I agree with Ben here and won’t repeat what he said.
In general, I think thinking things through yourself from first principles is a valuable exercise even if someone has already thought the thing through, so even if the economics field functioned better than it did, I would still not agree that “it probably doesn’t make sense to start over with a new framework, unless you could explain why the standard economics ones are problematic and how the new framework fixes those problems.”
Re comparative advantage: this is pretty easy to talk about in my framework; you can have different generators (with different conversion ratios and rates of conversion) in different locations, and produce faster feedback loops by using the more-efficient generators, which requires moving stuff around.
Re asymmetric information: also pretty easy, this is information produced in one location that is not communicated to other locations.
In general, I think thinking things through yourself from first principles is a valuable exercise even if someone has already thought the thing through
Given that the “first principles” must by necessity be much simplified compared to the real world, how do you know whether the derivations come anywhere close to explaining it? Academia has done a lot of the necessary vetting/testing for its frameworks so I don’t think it’s a good idea to start over with different principles (unless, again, you can explain why they’re likely to solve some problems in the standard ones).
Re asymmetric information: also pretty easy, this is information produced in one location that is not communicated to other locations.
Can your framework derive the well known consequences of asymmetric information in economics, such as it often leading to failure to agree upon mutually beneficial deals?
Given that the “first principles” must by necessity be much simplified compared to the real world, how do you know whether the derivations come anywhere close to explaining it?
Academia’s models are also simplified. Given things like the replication crisis, I am really not convinced that academia is good at vetting things outside STEM. Generation of known-good ideas can be separated into generating ideas and checking them; generating ideas can be useful even they can’t be fully checked yet. In practice, to vet my models, I look at things like: which conclusions are logically sound (e.g. that growing economies require positive material feedback loops), whether it matches up with information I have, compatibility with other models/intuitions (where incompatibility could mean either model is wrong), and so on. I don’t think this is that different from what the most generative academics do. If I were reading academic papers, I would be doing the same checks to determine what to trust and how to integrate the ideas into my own models.
Can your framework derive the well known consequences of asymmetric information in economics, such as it often leading to failure to agree upon mutually beneficial deals?
Kind of. I haven’t discussed rational agents yet. But, it’s possible to say that some systems will be more ecologically fit if they hide certain information, and some will be more ecologically fit if they only make trades given the info that making this trade would gain some necessary resource, from which it is derived that ecologically fit systems will fail to make trades that increase the fitness of both of them, where ecological fitness could be defined in terms of speed and sustainability of positive feedback loop. There are different advantages/disadvantages to thinking about things this way instead of in terms of rational agency.
Ok, this is much easier to respond to, thanks.
I agree with Ben here and won’t repeat what he said.
In general, I think thinking things through yourself from first principles is a valuable exercise even if someone has already thought the thing through, so even if the economics field functioned better than it did, I would still not agree that “it probably doesn’t make sense to start over with a new framework, unless you could explain why the standard economics ones are problematic and how the new framework fixes those problems.”
Re comparative advantage: this is pretty easy to talk about in my framework; you can have different generators (with different conversion ratios and rates of conversion) in different locations, and produce faster feedback loops by using the more-efficient generators, which requires moving stuff around.
Re asymmetric information: also pretty easy, this is information produced in one location that is not communicated to other locations.
Given that the “first principles” must by necessity be much simplified compared to the real world, how do you know whether the derivations come anywhere close to explaining it? Academia has done a lot of the necessary vetting/testing for its frameworks so I don’t think it’s a good idea to start over with different principles (unless, again, you can explain why they’re likely to solve some problems in the standard ones).
Can your framework derive the well known consequences of asymmetric information in economics, such as it often leading to failure to agree upon mutually beneficial deals?
Academia’s models are also simplified. Given things like the replication crisis, I am really not convinced that academia is good at vetting things outside STEM. Generation of known-good ideas can be separated into generating ideas and checking them; generating ideas can be useful even they can’t be fully checked yet. In practice, to vet my models, I look at things like: which conclusions are logically sound (e.g. that growing economies require positive material feedback loops), whether it matches up with information I have, compatibility with other models/intuitions (where incompatibility could mean either model is wrong), and so on. I don’t think this is that different from what the most generative academics do. If I were reading academic papers, I would be doing the same checks to determine what to trust and how to integrate the ideas into my own models.
Kind of. I haven’t discussed rational agents yet. But, it’s possible to say that some systems will be more ecologically fit if they hide certain information, and some will be more ecologically fit if they only make trades given the info that making this trade would gain some necessary resource, from which it is derived that ecologically fit systems will fail to make trades that increase the fitness of both of them, where ecological fitness could be defined in terms of speed and sustainability of positive feedback loop. There are different advantages/disadvantages to thinking about things this way instead of in terms of rational agency.