This article is a collaboration between Mark Steed and myself. He did most of the work. Mark was a participant at the last Re:Think Decision Making event as well as a member of the Good Judgment Project. I asked him to put together something on making better predictions. This is the result.
We all face decisions. Sometimes we think hard about a specific decision, other times, we make decisions without thinking. If you’ve studied the genre you’ve probably read Taleb, Tversky, Kahneman, Gladwell, Ariely, Munger, Tetlock, Mauboussin and/or Thaler. These pioneers write a lot about “rationality” and “biases”.
Rationality dictates the selection of the best choice among however many options. Biases of a cognitive or emotional nature creep in and are capable of preventing the identification of the “rational” choice. These biases can exist in our DNA or can be formed through life experiences. The mentioned authors consider biases extensively, and, lucky for us, their writings are eye-opening and entertaining.
Rather than rehash what brighter minds have discussed, I’ll focus on practical ideas that have helped me make better decisions. I think of this as a list of “lessons learned (so far)” from my work in asset management and as a forecaster for the Good Judgment Project. I’ve held back on submitting this given the breadth and depth of the FS readers, but, rather than expect perfection, I wanted to put something on the table because I suspect many of you have useful ideas that will help move the conversation forward.
1. This is a messy business. Studying decision science can easily motivate self-loathing. There are over one-hundred cognitive biases that might prevent us from making calculated and “rational” decisions. What, you can’t create a decision tree with 124 decision nodes, complete with assorted probabilities in split seconds? I asked around, and it turns out, not many people can. Since there is no way to eliminate all the potential cognitive biases and I don’t possess the mental faculties of Dr. Spock or C-3PO, I might as well live with the fact that some decisions will be more elegant than others.
2. We live and work in dynamic environments. Dynamic environments adapt. The opposite of dynamic environments are static environments. Financial markets, geopolitical events, team sports, etc. are examples of dynamic “environments” because relationships between agents evolve and problems are often unpredictable. Changes from one period are conditional on what happened the previous period. Casinos are more representative of static environments. Not casinos necessarily, but the games inside. If you play Roulette, your odds of winning are always the same and it doesn’t matter what happened the previous turn.
3. Good explanatory models are not necessarily good predictive models. Dynamic environments have a habit of desecrating rigid models. While blindly following an elegant model may be ill-advised, strong explanatory models are excellent guideposts when paired with sound judgment and intuition. Just as I’m not comfortable with the automatic pilot flying a plane without a human in the cockpit, I’m also not comfortable with a human flying a plane without the help of technology. It has been said before, people make models better and models make people better.
4. Instinct is not always irrational. The rule of thumb, otherwise known as heuristics, provide better results than more complicated analytical techniques. Gerd Gigerenzer, is the thought leader and his book Risk Savvy: How to Make Good Decisions is worth reading. Most literature despises heuristics, but he asserts intuition proves superior because optimization is sometimes mathematically impossible or exposed to sampling error. He often uses the example of Harry Markowitz, who won a Nobel Prize in Economics in 1990 for his work on Modern Portfolio Theory. Markowitz discovered a method for determining the “optimal” mix of assets. However, Markowitz himself did not follow his Nobel prize-winning mean-variance theory but instead used a 1/N heuristic by spreading his dollars equally across N number of investments. He concluded that his 1/N strategy would perform better than a mean-optimization application unless the mean-optimization model had 500 years to compete. Our intuition is more likely to be accurate if it is preceded by rigorous analysis and introspection. And simple rules are more effective at communicating winning strategies in complex environments. When coaching a child’s soccer team, it is far easier teaching a few basic principles, than articulating the nuances of every possible situation.
5. Decisions are not evaluated in ways that help us reduce mistakes in the future. Our tendency is to only critique decisions where the desired outcome was not achieved while uncritically accepting positive outcomes even if luck, or another factor, produced the desired result. At the end of the day I understand all we care about are results, but good processes are more indicative of future success than good results.
6. Success is ill-defined. In some cases this is relatively straightforward. If the outcome is binary, either it did, or did not happen, success is easy to identify. But this is more difficult in situations where the outcome can take a range of potential values, or when individuals differ on what the values should be.
7. We should care a lot more about calibration. Confidence, not just a decision, should be recorded (and to be clear, decisions should be recorded). Next time you have a major decision, ask yourself how confident you are that the desired outcome will be achieved. Are you 50% confident? 90%? Write it down. This helps with calibration. For all decisions in which you are 50% confident, half should be successes. And you should be right nine out of ten times for all decisions in which you are 90% confident. If you are 100% confident, you should never be wrong. If you don’t know anything about a specific subject then you should be no more confident than a coin flip. It’s amazing how we will assign high confidence to an event we know nothing about. Turns out this idea is pretty helpful. Let’s say someone brings an idea to you and you know nothing about it. Your default should be 50/50; you might as well flip a coin. Then you just need to worry about the costs/payouts.
