Chess and the Machine-Mediated Future of Work

I’ve been thinking about chess a lot lately. I blame a couple of authors – Tyler Cowen in “Average is Over,” and Jacob Morgan in “The Future of Work”. Both authors look to the future economy and what it will look like. Cowen’s seems more like a dystopia, but Morgan’s future has a smaller time line. For him the future of work will look like it does now, only more so. The real commonality is that they both really like Chess. Cowen uses it as a metaphor throughout his book, in that today’s chess matches mediated through computer help are tomorrow’s work situations. Morgan named his consulting firm after the game. I think it is a powerful metaphor for strategy, but a limiting one.

                I say that as someone who had chess thrust on me a lot when I was a kid. I was one of the smart kids, and in several environments, I was in a sort of “gifted” program it was anticipated that the smart kids would necessarily gravitate to the game. I was never particularly interested in it for whatever reason. That meant that I was beat by people who were more interested in that particular game. Here’s the thing, though. Researchers in artificial intelligence like chess because it is very bounded. There are only sixty-four squares, and there are sixteen pieces on each side. Each piece moved to set rules. There are, if I’m counting right, only twenty possible initial moves, and twenty possible second moves.  All the possible position and piece combinations can be mapped. It is a very large but finite number, but not so large if you have a perfect computer that can have all those possible positions in their memory that they can access. Each move is one more step along a decision tree that makes one side more or less likely to win. You could set a program up that maps out a route down the decision tree that makes the computer more likely to win in response to its opponents moves. You set two of these programs up against each other and you get white with a slight edge but the end would be mostly draw games?

                You know why I never really got into chess? Because chess is boring, and that scenario I drew out makes it even more so. Humans are not perfect computers. We play the game sub-optimally, where we often make choices that may make our opponent more likely to win. We operate with opening heuristics and planned end games that we try to get to because we know how they are supposed to go.  Strategy is interesting in the same way. If you are in either a cooperative or a zero-sum game, you have to anticipate your opponent’s moves in terms of all possibilities, not just the ones that may improve his lot. This is true for both bounded games like chess and for real life. As we move forward to Morgan or Cowen’s future, this is what I am afraid of – that mechanical mediation will make even the mindful jobs boring and that the workers of the machine will get more productive, but they will also become more machine-like. It would then be the owners of the machine who reap the benefits of that future, and the vast majority of the workers are just pawns on the board.

Somebody Ought To….

 I was reading something about the views of some tax payers on the right, who think the government is just an extractive institution. The problem is that the individual relationship with the state is a symbiotic relationship. It then becomes part of the environment so much that it is forgotten that it is even there. 

It starts from a state of nature, but in a large industrial society, there needs to be coordination of public goods. These are the things that everyone needs but few are willing to pay the upfront costs. It’s why transport was limited in the first 100 years of the country. 

The state helps coordinate transportation – somebody ought to help trade the interior, it was said. There was no private company willing to dig a big ditch. So the Governor of New York said, let’s do this. 

The same was with the railroads and helping utilities lay right-of-ways. 

So that’s fine. We have public goods. But how do you trade? You need to facilitate trade in some way. In a small community, you have your word and reputation. In a larger environment, you need something else. Somebody ought to arbitrate contracts and set the rules for the market place. So you have a government step in.

Now you have travel and commerce taken care of. How do you protect your property that you gained through that trade? Somebody ought to help out and protect private property. It’s too expensive to hire one guy for your own property.  Maybe you get together with some other local property to hire a guy to patrol everyone’s property. But who sets the limits on what that guy can do? You need a code of rules and regulations to say what he can or cannot do.

But why does that property need protection? Because for whatever reason there are people who covet what you own, much of these are losers on the transportation or the trade that was set up. What if instead of protecting your property, you made it so that you could help these people so that you did not be afraid for your property. What if you could do this at a lesser cost than it takes to protect your property and punish the trespassers?

Congratulations, you just built a functioning state. Now the real questions are about priorities and allocations of the funds you gained because the state set up the conditions for your prosperity. It is not that the state is being extractive, but about how much you are willing to share with the society that is only possible because of state coordination.

Economies of Scale Versus the Learning Curve

As Besank et al note, there is a difference between economies of scale and the learning curve (81). With the learning curve, a company can do something at a less expensive unit cost after time, where economies of scale are lower costs the more you do.

            These two concepts are independent of each other. Whether one is more important than the other seems to rely on the level of complexity within the task of a company. If something is simple, but takes a lot of capital to be done, economies of scale will take place. An example of a simple, capital-intensive business would be the operation of a strip mine. Once a business has the plot of land in which it wants to mine, all it needs is more equipment and relatively easily trained operators for that equipment. The more equipment, the more the company can carry out of the mine, and the cheaper each pound of rock is to take out.

