Liberation and Heuristics

I learned earlier this month that my blog isn’t making the numbers it should. This doesn’t surprise me for lots of reasons. My writing will only appeal to a small audience to begin with and very few things I write are quick reads. NextStage’s own research indicates that these are not winning elements in the greater blogosphere.


I know I have a readership…the emails I receive and comments posted to the blog are good indications that I’m reaching a reliable audience, simply not an audience large enough to warrant the overhead.

Strangely enough, I find the fact that my blog isn’t economically viable liberating. I don’t have to worry about posting something every day. When I do post something it’ll be important and/or fascinating to me, not something I feel compelled to post on.

Take heuristics, for example.

Lots of people are talking with me about heuristics lately. I don’t think they know that’s what they’re talking about. Often I listen to them and say, “I think you’re talking about heuristic solutions.”

What are heuristic solutions?

In a nutshell, I use the term to describe logical calculus solutions that determine best fits for all involved in a given ecological system. These are not optimal solutions for any one stakeholder although you could define them as optimal solutions for all stakeholders provided you recognize that optimal for all might not be optimal for any specific one.

Think of “All for one and one for all” where the former is traditional statistics and the latter is heuristics.

Heuristics draws its power from being able to provide ecological solutions with a recognizably small data set. “Recognizably small” is a mathematical way of saying “it fits in a breadbox” or “you don’t need an infinite data set to have confidence in your solutions.”

The great thing about heuristic solutions is that their use is how we’re wired to begin with. The great scientific axiom of Occam’s Razor is actually what’s called the “fluency heuristic”. Occam wanted us to go with the simpler solution, fluency wants us to go with what we know. By definition, what we already know is simpler to us than what we don’t know.

The kick here is that the more experience and education one has the more often what they know isn’t necessarily the simplest solution.

Go figure.

Or “Go heuris”.

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