Archive for November 2013
Perceived safety increases risk-taking
In many senses, life is a series of risk/reward calculations. Choosing which school to attend, buying a house, and choosing a spouse are all risky endeavors. According to the Peltzman effect, also known as risk compensation, people have a tendency to take greater risks when perceived safety increases.
I’m sure this conclusion comes as no surprise to you. Toddlers learning to walk soon start to run, or go down stairs, with the expected results. Teen drivers (particularly teen boys) get comfortable behind the wheel and dart off in a burst of testosterone, occasionally ending up in dire circumstances. This phenomenon was very common the Formula 2 racing series. Formula 2 is a development series for the global F1 competition, which is viewed as the pinnacle of motor racing. The problem is that the Formula 2 series was plagued with multiple accidents resulting from brash moves made by the young drivers. The reason? Analysts, including current F1 drivers, argued that Formula 2 racers were overly aggressive because their cars are so safe. Romain Grosjean, a Formula 2 driver who now competes for the Renault F1 team, was fined several times and sat out for an F1 race after being at fault in repeated incidents following his promotion.
Investors make similar risk/reward calculations. Wall Street investment bankers often take significant risks because their compensation schemes reward short-term success far more than they punish failure. Why would they take such risks? Because it’s part of their overall strategy. In the Wharton School’s corporate finance MOOC I’m taking on Coursera, Professor Franklin Allen argues that one’s sense of risk is inverted when you think of investing in a portfolio of stocks rather than in a single stock. For example, imagine that you buy stock in an oil company that finds oil in 1 out of 20 wells, and each producing well returns $100. You have a hit rate of 5% which, multiplied by the return of a good well, yields an expected value of $5. Now imagine that you have a separate investment in a research company that has a 1 in 50 chance of returning $250, otherwise gaining you nothing. This investment has a similar expected value to the previous example, because 2% (1 in 50) of $250 is $5.
Which of the two investments is less risky? If you look at the expected values, they’re equally risky. However, Professor Allen argues that, when considered as part of a portfolio, the latter investment is less risky because of its higher potential return. The crux of the argument is that a diversified portfolio with numerous independent risks will tend to have a higher return than a collection of pedestrian investments with relatively low risk. The end result is safety in numbers. Just as a fair coin flipped 1,000 times will tend to show heads in about 50% of the trials, investments with independent risks will tend to earn out at their expected rate, assuming you adjudged the risks correctly in the first place. Statistics on investment return since the year 1900 bear out his argument.
Improvisers can and should take risks to make great scenes. We can do it without fear because we know our fellow players will be there to make what we say and do the right thing. Similarly, businesses can take risks as part of a diversified portfolio of ideas. Just as you wouldn’t invest in a single stock such as, I don’t know…Enron, you shouldn’t discourage experimentation and risk. That said, you must understand that risks taken within a scene or business are dependent, not independent. There’s only so much we can do to fix things if you go too far overboard. If you can’t spread out your risk, you must moderate it to be successful.
Clustering and Streaks — Real or Imagined?
The folk wisdom that “bad things come in threes” is still popular in the U.S. Whenever two celebrities die on the same day, for example, even the most hardened critical thinker feels the urge to look for the third.
Is clustering real? Do events happen in streaks, or are they just a product of our pattern-seeking brains? George Carlin made fun of the “bad things happen in threes” adage by stating that bad things actually happen in 27’s, noting that “it just takes longer to see the pattern.” You can always find instances of “bad things” in the world to fill out your sets of three, but what does the research say? There have been a lot of studies on the subject, including Koehler and Conley’s “The “Hot Hand” Myth In Professional Basketball”, published in 2003 in the Journal of Sport and Exercise Psychology. The authors examined the National Basketball Association’s long distance shooting contest and looked for statistical aberrations in the sequences of made and missed shots. As in all but a few other studies, they found no significant deviation from chance. When they took each player’s base shooting accuracy into account, the effect disappeared.
Sports are physical contests and even little variations in physical conditions can affect performance, but what about chess? Chess is a mental game played with perfect information. That is, you know everything there is to know about a position and there’s no hidden information, such as a player’s hole cards in poker. As of this writing, I have played 19,738 games of blitz chess (each player has 3 or 5 minutes to make all moves in a game) at the Internet Chess Club since June 27, 2001. As I watch my online chess rating fluctuate from embarrassing to “not bad for me”, I wonder how much the streaks of wins, losses, and draws reflect my abilities and how much is the “luck” of an opponent making some horrible mistake.
The three-year graph of my rating shows huge swings, but the average is right about where I perceive myself as a player. Perhaps my streaks are due to luck. After all, I don’t seriously study the game and play to take a break from other work. The big changes make a strong visual impression, but there are a lot of small shifts in there, too.
Improvisers can make a fun game out of looking for apparent patterns and justifying reasons for believing streaks exist. The lesson for analysts? Carefully examine whether a sequence of events is due to some underlying cause or is just a sequence of events that might be due to chance. That said, given the strength of our innate need to discover patterns, is there any way to dispel what appears to be the myth of the hot hand? In a 2006 review of the literature, Michael Bar-Elia, Simcha Avugosa, and Markus Raab summarized the situation in this way:
As Amos Tversky, who initiated the hot hand research, used to say (cited by Gilovich in an online chat, September, 2002), ‘‘I’ve been in a thousand arguments over this topic, won them all, but convinced no one’’.