Quivers of Idiosyncrasy: Modern Statistics in A Modern Utopia

  • Genie BabbEmail author


In the opening paragraph of A Modern Utopia, Wells critiques previous utopias as predicated on a generic, idealised notion of human nature and on the naïve assumption that stable social arrangements can be implemented and sustained ad infinitum. In these utopias, he writes: ‘One beheld a healthy and simple generation enjoying the fruits of the earth in an atmosphere of virtue and happiness, to be followed by other virtuous, happy and entirely similar generations until the Gods grew weary.’1 As much as they might have to teach us, the problem with these utopias as a blueprint for ‘modern’ society, Wells argues, is that they are not grounded in ‘modern’—that is to say, scientific—‘conceptions’ (Wells, A Modern Utopia, p. 11). Wells singles out two elements in particular that need revision according to recent scientific advances: the conception of human nature and the understanding of the role of change in all of life. Not only must notions of human nature include the ‘perpetuity of aggressions’ and ‘Will to Live’ that evolutionary theory had made manifest, they must also reflect the irreducible uniqueness of individual human beings. Similarly, scientific advances had made clear that life is ‘kinetic’. Evolutionary change is a fundamental principle of the universe: ‘we do not resist and overcome the great stream of things, but rather float upon it’ (Wells, A Modern Utopia, p. 11). Therefore, any ‘modern’ utopia worth its salt must take into account ‘the fertilizing conflict of individualities’ on the one hand and the ‘kinetic’ nature of life on the other; it must be ‘a flexible common compromise, in which a perpetually novel succession of individualities may converge most effectually upon a comprehensive onward development’ (Wells, A Modern Utopia, p. 11).


Human Nature Science Fiction Statistical Thinking Parallel World Rock Crystal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© The Author(s) 2016

Authors and Affiliations

  1. 1.State University of New York, PlattsburghNew YorkUSA

Personalised recommendations