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You don't. Then we are back to power being a non-ordinal concept. ...more power... If power is not an ordinal concept, this phrase is meaningless. I'm also

You don't. Then we are back to power being a non-ordinal concept. ...more power... If power is not an ordinal concept, this phrase is meaningless. I'm also confused as to how you can even determine that such a "constant shift" isn't happening - in terms of edges in your influence graph, they shift regularly. Yesterday I met a new person at work and we now interact and influence each other. The power digraph has gained 2 edges. Could you explain how you quantify this?

pron 11y ago
> If power is not an ordinal concept, this phrase is meaningless. You're reading things that I'm not saying. It is an ordinal concept, but we don't have the ability to measure it well, only notice large differences. You can think of it as a quantity for which we have very inaccurate measurement devices. > I'm also confused as to how you can even determine that such a "constant shift" isn't happening - in terms of edges in your influence graph, they shift regularly. Yesterday I met a new person at work and we now interact and influence each other. The power digraph has gained 2 edges. Could you explain how you quantify this? Because in this discussion we are talking about groups (social mobility in individuals is an interesting, yet somewhat different discussion), and when we look at a large number of people, it's easier to approximate power through proxies such as income, percentage of powerful roles such as CEOs, leading journalists etc. (it's harder to compare power in individuals, because it's hard to tell which is more influential, a media personality or the CEO of a large company, especially as the latter's influence might sometimes be hidden). Shifts in power from nobility to the bourgeois class, or in the relative cultural and technological power of Europe, were sudden changes of relative homeostases. Similarly, you can track power distribution between men and women, or between whites and blacks in the US. They point at very stable states that are violently changed, or (usually in the case of women), a faster rate of smaller revolutions (or a more steady rate of change). The difference between male/female and race hegemonies is also well understood (and seen in many other cultures), because the classification of women as "strangers" is always very different from that of actual foreign ethnicities.
- yummyfajitas 11y ago
  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

pron 11y ago

> If power is not an ordinal concept, this phrase is meaningless. You're reading things that I'm not saying. It is an ordinal concept, but we don't have the ability to measure it well, only notice large differences. You can think of it as a quantity for which we have very inaccurate measurement devices. > I'm also confused as to how you can even determine that such a "constant shift" isn't happening - in terms of edges in your influence graph, they shift regularly. Yesterday I met a new person at work and we now interact and influence each other. The power digraph has gained 2 edges. Could you explain how you quantify this? Because in this discussion we are talking about groups (social mobility in individuals is an interesting, yet somewhat different discussion), and when we look at a large number of people, it's easier to approximate power through proxies such as income, percentage of powerful roles such as CEOs, leading journalists etc. (it's harder to compare power in individuals, because it's hard to tell which is more influential, a media personality or the CEO of a large company, especially as the latter's influence might sometimes be hidden). Shifts in power from nobility to the bourgeois class, or in the relative cultural and technological power of Europe, were sudden changes of relative homeostases. Similarly, you can track power distribution between men and women, or between whites and blacks in the US. They point at very stable states that are violently changed, or (usually in the case of women), a faster rate of smaller revolutions (or a more steady rate of change). The difference between male/female and race hegemonies is also well understood (and seen in many other cultures), because the classification of women as "strangers" is always very different from that of actual foreign ethnicities.

yummyfajitas 11y ago
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