Hobby: Holography

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In the early 1960s Holography emerged as one of the most exotic uses of the unique light produced by lasers.  By the early 1970s it began to make the transition from scientist only to hobbyist.  At first it was to exploit it as an artistic medium but the amateurs that saw its potential quickly developed innovations of media and techniques that became main-stream.

By the mid 1990s the nascent “World Wide Web” had one or two Usenet newsgroups called alt.holography dedicated to the hobby.  Between this and a couple of “how-to” books published by the 1970s superstars of the San Francisco Holography Art movement everyone had access to the technology.

The hardware came available with affordable Kodak holography plates and the surplus lasers from copy machines made the hobby approachable to interested amateurs.  holo1

My holography table was built from the aft bulkhead of a 747 from Boeing Surplus which is a two-inch high honey-comb sandwiched between two aluminum sheets.  I added padding and bricks and placed it in my basement.

In the configuration seen in the pictures it is making a reflection hologram of some plaster whales.  One beam illuminates the whales, while the other illuminates the plate at the far right.

As yholo2ou can see, this is all stuff hacked together from scraps.




Hobby: Rocketry

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The 1990’s was a wonderful time to be a hobbyist.

The contagious optimism of that time lent itself well to the enthusiasms of people and families [ :-(  mostly men and mostly white] who bonded in far flung disciplines that had always been the purview of “trained professionals”.

One of the hobbies that decade “launched” was high power rocketry.  There were always two reactions the first time anyone ever saw a launch of anything bigger than the “Estes”rockets you could buy at any hobby store:  Awe and fear that you could do more than take someone’s eye out.  To assuage such fears, the hobby community itself, the US rocketsFederal Aviation Administration and the Bureau of Alcohol, Tobacco and Firearms provided strict regulations around the storage, purchase and use of any rocket motors with a specific impulse above 36 Lb-seconds.  In order to better assure that the hobbyist would align with these regulations, hobbyists associations coordinate the launches and supply amateurs with the supplies and training they need.

In 1996 I passed my level 1 certification and my level 2 in 1997.  In order to be certified to level 3 (allowing launch of rockets with a specific impulse exceeding 1,150 Lb-seconds) it was traditional to build something novel into the design.  In those days before cell phones and micro-power electronics the favorite was altimeter-based parachute deployment.

EricalchMine was a pic-based system with a clunky altimeter and an enormous T-1 accelerometer but what I really wanted to study was what’s called the mach disks of the rocket’s exhaust.  This is actually a phenomena that reveals volumes about the sonics, plasma and nozzles of all jet and rocket propulsion.

Because of my Afghan ancestry, I stopped any and all activity in this hobby shortly after 9/11.



Radio Men

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When an old guy reflects on his past, it always comes off as the pointless ramblings of a grandpa, freshly awakened by a vivid dream, recalling his past.

The truth is, it isn’t always just mawkish sentimentality. Sometimes we’re overcome by the realization that each generation asserts that their imagined future will render all future imagination obsolete. Its a realization that compels us to remind them that any ‘imagined future’ is, by definition, a rainbow – just out of reach…that the real prize is what that vision motivates in us now.

What an ironic compulsion: Struggle to make plain, an ineffable statement about an unattainable reality!  What’s the point?  So they don’t end up with the same frustrating compulsion?

What I’ve chronicled in this blog as current passions are quickly aging and becoming anachronistic.

That’s normal.  It’s the way time passes.  The problem is that as it ages, it obtains a patina that makes it look more like older, more cherished memories that really are mawkishly sentimental. It makes it easier for me to justify boring everyone with all the outdated technologies I’ve played with.

Let us agree that once there were a bunch of kids (all gone now), as geekish as we are, who proudly called themselves “Radio Men” (Let’s acknowledge-and-then-bracket the fact that they were largely sexist, racist, homophobes who never questioned the order of their world).

The thing to carpe in their diem was wireless transmission of messages over great distance.  It was the first time using something we couldn’t perceive with our senses in day-to-day technologies that everyone would use.  Up until that time, all technological revolutions had improved transmitting power via pistons, gears, belts and wires. Now we were harnessing an invisible quality of light to change (and perceive the change in) the æther around us. It was applied theory for consumers.

