There are a few distinctions that might help with your update:
determinism: knowledge of some system of causes now allows prediction of their outcomes until the end of time
closed world: we know all there is to know about the topic. Any search through our knowledge that fails to prove some hypothesis means that the hypothesis is false.
defeasibility: new observations can contradict earlier beliefs and result in withdrawal of earlier beliefs from one’s knowledge.
It seems like your use of the solar system example allows you to assume the first two distinctions apply to knowledge of the solar system. I’m not sure a physicist would agree with your choice of example, but I’m OK with it.
Human reasoning is defeasible, but until an observation provides an update, we do not necessarily consider the unknown beyond making passive observations of the real world.
From my limited understanding of the philosophy behind classic EA epistemics, believing what you know leads to refusing new observations that update your closed world. Thus the emphasis on incomplete epistemic confidence most of the time. So the thinking goes, it ensures that you’re not close-minded to always hold out that you think you might be wrong.
When running predictions, until someone provides a specific new item for a list of alternative outcomes (e.g, a new s-risk), the given list is all that is considered. Probabilities are divided among its alternatives when those alternatives are outcomes. The only exhaustive list of alternatives is one that includes a contradictory option, such as:
A
B
C
not A and not B and not C
and that covers all the possibilities. The interesting options are implicit in that last “not A and not B and not C”. This is not a big deal, since it’s usually the positive statements of options (A, B, or C) that are of interest.
So what’s a discovery? It seems like, in your model, it’s an alternative that is not listed directly. For example, given:
future 1
future 2
not future 1 and not future 2
An unexpected discovery belongs to future 3. All we know about it is that it is not future 1 and not future 2. One way to reframe your line of thought would be to ask:
how can we weight future 3?
A concrete example of discoveries of road surfacing strategies:
road paving that is concrete that absorbs CO2 with X efficiency (20%)
road paving that is made of plastic (5%)
road paving that is not concrete and that is not plastic (50%)
not road paving but serves to provide a road surface (24%)
something better than roads (1%)
That actually looks ridiculous. How do we know that there’s a 1% chance that we discover something better than roads?
In a longtermist framework, reasoning by analogy, lets consider some futures, and this example is fiction, not what I believe:
we make the planet slightly hotter and kill some species (1%)
something bad kills us all (29%)
something bad makes us suffer (15%)
not 1 or 2 or 3 (55%)
Future 4 has a probability of 55%. But future 4 is simply the unknowable future. What in heck is going on here?
If I understand what you’re trying to say, it’s that futures like future 4 in that example cannot be assigned a probability or risk. Furthermore, given that future 4 is a mutually exclusive alternative to futures 1, 2, and 3, those futures cannot be assigned a probability either.
Have I made an error in reasoning or did I misunderstand you?
Basically, predictions about the future are fine as long as they include the caveat “unless we figure out something else.” That caveat can’t be ascribed a meaningful probability because we can’t know discoveries before we discovery them, we can’t know things before we know them.
Well, my basic opinion about forecasting is that probabilities don’t inform the person receiving the forecast. Before you commit to weighting possible outcomes, you commit to at least two mutually exclusive futures, X and not X. So what you supply is a limitation on possible outcomes, either X or not X. At best, you’re aware of mutually exclusive alternative and specific futures. Then you can limit what not X means to something specific, for example, Y. So now you can say, “The future will contain X or Y.” That sort of analysis is enabled by your causal model. As your causal model improves, it becomes easier to supply a list of alternative future outcomes.
However, the future is not a game of chance, and there’s no useful interpretation to supply meaningful weights to the future prediction of any specific outcome, unless the outcomes belong to a game of chance, where you’re predicting rolls of a fair die, choice of a hand from a deck of cards, etc.
What’s worse, that does not limit your feelings about what probabilities apply. Those feelings can seem real and meaningful because they let you talk about lists of outcomes and which you think are more credible.
As a forecaster, I might supply outcomes in a forecast that I consider less credible along with those that I consider more credible. but if you ask me which options I consider credible, I might offer a subset of the list. So in that way weights can seem valuable, because they let you distinguish which you think are more credible and which you can rule out. But the weights also obscure that information because they can scale that credibility in confusing ways.
For example, I believe in outcomes A or B, but I offer A at 30%, B at 30%, C at 20%, D at 10%, and E at 10%. Have I communicated what I intended with my weights, namely, that A and B are credible, that C is somewhat credible, but D and E are not? Maybe I could adjust A and B to 40% and 40%, but now I’m fiddling with the likelihoods of C, D, and E, when all I really mean to communicate is that I like A or B as outcomes and C as an alternate. My probabilities communicate more and differently than I intend. I could make it clear with A and B each at 48% or something, but really now I’m trying to pretend I know what the chances of C, D, and E are, when all I really know about them is that my causal model doesn’t support their production much. I could go back and quantify that somehow, but information with which to do that is not available , so I have to pretend confidence in some estimation of the outcomes C, D, and E. My information is not useless, but it’s not relevant to weighting all possible outcomes against each other. If I’m forced to provide weights for all the listed outcomes, then I’m forced to figure out how to communicate my analysis in terms of weights so that the audience for my forecast understands what I intend to mean.
