When we set out to do anything we form a mental picture or "forecast" of what can be achieved with proper focus and execution.  Forecasts, given their nature of predicting the future are often wrong.  Ideally they're wrong in a favorable sense, where we exceed our aim.  Obviously sometimes we miss the mark on the downside.  Both are equal opportunities to learn.

When we develop a prediction or forecast the point is to think thoroughly and clearly through the details that support the forecast.  It's this discipline and thinking that provide 99% of the value and give us the ability to learn when our forecast inevitably is missed (either to the upside or downside).  The miss to plan is the opportunity.  

Why did we miss?

Was it a flaw in our mental model? Flaw in the data? Mis-calculation or missed assumption somewhere? Did we have enough Retained Optimism?

The concept of Retained Optimism is fairly simple: give your predictions enough breathing room for upside surprises. This, I think, is where outsized value creation most often occurs.

To be clear, this isn't about being overly conservative.  In fact, being overly conservative leads to a bad learning environment where you overachieve simply because you set the bar too low. And its important to stretch yourself on focus, execution and constraint-driven innovation. The idea behind Retained Optimism is more subtle.

For example, let's say that you want to launch a new product. In order to forecast adoption and growth potential the process of understanding the market wants, needs, motivations, insecurities, competition, alternatives, etc., is foundational to your analysis.  You build up a model to support your prediction.  You write it all down so it forms the historical baseline for your eventual learning when you arrive at the future state.  Every good plan requires stretch to your focus and execution to keep you sharp.

Retained Optimism on the other hand is a variable in your second-level analysis of the macro market and trends.  Meaning.. are there things that you've contemplated that could, if they hit, cause a favorable surprise to the upside?  In other words, is there a reasonable adjacent variable that could give the existing predictions a boost? Maybe this is a competitor being acquired and then distracted, or running into headwinds through poor leadership? Or perhaps an anticipated shift in market dynamics that gives you a tailwind you didn't directly apply to your forecast model?  These sorts of things are the essence of Retained Optimism.

We all have many opportunities to set forecasts, plans and predictions. And we always learn the most when we find a flaw in our models. That learning most often occurs when the future turns out different than we thought it might.  The thing that makes great companies, great leaders, and great investors is their ability to most accurately model probabilities of the future with a thoughtful dose of Retained Optimism.