An evolutionary model explaining the Neolithic transition from egalitarianism to leadership and despotism

Powers, S.T., Lehmann, L. (2014). An evolutionary model explaining the Neolithic transition from egalitarianism to leadership and despotism. Proceedings of the Royal Society B: Biological Sciences, 281, (1791), .


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Abstract

The Neolithic was marked by a transition from small and relatively egalitarian groups to much larger groups with increased stratification. But, the dynamics of this remain poorly understood. It is hard to see how despotism can arise without coercion, yet coercion could not easily have occurred in an egalitarian setting. Using a quantitative model of evolution in a patch-structured population, we demonstrate that the interaction between demographic and ecological factors can overcome this conundrum. We model the coevolution of individual preferences for hierarchy alongside the degree of despotism of leaders, and the dispersal preferences of followers. We show that voluntary leadership without coercion can evolve in small groups, when leaders help to solve coordination problems related to resource production. An example is coordinating construction of an irrigation system. Our model predicts that the transition to larger despotic groups will then occur when: (i) surplus resources lead to demographic expansion of groups, removing the viability of an acephalous niche in the same area and so locking individuals into hierarchy; (ii) high dispersal costs limit followers' ability to escape a despot. Empirical evidence suggests that these conditions were probably met, for the first time, during the subsistence intensification of the Neolithic.
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Authors

Simon T. Powers
Lecturer
S.Powers@napier.ac.uk
+44 131 455 2718

Areas of Expertise

Bio-inspired Computing
The Bio-Inspired Algorithms group within the Centre for Algorithms, Visualisation and Evolving Systems is a large and thriving group with interests in nature-inspired computing that include Evolutionary Computing, Hyper-Heuristics, Artificial Immune Systems and Swarm Intelligence.

Associated Projects