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Previously you said this: If groups with power were not to resist social mobility (i.e. for less powerful groups to obtain more power) we'd see a constant shift

Previously you said this: If groups with power were not to resist social mobility (i.e. for less powerful groups to obtain more power) we'd see a constant shift in power distribution... You now say this: "it's easier to approximate power through proxies such as income, percentage of powerful roles" Lets work with this specific prediction. If a group has a tendency to enter a powerful role with probability p, and there are N members of this group, then the shifts in power distribution will have standard deviation (in percentage terms) of 1/sqrt(Np(1-p)). That doesn't seem to agree with your "constant shift in power distribution" at all.

pron 11y ago
Read it again. I said that if the consensus theory is wrong then we'd see constant power shifts. Instead, we see long stable periods disrupted by short rare, revolutions or a relatively slow, very visible struggle. Hence, hegemonies do resist social mobility. IIRC, you made the exact same mistake last time we discussed the issue. Also, I fail to see how your formula is meaningful at all, as it has nothing to do with power dynamics. At best, it describes the distribution of the number of powerful people in a random sample. Well, OK, how is that helpful? It cannot describe any shifts in power as it assumes independence, where our understanding of power is anything but (you are more likely to be powerful if you're close to powerful people). The actual dynamics is a collection of non-linear processes in a complex system. A very, very crude example of a somewhat similar process is that of segregation[1] with the addition of random populations during the run. It is processes with strong correlations between neighboring agents that tend to show stability pockmarked with rare bifurcations, which is exactly what we see in human society. When I went to study history in graduate school, I dreamt of applying math to historical events. Playing with cellular automata to recreate certain shifts in history is fun, but eventually teaches us little, as we cannot say much more about the model than the same qualitative descriptions historians give anyway. It is sometimes cool to figure out the values of certain variables in the model and assign them meaning (like the "despair level prior to the French Revolution", but even these numbers don't tell us much because the models are so sensitive we can't be sure we got them right, and even if we did, it's virtually impossible to measure the values directly rather than figuring them out retrospectively. Therefore, speaking about events in broad qualitative terms is often the best we can do (compound cellular automata's Turing completeness with the non-linear ODEs defining the automaton's rules and the difficulty measuring variables directly and you get very little predictive ability). [1]: http://ccl.northwestern.edu/netlogo/models/Segregation http://ccl.northwestern.edu/netlogo/models/Segregation
- yummyfajitas 11y ago
  I said that if the consensus theory is wrong then we'd see constant power shifts. This is clearly wrong - I demonstrated a stupidly simple alternate theory which disagrees with the consensus, but still lacks constant power shifts. Rapid shifts in the composition of various fields would debunk basically every theory that presupposes human nature doesn't rapidly shift. As such, your theory proves very little, and other theories (e.g. mine) make the same predictions. So why cling to that theory, rather than, say, my theory of p=A - B x math_content + second_order_corrections? My theory predicts more things (e.g., it predicts % women in academia, % women in finance, % women in tech vs non-tech roles) and is vastly simpler. How is that not a win?
  - pron 11y ago
    > This is clearly wrong - I demonstrated a stupidly simple alternate theory which disagrees with the consensus, but still lacks constant power shifts. Your theory didn't even explain the data (in the software industry), and had nothing to say about power. Basically, it can comfortably exist beside the consensus theory as a second-order effect (and the data actually looks like that is probably the case, explaining representation differences of up to 2:1). > So why cling to that theory, rather than, say, my theory of p=A - B x math_content + second_order_corrections? Two reasons: 1. your theory doesn't explain the anomalous software industry (neither the very low participation nor the sudden change) while the consensus theory does, and 2. your theory doesn't explain the lack of representation (and possible correction after well-documented struggles) in fields of high prestige and little math (such as law, medicine and politics). In all those fields: law, medicine, politics and software, women participation was (or is) far lower than 30%. It was/is lower than 20%. You might think that singling out software is uninteresting from a statistical perspective, as in Scott Alexander's regression it was just a single (outlier) data point, but in fact, the software industry is larger than all "heavy math content" professions combined, and holds more power in today's society. > My theory predicts more things (e.g., it predicts % women in academia, % women in finance, % women in tech vs non-tech roles) and is vastly simpler. How is that not a win? Because it predicts fewer things, and not even what you claim it does. At best, it predicts fewer women than men in certain subjects, but that's it. However, we see that while the binary relation "fewer" may remain, how much fewer decreases over time. Second, it only holds (even as a binary prediction) when you look at the past few decades -- far too short to look at social change -- which means it does not explain the exclusion of women from law, medicine and politics. Third, it does not explain the way-too-low representation in CS, nor the drop in participation. The consensus theory explains all of it, for any time duration. It even explains interesting shifts we see in, say, education (more women as prestige drops, while in CS fewer women as prestige rises). Yes, it's more complicated, but fits reality better, plus it's supported by lots and lots of data.

