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Toward a Scientific History. Robert Aunger. Many scales:. Many schools : Marxist Structuralist Deconstructionist Anthropological Psychological Economic Feminist. Local World/global ‘Big’ history. No consensus?. History as ‘story-telling’.

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Many scales l.jpg
Many scales:

  • Many schools:

    • Marxist

    • Structuralist

    • Deconstructionist

    • Anthropological

    • Psychological

    • Economic

    • Feminist



‘Big’ history

No consensus?

History as story telling l.jpg
History as ‘story-telling’

  • narrative interpretation of past events emphasizing –

  • contingency

  • human agency

  • ‘big men’

  • (Etymology: Latin historia = “narrative, account, tale, story”)

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    No theory please!

    • “After a century of grand theory, from Marxism and Social Darwinism to structuralism and postmodernism, most historians have abandoned the belief in general laws. We no longer search for grand designs and dialectics. Instead, we concentrate on the particular and sometimes even the microscopic…not because we think we can see the universe in a grain of sand but because we have developed an increased sensitivity to the complexities that differentiate one society or one subculture from another.”

    • (Darnton 1999)

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    Historical regularities

    (Turchin, 2005)

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    Historical progress

    • ‘Arrow’ in history, resulting in increasing complexity over time

    • This progress attributed to –

    • energy flows (LH Morgan, Leslie White, Chaisson)

    • information accumulation (Lenski, Wright)

    • dialectical mechanism (Hegel, Marx, Wallerstein)

    • (often related to technological advances)

    Cycles in history l.jpg
    Cycles in history

    • Recurrent patterns in the rising and falling sequence of some variable:

    • civilizations/dynasties (Vico, Gibbon, Toynbee)

    • population size/demographics (Turchin)

    • economic waves (Kondratiev)

    • spiritual development (Sarkar)

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    Leap theories

    • Punctuated equilibrium (Eldredge & Gould)

    • ‘Hopeful monster’ saltationism (Goldschmidt)

    • Major transitions (Maynard Smith & Szathmáry)

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    Identification strategies

    • Define periods of specific length beginning from some arbitrary starting point

      • – e.g., BCE, the 60s

    • Pick events by particular characteristics

      • – e.g., the Middle Ages, the French Revolution

    • 3. Begin with a theoretical criterion

      • – e.g., change in level of social organisation

    Atoms of history l.jpg
    ‘Atoms’ of History

    • A science of history is possible if can identify class of objects analogous to the ‘natural kinds’ in other sciences (i.e., atoms, stars, genes, beliefs)

    • – periods?

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    • Use theoretical criterion as most likely to be scientific

    • Largest possible temporal scale to maximize chances of finding patterning in events – universal/cosmic

    • The only ‘currency’ applicable to cosmic/planetary/biological/cultural scale is energy; the common currency paying for all biological information or organisation in a self-organizing system is energy flow (Morowitz, 1979); transitions in rates of energy flow through thermodynamic systems are already established as the currency of ‘big’ history (Chaisson/Spiers)

    • Interested in explaining long-term increase in complexity, not medium-term cycles – so linear or leap theory

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    Maximum Entropy Production

    • (Paltridge, 1975; Attard, 2006; Niven, 2009)

    • A generalization of Boltzmann’s probability distribution to non-equilibrium case

    • States that open thermodynamic systems with sufficient degrees of freedom (i.e., many elements) will converge to a steady state at which the production of entropy is maximized given the constraints of the system

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    Systems far from equilibrium

    • A wide variety of dynamical systems adjust to maximize entropy production as a fundamental physical necessity (Lineweaver, 2005)

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    Quantitative predictions

    • As an optimization solution, MEP ignores details of system dynamics, but nevertheless gives precise predictions

      • E.g., MEP models of Earth’s climate include accurate predictions of –

    • mean latitudinal air temperature

    • fractional cloud cover

    • meridional & vertical heat flux

    • mean vertical air temperature profile

    • historical latitudinal air temperature gradients

    (Lorenz, 2004)

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    Darwinian Thermodynamics

