Your example 1 can be easily modeled as a 100-point power source, with two lamps that share it. You don’t need statistics or uncertainty for most calculations there. Your example 2 doesn’t click with me—why is the abstraction/interpretation/modeling limited in that way? Can you give a more concrete example, as with the lights?
The analogy to probability makes some sense—for a given outcome, all probabilities add up to 1. But that’s about your knowledge, not the universe; once it’s resolved, the probability for one outcome is 1 and the rest are 0.
Your coin example loses me again—SOMETHING will happen, and both the end result and the intermediate states (“merging”?) are observable and enumerable. Making a table of every state will allow you to put probabilities to them, and then you’re back to classical probability, without any reference to sharing or quantum weirdness.
Like a Dirichlet distribution. The two lamps split weights 0-1, and then get that percentage of some absolute quantity, like lumens. A real-world use case might be lighting an interior room, where you want a certain absolute amount of brightness, like 1000 lumens, but maybe the ratio changes so the lamp behind you gets brighter and the one in front gets dimmer so you can read.
I think this statement is true: there are two types of systems (among other types), the ones where objects do share properties/identities, and the ones where they don’t. I believe this is true regardless of how the systems are modeled. If you model both systems with the same mathematical theory, it doesn’t mean there are no difference between the systems. (Maybe some important details are different, maybe some assumptions are different.)
And those two systems lead to different predictions. Even if they’re described in terms of the same mathematical theory.
Imagine you ask “Is it easy to be smarter than a human?”. Then you encounter beings much smarter than humans. In the classical situation, you just update towards answering “yes”. In the situation where “intelligence” is shared between all beings, something more complicated may happen: you may partially update towards “yes”, but also partially update towards “no” (because you re-evaluated how smart humans are or how much intelligence is left for other beings).
Sorry if I don’t have a specific example. I was asking my questions hoping that some interesting/important examples of systems with shared properties/identities already exist.
The analogy to probability makes some sense—for a given outcome, all probabilities add up to 1. But that’s about your knowledge, not the universe; once it’s resolved, the probability for one outcome is 1 and the rest are 0.
I think that sometimes “uncertainty” is the property of the universe (I’m not talking about quantum mechanics right now). See fuzzy logic, fuzzy sets. Predictive probability is about uncertainty in the map (usually). Descriptive “probability” (fuzziness) is about uncertainty in the territory.
I think uncertainty related to shared properties/identities is interesting because it’s a mix between “uncertainty in the map” and “uncertainty in the territory”.
Your coin example loses me again—SOMETHING will happen, and both the end result and the intermediate states (“merging”?) are observable and enumerable. Making a table of every state will allow you to put probabilities to them, and then you’re back to classical probability, without any reference to sharing or quantum weirdness.
I think this argument may have a problem. Here’s an analogy: you may translate C++ into bytecode and even movement of particles, but it doesn’t mean that C++ doesn’t exist.
Your example 1 can be easily modeled as a 100-point power source, with two lamps that share it. You don’t need statistics or uncertainty for most calculations there. Your example 2 doesn’t click with me—why is the abstraction/interpretation/modeling limited in that way? Can you give a more concrete example, as with the lights?
The analogy to probability makes some sense—for a given outcome, all probabilities add up to 1. But that’s about your knowledge, not the universe; once it’s resolved, the probability for one outcome is 1 and the rest are 0.
Your coin example loses me again—SOMETHING will happen, and both the end result and the intermediate states (“merging”?) are observable and enumerable. Making a table of every state will allow you to put probabilities to them, and then you’re back to classical probability, without any reference to sharing or quantum weirdness.
Like a Dirichlet distribution. The two lamps split weights 0-1, and then get that percentage of some absolute quantity, like lumens. A real-world use case might be lighting an interior room, where you want a certain absolute amount of brightness, like 1000 lumens, but maybe the ratio changes so the lamp behind you gets brighter and the one in front gets dimmer so you can read.
I think this statement is true: there are two types of systems (among other types), the ones where objects do share properties/identities, and the ones where they don’t. I believe this is true regardless of how the systems are modeled. If you model both systems with the same mathematical theory, it doesn’t mean there are no difference between the systems. (Maybe some important details are different, maybe some assumptions are different.)
And those two systems lead to different predictions. Even if they’re described in terms of the same mathematical theory.
Imagine you ask “Is it easy to be smarter than a human?”. Then you encounter beings much smarter than humans. In the classical situation, you just update towards answering “yes”. In the situation where “intelligence” is shared between all beings, something more complicated may happen: you may partially update towards “yes”, but also partially update towards “no” (because you re-evaluated how smart humans are or how much intelligence is left for other beings).
Sorry if I don’t have a specific example. I was asking my questions hoping that some interesting/important examples of systems with shared properties/identities already exist.
I think that sometimes “uncertainty” is the property of the universe (I’m not talking about quantum mechanics right now). See fuzzy logic, fuzzy sets. Predictive probability is about uncertainty in the map (usually). Descriptive “probability” (fuzziness) is about uncertainty in the territory.
I think uncertainty related to shared properties/identities is interesting because it’s a mix between “uncertainty in the map” and “uncertainty in the territory”.
I think this argument may have a problem. Here’s an analogy: you may translate C++ into bytecode and even movement of particles, but it doesn’t mean that C++ doesn’t exist.