About Sam Bhagwat

Sam Bhagwat is an economist/data scientist by training. His ribbonfarm posts explore complex economic systems. Follow him on Twitter.

Playing Games to Leave Games

October 21, 2014 By

Sam is a 2014 blogging resident visiting us from his home blog at Moore’s Hand.

When I was a kid I played a lot of chess. On Saturdays my mom and I would get up early and drive an hour to a high school somewhere around Michigan. She would bring a box of old New York Times and read as I played five rounds of chess against other 6-, 7-, and 8-year-olds.

The games were typically G/30 or G/45, which means each player had 30 or 45 minutes to make all their moves. If you finished your game early you would have to wait for all the other games to finish and the organizers to calculate the rankings and matchups for the next round.  That process would usually take about an hour, which doesn’t seem like a long time now but of course did at the time.

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Replaceability and the Economics of Disequilibrium

April 30, 2014 By

Sam is a 2014 blogging resident visiting us from his home blog at Moore’s Hand.

One characteristic of personal meaning is irreplaceability. If you’re the only one who can do what you’re doing, your actions suddenly seem a lot more important.

We’re familiar with this principle in our personal relationships — perhaps a friend needs a piece of advice, or a child needs parenting, that only we can give.

But larger systems are designed to make most people replaceable.

If you are replaceable, the system will equilibrate without you. If you aren’t replaceable, it won’t.

This is good for you but bad for the system. So your manager’s job is to make sure that the company will survive you being hit by a bus.

Replaceability is a spur to the ambitious. Every law school grad applying for a Supreme Court clerkship; every Ivy League grad interviewing at Goldman Sachs or McKinsey, knows that ten other people want the job, and can do it.

But replaceability is existentially demotivating. “Just a cog in the machine” has been an epithet for employment since Charlie Chaplin filmed Modern Times.

If a system will achieve roughly the same outcome no matter who’s inside it, these people are by definition replaceable.

And so in seeking irreplaceability, we must ask: what systems are capable of achieving genuinely different equilibrium outcomes?

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Authors and Directors

March 12, 2014 By

Sam is a 2014 blogging resident visiting us from his home blog at Moore’s Hand.

I grew up in Michigan, the older of two kids, but the second-oldest of all of my cousins. Every Thanksgiving we would drive to Cleveland for our family gathering, where I would hear about my older cousin Amruta’s exploits, and a couple years later, reproduce them.

She went to Penn; I went to Stanford. She spent a year living in India, I spent two. Post-graduation she started in management consulting; three years later, I followed suit.

Our paths led us both to San Francisco, but while Amruta got a J.D. and became a law associate, I quit my consulting job, taught myself to code, and became a programmer.

If you ask Amruta, she’ll say that the common thread of McKinsey and Big Law has been learning to operate in tough workplace environments. Her one-on-ones at McKinsey gave her detailed personality feedback; she was told point-blank to focus on her assertiveness.

Amruta was receiving training about how to effectively implement ideas in an organizational context — engaging with stakeholders, overcoming opposition, and so on.

My main job perk, on the other hand, is being able to sit on a couch all day and think. I give substantive status reports once or twice a week, as opposed to multiple times a day in consulting.

Amruta is a knowledge worker who primarily implements her ideas in an organizational context. I am a knowledge worker whose ideas primarily execute, and replicate, themselves in browsers and on servers.

We’ll call Amruta’s type Directors and my type Authors.

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Algorithmic Governance and the Ghost in the Machine

December 1, 2013 By

This is a guest post by Sam Bhagwat from Moore’s Hand.

 Moore’s Law has granted to 21st-century organizations two new methods for governing complexity:  locally powerful god-algorithms we’ll call Athenas and omniscient but bureaucratic god-algorithms we’ll call Adjustment Bureaus. 

As an example of an Athena, consider this case:

Eighteen months ago a Buddhist convert in Los Angeles named Rick Ruzzamenti donated his kidney. It was flown on ice on a Continental red-eye, to a retiree in Newark in need of a transplant. The retiree’s niece then sent her kidney to a woman in Madison. The woman’s ex-boyfriend sent his kidney to a secretary in Pittsburgh. The secretary’s boyfriend sent his to a young father in San Diego.

When the chain halted six months later, in December 2012, with a final transplant in Chicago, sixty operations had taken place, enabled by an algorithm that crunched through billions of match possibilities.

And for an example of an Adjustment Bureau, consider this case:

For several months in 2011, including the holiday sale season, JC Penney was at the top of a curious number of Google search results. “Dresses.” “Bedding.” “Area rugs.” “Skinny jeans.” “Home decor.” “Furniture.” Even “grommet top curtains.”

The placement generated huge traffic – 3.8 million monthly visitors just for ‘dresses’ – and  revenue in the tens to hundreds of millions of dollars.  Then an enterprising reporter noticed thousands of paid links in link directories, and brought the matter to the attention of Google’s webspam team, which flagged the site:

At 7 p.m. Eastern time on Wednesday, J. C. Penney was still the No. 1 result for “Samsonite carry on luggage.”

Two hours later, it was at No. 71.

At 7 p.m. on Wednesday, Penney was No. 1 in searches for “living room furniture.”

By 9 p.m., it had sunk to No. 68.

In other words, one moment Penney was the most visible online destination for living room furniture in the country. The next it was essentially buried.

Wide-eyed advocates of ‘algorithmic governance’ (Tim O’Reilly) beware: each god may grant you your local optimization, but their intervention is far from free.

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