pron 11y ago

Read it again. I said that if the consensus theory is wrong then we'd see constant power shifts. Instead, we see long stable periods disrupted by short rare, revolutions or a relatively slow, very visible struggle. Hence, hegemonies do resist social mobility. IIRC, you made the exact same mistake last time we discussed the issue. Also, I fail to see how your formula is meaningful at all, as it has nothing to do with power dynamics. At best, it describes the distribution of the number of powerful people in a random sample. Well, OK, how is that helpful? It cannot describe any shifts in power as it assumes independence, where our understanding of power is anything but (you are more likely to be powerful if you're close to powerful people). The actual dynamics is a collection of non-linear processes in a complex system. A very, very crude example of a somewhat similar process is that of segregation[1] with the addition of random populations during the run. It is processes with strong correlations between neighboring agents that tend to show stability pockmarked with rare bifurcations, which is exactly what we see in human society. When I went to study history in graduate school, I dreamt of applying math to historical events. Playing with cellular automata to recreate certain shifts in history is fun, but eventually teaches us little, as we cannot say much more about the model than the same qualitative descriptions historians give anyway. It is sometimes cool to figure out the values of certain variables in the model and assign them meaning (like the "despair level prior to the French Revolution", but even these numbers don't tell us much because the models are so sensitive we can't be sure we got them right, and even if we did, it's virtually impossible to measure the values directly rather than figuring them out retrospectively. Therefore, speaking about events in broad qualitative terms is often the best we can do (compound cellular automata's Turing completeness with the non-linear ODEs defining the automaton's rules and the difficulty measuring variables directly and you get very little predictive ability). [1]: http://ccl.northwestern.edu/netlogo/models/Segregation http://ccl.northwestern.edu/netlogo/models/Segregation

yummyfajitas 11y ago
I said that if the consensus theory is wrong then we'd see constant power shifts. This is clearly wrong - I demonstrated a stupidly simple alternate theory which disagrees with the consensus, but still lacks constant power shifts. Rapid shifts in the composition of various fields would debunk basically every theory that presupposes human nature doesn't rapidly shift. As such, your theory proves very little, and other theories (e.g. mine) make the same predictions. So why cling to that theory, rather than, say, my theory of p=A - B x math_content + second_order_corrections? My theory predicts more things (e.g., it predicts % women in academia, % women in finance, % women in tech vs non-tech roles) and is vastly simpler. How is that not a win?
- pron 11y ago
  > This is clearly wrong - I demonstrated a stupidly simple alternate theory which disagrees with the consensus, but still lacks constant power shifts. Your theory didn't even explain the data (in the software industry), and had nothing to say about power. Basically, it can comfortably exist beside the consensus theory as a second-order effect (and the data actually looks like that is probably the case, explaining representation differences of up to 2:1). > So why cling to that theory, rather than, say, my theory of p=A - B x math_content + second_order_corrections? Two reasons: 1. your theory doesn't explain the anomalous software industry (neither the very low participation nor the sudden change) while the consensus theory does, and 2. your theory doesn't explain the lack of representation (and possible correction after well-documented struggles) in fields of high prestige and little math (such as law, medicine and politics). In all those fields: law, medicine, politics and software, women participation was (or is) far lower than 30%. It was/is lower than 20%. You might think that singling out software is uninteresting from a statistical perspective, as in Scott Alexander's regression it was just a single (outlier) data point, but in fact, the software industry is larger than all "heavy math content" professions combined, and holds more power in today's society. > My theory predicts more things (e.g., it predicts % women in academia, % women in finance, % women in tech vs non-tech roles) and is vastly simpler. How is that not a win? Because it predicts fewer things, and not even what you claim it does. At best, it predicts fewer women than men in certain subjects, but that's it. However, we see that while the binary relation "fewer" may remain, how much fewer decreases over time. Second, it only holds (even as a binary prediction) when you look at the past few decades -- far too short to look at social change -- which means it does not explain the exclusion of women from law, medicine and politics. Third, it does not explain the way-too-low representation in CS, nor the drop in participation. The consensus theory explains all of it, for any time duration. It even explains interesting shifts we see in, say, education (more women as prestige drops, while in CS fewer women as prestige rises). Yes, it's more complicated, but fits reality better, plus it's supported by lots and lots of data.