Pascal’s Wager (Applied to Induction and Risk Management)
Taleb applies Pascal’s asymmetry to induction: use historical patterns to find opportunities, while separately deciding how much loss you can bear and when to exit. A stop-loss order is an intended exit point, not a guarantee against severe losses.
THE IDEA
Pascal’s Wager
My method of dealing with the problem of induction starts with an asymmetry. The philosopher Pascal proclaimed that the optimal strategy for humans is to believe in the existence of God. For if God exists, then the believer would be rewarded. If he does not exist, the believer would have nothing to lose. Accordingly, we need to accept the asymmetry in knowledge; there are situations in which using statistics and econometrics can be useful. But I do not want my life to depend on it.
Like Pascal, I will therefore state the following argument. If the science of statistics can benefit me in anything, I will use it. If it poses a threat, then I will not. I want to take the best of what the past can give me without its dangers. Accordingly, I will use statistics and inductive methods to make aggressive bets, but I will not use them to manage my risks and exposure. Surprisingly, all the surviving traders I know seem to have done the same. They trade on ideas based on some observation (that includes past history) but, like the Popperian scientists, they make sure that the costs of being wrong are limited (and their probability is not derived from past data). Unlike Carlos and John, they know before getting involved in the trading strategy which events would prove their conjecture wrong and allow for it (recall that Carlos and John used past history both to make their bets and to measure their risk). They would then terminate their trade. This is called a stop loss, a predetermined exit point, a protection from the black swan. I find it rarely practiced.
When to use it
A startup founder uses market research to decide which product features to build (aggressive bet) but limits development costs to avoid catastrophic failure.
A scientific researcher tests a hypothesis based on prior observations but designs the experiment so that a negative result costs little (e.g., small sample pilot study).
An investor limits the amount allocated to a backtested strategy and plans exits while accounting for the possibility that stop-loss orders execute below their trigger prices.
A policy maker uses historical data to design a social program (aggressive bet) but builds in automatic sunset clauses to limit long-term downside if outcomes are poor.
FROM THEORY TO EVERYDAY LIFE
In practice
Applying Pascal’s Wager to Investment Decisions
An investor uses historical data to identify a promising stock and places a stop-loss order 10% below the purchase price. That specifies an intended exit independently of the estimated frequency of losses. A price gap or poor execution can still produce a larger loss.
Trading Strategy with Stop Loss
A trader enters a position based on a pattern in past market behavior and decides in advance what would invalidate the idea. The trader plans an exit at that point and separately limits the amount exposed. The exit may not execute at the intended price.
A MOMENT TO REFLECT
Put it to work.
In what areas of your life do you rely on statistics or past data? How might you apply Pascal’s asymmetry (use for gains but not for risks)?
Think of a recent decision based on historical data. What would have shown your assumption was wrong, and what could have limited the resulting loss?
How can you distinguish between using induction for aggressive bets versus for exposure? What criteria would you use?
Often confused with
CONTINUE THE CONVERSATION
Source texts
- Fooled by Randomness The Hidden Role of Chance in the Markets and in Life (Nassim Nicholas Taleb)