            Conversely, throwing capital at different problems is not always the answer. Sometimes human capital is the most important element of a business. Often a service provider can be an example, but it exists in skilled manufacturing. For example, some of the most expensive watches in the world are still made by hand in Switzerland. These watches are miracles of the jeweler’s craft, even if the best made watch will never keep time as well as a twenty-dollar digital watch. The companies that make these watches position their wares as luxury goods, so that they are not in the same market as the cheap digital timepiece. They are made at a small scale for discerning buyers. Here is where the learning curve is important. Well-trained watchmakers will be able to make more watches faster and with higher accuracy. This allows a company that employs these artisans to have lower costs per unit. It also discourages new entrants to the market, keeping the retail value of the watches they sale inflated.

            With the two examples, it becomes possible to say that both the learning curve and economies of scare are important to the financial health of companies. The important difference is the human factor between which one will be more important.

          References

Besanko, D., Dranove, D., Shanley, M., & Schaefer, S. (2013). Economics of Strategy (6th ed.). New York: Wiley.

Merger Happy: Lessons of ExxonMobil

            Standard Oil, Rockefeller’s oil conglomerate was broken up in 1911, into 34 separate companies (“About Us”). Two of those companies were the forerunners to the companies that came to be known as Exxon and Mobile. After almost a century apart, they joined back together on November 30, 1999 after over 18 months of talks (“About Us”). The merger did not happen in a vacuum. The head of the Bureau of competition at the Federal Trade Commission at the the time noted that there were several other contemporary mergers: “In recent months, we have seen the merger of BP and Amoco - which was the largest industrial merger in history until Exxon/Mobil was announced --and the combination of the refining and marketing businesses of Shell, Texaco and Star Enterprises to create the largest refining and marketing company in the United States” (“Remarks”).

Exxon was the larger of the companies in a merger worth $75.3 billion dollars, larger again by almost half of the merger between BP and Amoco (“12 Years Later”). Both companies had full control of their revenue streams, from extraction to refining to retail sales, though they often had partnerships. This merger of equals on a horizontal basis allowed them to create corporate synergies totaling up to $3.8 billion in pretax savings (“12 Years Later”)

The merger was not without its critics. Public Citizen, and advocacy group, put out a list of the things to worry about with the merger, including “If Exxon-Mobil were a nation it would have the 18th largest economy in the world larger than Denmark, Finland, Austria, and Greece,” and “Exxon-Mobil, with more than 50 refineries in a dozen countries, will be the most powerful oil refiner in the world. This position will allow Exxon-Mobil to shift production to the cheapest, most worker-unfriendly environment” (“10 Facts”).

The company countered that the market had changed. By their measure, the Standard Oil giant had over 80% of the market for oil, whereas a combination of Exxon and Mobile would only control 11% of the market (“Exxon, Mobile Divestures”).  In the end, the regulatory bodies were worried about monopoly conditions one the retail side, especially in the north east, where both companies had been originally based after the split up of Standard Oil. To get approval from the FTC, they had to sell 1800 gas stations to outside firms (“Deal Nears OK)”.

The result for consumers is hard to ferret out. The average gas price for consumers the month the merger was announced was $0.873 a gallon. A year later, it had risen to $1.124 nationwide (“U.S. Total Gasoline”). It is hard to tell how much of that increase was from the merger of the two companies and how much from other contemporary economic effects. Since gas prices eventually came back close to the pre-merge low before taking off based on large geopolitical issues, it looks as if there was little overall consumer effect in the long run. As for shareholders, it looks as if the merger was a wash. For the past fifteen years, the total return of the XOM stock has moved in tandem with an index of other oil company stocks, but has beat the S&P 500 over that time by 2.75% (Morningstar). If there had been true efficiencies to work out through scale, it would be expected that the value of the combined companies would surpass an index of comparable publically traded companies.  That there was little effect overall for consumers shows that the merger was not necessary, but it did not hurt them. Perhaps the FTC-mandated divesture was enough to make the long-run monopoly concerns not an issue. That there were not greater gains to scale in terms of returns in the equities market should show future managers that merging may bring headlines, but not necessarily growth.

References

Baer, William J.. (1999). Statement of the Federal Trade Commission. Federal Trade Commission. Retrieved from http://www.ftc.gov/sites/default/files/documents/public_statements/prepared-statement-federal-trade-commission-exxon/mobil-merger/exxonmobiltestimony.pdf

CNN Money (1999, November 23). Exxon-Mobil deal nears OK. CNN Money. Retrieved from http://money.cnn.com/1999/11/23/deals/exxon/

Corcoran, Gregory (2010, November 30). Exxon-Mobil 12 Years Later: Archetype of a Successful Deal. Wall Street Journal. Retrieved From http://blogs.wsj.com/deals/2010/11/30/exxon-mobil-12-years-later-archetype-of-a-successful-deal/

