The absolute genius of game designers is that they understand economics at a very sophisticated and subtle level.  What they do to manipulate people’s learned value of engagement is only now being appreciated by educators, social scientists and economists as a real tool.

This is what truly excited me about the possibilities of this industry.  How did these geniuses build up a sense of value in a virtual good such that people would willingly spend millions of dollars to possess?  and then brag about it?  The artificial creation of value is what lies at the heart of economic growth and collapse and these guys had mastered it into an art.  Literally, an art.

That’s when I met the marketing department.

For us managers, marketers and feather merchants, artistry is just another asset to be exploited.  Our main ‘sploit’ seems to be to convince the game designers that we have hidden knowledge of the players that they do not.  We do this by leveraging apophenia.

Apophenia is the tendency of humans to see patterns in random.  It is not just a comfortable delusion…It’s a savanna-bred imperative for survival and tops the agenda of most business meetings.

In the case of the game industry it works like this: Take any given population of players and say they’re all motivated to act differently.  Let’s say we actually graph their likelihood to spend money on something over time and find that the curves those ‘likelihoods’ trace includes every shape from a ski slope to a bell curve to a wall.  The deeper we go, the more random we find people’s preferences, motivations and willingness to spend money are any given point in time.

Now take a game in the Apple or Google store and solicit random downloads from among this population and graph the likelihood they’ll spend on level 7 of your game.  You’ll get a bell curve…but a bell curve with really fat tails.

 

You can convolve a random selection from a set of random distributions (with varying σ), but you’re no longer able to derive their moments (mean, standard deviation etc.).  Instead the best description of their central tendency is by what astronomer’s use to describe the brightness of stars that appear fuzzy around the edges: Full width, half maximum (FWHM).

The next day you’ll get a different random selection of distributions (even from the same players) but everyone staring at the chart will clearly see a middle and call it average.  Stats classes do us a severe dis-service by stating everything in terms of normal distributions because forevermore, we’ll use the mean of a normal distribution (think “bell curve”)  for the expected value and central tendency of any set of measurements.

So what happens when we design changes in the economy of a games points/currencies and power based on a mistaken distribution of player behavior?  BAM!  SPLAT! Game over!

When the product owners completely change of direction in the game at every scrum retrospective (with the predictability of a Roomba) you can be pretty sure you’re not measuring expected values. But the apophenia persists and we’re sure we’d have a hit, if we could only expose it!

When the first dozen diagnostics don’t change outcomes, managers double down on their ability to see patterns in the data and recursively make the situation worse.  They’ll ask their data analysts for increasingly obscure cross tabulations to feed an endless cycle of “AHA!  I knew it!…wait, that doesn’t work?!  Gimme….” moments.

So what happens to insight analysts in this environment?  How do they maintain they’re worth in the face of a habit of failed recommendations?

Here is where I met the fourth leg of the video game industry…the programmers.

 

 

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