Figure 1.2 Ed Adelson Checkerboard Illusion Answer
But what about experts? Surely, experts are beyond the grip of such cognitive bias? We often assume that professionals with years of experience and expertise in a particular field are better equipped and incentivized to make unbiased decisions. Unfortunately for experts, and for those who rely on them, the academic evidence is unequivocal: systematic decision-making, which relies on models, outperforms discretionary decision-making, or experts. We will come back to this point in a moment, but first let's discuss some other reasons experts might not always provide flawless advice.
When paying a financial expert to manage your money, a good question to ask is the following: What are the experts' incentives? This is important to know, because even if the expert has true knowledge about financial markets, misaligned incentives can destroy an edge the expert has, or make the expert look better than he really is. Here are a few examples of when experts' incentives might not be properly aligned:
• Focusing on short-term vs. long-term results. Consider a financial expert creating a value strategy with an assumed “edge,” or ability to beat the market in the long run. This expert can decide to invest in 200 of her best stock ideas or 50 of her best stock ideas. The expert faces a trade-off between these two approaches. On one hand, the expert knows that, over the long-haul, buying the cheapest 50 value stocks will be a better risk-adjusted bet than the 200-stock portfolio, since the larger portfolio would be dilutive to performance in the long run. On the other hand, the expert also understands that the 50-stock portfolio has a higher chance of losing to a standard benchmark in a given year, which will could cause her to lose clients in that year. The expert, who assumes, correctly, that most investors focus on short-term results, will opt for a 200-stock portfolio in order to minimize downside risk (and retain clients), and thus, will create a suboptimal product that doesn't fully leverage her expertise. In effect, the expert is indeed an expert, but there is an incentive alignment problem between the expert and investors that negates the benefits of her expertise.
• Exploiting authority to generate business. Let's say we have two financial experts. One expert shows up in a pair of jeans and a sweatshirt and states that simply investing in the S&P 500 from 1927 to 2013 has a return of 9.91 percent on average. The second expert shows up in an Armani suit, with his research team of PhDs (also in suits) behind him, and tells you that with his investment technique, $100 would have grown to $371,452 from 1927 to 2013. “Wow,” you would say, and then ask, “So what are the details of the strategy?” Our straight-talking sweatshirt and jeans expert might say, “Well, you simply buy and hold the S&P 500 Index and reinvest dividends to achieve the 9.91 percent return.” However, our Armani-suited PhD squad may respond with the following: “Our strategy is proprietary, is built off of 30 years of research by 15 PhDs, and seeks to dynamically allocate to certain sectors of the market, with more weight going toward better-performing securities.” Sounds impressive, but the strategy is the same: Buy and hold the market! Sadly, that is the expert's power over the layman. If you are unable to fully interpret the advice of an expert, you may be beguiled by his overblown rhetoric masquerading as skill. Overall, an investor needs to be aware of experts' incentives to leverage their position of authority. If an expert cannot explain his strategy to you in a simple, understandable way, we recommend walking in the other direction.
• Favoring complexity over simplicity. All else equal, a financial expert prefers a more complex model to a simple one. Why? Because complex models allow them to charge higher fees! As we will show later in the book, simple models beat complex models, and they certainly beat human experts. Why would an expert, many of whom are informed of this fact, recommend a complex solution other than for an increased fee? Consider two asset-allocation alternatives: The first option is an “optimized, time-varying, strategic allocation approach, based on years of research,” whereas, the second option is a 50/50 split between stocks and bonds, buy and hold forever. Also consider that both approaches sell for a 1 percent management fee and you have to choose one of the options. Your instinct probably suggests the more advanced version. But why? What if the simpler option is actually superior to the more complex one?
Overall, there are some true experts in the field. We recommend focusing on those experts who have long-term goals, are transparent about their investment strategy, and have an ability to explain their approach in one sentence.
To be clear: We are not making the claim that human experts are worthless across all aspects of the decision-making process. Dentists are great at filling cavities, surgeons are quite handy at repairing ACLs, and the right financial advisor can protect us from making expensive mistakes. Experts are critical, but only for certain elements of the decision-making process. To better frame the decision-making problem, we break the decision-making process into three components (see Figure 1.3):
• Research and development (build systems)
• Systematic implementation (implement systems)
• Evidence-based assessment (assess systems)
Figure 1.3 The Decision-Making Process
We would argue that human experts are required for the first and third phases of a decision-making process, which are the research and development phase and the assessment phase, respectively. The crux of our argument is that human experts should not be involved in the second phase of decision-making, or the implementation phase.
During the research and development phase of decision-making, experts build and test new ideas. In this phase, experts are required to create a sensible model. In the second phase – implementation – one should eliminate human involvement and rely on systematic execution. Finally, during the assessment phase of decision-making, one should once again rely on human experts to analyze and assess model performance to make improvements and incorporate lessons learned from the implementation phase.
We look to the real world for insights into how this three-phase decision-making framework might be applied in practice. A great case study exists within the US Marine Corps (USMC), where Wes spent nearly four years as an officer deployed in a variety of combat situations. The USMC relies on “standard operating procedures,” or SOPs, particularly when Marines are in harm's way. SOPs are developed according to the three-step process mentioned above, which is designed to establish the most robust, effective, and systematic decision-making process possible.
One example is the SOP for setting up a defensive position in a combat situation.7 In the first phase of SOP development, experienced combat veterans and expert consultants review past data and lessons from the field to develop a set of rules that Marines will follow when establishing a defensive position. These rules are debated and agreed upon in an environment that emphasizes slow, deliberate, and critical thought. The current rules, or SOP, for a defensive position is summarized by the acronym SAFE:
• Security
• Automatic weapons on avenues of approach
• Fields of fire
• Entrenchment
During the second phase – the implementation phase – of SAFE, Marines in combat are directed to “follow the model,” or adhere to the SOP. The last thing a Marine should do is disregard SOPs in the middle of a firefight, when the environment is chaotic, Marines are tired, and human decisions are most prone to error. Marines are trained from the beginning to avoid “comfort-based”