We have a bias towards using discrete mental models over continuous ones because they’re more convenient. But they’re not always the best choice.
I’ve heard a lot of different definitions for “dead”. Some people say living things have souls, and they die when the soul leaves. Other people say death is when the heart stopped beating. Some people say it’s when the “brain dies”.
None of these definitions ever satisfy me. There are always questions people can’t answer. What about someone whose heart stops beating but starts again? Can we look at a scan of someone’s brain and point to the moment they die? What if we revive someone from cryonic suspension—does that mean the soul never left?
You could say this is a question of definitions. If we define death as “heart not beating” then we can definitively say when someone is dead. Of course, this would mean that we can bring people back to life after they’ve died because CPR works when you are “heart-stopped-dead”. But what if someone is too far gone, and we can’t bring them back? What if they’re “actually-dead”, then what’s the definition of that kind of dead? We’re back to square one.
The problem is that alive and dead aren’t binary states where something is either dead or not. Instead, it’s a continuous spectrum of “more dead” to “less dead”. You could measure it as “probability we can bring back to life”. The question of “what does it mean to die” doesn’t make sense because there is no single point where someone is fully dead. Why was this not obvious to begin with?
Language is discrete by nature. Words usually represent distinct states and ideas because they themselves are discrete. Describing continuity is an abstract task where we draw a line between two discrete points using multiple words. For example to describe the spectrum of alive to dead I need you to visualise each endpoint and the continuum between them. Because language is discrete, a lot of our communication and thinking also becomes discrete. This is at odds with the world, which is mostly continuous.
I’m not making a claim about the nature of reality or the underlying physics. That’s above my pay grade. I’m saying, at the level of abstraction we operate at, the world mostly acts as if it is continuous. Yes, some things are discrete, like the number of trees in a forest or the number of buildings in a city. But for the most part, moments in time, objects in space, or states of the world are part of a continuum. For example, we’re comfortable thinking about time as continuous because we speak about it in numbers which we already intuitively understand are on a spectrum.
Discrete language trades accuracy for convenience. Because language is discrete, we need more words when we speak about continuous things. Most of the time the extra speed and convenience are a good thing. Saying I’m hungry or full is usually enough—I don’t need to put a number on it. But sometimes accuracy is more valuable than speed and in those cases, it’s valuable to be aware of the bias so you can correct it.
If you go to the doctor for a check-up and he says you’re healthy, that isn’t true. You are always at least a little bit “sick” because you’re somewhere on the sick to healthy spectrum. Saying you’re healthy means you’re above an arbitrary point on that spectrum where there are no obvious signs of illness. But if you walk out of their office thinking you’re healthy, you’re less likely to take action until you dip below that threshold, at which point you’ve done more damage than was necessary.
Sick vs. healthy, dead vs. alive, happy vs. unhappy, hungry vs. full, smart vs. stupid. All of these are actually continuous spectrums. Treating them as discrete states is a less accurate mental model, that leads to poor decisions. When you notice yourself categorising things into discrete states, ask yourself what spectrum they live on.
The World is Continuous, Not Discrete
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We have a bias towards using discrete mental models over continuous ones because they’re more convenient. But they’re not always the best choice.
I’ve heard a lot of different definitions for “dead”. Some people say living things have souls, and they die when the soul leaves. Other people say death is when the heart stopped beating. Some people say it’s when the “brain dies”.
None of these definitions ever satisfy me. There are always questions people can’t answer. What about someone whose heart stops beating but starts again? Can we look at a scan of someone’s brain and point to the moment they die? What if we revive someone from cryonic suspension—does that mean the soul never left?
You could say this is a question of definitions. If we define death as “heart not beating” then we can definitively say when someone is dead. Of course, this would mean that we can bring people back to life after they’ve died because CPR works when you are “heart-stopped-dead”. But what if someone is too far gone, and we can’t bring them back? What if they’re “actually-dead”, then what’s the definition of that kind of dead? We’re back to square one.
The problem is that alive and dead aren’t binary states where something is either dead or not. Instead, it’s a continuous spectrum of “more dead” to “less dead”. You could measure it as “probability we can bring back to life”. The question of “what does it mean to die” doesn’t make sense because there is no single point where someone is fully dead. Why was this not obvious to begin with?
Language is discrete by nature. Words usually represent distinct states and ideas because they themselves are discrete. Describing continuity is an abstract task where we draw a line between two discrete points using multiple words. For example to describe the spectrum of alive to dead I need you to visualise each endpoint and the continuum between them. Because language is discrete, a lot of our communication and thinking also becomes discrete. This is at odds with the world, which is mostly continuous.
I’m not making a claim about the nature of reality or the underlying physics. That’s above my pay grade. I’m saying, at the level of abstraction we operate at, the world mostly acts as if it is continuous. Yes, some things are discrete, like the number of trees in a forest or the number of buildings in a city. But for the most part, moments in time, objects in space, or states of the world are part of a continuum. For example, we’re comfortable thinking about time as continuous because we speak about it in numbers which we already intuitively understand are on a spectrum.
Discrete language trades accuracy for convenience. Because language is discrete, we need more words when we speak about continuous things. Most of the time the extra speed and convenience are a good thing. Saying I’m hungry or full is usually enough—I don’t need to put a number on it. But sometimes accuracy is more valuable than speed and in those cases, it’s valuable to be aware of the bias so you can correct it.
If you go to the doctor for a check-up and he says you’re healthy, that isn’t true. You are always at least a little bit “sick” because you’re somewhere on the sick to healthy spectrum. Saying you’re healthy means you’re above an arbitrary point on that spectrum where there are no obvious signs of illness. But if you walk out of their office thinking you’re healthy, you’re less likely to take action until you dip below that threshold, at which point you’ve done more damage than was necessary.
Sick vs. healthy, dead vs. alive, happy vs. unhappy, hungry vs. full, smart vs. stupid. All of these are actually continuous spectrums. Treating them as discrete states is a less accurate mental model, that leads to poor decisions. When you notice yourself categorising things into discrete states, ask yourself what spectrum they live on.