ExxonMobil. (2015). Our History. ExxonMobil.  Retrieved from http://corporate.exxonmobil.com/en/company/about-us/history/overview

Morningstar. (2015). Exxon Mobil Corporation XOM . Morningstar. Retrieved from http://performance.morningstar.com/stock/performance-return.action?t=XOM&region=usa&culture=en-US

Public Citizen. (2015). 10 Facts About the Exxon-Mobil Merger. Public Citizen. Retrieved From http://www.citizen.org/cmep/article_redirect.cfm?ID=6307

U. S. Enegry Information Administration. (2015, February 2). Petroleum & Other Liquids. UEIA. Retrieved from http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=EMA_EPM0_PTC_NUS_DPG&f=M

Wilke, J. and Liesman, S. (1999, January 20). Exxon, Mobil Divestitures Are Seen To Obtain U.S. Approval of Merger. Wall Street Journal. Retrieved from http://www.wsj.com/articles/SB916791504585647500

Levels of Control in Data Gathering

In scientific data gathering, there are three different ways to find the data needed to look at the relationships being studied. These three methods will tell you different things, and have their own strengths and weaknesses. The three methods are the experimental, the quasi experimental, and the correlation method.

In the experimental method, the scientist is trying to isolate causation from a single variable.  To see if a particular variable has a consequence the scientist must hold all other variable to be the same and only change the one thing that they are studying. To make sure that the experiment is being done correctly, the choice of who is exposed to the independent variable must be at random (Salkind 2014 p. 8). For example, say that the scientist thinks that college men wearing a baseball cap will be able to run faster. The scientist will then distribute baseball caps to the study group and then measure the running speeds of all the participants. If the study finds that the average speed of the baseball cap wears was in fact faster than the average speed of the non-cap wearers, then the hypothesis is confirmed.

Not all variables are as easily tested as the question of speed of college-age men and baseball cap wearing. Sometimes what is to be measured is not so easy to control.  If the experiment designer cannot pick who receives a variable, then there is a measure of control lost. This becomes part of a quasi-experimental method (Salkind 2014 p. 9). An example where a quasi-experimental method would be used is one where a scientist was curious at who was better at chess, all other things being equal, left-handed people or right-handed people. Since nature has already chosen who will be left-handed and who will be right handed, the element of randomness has been taken away from the experimenter. The ultimate results of the quasi-experiment may be less certain than a strictly experimental method because there may be other variables that the left-handers possess other than their dominate hand that may skew the results.

The final method for looking at relationships between two variables is the correlational method. In this method, there is no experiment run, but the scientist looks at two sets of data to see if there is a relationship between them. Does one go up while at the same time the other goes down? Alternatively, do they move in tandem together? If they do either one, then the indicators are said to be correlated. The problem with looking for correlation is that scientist cannot tell if there is a direct causal relationship (Salkind 2014 p. 10). Say a scientist can look at the sales of Happy Meals in America as well as the average weight of American children. If the scientist sees that both variables increased over the same time, then a correlation can be said to exist. The issue is that there is no way to say directly what caused what. Did children gain weight because they were eating too many Happy Meals, or did already-obese children demand more Happy Meals?

The overall result is that the more control a scientist has over the independent variables that they are studying, the more certain they can be with the validity of their results. In the use of data, more control is the desired starting point, but it may not always be possible to attain. That is why the other options exist.

Bertrand Russell Deserves a Seat at Your Table: On Sceptical Essays

I grabbed this book because it was in the Journal’s recommend books for year-end last year. I had read his “Why I am not a Christian,” and was aware of Russell as a philosopher and mathematician. I did not know he was such a clear writer. I have to respect a free  thinking, socialist, atheist from 100 years ago who was not afraid to follow the strength of his convictions even though they led him against the grain. He lost potential jobs, and went to jail for his beliefs. Maybe he was never in any real danger, but I don’t know – still brave.

Reading this book made me think of that hypothetical situation where you can have a dinner with anyone you want, living or dead. I think I’d have Russell at my table. His writing, reading it now, sounds contemporary.  These essays, for the most part, would not be out of place in current conversation. I say for the most part, because there are a couple that strike wrong notes. One essentializes all “Chinese,” the other talks about the benefits of behavorialism and is perhaps too enthuastical about the problems that science could solve. Other than that, I liked all the essays. In fact, I liked them so much that it is hard to point out what was good. I normally read with a pen so I can take notes and engage with the text, but I couldn’t with this book. It just had narrative and argumentative momentum that I couldn’t dent. I instead dog-eared the pages where there was a striking turn of phrase of interesting way of looking at a subject that I hadn’t previously considered. By the end of the book, my wife remarked at just how many dog-ears were in the book. I can’t summarize it here and give it justices. You need to read Russell to appreciate him. I’m just a shadow on the cave wall.