It fueled the new tropes of science fiction and captured the same child-like wonder you find in Makespaces today.  It built icons of a national character no less admired than those today.  It spawned practitioners across the spectrum from enthusiast to hobbyist to student to theoretician.  There were sights and smells, unique to that technology, that instantly jolted anyone from that era back to exact moments in time and states of excitement.

How is that qualitatively different than today?  Not at all.

How relevant is that fact?  Not at all.

All I’ve experienced will someday be relegated to the same dustbin as the fact that there was a distinctive quality to the optimism held by “radio men”.  Unless I’m willing to take the time to write a thoroughly researched and peer-reviewed history, these should be set aside as anecdotes.  They’re mine and they were fine but they’re of little importance to anyone but me. And I worry that they’ll pollute the optimism of today’s radio men.

I’ll include hobbies that are still popular but I won’t dig into all the hobbies of my mis-spent youth.

Oligo sensor

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A lifelong ambition of mine has always been to be part of a team of people who race past the conventional boundaries and are surprised by every new possibility.  Where being creative was a habit that you couldn’t turn off and no terrain of thought was forbidding.

There is a cozy little shop in Spokane, Washington that smells of photoresist, solder and machining coolant oil where that happens every day.

NLIWhen I was there from 2001 through 2003 we had just enough contractual work to  produce just enough surplus energy to get the next contract.  But the culture there placed such a high value on creative and innovative approaches that it was the funnest place I’ve ever worked at.

The principal (seated on the right) is the president of the company who holds several patents including several very successful patents in holography.

One of the ideas he tried to patent was an electronic sensor of oligonucleotides based on two facts: The different weight of the base pairs in a single strand of DNA and the fact that there exists a resonant frequency for any oscillating body.

My work involved preparing gold plated test slides for attaching the 5′ end of oligonucleotides of known composition.  When a complimentary nucleotide bonded with this, it could be attached to the sensor and then separated by heating and tested for its composition.

Each sensor cell consisted of a gold foil diaphragm that would be set to oscillating by a frequency generator with reflected light measuring the degree of deformation of the diaphragm.  The frequency that took the least energy to maintain would be the resonant frequency of that cell of the sensor.

Adding weight to the cell would change that frequency.

Unfortunately someone else was awarded this patent first and the proof of concept development stopped.

Strange that.  Why is it that every time I join efforts with really creative people, I end up building the ACME widget that ends up with Wiley Coyote at the bottom of a grand canyon?

Deconstruction: Cauchy time with the sink

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In economics there’s something called the Edgeworth box which attempts to illustrate how two people with different production possibilities might trade with each other.

I’ve always maintained that a similar construct could be used to illustrate how two people with slightly different idea/word-meaning values might communicate (i.e. trade and barter meanings).

This theory neatly explains how spectacularly I fail to communicate with programmers.  It’s because I completely lack any meanings or ideas that they want to trade in.

In the game industry of Seattle, everyone integrates easily with the same geek-chic humor we’ve had since the early 1990’s but programmers quickly separate themselves from the non-linear thinking of non-programmers.  When they saw us data wonks floundering because our managers keep asking for different slices of the data, 5 programmers slammed the same sticky note to the Scrum board: Automate finding patterns in the data.  At that point, I had nothing of value to add to the conversation.

My abstractions about player values and the equilibrium of the game’s economy solicited the same sympathetic disdain they show artists debating the color schemes of a game.  They quickly disappear into the safety of their code.  (Meanwhile the concept artists are drawing ridiculously chibi-headed caricatures of the programmers as zombie fodder for their next project.)

The product they came up with is what is hailed as the newly minted discipline of Data Science.  The application of apps to data from apps to build better apps from.

But instead of actually improving the outcomes it simply puts the mantra of “fail cheaply and fail often” on steroids.  Although you can’t derive any moments from data that comes from a Cauchy-generating process, you can subject it to a power series that will quickly reveal…you guessed it, something to find patterns in.

One of the places I saw this clearly was in the balance of soft and hard currencies in a builder game we were designing.

In the early 2000’s everybody was enamored with agent model simulations that could simulate the data you’d get with agents that a finite set of rule-based behaviors.  I don’t know why we thought this was so special.  Game designers have been using simulations on board games forever.

