“What You See Is All There Is.” This is a phrase Kahneman frequently uses in his book to describe System 1’s tendency to focus solely on the information at hand, failing to account for relevant information that might be missing. It attempts to construct a story with that information to make sense of it and judges its validity based on the coherence of that story rather than on the quality of the supporting evidence.
For example, consider a poor-performing mutual fund that replaces its manager with a star who has a long record of success running a different portfolio at the firm. Three years later, it is among the best-performing funds in its category. That story seems to support the obvious conclusion that the new manager turned things around. But there’s not enough information to know that.
System 1 doesn’t stop to consider alternatives that weren’t presented. For instance, the performance turnaround may not have come from any changes to the portfolio. Rather, after a stretch of underperformance, the holdings the original manager selected may have been priced to deliver better returns going forward. Luck could have also played a role. However, the stories that System 1 creates leave little room for chance, often searching for causal relationships where there are none.
We can do better by asking what other information might be relevant and how reliable the source is, and accounting for chance. The best way to do that is to start with the base rates of the group of which the specific case is a part and adjust from there based on the dependability of the information available about the case. For example, to gauge the likelihood that an active manager will outperform, start with the success rates for that Morningstar Category in the Active/Passive Barometer and make small adjustments to those figures based on what you know about the manager.
Availability Bias. This is the tendency to judge the likelihood of an event based on how easily it is to think of examples. This bias is an extension of System 1’s focus on the information at hand and eagerness to answer an easier question. It suggests that perceptions of risk are influenced by recent experiences with losses. After a market downturn, the market feels riskier than it does after a long rally and memories of past losses are more distant. This may even cause the market to sell off more than it should during market downturns, as perceptions of risk change more statistical measures of risk.
While it’s possible to fight this bias by becoming better informed about the relevant statistics, it’s difficult to overcome. Examples that resonate with us, particularly personal experiences, ring louder than abstract statistics. The best we can do is to recognise that innate bias and educate ourselves.
For most, losing $500 feels worse than the joy that winning $500 brings. Even though such changes have very little impact on most people’s wealth, it is natural to fixate on losses. And $500 feels like a bigger deal when going from a $500 loss to a $1,000 loss than it does going from $10,000 to $10,500. System 1 is the source of these feelings, and it automatically evaluates potential gains and losses. Unfortunately, System 1 is not well-suited to evaluate probability. It tends to overweight unlikely outcomes and underweight high-probability events. This behaviour is described by Prospect Theory, which makes a set of predictions, as shown in Exhibit 1.
When the likelihood of gains is high, System 1 underweights the value of the probability and it becomes less sensitive to changes in wealth as they increase. This leads to an undervaluation of the bet and risk-averse behaviour, where a smaller gain with no risk would be more attractive to most. For example, this may explain why someone with a strong case might settle for less than the expected value of the lawsuit.
When the likelihood of losses is high, these same effects—underweighting the value of the probability and diminishing sensitivity to changes in wealth— lead to risk-seeking behaviour. This is the most surprising prediction of Prospect Theory. To someone who is already in the hole, it may seem attractive to take even more risk to get back to even (such as pouring more money into a stock with deteriorating fundamentals) rather than to accept those losses.
System 1 doesn’t do so great with low-probability events either. We tend to think in terms of possibility rather than probability. This leads to risk-seeking behaviour in the realm of gains—like buying lottery tickets—and risk-averse behaviour when losses are possible, where we are willing to pay more than the expected value of the potential loss to eliminate the risk. This is how insurance companies stay in business.
The best remedy for many of the behavioural biases Kahneman highlights is to slow down, think critically, and be skeptical of your first instinct when the stakes are high. Reframing financial decisions more broadly can also facilitate more-prudent risk-taking and behaviour. For example, rather than viewing investments in isolation, it’s better to focus on the entire portfolio and evaluate potential gains and losses based on their impact on your long-term wealth. That means if you have a long investment horizon, it isn’t necessary to lose sleep over market downturns. It’s not always easy, but the added effort and restraint should be worth it.