Doesn’t the act of combining many outside views and their reference classes turn you into somebody operating on the inside view? This is to say, what is the difference between this and the type of “inside” reasoning about a phenomenon’s causal structure?
Is it that inside thinking involves the construction of new models whereas outside thinking involves comparison and combination of existing models? From an machine intelligence perspective, the distinction is meaningless. The construction of new models is the extension of old models, albeit models of arbitrary simplicity. Deductive reasoning is just the generation of some new strings for induction to operate on to generate probabilities. Induction has the final word; that’s where the Bayesian network is queried for the result. Logic is the intentional generation of reference classes, a strategy for generating experiments that are likely to quickly converge that probability to 0 or 1.
Inside thinking also, analogously in humans, is the generation of new reference classes; after casting the spell called Reason, the phenomenon now belongs to a class of referents that upon doing so produce a particularly distinguishing set of strings in my brain. The existence of these strings, for the outside thinker, is strong evidence about the nature of the phenomenon. And once the strings exist, the outside thinker is required to combine the model that includes them with her existing model. And unbeknownst to the outside thinker, the strategy of seeking new reference classes is inside thinking.
I think that’s only true if you allow yourself free choice of outside views. If you had a fixed frameworks of which outside views to take into account, i.e. how to select the ten experts you’re going to ask for their opinion, and you decide that before you formulate the problem, the composite model you get shouldn’t suffer from the arbitrariness of the inside view. Right?
Doesn’t the act of combining many outside views and their reference classes turn you into somebody operating on the inside view? This is to say, what is the difference between this and the type of “inside” reasoning about a phenomenon’s causal structure?
Is it that inside thinking involves the construction of new models whereas outside thinking involves comparison and combination of existing models? From an machine intelligence perspective, the distinction is meaningless. The construction of new models is the extension of old models, albeit models of arbitrary simplicity. Deductive reasoning is just the generation of some new strings for induction to operate on to generate probabilities. Induction has the final word; that’s where the Bayesian network is queried for the result. Logic is the intentional generation of reference classes, a strategy for generating experiments that are likely to quickly converge that probability to 0 or 1.
Inside thinking also, analogously in humans, is the generation of new reference classes; after casting the spell called Reason, the phenomenon now belongs to a class of referents that upon doing so produce a particularly distinguishing set of strings in my brain. The existence of these strings, for the outside thinker, is strong evidence about the nature of the phenomenon. And once the strings exist, the outside thinker is required to combine the model that includes them with her existing model. And unbeknownst to the outside thinker, the strategy of seeking new reference classes is inside thinking.
Yes. So does the act of selecting just one outside view as your Personal Favorite, since it gives the right answer.
I think that’s only true if you allow yourself free choice of outside views. If you had a fixed frameworks of which outside views to take into account, i.e. how to select the ten experts you’re going to ask for their opinion, and you decide that before you formulate the problem, the composite model you get shouldn’t suffer from the arbitrariness of the inside view. Right?