In general, analyzing causal models that determine possible futures is a distinct activity from weighting those futures. The valuable information is in the causal models and in the selection of futures based on those models. The extra information on epistemic confidence is not useful and pretends more information than a forecaster likely has. I would go as far as two tiers of selections, just to qualify what I think my causal model implies,
“A or B, and if not those, then C, but not D or E”.
Actually, I think someone reading my forecast with weights will just leave with that kind of information anyway. If they try to mathematically apply the weights I chose to communicate my tiers of selections, then they will be led astray, expecting precision when there wasn’t any. They would do better to get details of the causal models involved and determine whether those have any merit, particularly in cases of:
very different forecasts (I forecast A or B, everyone else forecasts D)
a single forecast predicting very different outcomes (A or B are contradictory outcomes)
a homogenous bunch of forecasts (A, B, or A or B)
a heterogenous bunch of forecasts (A, B, C or D, A or B, E)
so basically in all cases. What might distinguish superforecasters is not their grasp of probability or their ability to update bayesian priors or whatever, but rather the applicability of causal models they develop, and what those causal models emphasize as causes and consequences.
That’s the background of my thinking, now here’s how I think it relates to what you’re saying:
If discoveries influence future outcomes in unknown ways, and your information is insufficient to predict all outcomes, then your causal model makes predictions that belong under an assumption of an open world. You are less useful as a predictor of outcomes and more useful as an supplier of possible outcomes. If we are both forecasting, and I supply outcomes A and B; you might supply outcomes C and D; someone else might supply E, F, and G; yet another person might supply H. Our forecasts run from A to H so far, and they are not exhaustive. As forecasters, our job becomes to create lists of plausible futures, not to select from predetermined lists.
I think this is appropriate to conditions where development of knowledge or inventions is a human choice. Any forecast will depend not only on what is plausible under some causal model, but also on what future people want to explore and how they explore it. Forecasts in that scenario can influence the future, so better that they supply options rather than weight them.
There are a few distinctions that might help with your update:
determinism: knowledge of some system of causes now allows prediction of their outcomes until the end of time
closed world: we know all there is to know about the topic. Any search through our knowledge that fails to prove some hypothesis means that the hypothesis is false.
defeasibility: new observations can contradict earlier beliefs and result in withdrawal of earlier beliefs from one’s knowledge.
It seems like your use of the solar system example allows you to assume the first two distinctions apply to knowledge of the solar system. I’m not sure a physicist would agree with your choice of example, but I’m OK with it.
Human reasoning is defeasible, but until an observation provides an update, we do not necessarily consider the unknown beyond making passive observations of the real world.
From my limited understanding of the philosophy behind classic EA epistemics, believing what you know leads to refusing new observations that update your closed world. Thus the emphasis on incomplete epistemic confidence most of the time. So the thinking goes, it ensures that you’re not close-minded to always hold out that you think you might be wrong.
When running predictions, until someone provides a specific new item for a list of alternative outcomes (e.g, a new s-risk), the given list is all that is considered. Probabilities are divided among its alternatives when those alternatives are outcomes. The only exhaustive list of alternatives is one that includes a contradictory option, such as:
A
B
C
not A and not B and not C
and that covers all the possibilities. The interesting options are implicit in that last “not A and not B and not C”. This is not a big deal, since it’s usually the positive statements of options (A, B, or C) that are of interest.
So what’s a discovery? It seems like, in your model, it’s an alternative that is not listed directly. For example, given:
future 1
future 2
not future 1 and not future 2
An unexpected discovery belongs to future 3. All we know about it is that it is not future 1 and not future 2. One way to reframe your line of thought would be to ask:
how can we weight future 3?
A concrete example of discoveries of road surfacing strategies:
road paving that is concrete that absorbs CO2 with X efficiency (20%)
road paving that is made of plastic (5%)
road paving that is not concrete and that is not plastic (50%)
not road paving but serves to provide a road surface (24%)
something better than roads (1%)
That actually looks ridiculous. How do we know that there’s a 1% chance that we discover something better than roads?
In a longtermist framework, reasoning by analogy, lets consider some futures, and this example is fiction, not what I believe:
we make the planet slightly hotter and kill some species (1%)
something bad kills us all (29%)
something bad makes us suffer (15%)
not 1 or 2 or 3 (55%)
Future 4 has a probability of 55%. But future 4 is simply the unknowable future. What in heck is going on here?
