In The Simpson's episode Homer's Enemy ,  Frank Grimes is a unlucky man who's worked hard his life but failed to reap any of the benefit.  When confronted with the inequity of his circumstances compared to Homer's he breaks down into a self destructive fit of jealousy that eventually ends his life prematurely.   The episode is a 1990s comedic expression of the general observation that life is not fair.  Several decades later, as income inequality has increased it's become a popular talking point that, "Hard work alone doesn't make you successful."  In Gen A slang a, "Try Hard" is someone like Frank Grimes who does himself discredit by giving it too much effort. For us conscientious types, this is not only counter intuitive but also elicits an emotional reaction.  Folk heroes like Rocky show us that while God may not have blessed us with any particularly useful talent, we can make up for it for it with heart.  Hercules taught us that even wi

We tend to think of flexibility as a good thing.  When it comes to interpersonal relationships, the ability to compromise is considered a sign of maturity.  Shouldn't this be the case for software?  After all, software is just a human solution to a human problem. Well, it turns out that it depends on what exactly we mean by "flexibility."   Flexibility as software extensibility is generally positive.  It's unlikely that we perfectly capture the problem space in an optimal fashion in the first go.  We should strive to make our future selves' lives easier.  Extensibility means, holding all else equal, building a version of the solution that is easier to expand upon in the future.  This kind of flexibility is good.  It means spending a little extra time now to save time in an ongoing fashion in the future.  We'll return to this idea later.   When by "flexibility" we mean producing a solution to an ambiguously defined problem, that's where we get int

In finance, the process of retrospectively evaluating the efficacy of investment decisions is referred to as attribution.  It's used to ask, "How'd we do?" and offer a granular, objective answer.  Using the risk/reward framework, widely accepted risk factors are used to explain a position's performance. As a cartoon example, let's say you own a share of IBM.  For equities, we'll perform attribution using Beta , roughly speaking a measure of correlation to the underlying market.  If IBM has a Beta of 0.75 and the SnP5000 moved by 2% then Beta predicts that IBM would move by 0.02 * 0.75 = 1.5%.  If instead we observed a measure of 1.7% then we may say that 1.7 - 1.5 = 20bps (basis point = 1/100 of 1%) is due to expert skill aka " alpha ."  Alpha is often used as a remuneration metric for professional portfolio managers. There's actually a whole lot more to attribution but hopefully this example communicates the general intuition.  Interesting