Alan Turing, one of the most brilliant minds to have graced the earth, devised a test to determine whether or not a machine possessed intelligence. This test became known as the Turing Test.
A pair of researchers have developed a similar test to determine whether humans can tell the difference between actual financial market returns and randomly generated data:
[Jasmina Hasanhodzic and Andew Lo] have devised a simple experiment.
They have created a computer game in which a player is shown two time-series of data. One is real data from a financial market such as the US Dollar Index, or the spot price of Gold. The other is the same data randomly rearranged. The player has to guess which is the real series and is immediately told whether the guess is right or wrong.
They found that very quickly subjects were able to determine what was randomly generated data and what was actual data. Actual data was much smoother and therefore easy to spot against the bumpy, randomized data.
It is an interesting experiment, but it doesn’t break any ground in terms of human cognition. We’ve long known that humans seem hardwired to quickly visualize patterns, likely for evolutionary reasons since it is advantageous to quickly recognize predators. We’ve all experienced the sensation of effortlessly seeing faces or objects in clouds, for example. This experiment just confirms the notion.
What we see in reality are millions of corpses of businesses and ideas that have made their impact (or not) and then petered out into oblivion without leaving much more than a memory. Some of them get bought and swallowed by a bigger company, others have their ideas copied and commodotized and many just don’t have the business or financial chops to make it all work for more than a few years.
So what if instead of worrying about all that you just decided at the beginning you were going to end it all six years in?
I love questions like this, and Noah is great at asking them. He suggests that such an arrangement may solve the problems that arise when management sacrifices the long-term interests of the company for the short-term, making decisions that optimize their current job security but may create problems for the firm down the road:
Company management doesn’t know how long the company will last, so they optimize for the now (they also don’t know how long their jobs will last, but I’ll get to that in a minute). It may be overly hopeful, but as long as one choose a reasonable time-frame (5-10 years) I wonder if you couldn’t lift the decision-making out of the immediate.
It is an interesting idea, but I think that what Noah is mostly interested in here is a shift in how employment is structured (i.e. knowing up front when one’s job will terminate), rather than how businesses as a whole are set up. (If the business will shutter its doors in 10 years what precisely are the long-term interests of the firm?) Additionally, he focuses more on the employment issue towards the end of the post. In either case, I think that on the whole the uncertainty that exists in terms of business and employment termination is superior to expiration dates. Here’s why: If businesses were set up at the outset with a planned time frame at the end of which the business would wind down it would likely play havoc with their ability to compete in the marketplace. Additionally, the beneficial economic conditions that obtain through competition would be warped. Why? Because businesses would not have to operate in the shadow of the future. Continue reading
Tim Hartford (whose blog at FT.com you really must read) discusses the results of a recent survey that suggest the answer is yes:
A recent survey by Yoram Bauman and Elaina Rose, two economists from the University of Washington, explains that in experiments, economics students are less generous, more likely to choose an unco-operative approach and more likely to accept bribes.
Bauman and Rose’s survey built upon an earlier study 30 years ago which demonstrated that “postgraduate students of economics were more likely than others to “free ride” in a laboratory game, effectively exploiting other players for their own benefit.”
I tend to agree with Hartford’s supposition that what is really going on here is that economists–as well as political scientists and sociologists–are simply choosing optimal strategies based on the game theoretic models upon which the laboratory experiments are based. Cooperation is not inherently a good strategy, but rather one that is determined by the structure of a game or experiment (e.g. what is the payoff structure of particular combinations of choices, is the game a one-shot deal or is it iterated, etc). Social scientists are trained in, and therefore comfortable with, game theory and the various structures and payoffs that exist. It is reasonable then to assume that if placed in an experiment that mimics those structures and payoffs they are more likely to play the most dominant strategies. Continue reading
Investors are sometimes accused of treating the stock market like a casino. Now, one Wall Street firm wants to treat casinos like the stock market.
Bond-trading specialist Cantor Fitzgerald in March took over the management of sports betting at the M Resort, a new 390-room hotel and casino on the Strip’s southern edge.
“We wanted to turn gamblers into traders,” says Lee Amaitis, the 60-year-old Cantor executive who runs the gambling division, Cantor Gaming.
To do that, the company has transformed Las Vegas sports betting into something it thinks is akin to derivatives trading. By using financial-markets technology, Cantor allows bettors to wager not only on who might win the game or by how much, but also on whether a team can complete its next pass or make a field goal.
Essentially, in-game betting of this nature is not new. However, it is not a robust practice here in the United States. In fact, Cantor is seemingly the only firm that allows for such bets at this time (the practice is apparently common in Europe). What is interesting is that such an approach allows gamblers (or what are essentially traders) to effectively hedge their bets (investments): Continue reading
Sunday’s Wall Street Journal reported that Amazon has stopped selling Kindle versions of all Macmillan titles. John Sargent, Macmillian’s CEO, recently went to Amazon’s headquarters to try and negotiate new terms for the sale of e-books published by his company. In general, the publishing industry has been unhappy with Amazon’s insistence that most books be priced at $9.99. Apparently, the discussions resulted in Amazon pulling all Macmillan e-books from it’s website.
