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Think Twice: Harnessing the Power of Counterintuition Paperback – November 6, 2012
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Perhaps the best way to describe the content of the book is to summarize the key points, roughly in the order they appear in the book:
(1) "Think twice" to avoid errors in judgment and decision making, especially in situations where stakes are high.
(2) Learn from the experiences of others in similar situations (making use of statistics when possible), rather than relying only on your own perspective, and don't be excessively optimistic about expecting to beat the odds.
(3) Beware of anecdotal information, since it can paint a biased picture. Related to this point, don't infer patterns which don't exist, especially when the available data is limited, and avoid the bias of favoring evidence which supports your beliefs while ignoring contradictory evidence (deliberately seek dissenting opinions if necessary).
(4) Avoid making decisions while at an emotional extreme (stress, anger, fear, anxiety, greed, euphoria, grief, etc.).
(5) Beware of how incentives, situational pressures, and the way choices are presented may consciously or subconsciously affect behavior and shape decisions.
(6) In areas where the track record of "experts" is poor (eg, in dealing with complex systems), rely on "the wisdom of crowds" instead. Such crowds will generally perform better when their members are capable and genuinely diverse, and if dissent is tolerated (otherwise the crowd will be prone to groupthink).
(7) Use intuition where appropriate (eg, stable linear systems with clear feedback), but recognize its limitations otherwise (eg, when dealing with complex systems).
(8) Avoid overspecialization, aiming to have enough generalist background to draw on diverse sources of information.
(9) Make appropriate use of the power of information technology.
(10) Overcome inertia by asking "If we did not do this already, would we, knowing what we now know, go into it?"
(11) Because complex systems have emergent properties (the whole is more than the sum of the parts), avoid oversimplifying them with reductionistic models (simulation models are often helpful), remember that the behavior of components is affected by the context of the system, and beware of unintended consequences when manipulating such systems.
(12) Remember that correlation doesn't necessarily indicate causality.
(13) Remember that the behavior of some systems involves nonlinearities and thresholds (bifurcations, instabilities, phase transitions, etc.) which can result in a large quantitative change or a qualitative change in system behavior.
(14) When dealing with systems involving a high level of uncertainty, rather than betting on a particular outcome, consider the full range of possible outcomes, and employ strategies which mitigate downside risks while capturing upside potential.
(15) Because of uncertainties and heterogeneities, luck often plays a role in success or failure, so consider process as much as outcomes and don't overestimate the role of skill (or lack thereof). A useful test of how much difference skill makes in a particular situation is to ask how easy it is to lose on purpose.
(16) Remember that luck tends to even out over time, so expect outcomes to often "revert to the mean" (eventually move close to the average). But this isn't always the case, since outliers can also occur, especially when positive feedback processes are involved (eg, in systems in which components come to coordinate their behavior); in a business context, remember to make a good first impression.
(17) Make use of checklists to help ensure that important things aren't forgotten.
(18) To scrutinize decisions, perform a "premortem" examination. This involves assuming that your decision hasn't worked out, coming up with plausible explanations for the failure, and then revising the decision accordingly to improve the likelihood of a better outcome.
While this book doesn't really present any new material, I still found it to be a good resource, so I recommend it. After all, this subject matter is important and practical, yet also counterintuitive, so it makes sense to read many books to help these insights sink in and actually change one's habits.
The author has picked up many cognitive biases and woven a nice little story around them. To make the 'package' more attractive, he has also thrown in the tagline of the book being for 'investors', the implicit assertion being that we can make more money if we could eliminate some of the biases (as if we are already making millions in markets and could suddenly make billions!). I found nothing useful here for traders or investors (full disclaimer: I did not buy the book for that purpose, my sole interest was cognitive bias only). If you can separate the marketing hype and take out the 'trader/investor' framework, it is a mediocre book on cognitive biases at best, simply because it does not cover all of them, or even the most important ones. If you want some hard hitting stuff on biases, you are better off reading Kahneman, Tversky, et. al., directly. This book is a nice filler if heavy and comprehensive tomes are not your thing (though Kahneman happens to be very readable).
Ok, with the disclaimer done, some questions and possibilities.
1. There are too many implicit statements in the book, with the author neither making open statements, nor taking them to their logical conclusion. For instance, the episodes related to portfolio managers doing worse than the market are mentioned numerous times. Each time, it is implicitly stated that the investors would well to invest in index funds rather than go with portfolio managers. Hmmm...
One of the major themes on crowdsourcing posits that markets run up and become ripe for crashes as diversity gets eliminated from the market and all agents start conforming to the dominant view of the market. That issue has been known in the financial literature in many forms for decades. It is the reason behind once successful trading strategies becoming useless in addition to causing market cycles (in fact, the famed Eugene Fama paper on Efficient Market Hypothesis hinges on this very feature, you cannot have a lasting advantage or a trading strategy in the market). The "Index Fund Strategy" also has to be viewed in this light. After all, Index Funds do not invest in 10,000 assets. Most leading indices comprise of 30, 40, 50 or 100 stocks or securities at best. If everyone starts to follow and invest in these indices only, diversity would get knocked out of this decision. And very rapidly too. Logically, stock pickers who were investing outside the indices would do better than indices. All of a sudden, index funds will start looking bad and money managers will start looking good, for some time...
