How do you assign probabilities to expected lengths of inferential differencies needed to technically understand clearly ‘cascading’ issues? I mean, the grass is green. I know it has to do with light being selectively reflected off it, because of photosynthesis, because of the plant needing energy to live. I estimate the ID in the range of c. 5 steps (the level at which I’d feel comfortable in predicting outcomes of adding fertilizers to vegetable garden) to 50 (answering an exam question). Yet, I happen to need knowledge of photosynthesis now and again, a semi-technical one. And I usually feel, well, like neither the ‘5’ nor the ’50′ resolution fits.
I know about ‘emergent properties’ and such, but—can you share your own eхperiences? Maybe there’s some explainable variation between fields.
How do you assign probabilities to expected lengths of inferential differencies needed to technically understand clearly ‘cascading’ issues? I mean, the grass is green. I know it has to do with light being selectively reflected off it, because of photosynthesis, because of the plant needing energy to live. I estimate the ID in the range of c. 5 steps (the level at which I’d feel comfortable in predicting outcomes of adding fertilizers to vegetable garden) to 50 (answering an exam question). Yet, I happen to need knowledge of photosynthesis now and again, a semi-technical one. And I usually feel, well, like neither the ‘5’ nor the ’50′ resolution fits.
I know about ‘emergent properties’ and such, but—can you share your own eхperiences? Maybe there’s some explainable variation between fields.