home; we assess how much time it will take to reach our destination and choose between different modes of transport; we think about what kind of foods and what amount to buy while grocery shopping given our plan for the week. Forecasting and foresight are indispensable parts of our life. Living in the moment sounds very poetic, but is not very realistic.

In my research findings, the ability to make accurate forecasts turned out to be strongly correlated with critical thinking and good judgment — and it could be deconstructed into measurable and observable skills that can be learned and taught.

Interestingly, even though forecasting and its statistical underpinnings are commonly studied in the context of quantitative research, when I entered the field in 2019, I found a massive overlap of the data with my previous qualitative research on paradox, cognitive biases, courage, cursioty, and grounded confidence. Effective forecasting became the previously missing piece in my vision of daring leadership — just as essential for finance corporations, investors, and IT startups as it was for policy-making institutions and individuals.

With all my forecasting research now summarized into a separate course, below are a few critical points.

  1. Meaningful forecasting requires calibration and accountability. Therefore is mandates the transparency of definitions, time frames, and expectations. There's no room for vague verbiage, bullshiting, and evasive rhetoric. That's why, according to Phil Tetlock, the world's most famous forecasting pundits have the measured prediction ability slightly higher than that of a dart-throwing chimpanzee: their forecasts cannot be reliably tested as they routinely emlpoy language like "could", "might", "significant", "negligible", "at a certain point" etc. when talking about risks, time frames, and probabilities, as a result making accountability impossible and always having a way to rationalize a bad forecast and take credit for a good one they never actually made.
  2. Efficient forecasting is in service of the work, not the ego. It can only happen when we prioritize and practice getting things right over being right. That's difficult when forecasting questions at hand are highly emotionally charged because of the size of the decision-making impact, particularly in politics, governmental institutions, and high-profile finance organizations. Self-awareness and a daring leadership culture become individual and collective prerequisites — and reliable predictors — of the leadership's forecasting ability.
  3. In order to get better at forecasting, we have to learn both from our successes and errors, also embedding the lessons from those of other people. That is predicated on shame resilience, courage, and curiosity. We also have to watch for moral judgment getting in the way. The point of making a good forecast is accuracy, and not whether we like or dislike a certain outcome.
  4. While effective forecasting requires some numerate literacy, it doesn't require you to be a math genius. Even if math wasn't a breeze for you in middle school, high school, or college, the basic principles of statistics are teachable and learnable for everyone. In fact, you already use some of them without your awareness when you're planning for time and budgets. The point is to refine these skills and get acutely aware of fallacies and biases that get in the way. If you're still anxious that you're not good enough at math — rest assured: in the modern era, you can delegate the statistical heavy-lifting to computers. Your main job is to correctly frame the forecasting question and to constructively assess the results.
  5. Forecasting ability is highly correlated with perspective-taking (part of the empathy skillset), emotional agility, and the conscious drive to update one's knowledge and vision, all the while having the skills to filter helpful pieces of data from unhelpful ones. Good forecasters aren't burdened with "one big idea", worth defending and sticking to no matter what.
  6. Forecasting requires awareness about the most common cognitive biases, including the hindsight bias and the base-rate neglect. It also involves granular thinking: distinguishing the knowable from the unknowable, deconstructing big intractable questions into small tractable ones, and finding the sweet spot between prudence and decisiveness.
  7. The level of collective courage in an organization's culture is one of the best predictors of its leadership's forecasting ability: because collective courage is inversely correlated with groupthink. This translates into the organization's market agility, innovation, and long-term success.
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