So am not so sure what the blinding insight here is. That investors should start investing in index funds? That would start a different cycle, at least some money managers will start outperforming indices significantly. Go with the money managers, index funds will get better. It is a forever oscillating system.
More than that, different chapters conflate a lot of concepts. For example, a baseball team owner venting on the team because it loses 8 out of its first 12 games... The author argues that the owner was wrong because there WAS real skill involved. Why wouldn't the same thing apply to the big pension funds firing their money managers because they had a bad year or quarter? Simply pointing out that the fired folks suddenly outperformed the ones who replaced them proves neither mean reversion nor lack of skill.
This conflation continues throughout the book, though I am not complaining about it. In fact, I quite enjoyed it because it put the same conflicting positions in juxtaposition as the incident of 'global oil supply debate' that the author has listed in the book.
2. Again, the idea of crowdsourcing has 'second order effect' questions that the book neither raises nor answers. We are seeing a situation where the crowd is better than experts, for the time being. I have no problem with computer based predictive systems being better than the experts. But there is a bit of a problem with overall crowdsourcing paradigm.
First and foremost, all the successful predictive market and crowd wisdom experiments have needed to be well controlled and well set up. You suddenly don't go asking people on the street what the price of crude oil six months later will be. As listed by the author, people need to have right kind of incentives, they need to know about the subject well enough and so on. Question: if crowdsourcing is so effective, why is a survey of 50 or 100 economists about economic indicators usually wrong and typically wide off the mark? If expertise is such a hindrance, then what level of familiarity with the subject is the right level for getting crowdsourcing right?
Next order question, of course, is the same as what tanked the likes of LTCM. If experts also typically end up creating crude mental models while predicting (simple extrapolation), wouldn't the same thing apply to all the participants? May be in a stable system (the world where LTCM's models predicted everything accurately, till the normal distributions worked), the crowd would be right. What about fundamental shifts in the market (what caused LTCM to fail and other such major turning points in financial history)? Will the crowds be able to effectively pick those? Not so sure again, as we simply don't have enough evidence. And I am not even touching upon the diversity issue here.
3. At some point, the author makes a really tired point about 'the market being more accurate than the individual trader or investor'. If you are a seasoned trader or investor, I am sure you would probably get a bemused chuckle at best. If individual biases do not add up, then why do we have bubbles at all? I know he has conveniently laid the blame at the door of market losing diversity. But if all or most of the participants in the collective are the same, just change their opinion (as it often happens in bubbles), at what point do you say the market has lost diversity? The collective argument simply means that the trend following systems of the '80s and the madness of the last decade were actually legitimate. The collective consciousness of the market WAS driving everything up after all. And that too for multiple years.
4. I think the financial crisis has been just a favorite horse to flog for too many writers. No harm in analyzing it one more time and earning some quick bucks. But for every Taleb who made money in the crisis, there are many who bet against the 'madness' in 2003, 2004 or 2005, and lived to the rue the day. And there have been plenty, just that those with the staying power eventually triumphed. There have been cassandras at all times, just that the crisis happened and the cassandras of the day (Roubini, et. al.) collected the accolades.
5. Laying all the blame for the last financial crisis at the doorstep of 'bad modelling' or 'cognitive biases' alone is probably too narrow a view anyway. Probably the biggest factor was the incentive built into the system. Unprecedented amount of liquidity pumped into the system without any apparent reason (why did Greenspan keep pumping money even when the global economy was on the boil is a question no one has an answer to), no financial oversight, individual incentives adding up to collective disaster (the author is right about this); these are probably bigger reasons for it than anything else. If your model is right and you are predicting disaster, but listening to you would force the corporation to forgo billions in profit for next few years and its bosses to lose millions in bonuses; there is only one logical outcome possible, you get fired. If you are lucky enough to be able to raise money and bet against the system, you may have the last laugh. Otherwise, you don't stand a chance. I don't think cognitive bias has a lot to do with it.
6. Coming back to the investor / trader premise, what about automated systems? Much of the trading on the exchanges today is carried out by automated systems. Occasional glitches do bring the problems to the surface. But how do you trade / invest in markets that pit sophisticated algorithms and computers against you? Market information may also be getting less transparent with dark pools, etc., emerging in a big way. How do these affect the collective wisdom of the market? If half the participants in the market are computers and algorithms, what shape would investor biases take then? Will the market stop having bubbles (since the computers will beat the biased humans in a big way, all of us humans will go bankrupt and there will be no bubble)? Again, I am not holding the author to it, as the book is not really addressing investing in any serious manner.
Okay, with the questions over, now let us turn to why I think this book is worth four stars. One, it is a thoroughly enjoyable book with exhaustive research, a great bibliography and good anecdotes interspersed. Two, the book may not delve too deep in the topics, but at least asks the right questions. If you stay with the thoughts and are willing to push the questioning further on your own, the book gives you enough material for doing that too. Three, there are some things that you can use in day to day decision making. Ideas like the collective, etc., are difficult to deploy without going through elaborate processes and large scale organizational buy-in. But the checklists at the end of the chapters are handy in case of some of the biases. Finally, the book is a nice and light read. I would prefer it any day over a fiction book for its sheer reading pleasure.
Overall, worth a read. Just don't expect it will help you make 'more money', and be happy with some research being quite dated.
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If one considers how to improve his decision making on any level, reading this will only help.
He will get you thinking and hopefully get you to slow your decision making to prevent long lasting errors.