DeDeo and Hobson do a commentary on a model developed by
Kawakatsu et al. (open source) that explains the emergence of hierarchy in networked endorsement dynamics. I pass on a few clips from both and after that list titles with links to a number of previous MindBlog posts that have presented explanations of why inequality and hierarchy are features of all natural systems. First, from DeDeo and Hobson:
As an old Scottish proverb says, “give a Dog an ill Name, and he’ll soon be hanged.” Even when the signal has little to do with underlying reality, endorsement—or contempt—can produce lasting consequences for a person’s social position. The ease with which such pieces of folk wisdom translate across both time and species suggests that there is a general, and even perhaps universal, logic to hierarchies and how they form. Kawakatsu et al. make an important advance in the quest for this kind of understanding, providing a general model for how subtle differences in individual-level decision-making can lead to hard-to-miss consequences for society as a whole...Their work reveals two distinct regimes—one egalitarian, one hierarchical—that emerge from shifts in individual-level judgment. These lead to statistical methods that researchers can use to reverse engineer observed hierarchies, and understand how signaling systems work when prestige and power are in play. The results make a singular contribution at the intersection of two distinct traditions of research into social power: the mechanistic (how hierarchies get made) and the functional (the adaptive roles they can play in society).
Kawakatsu et al.'s abstract:
Many social and biological systems are characterized by enduring hierarchies, including those organized around prestige in academia, dominance in animal groups, and desirability in online dating. Despite their ubiquity, the general mechanisms that explain the creation and endurance of such hierarchies are not well understood. We introduce a generative model for the dynamics of hierarchies using time-varying networks, in which new links are formed based on the preferences of nodes in the current network and old links are forgotten over time. The model produces a range of hierarchical structures, ranging from egalitarianism to bistable hierarchies, and we derive critical points that separate these regimes in the limit of long system memory. Importantly, our model supports statistical inference, allowing for a principled comparison of generative mechanisms using data. We apply the model to study hierarchical structures in empirical data on hiring patterns among mathematicians, dominance relations among parakeets, and friendships among members of a fraternity, observing several persistent patterns as well as interpretable differences in the generative mechanisms favored by each. Our work contributes to the growing literature on statistically grounded models of time-varying networks.
And, I list a few relevant past MindBlog posts:
Wealth inequality as a law of nature.
The science of inequality.
The Pareto Principle - unfairness is a law.
Simple mechanisms can generate wealth inequality.
A choice mind-set perpetuates acceptance of wealth inequality.
