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ColdwaterCowboy_1

From 60,000 feet above sea level...we look pretty puney.

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One of the development teams that I was lucky to work for was large and had a very healthy brain pool...except for me...I was proud to have worked with so many good human beings.

For each major piece of an enterprise application there were literally billions of entities . To build algorithms around them you needed to understand these entities intimately and more important their relationship to the billions of other entities'...and of course in the end you did because you described them and created them.    ( We had to beg some coders for documentation because if they disappeared there would be intellectual property left over that no one else understood. Things might get broken that couldn't be fixed)

So my point is.....aaarg:cap_old:

Financial metrics had the largest say in the tuning of the code (built right into the code) to meet certain requirements of the development overhead......requirements meaning enough money to make the payroll.

The cash stream for our survival was designed from mining your habits (e.g. $/game played) You might be the most visible person in the game but if you are not an earner then you are expendable.)

There is a metric for each and every unit of goodwill in a game app that can predict it's contribution in cash. That metric flows right into the main algorithms of the application.

In the end it is the "metric" that speaks on the content of each release.

It has the highest success rate.

Listening to every user can only help you go broke.

 

 

 

 

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2 hours ago, ColdwaterCowboy_1 said:

In the end it is the "metric" that speaks on the content of each release.

It has the highest success rate.

Listening to every user can only help you go broke.

This is quite true under partial equilibrium, i.e. when the changes you make don't change the fundamental structure of the system or otherwise make it behave outside the historical trends your data is based on.

However, it's worth noting that there are certain types of changes that don't get modeled well by the standard techniques in this domain. Two that are relevant here are spillover effects, in this case something like "social snowballing"; and the other is changes that have a large enough systemwide impact that dormant factors (locally zero or constant under normal conditions) begin kicking in. Both of these types of change tend to break pre-existing models, because those models are built and tuned with data that didn't account for, e.g. hysterical change. We'll see how well WG's analysts and devs do on this one.

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On ‎2‎/‎1‎/‎2019 at 10:44 AM, Edgecase said:

"social snowballing"

it tends to go away eventually....but it does a lot of short term damage.

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