This video has been making the rounds recently. One doesn’t immediately think of rap and macroeconomic theory together, but the video does a really good job of boiling down the two dominant schools of thought and what their disagreements primarily revolve around. There are some really smart touches in the video (e.g. Hayek peering into his hotel room’s nightstand and seeing a copy of Keynes’ General Theory)–it’s well worth the 7 minutes it takes to view it.
The Limits of Innovation: “And the last argument for the limits of innovation has to do with human nature. Why we choose to adopt things is not a logical process, and is fueled by culture, psychology, timing, and a dozen factors, many which have little to do with new idea X being better than old idea Y in technological or design terms. Those are terms technologists and designers obsess about, despite history’s strong suggestion that those factors are overestimated in their role for what becomes dominant, and when.”
Ant Colonies are Super-Organisms: “There are economies of scale within a single organism but not across.Except with ant colonies. The mass to energy ratio of the colony as a whole follows the same law that governs indivduals of non-colony animals.”
Prediction Without Markets: “In a new study, Daniel Reeves, Duncan Watts, Dave Pennock and I compare the performance of prediction markets to conventional means of forecasting, namely polls and statistical models. Examining thousands of sporting and movie events, we find that the relative advantage of prediction markets is remarkably small.” Jeff at Cheap Talk offers a methodological critique of the study.
A Little Less Conversation: “When you have a team of one person, you have no communication requirements. None. Add a second person, and now you have a single connection: Adam and Mary have to talk to each other once in a while. Now add a third person, say, Srinivas, and suddenly we’ve gone from one connection to three, since Srinivas has to talk to Adam and Mary. Add a fourth person. I’m running out of names here to help me out — OK: Britney. If we add her, and she needs to coordinate with all of them, you get six connections. For the mathematically inclined, the formula is that if you have n people on your team, there are (n2-n)/2 connections. In 2006, Moishe Lettvin, a former programmer at Microsoft, wrote a blog post describing the year he spent coordinating the list of items that would be featured on one menu in Windows Vista — the menu you use to turn off your computer. Lettvin figured that 43 people all had a voice in designing this one menu. Forty-three! By Brooks’s formula, that means managing 903 connections. Lettvin says he spent so much time on coordination tasks that, in 12 months, he produced fewer than 200 lines of code.”
Over lunch the other day, Noah mentioned this really interesting product that was announced at CES. It’s called Airnergy and it somehow harvests the energy emitted by nearby WiFi signals and converts it into electricity that can be used to power and recharge various devices.
It reminded me of a concept I’ve been toying with for a while–free-rider businesses. Mancur Olson discussed the problem of free-riders and the incentives that produced them in his classic The Logic of Collective Action. Large groups have trouble creating public goods since each individual has an incentive to free ride on the efforts of others (since the good is non-excludable, meaning there isn’t a practical way to prevent specific individuals from benefiting from the public good once it’s produced). In many instances there will be individuals or small groups that have a large enough incentive to create a particular good, thereby creating the opportunity for their work to be exploited by free-riders. My thought was that there are some business that are free-riders, or at least benefit from this practice. Free-rider businesses are those that develop a product or service that relies in some significant way on the physical resources or creative content produced by others without providing compensation to those third parties or having helped create the resource or content themselves. In most cases, the third party resources serve as a critical input (sometimes even the main content) for a firm’s key product or service. The most obvious example of this type of firm is Google. Continue reading
Joel Spolsky, an entrepreneur and columnist for Inc., wrote an interesting piece last month asking whether his strategy of slow, consistent growth was actually a recipe for failure:
I have always believed that there is a natural, organic rate at which a business should grow, and that if we expanded too fast, the wheels would come flying off. Then I came across a quote from Geoffrey Moore, who is best known for his best-selling book Crossing the Chasm, which is about how businesses cross over from their initial niche markets to dominate larger markets. In another book, called Inside the Tornado, Moore writes about the great battle between Oracle and Ingres in the early 1980s. The winner of that battle is well known: Oracle now has a market cap of more than $100 billion, and I’ll bet you’ve never heard of Ingres. Moore explains that “for pragmatist customers, the first freedom in a rapidly shifting market is order and security. That can only come from rallying around a clear market leader. Once the apparent leader-to-be emerges, pragmatists will support that company, virtually regardless of how arrogant, unresponsive, or overpriced it is.”
The reasons why breakneck growth may be preferable to steady, conservative growth can be summed up in two bullet points:
As noted above. network effects that attach to the market leader
Generation of revenue and capital that can be reinvested in the firm to improve systems, products, as well as increase advertising spend and bolster sales efforts
To be sure, these are logical arguments. However, I think to what extent the logic holds is dependent on a few factors, such as product and/or industry. Additionally, whether a high-growth strategy will be successful depends in large part on whether their is a plan at the outset that includes provisions for how to effectively manage the growth. Continue reading
A little post-Christmas cold has thrown a wrench in my plans to catch up on posting. In the meantime, be sure to check out the current series on Galleon and its founder, Raj Rajaratnam, in the Wall Street Journal. The Galleon case is the largest insider-trading case brought by Federal prosecutors in many years. Rajaratnam built a dizzying network of corporate informants who passed material nonpublic information to the hedge fund titan over the course of 20 years:
I love questions like this, and Noah is great at asking them. He suggests that such an arrangement may solve the problems that arise when management sacrifices the long-term interests of the company for the short-term, making decisions that optimize their current job security but may create problems for the firm down the road:
