The Four Quadrants of Decision-Making
Decisions fall into four quadrants based on whether exposures are binary or open-ended, and whether the environment is Mediocristan (thin-tailed) or Extremistan (fat-tailed). This framework helps assess risk and impact in different domains.
THE IDEA
DECISIONS
When you look at the generator of events, you can tell a priori which environment can deliver large events (Extremistan) and which environment cannot deliver them (Mediocristan). This is the only a priori assumption we need to make. The only one.
So that’s that.
I. The first type of decision is simple, leading to a “binary” exposure: that is, you just care about whether something is true or false. Very true or very false does not bring you additional benefits or damage. Binary exposures do not depend on high-impact events as their payoff is limited. Someone is either pregnant or not pregnant, so if the person is “extremely pregnant” the payoff would be the same as if she were “slightly pregnant.” A statement is “true” or “false” with some confidence interval. (I call these M0 as, more technically, they depend on what is called the zeroth moment, namely on the probability of events, and not on their magnitude—you just care about “raw” probability.) A biological experiment in the laboratory and a bet with a friend about the outcome of a soccer game belong to this category.
Clearly, binary outcomes are not very prevalent in life; they mostly exist in laboratory experiments and in research papers. In life, payoffs are usually open-ended, or, at least, variable.
II. The second type of decision is more complex and entails more open-ended exposures. You do not just care about frequency or probability, but about the impact as well, or, even more complex, some function of the impact. So there is another layer of uncertainty of impact. An epidemic or a war can be mild or severe. When you invest you do not care how many times you gain or lose, you care about the cumulative, the expectation: how many times you gain or lose times the amount made or lost. There are even more complex decisions (for instance, when one is involved with debt) but I will skip them here.
The other dimension is the event generator:
A. Event generators belong to Mediocristan (i.e., it is close to impossible for very large deviations to take place), an a priori assumption.
B. Event generators belong to Extremistan (i.e., very large deviations are possible, or even likely).
Crossing the exposure type with the event generator gives four quadrants: binary exposure in Mediocristan, binary exposure in Extremistan, open-ended exposure in Mediocristan, and open-ended exposure in Extremistan.
When to use it
Evaluating investment strategies by examining both the frequency and size of gains and losses, including rare outcomes that a win/loss tally would hide.
Assessing public health policies: An epidemic is an open-ended exposure in Extremistan; binary thinking (just whether it occurs or not) is insufficient.
Comparing career paths by considering both the probability of continued employment and the range of financial outcomes, rather than assuming a salaried job has a binary payoff.
Legal judgments: Guilt/innocence is a binary exposure, but sentencing can be open-ended—consider the interplay of the two quadrants.
FROM THEORY TO EVERYDAY LIFE
In practice
Binary Exposure: Pregnancy Test
A pregnancy test yields a true/false result. Being 'slightly pregnant' or 'extremely pregnant' makes no difference to that classification. This illustrates the binary dimension of a Type I decision; it does not, by itself, establish whether the underlying event generator belongs to Mediocristan.
Open-Ended Exposure: Investing in Stocks
An investor cares not just about the frequency of gains and losses, but about their magnitude. A single extreme positive return (e.g., from a black-swan event) can outweigh many small losses. This is a Type II decision, and if the stock market belongs to Extremistan, the tail risks dominate.
A MOMENT TO REFLECT
Put it to work.
Think of a recent decision you made. Was it binary or open-ended? Which environment (Mediocristan or Extremistan) did the underlying event generator belong to?
How does framing a decision as binary versus open-ended change your approach to risk? Can you identify a situation where ignoring the magnitude of impact led to a bad outcome?
In what domains of life do we mistakenly treat open-ended exposures as binary ones, and what are the consequences?
Often confused with
CONTINUE THE CONVERSATION
Source texts
- The Black Swan The Impact of the Highly Improbable With a new section On Robustness and Fragility (Nassim Nicholas Taleb)