If I understand what you’re trying to say, it’s that futures like future 4 in that example cannot be assigned a probability or risk. Furthermore, given that future 4 is a mutually exclusive alternative to futures 1, 2, and 3, those futures cannot be assigned a probability either.
Have I made an error in reasoning or did I misunderstand you?
Beautiful! We can’t determine “something we haven’t thought of” as simply “1 - all the things we’ve thought of”.
Basically, predictions about the future are fine as long as they include the caveat “unless we figure out something else.” That caveat can’t be ascribed a meaningful probability because we can’t know discoveries before we discovery them, we can’t know things before we know them.
Well, my basic opinion about forecasting is that probabilities don’t inform the person receiving the forecast. Before you commit to weighting possible outcomes, you commit to at least two mutually exclusive futures, X and not X. So what you supply is a limitation on possible outcomes, either X or not X. At best, you’re aware of mutually exclusive alternative and specific futures. Then you can limit what not X means to something specific, for example, Y. So now you can say, “The future will contain X or Y.” That sort of analysis is enabled by your causal model. As your causal model improves, it becomes easier to supply a list of alternative future outcomes.
However, the future is not a game of chance, and there’s no useful interpretation to supply meaningful weights to the future prediction of any specific outcome, unless the outcomes belong to a game of chance, where you’re predicting rolls of a fair die, choice of a hand from a deck of cards, etc.
What’s worse, that does not limit your feelings about what probabilities apply. Those feelings can seem real and meaningful because they let you talk about lists of outcomes and which you think are more credible.
As a forecaster, I might supply outcomes in a forecast that I consider less credible along with those that I consider more credible. but if you ask me which options I consider credible, I might offer a subset of the list. So in that way weights can seem valuable, because they let you distinguish which you think are more credible and which you can rule out. But the weights also obscure that information because they can scale that credibility in confusing ways.
For example, I believe in outcomes A or B, but I offer A at 30%, B at 30%, C at 20%, D at 10%, and E at 10%. Have I communicated what I intended with my weights, namely, that A and B are credible, that C is somewhat credible, but D and E are not? Maybe I could adjust A and B to 40% and 40%, but now I’m fiddling with the likelihoods of C, D, and E, when all I really mean to communicate is that I like A or B as outcomes and C as an alternate. My probabilities communicate more and differently than I intend. I could make it clear with A and B each at 48% or something, but really now I’m trying to pretend I know what the chances of C, D, and E are, when all I really know about them is that my causal model doesn’t support their production much. I could go back and quantify that somehow, but information with which to do that is not available , so I have to pretend confidence in some estimation of the outcomes C, D, and E. My information is not useless, but it’s not relevant to weighting all possible outcomes against each other. If I’m forced to provide weights for all the listed outcomes, then I’m forced to figure out how to communicate my analysis in terms of weights so that the audience for my forecast understands what I intend to mean.
In general, analyzing causal models that determine possible futures is a distinct activity from weighting those futures. The valuable information is in the causal models and in the selection of futures based on those models. The extra information on epistemic confidence is not useful and pretends more information than a forecaster likely has. I would go as far as two tiers of selections, just to qualify what I think my causal model implies,
“A or B, and if not those, then C, but not D or E”.
Actually, I think someone reading my forecast with weights will just leave with that kind of information anyway. If they try to mathematically apply the weights I chose to communicate my tiers of selections, then they will be led astray, expecting precision when there wasn’t any. They would do better to get details of the causal models involved and determine whether those have any merit, particularly in cases of:
very different forecasts (I forecast A or B, everyone else forecasts D)
a single forecast predicting very different outcomes (A or B are contradictory outcomes)
a homogenous bunch of forecasts (A, B, or A or B)
a heterogenous bunch of forecasts (A, B, C or D, A or B, E)
so basically in all cases. What might distinguish superforecasters is not their grasp of probability or their ability to update bayesian priors or whatever, but rather the applicability of causal models they develop, and what those causal models emphasize as causes and consequences.
That’s the background of my thinking, now here’s how I think it relates to what you’re saying:
If discoveries influence future outcomes in unknown ways, and your information is insufficient to predict all outcomes, then your causal model makes predictions that belong under an assumption of an open world. You are less useful as a predictor of outcomes and more useful as an supplier of possible outcomes. If we are both forecasting, and I supply outcomes A and B; you might supply outcomes C and D; someone else might supply E, F, and G; yet another person might supply H. Our forecasts run from A to H so far, and they are not exhaustive. As forecasters, our job becomes to create lists of plausible futures, not to select from predetermined lists.
I think this is appropriate to conditions where development of knowledge or inventions is a human choice. Any forecast will depend not only on what is plausible under some causal model, but also on what future people want to explore and how they explore it. Forecasts in that scenario can influence the future, so better that they supply options rather than weight them.
I love it. Creating lists of plausible outcomes is very valuable, we can leave alone to idea of assigning probabilities.