A healthy 30 year old, uninvolved in crime or drugs, has less than a 0.1% chance of death. Do you think it is irrational for them to buy some cheap life insurance to protect their family in the small fraction of worlds where they do die?
That analogy is flawed because it equates a pooled risk portfolio with a singular risk.
An insurance company isn’t taking a 0.1% gamble. It’s managing a statistical certainty across a portfolio of millions, where the law of large numbers guarantees that the 0.1% rate is predictable and profitable.
An existential risk is a one-shot scenario for humanity. We are the single participant. There is no larger portfolio to average out the 99.9% chance of failure.
The analogy fails because joining a predictable risk pool is not logically equivalent to taking on an unpooled, singular gamble.
I didn’t ask about the insurance company’s perspective—I asked about the individual buying the life insurance. For them, buying the insurance is a 0.1% gamble, they are a single person who can only die once.
So let’s view from the individual’s perspective. I think looking at that perspective reveals a fundamental difference in the types of value we’re comparing. The analogy breaks down because it incorrectly assumes that the value of money and the value of lives are structured in the same way.
1. The Non-Linear Utility of Money (The Insurance Case)
For an individual, money has non-linear utility. The first $1000 you earn is life-changing, while an extra $1000 when you’re a millionaire is not.
Losing your last dollar is catastrophic—a state of ruin. Therefore, it is perfectly rational to pay a small premium, which represents a tiny certain loss of your least valuable money, to prevent a small chance of a catastrophic loss of your most valuable money. The insurance decision is rational precisely because of this non-linear value.
2. The Linear Value of Saving Lives (The EA Dilemma)
In contrast, the moral value of saving lives is treated as linear in these calculations. The first life saved is just as valuable as the 1000th. There is no “diminishing return” on a human life.
Because the value is linear, we don’t need to worry about utility curves. We can compare the outcomes directly. My argument is that when comparing these linear values, a guaranteed outcome (saving 1 life) is rationally preferable to an action with a 99.9% chance of achieving nothing.
The insurance analogy relies on the non-linear utility of money to be persuasive. Since that feature doesn’t exist in our dilemma about saving lives, the analogy is flawed and doesn’t challenge the original point.
An existential risk is a one-shot scenario for humanity. We are the single participant. There is no larger portfolio to average out the 99.9% chance of failure
But there may well be a large portfolio of actions we can take to reduce existential risk. In most cases there are many shots we can take.
That’s an interesting point about the portfolio of actions. You’re suggesting that while the existential risk itself is a one-shot scenario for humanity, we might be able to take multiple ‘shots’ at reducing it. In essence, you’re proposing that if a single, highly uncertain intervention isn’t reliable, we can average out the risk by attempting many such interventions.
However, this then shifts the problem, rather than solving it. Even if we could, in theory, take a ‘thousand shots’ at reducing x-risk, we still lack a robust framework for comparing the aggregated expected value of these highly speculative, low-probability interventions against the more certain, smaller-scale interventions. My original argument is precisely that our current EV tools are inadequate for such comparisons, especially when dealing with epistemic uncertainty and truly one-shot, high-stakes scenarios for which a ‘portfolio’ approach might still be an insufficient or unproven solution.
A healthy 30 year old, uninvolved in crime or drugs, has less than a 0.1% chance of death. Do you think it is irrational for them to buy some cheap life insurance to protect their family in the small fraction of worlds where they do die?
That analogy is flawed because it equates a pooled risk portfolio with a singular risk.
An insurance company isn’t taking a 0.1% gamble. It’s managing a statistical certainty across a portfolio of millions, where the law of large numbers guarantees that the 0.1% rate is predictable and profitable.
An existential risk is a one-shot scenario for humanity. We are the single participant. There is no larger portfolio to average out the 99.9% chance of failure.
The analogy fails because joining a predictable risk pool is not logically equivalent to taking on an unpooled, singular gamble.
I didn’t ask about the insurance company’s perspective—I asked about the individual buying the life insurance. For them, buying the insurance is a 0.1% gamble, they are a single person who can only die once.
So let’s view from the individual’s perspective. I think looking at that perspective reveals a fundamental difference in the types of value we’re comparing. The analogy breaks down because it incorrectly assumes that the value of money and the value of lives are structured in the same way.
1. The Non-Linear Utility of Money (The Insurance Case)
For an individual, money has non-linear utility. The first $1000 you earn is life-changing, while an extra $1000 when you’re a millionaire is not.
Losing your last dollar is catastrophic—a state of ruin. Therefore, it is perfectly rational to pay a small premium, which represents a tiny certain loss of your least valuable money, to prevent a small chance of a catastrophic loss of your most valuable money. The insurance decision is rational precisely because of this non-linear value.
2. The Linear Value of Saving Lives (The EA Dilemma)
In contrast, the moral value of saving lives is treated as linear in these calculations. The first life saved is just as valuable as the 1000th. There is no “diminishing return” on a human life.
Because the value is linear, we don’t need to worry about utility curves. We can compare the outcomes directly. My argument is that when comparing these linear values, a guaranteed outcome (saving 1 life) is rationally preferable to an action with a 99.9% chance of achieving nothing.
The insurance analogy relies on the non-linear utility of money to be persuasive. Since that feature doesn’t exist in our dilemma about saving lives, the analogy is flawed and doesn’t challenge the original point.
But there may well be a large portfolio of actions we can take to reduce existential risk. In most cases there are many shots we can take.
That’s an interesting point about the portfolio of actions. You’re suggesting that while the existential risk itself is a one-shot scenario for humanity, we might be able to take multiple ‘shots’ at reducing it. In essence, you’re proposing that if a single, highly uncertain intervention isn’t reliable, we can average out the risk by attempting many such interventions.
However, this then shifts the problem, rather than solving it. Even if we could, in theory, take a ‘thousand shots’ at reducing x-risk, we still lack a robust framework for comparing the aggregated expected value of these highly speculative, low-probability interventions against the more certain, smaller-scale interventions. My original argument is precisely that our current EV tools are inadequate for such comparisons, especially when dealing with epistemic uncertainty and truly one-shot, high-stakes scenarios for which a ‘portfolio’ approach might still be an insufficient or unproven solution.