For games that monetized based on currency shortages, however, you had to build separate simulators for currencies so that you wouldn’t accidentally nerf or buff a virtual good that you just spent the last 5 levels increasing the value of.  In mid-2012 Joris Dormans created an online simulator called Machinations to do just that.  You constructed sources and sinks of each currency and a few other logical conditions to build an animation of the flows along with a graphic that simulated what you would see from an instrumented game in Beta.  Now with a model of the mechanics, a par sheet of probabilities and well constructed on-boarding I should be able to balance the economy right?  Well…except for those pesky players who refused to play as scripted.

No matter how well we balanced the economy in the builder game, results came back from tests in Canada that players were not making choices that could be classed as strategies or player-types.

When our programmer got hold of Joris’ open source program, he immediately bolted it to a genetic algorithm to maximize the flow of resources (including In App Purchases). This would seem the right thing to do as a programmer but it ignores the fact that entrepreneurs and gamers live by:  One plays to beat expected values, not to meet them.

Once again, the charts produced by Machinations that most closely matched actual data were those with the fattest tails of a distribution (Cauchy).

No matter how many ways the problem was ‘diagnosed’ player retention dropped (with a freemium ‘Thud!’) right at the point the player met a paywall.

Now it was time to bring out the big guns of behavior analysis…psychology.

Deconstruction: Apophenia. Goddess to Feather Merchants

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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.



Deconstruction: Toxic Player Taxonomies

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A metaphor I think is useful here is the story Sylvie and Bruno by Lewis Carroll.  In it a king orders the court cartographers to make maps of increasing accuracy until they finally have a 1-1 scale map of everything in the kingdom.  The hero’s quest is to convince the king he’ll win a Darwin Award if he unrolls the map.

There is a constant debate among those of us who assay life as to just how closely our models of reality have to agree with empirical data in order to be useful.  Unfortunately, despite George Box’s hopeful assertion, models are only ever useful for deceiving oneself into believing that natural law has anything to do with human behavior.

The first wrong model I used was a taxonomy  for classifying player types developed by Richard Bartle in 1990.  In Bartle’s taxonomy there are four basic types of players in games.  Achievers, Explorers, Socializers and Killers.  I was definitely the explorer he describes as players for whom “The real fun comes only from discovery and making the most complete set of maps in existence.”  I should’ve known better than trust the ramblings of the self-proclaimed “Wizards of MUDS” but it turns out Bartles accurately predicted my final score as an “Explorer” in the game of Freemium game design.

You can imagine what happens to characters like me when the game industry is dominated by people whose ludo type is Killer.  According to Bartle, “Killers use words like: ‘Ha!’, ‘Coward!’, ‘Die!’ and ‘Die, Die! Die!’ (Killers are people of few words).  “

It’s amazing how fast you find this out how dominated a strategy being an explorer is but what you don’t realize is how quickly you also learn to change your game.

The learning process in game play is nicely described by theories of Q-learning or re-enforcement learning models.  The problem is that those models assume a well defined Markov decision chain that every rational player would follow…and of course All players are rational.


At about the same time Bartle was deciding how people played Dungeon’s and Dragons, Nigel Howard was doing the same thing for international arms talks.  Only Howard didn’t assume rationality.

Howard’s Drama theory holds that as one learns the game they are only holding their definition of the game as a provisional assumption.  If they get enough evidence that the game isn’t what they thought it was, they’ll re-define the game to better suit the data.  Very Bayes.  The result of this was that players would fall into predictable Markov decision processes only so long as there was no divergence from expected outcomes.  As soon as something unexpected happened, the game would change.  And nothing’s more predictable than the unexpected in the game industry.

In 2011 Seattle hosted Casual Connect, a conference of casual game developers, at which everyone was told that any company without several statisticians on staff would be at a competitive disadvantage against teams that could see into the hearts of its players.

Everyone got the message and I truly believe that, but for that message, I would not have been hired by a game company.
The problem was that they were expecting this guy, while I thought I was there to make the most complete maps of player behavior in existence.

What happens in a 2-player game when each player is given the rules to completely different games?

If they have the expectation that everyone is playing the same game, the person with the fewest words moans “Lame” and struts to the next machine in the arcade.

The thing is, that because they are learning and adapting, the way they play the next game is different than how they would have played in the absence of analytics.

They’re haunted by the possibility that within the crystal ball of analytics there lies the super-power to make people like you…even if only for cosplay at a conference.





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