    • (Ao 2005; Sella and Hirsch, 2005)

    • Applies Sewall Wright’s adaptive landscape idea to statistical mechanics

    • Fitness or entropy is maximized, subject to constraints

    • Provides an intrinsically dynamic foundation for non-equilibrium thermodynamics, which is currently limited by its foundation in equilibrium and steady-state descriptions (Ao, 2005)

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    MEP vs DT

    • Maximum Entropy Production Principle:

      • a logical generalization of the second law of thermodynamics to non-equilibrium processes

      • Empirically confirmed in studies of various systems of physical, chemical or biological origin

      • Not contradicted by Prigorine’s minimum entropy production principle (which is a consequence of MEP in special circumstances)

      • Appropriate when dynamics are difficult to observe

      • (Martyushev and Seleznev, 2006)

    • Darwinian Thermodynamic Theory:

      • the maximum entropy outcome is produced by the dynamics; MEP doesn’t have a single outcome; instead the canonical (maximum) solution must be ‘chosen’ by convention

      • not restricted to the linear near-equilibrium (Onsager) regime

      • (Ao, 2005)

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    Steady-state response

    • Small flux perturbations cause control mechanisms to stabilize energy flow rates, maintaining existing steady-state

    (Kleidon, 2009)

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    Transitional response

    • If the boundary conditions shaping the optimum change, then a perturbation of the state can be amplified until the new optimum is reached (i.e., positive feedback to the perturbation leads to a search for a new output level)

    (Kleidon, 2009)

    Dual responses l.jpg
    Dual responses

    • Complex thermodynamic systems are able to –

    • stabilize in the face of small fluctuations in energy flow (and so persist for some time)

    • respond optimally to large fluctuations by finding a new steady state with higher (maximal) entropy production

    • The result historically should be a pattern of periodic jumps to new, higher levels of entropy production

    Result l.jpg

    • the fundamental unit of scientific ‘big’ history should be associated with a ‘leap’ in energy flow through a system far from thermodynamic equilibrium

    • What are the internal mechanics of such leaps?

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    Control volume analysis

    white elements = negative entropy producers (i.e., perform useful work)

    black elements = positive entropy producers (i.e. regions of high dissipation)

    energy flows


    Flow-controlled system

    (Niven, 2009)

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    Energy novelty

    energy flows


    A random fluctuation in internal flux or external force will generally not lead to a sustained increase in entropy production

    Energy novelty23 l.jpg
    Energy novelty


    energy flow

    energy flow

    A significant change in output is more likely to start with a new kind or level of energy flow in a system, due to a new way of extracting energy from the system’s surroundings. (Smil, 1994; Vermeij, 2004)

    Structural adjustment l.jpg
    Structural adjustment


    energy flow

    X Y

    energy flow

    Increased energy flow can cause new physical structures to form. Thermodynamic disequilibrium at point X induces a flow of energy toward an energy sink at point Y, producing a transient flow which increases disequilibrium at point Y. This new situation can cause a new or modified transport structure to form linking them (e.g., Belusov–Zhabotinsky reaction, self-organising nanostructures).

    Control l.jpg


    energy flow

    X Y

    energy flow

    • Control occurs in dynamical systems when a mechanism manipulates the inputs such that the stability of its output is increased. In particular, controls constrain the flow of energy through structures via feedback or interaction effects.

    • Adjustments of control typically must follow the creation of new structures, eliminating some structures and modifying others to make them appropriate within the context of the system as a whole. (Freeman & Louçã, 2001; Turchin, 1977)

    Nessts l.jpg


    A major energy novelty invokes several consequences, suggesting that these groups of events are linked together and so should be treated as inter-independent.

    Such structured processes can be called ‘non-equilibrium steady-state transitions’ (NESSTs).

    NESSTs identify the time/place/mechanisms for the birth of new historical periods.