8. Probabilities are one thing, payouts are another. You might feel 50/50 about your chances but you need to know your payouts if you are right. This is where the expected value comes in handy. It’s the probability of being right multiplied by the payout if you are right, plus the probability of being wrong multiplied by the cost. E= .50(x) + .50(y). Say someone on your team has an idea for a project and you decided there is a 50% chance that it succeeds and, if it does, you double your money, if it doesn’t, you lose what you invested. If the project required $10mm, then the expected outcome is calculated as .50*20 + .50*0 = 10, or $10mm. If you repeat this process a number of times, approving only projects with a 2:1 payout and 50% probability of success you would likely end up with the same amount you started with. Binary outcomes that have a 50/50 probability should have a double-or-nothing payout. This is even more helpful given #7 above. If you were tracking this employee’s calibration you would have a sense as to whether their forecasts are accurate. As a team member or manager, you would want to know if a specific employee is 90% confident all the time but only 50% accurate. More importantly, you would want to know if a certain team member is usually right when they express 90% or 100% confidence. Use a Brier Score to track colleagues but provide an environment to encourage discussion and openness.
9. We really are overconfident. Starting from the assumption that we are probably only 50% accurate is not a bad idea. Phil Tetlock, a professor at UPenn, Team Leader for the Good Judgment Project and author of Expert Political Judgment: How Good Is It? How Can We Know?, suggested political pundits are about 53% accurate regarding political forecasts while CXO Advisory tracks investment gurus and finds they are, in aggregate, about 48% accurate. These are experts making predictions about their core area of expertise. Consider the rate of divorce in the U.S., currently around 40%-50%, as additional evidence that sometimes we don’t know as much as we think. Experts are helpful in explaining a specific discipline but they are less helpful in dynamic environments. If you need something fixed, like a car, a clock or an appliance then experts can be very helpful. Same for tax and accounting advice. It’s not because this stuff is simple, it’s because the environment is static.
10. Improving estimations of probabilities and payouts is about polishing our 1) subject matter expertise and 2) cognitive processing abilities. Learning more about a given subject reduces uncertainty and allows us to move from the lazy 50/50 forecast. Say you travel to Arizona and get stung by a scorpion. Rather than assume a 50% probability of death you can do a quick internet search and learn no one has died from a scorpion bite in Arizona since the 1960s. Overly simplistic, but, you get the picture. Second, data needs to be interpreted in a cogent way. Let’s say you work in asset management and one of your portfolio managers has made three investments that returned -5%, -12% and 22%. What can you say about the manager (other than two of the three investments lost money)? Does the information allow you to claim the portfolio manager is a bad manager? Does the information allow you to claim you can confidently predict his/her average rate of return? Unless you’ve had some statistics, it might not be entirely clear what clinical conclusions you can draw. What if you flipped a coin three times and came up with tails on two of them? That wouldn’t seem so strange. Two-thirds is the same as 66%. If you tossed the coin one-hundred times and got 66 tails, that would be a little more interesting. The more observations, the higher our confidence should be. A 95% confidence interval for the portfolio manager’s average return would be a range between -43% and 45%. Is that enough to take action?
11. Bayesian analysis is more useful than we think. Bayesian updating helps direct given false/true positives and false/true negatives. It’s the probability of a hypothesis given some observed data. For example, what’s the likelihood of X (this new hire will place in the top 10% of the firm) given Y (they graduated from an Ivy League school)? A certain percentage of employees are top-performing employees, some Ivy League grads will be top-performers (others not) and some non-Ivy League grads will be top-performers (others not). If I’m staring at a random employee trying to guess whether they are a top-performing employee all I have are the starting odds, and, if only the top 10% qualify, I know my chances are 1 in 10. But I can update my odds if supplied information regarding their education. Here’s another example. What is the likelihood a project will be successful (X) given it missed one of the first two milestones (Y)?. There are lots of helpful resources online if you want to learn more but think of it this way (hat tip to Kalid Azad at Better Explained); original odds x the evidence adjustment = your new odds. The actual equation is more complicated but that is the intuition behind it. Bayesian analysis has its naysayers. In the examples provided, the prior odds of success are known, or could easily be obtained, but this isn’t always true. Most of the time subjective prior probabilities are required and this type of tomfoolery is generally discouraged. There are ways around that, but no time to explain it here.
12. A word about crowds. Is there a wisdom of crowds? Some say yes, others say no. My view is that crowds can be very useful if individual members of the crowd are able to vote independently or if the environment is such that there are few repercussions for voicing disagreement. Otherwise, I think signaling effects from seeing how others are “voting” is too much evolutionary force to overcome with sheer rational willpower. Our earliest ancestors ran when the rest of the tribe ran. Not doing so might have resulted in an untimely demise.
13. Analyze your own motives. Jonathan Haidt, author of The Righteous Mind: Why Good People Are Divided by Politics and Religion, is credited with teaching that logic isn’t used to find truth, it’s used to win arguments. Logic may not be the only source of truth (and I have no basis for that claim). Keep this in mind as it has to do with the role of intuition in decision making.
Just a few closing thoughts.
We are pretty hard on ourselves. My process is to make the best decisions I can, realizing not all of them will be optimal. I have a method to track my decisions and to score how accurate I am. Sometimes I use heuristics, but I try to keep those to within my area of competency, as Munger says. I don’t do lists of pros and cons because I feel like I’m just trying to convince myself, either way.
If I have to make a big decision, in an unfamiliar area, I try to learn as much as I can about the issue on my own and from experts, assess how much randomness could be present, formulate my thesis, look for contradictory information, try and build downside protection (risking as little as possible) and watch for signals that may indicate a likely outcome. Many of my decisions have not worked out, but most of them have. As the world changes, so will my process, and I look forward to that.
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