Author Archives: Peter McCluskey

White Swans Painted Black

Nassim Taleb has an article related to the current financial crisis. While much of what he says is true, he misleads when he implies that the recent collapse of financial companies resulted from a Black Swan. He claims:

use of probabilistic methods for the estimation of risks did just blow up the banking system

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Randomised Controlled Trials of Parachutes

It is tempting to react to unscientific methods of medical practice by rejecting any treatment that isn’t supported by rigorous scientific evidence.  Here’s a parody of naive implementations of evidence-based medicine that demonstrates the pitfalls of doing so:
Smith GCS, Pell JP. (2003). Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials. BMJ, 327(7429), 1459-1461.

From the paper:

Results We were unable to identify any randomised controlled trials of parachute intervention.

Conclusions As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised the adoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute.

There are some interesting comments on the paper here and here.

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Biases and Investing

This Wired article about the Netflix prize provides an important hint about a valuable result of understanding human biases:

Couldn’t a pure statistician have also observed the inertia in the ratings? Of course. But there are infinitely many biases, patterns, and anomalies to fish for. And in almost every case, the number-cruncher wouldn’t turn up anything. A psychologist, however, can suggest to the statisticians where to point their high-powered mathematical instruments. "It cuts out dead ends,"

This approach applies to a wide variety of problems, including beating markets.
Not only is it important for investors to avoid dead ends in the sense of failing to find patterns, it’s important to distinguish patterns that are sustained by strong human biases from patterns that will vanish when a modest number of people figure out how to exploit them or patterns that are a byproduct of data that are not random samples from the space of all possible market behavior. Or as Coase is reported to have said, "if you torture the data enough, nature will always confess".

There are a variety of known strategies that seem to work even though many people are aware of them. Most seem to be sustained by some combination of Status Quo Bias, Endowment Effect, and Recency Bias, although the benefits of these strategies seem to diminish over time.

Also, since Robin’s advice to welcome diversity in analysis rather than beliefs seems too abstract for some, here’s how I think of putting it into practice when investing: focus on asking questions that few other people are asking. The more widely discussed a question is, the more likely it is that markets reflect the best answer to it. My best investments have been in companies that few people have heard of, often found by looking through more earnings reports than most investors would be willing to. And one infrequently mentioned result from the cognitive science literature has been more helpful in automating my search for underpriced companies than years of studying what other investors are doing.

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Truth Bias

The book Detecting Lies and Deceit by Aldert Vrij mentions evidence of a truth bias, i.e. people are more likely to correctly judge that a truthful statement is true than that a lie is false.

This appears to be a fairly robust result that is not just a function of truth being the correct guess where the evidence is weak – it shows up in controlled experiments where subjects have good reason not to assume truth (for example, this paper).  Vrij proposes several explanations for this bias.

  • The higher frequency of truthful statements in daily life make cause the availability heuristic to bias our judgment.
  • Politeness: there’s more social harm in daily life from mistakenly believing someone to be a liar or from asking someone to prove all claims than there is from mistakenly believing false claims.
  • People rely on stereotypes which are less accurate for liars than for truth-tellers [it’s unclear why this is a separate explanation from the first].

Since it’s unclear whether these effects make it in your interest to be suspicious, let’s also look at how being suspicious affects others. Increased suspicion may do a little harm to your friends by making them a bit more uncomfortable. But if it spreads, it should also improve political systems by making it a bit harder for people to get away with lies. This suggests that altruists ought to be more comfortable with friend who question their honesty in order to encourage social norms under which political choices are based on more accurate beliefs.

One concern I don’t know how to analyze is how this would affect the kind of social capital that Fukuyama talks about in his book Trust.

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Whose Framing?

Book review: Whose Freedom?: The Battle Over America’s Most Important Idea by George Lakoff

This book makes a few good points about what cognitive science tells us about differing concepts associated with the word freedom. But most of the book consists of attempts to explain his opponents’ world view that amount to defending his world view by stereotyping his opponents as simplistic.

Even when I agree that the people he’s criticizing are making mistakes due to framing errors, I find his analysis very implausible. E.g. he explains Bush’s rationalization of Iraqi deaths as "Those killed and maimed don’t count, since they are outside the war frame. Moreover, Bush has done nothing via direct causation to harm any Iraqis and so has not imposed on their freedom". Anyone who bothers to listen to Bush can see a much less stupid rationalization – Bush imagines we’re in a rerun of World War II, where the Forces of Evil have made it inevitable that some innocent people will die, and keeping U.S. hands clean will allow Evil to spread.

Lakoff’s insistence that his opponents are unable to understand indirect, systematic causation is ironic, since he shows no familiarity with most of the relevant science of complex effects of human action (e.g. economics, especially public choice economics).

He devotes only one sentence to what I regard as the biggest single difference between his worldview and his opponents’: his opponents believe in "Behavior as naturally governed by rewards and punishments."

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Should Prediction Markets be Charities?

Advocates of prediction markets often focus their attention on markets that can be run for profit. This may be due to a bias resulting from the fact the prediction markets mainly interest people who are trying to combat prejudices against profit-oriented activities.
Profits are often better measurements of whether something is valuable than the average person realizes, but there is little reason to believe this applies to public goods. And much of the motivation behind prediction markets advocacy is the need to make better information available to voters about the effects of policy choices. That information is about as clear an example of a public good as one can get.
An assumption that a for-profit corporation is the best way to produce a public good should normally be treated with some suspicion (although alternative types of institutions deserve suspicion as well).

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Macro Shares: Prediction Markets via Stock Exchanges?

Today the American Stock Exchange started trading the first pair of a new kind of security that works in roughly the way prediction markets could be designed if SEC regulation and a stock market style user interface turn out to be the best way to run prediction markets. They are based on Robert Shiller’s idea of macro securities, which he describes on pages 126-128 of his book The New Financial Order (and may describe more fully in his book Macro Markets, which I haven’t read).
This first pair of such securities created by Claymore MACROshares merely helps investors do through a stockbroker roughly what existing oil futures markets provide, but Claymore expects to add other securities that provide exposure to things that aren’t currently traded, and there are no obvious barriers to basing them on the ideas that prediction markets trade.
One drawback is that Claymore owns patents covering this approach.
While it seems unlikely that SEC regulation is what I would want for prediction markets, it is nice to see signs that such an option is available should CFTC regulations pose a bigger obstacle than I currently expect to prediction markets that resemble markets the CFTC regulates. Alas, it also increases the risk that stock exchanges will see prediction markets as competitors and lobby to create barriers.

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Asymmetric Paternalism

An article titled Regulation  for  Conservatives: Behavioral Economics and the Case for Asymmetric Paternalism provides a fairly good perspective on how paternalistic laws should be evaluated, but is a bit weak on the public choice considerations that should make us skeptical of laws. They provide good arguments showing that cognitive biases imply that a government run by angels ought to be sometimes paternalistic, because we can imagine a wide variety of laws which provide significant benefits to people who are acting irrationally while having much less effect on people who are acting irrationally. The examples in this paper show that’s it’s not hard to imagine laws like that appear to do this by mandating defaults that rational people can override, by requiring better disclosure, and by requiring delays for certain purchases. But the examples also show that it’s hard to tell whether a significant fraction of those laws are beneficial in practice.

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