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    Criteria for identifying historical ‘leap’

    Scan the events covered by ‘big’ history (cosmological, geological, biological, cultural) looking for this pattern:

    Event associated with a sustained increase in energy flow (with entropy production) through a system

    followed ‘rapidly’ in the same area by an organisational event and a control event

    which can be causally linked

    Entropy production measure l.jpg

    Entropy production measure

    ‘Energy flow density’:

    the amount of free energy flowing through a gram of the relevant structure per second, in ergs (Chaisson, 2001)

    (NOTE: free energy [to do work] and entropy are linearly related in the Gibbs, Helmholtz and negative Massieu formulations, and so good enough measure for general trends; e.g., S = (E-F)/T where ; S = entropy; E = internal energy; F = Helmholtz free energy; and T = absolute temperature)

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    Population genetics = statistical mechanics

    • ‘There is a close analogy between statistical thermodynamics and the evolution of allele frequencies under mutation, selection and random drift. This analogy brings together previous ideas in a general framework, and justifies a maximum entropy approximation to the dynamics of quantitative traits.’

      • Wright's formula for the stationary distribution of allele frequencies is analogous to the Boltzmann distribution in statistical physics.

      • Population size plays the role of the inverse temperature and determines the magnitude of random fluctuations.

      • Log mean fitness tends to increase under selection, and is analogous to (negative) energy.

      • An entropy can be defined which measures the deviation from the distribution of allele frequencies expected under random drift alone.

      • The sum gives a free fitness that increases as the population evolves towards its stationary distribution.

    • (Barton & Coe, 2009; building on Ao 2005, 2008)

    Inevitable life l.jpg
    Inevitable life

    • Self-organised systems produce more entropy, more quickly, than a disordered collection; thus MEP favours increasing degrees of order

    • E.g., under the conditions of early Earth, life was the best way to release the build-up of geothermal energy. (Morowitz & Smith, 2007)

    • Dissipative structures which produce high entropy – including living systems – can be viewed as highly probable phenomena. (Dewar, 2005)

    Biological nesst l.jpg
    Biological NESST

    ‘metabolism first/compartment intermediate/replication last’ position

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    • Culture evolves as the amount of energy harnessed per capita per year is increased, or as the efficiency of the instrumental means of putting energy to work is increased’

    (Leslie White, 1949)

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    • ‘Big’ history covers cosmological, planetary, biological and cultural history

    • – but as a single narrative (Christian, 2004)

    • Use of the NESST concept and non-equilibrium thermodynamics produces a scientific ‘big’ history

    • (Etymology: Perasma = Gk “transition or crossing”)

    Eras in perasmology l.jpg





    Eras in Perasmology








    Origin of life

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    Trend analysis








    increasing complexity (more degrees of freedom)

    increasing hierarchy

    increasing levels of energy disequilibrium

    Eras in perasmology45 l.jpg
    Eras in Perasmology

    Major transitions in biology l.jpg
    Major transitions in biology

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    Generalizing MTT

    • NESST model extends ‘major transition theory’ (Maynard Smith and Szathmáry, 1995) into cosmological and geological time

    • Turns MTT transitions into components of a larger explanatory framework (NESST)

    • Shows how ‘egalitarian’ and ‘fraternal’ transitions (Queller, 1997) are related – as organisational and control elements of NESSTs, respectively

      • e.g., multi-cellular organism is organisation in multi-cellular NESST; sexual reproduction is control element of complex cell NESST

    Conclusions l.jpg

    • The NESST concept and non-equilibrium thermodynamics provide a framework – strongly grounded in physical and biological theory – which can be used to explain macro-historical trends since the origin of the universe

    • This framework allows rigorous identification of historical ‘leaps’, which define periods beginning with a NESST

    • ‘Big’ history becomes a scientific discipline: ‘Perasmology’

    • Historical trends in energy, organisation and information are linked in a single framework, generalizing MTT

    • NESSTs accelerate once information inheritance is invented

    Acknowledgements l.jpg

    • Prof. Walter Alvarez, Earth and Planetary Sciences, University of California, Berkeley

    • Prof. Ping Ao, Mechanical Engineering, University of Washington

    • Prof. Eric Chaisson, Physics and Astronomy, