The Moral Value of Information

I’m going to start the talk with some spoilers. Basically, I want nothing here to be a surprise. The first thing that I’m going to claim, and I hope you find it plausible, is that we generally prefer interventions with more evidential support, all else being equal. I’ll go into detail about what that means. The second is, I’m going to argue that having less evidence in favor of a given intervention means that your credences about the effectiveness of that intervention are what I call “low resilience”.

In this article I'm going to start with making two claims. The first claim is that we generally prefer interventions with more evidential support, all else being equal. I hope you find that claim plausible and I’ll go into detail later about what it means. The second claim I’m going to argue for is that having less evidence in favor of a given intervention means that your credences about the effectiveness of that intervention are what I call “low resilience”.

This is something that has been explored in decision theory to some extent. That’s true even if your credences about the effectiveness of that intervention are the same value. So, if I thought there was a 50% chance that I would get $100, there’s actually a difference between a low resilience 50% and a high resilience 50%.

This second claim has been explored in decision theory to some extent. It holds even if your credences about the effectiveness of that intervention are the same value. So, if I thought there was a 50% chance that I would get $100, there’s a difference between a low resilience 50% and a high resilience 50%.

I’m going to argue that, if your credences are low resilience, then the value of information in this domain is generally higher than it would be in a domain where your credences are high resilience. And, I’m going to argue that this means that actually in many cases, we should prefer interventions with less evidential support, all else being equal. Hopefully, you’ll find that counterintuitive and interesting.

I’m going to argue that, if your credences in a particular domain are low resilience, then the value of information in this domain is generally higher than it would be in a domain where your credences are high resilience. And, I’m going to argue that this means in many cases, we should prefer interventions with less evidential support, all else being equal. Hopefully, you’ll find that conclusion counterintuitive and interesting.

The first thing to say is that we generally think that expected value calculations are a pretty decent way of estimating the effectiveness of a given intervention. An example here is one where we imagine that there is a Disease A, very novelly and interestingly named, and another disease equally interestingly named Disease B (Figure 1).

The first thing to say is that we generally think that expected value calculations are a good way of estimating the effectiveness of a given intervention. For example, lets imagine that there are two diseases: (very novelly named) Disease A and Disease B [Figure 1].

Basically, the idea is that these two diseases are virtually impossible to differentiate. They all have the same symptoms, they cause the same reduction in life expectancy, etc. The key difference is that they respond very differently to different treatments, so any doctor who finds themselves with a patient with one of these conditions is in a difficult situation.

Say these two diseases are virtually impossible to differentiate. They both have the same symptoms, and they cause the same reduction in life expectancy, etc. Their key difference is that they respond very differently to different treatments, so any doctor who finds themselves with a patient with one of these conditions is in a difficult situation.

https://images.ctfassets.net/ohf186sfn6di/46wOcwQAe4Q6eM6cq6cOOm/b7e49ca718c76b6b53e38ea8ef577060/moral_value1.png?w=768&q=70

They can prescribe Drug A. Drug A costs $100. If the patient has Disease A, then Drug A will basically extend their life by another 10 years. If on the other hand they have Disease B, it won’t extend their life at all. They will die of Disease B, because Disease B is completely non-responsive to Drug A. So the expected years of life that we get from Drug A is 0.05 per dollar. Drug B works in a very similar way, except it is used to treat Disease B. If you have Disease A, it will be completely non-responsive. So, it’s got the same expected value.

They can prescribe Drug A which costs $100. If the patient has Disease A, then Drug A will extend their life by another 10 years. If on the other hand the patient had Disease B, it won’t extend their life at all. They will die of Disease B, because Disease B is completely non-responsive to Drug A. Therefore, the expected years of life that we get from Drug A is 0.05 per dollar. Drug B works in a very similar way, except it is used to treat Disease B. If you have Disease A, it will be completely non-responsive. So, it’s got the same expected value as Drug A.

Then, we have Drug C. Drug C costs $100, but regardless of whether you have Disease A or Disease B, it will in fact be responsive to Drug C. So, this is a new and interesting drug. This means that the expected value for Drug C is greater than the expected value for either Drug A or Drug B. So we think, “Okay, great. Kind of obvious that you should prescribe Drug C.”

Then, we have Drug C, which also costs $100. Both Disease A or Disease B are somewhat responsive to Drug C so this is a new and interesting drug. From Figure 1 we can see the expected value for Drug C is greater than the expected value for either Drug A or Drug B. So we think, “Okay, great. Kind of obvious we should prescribe Drug C.”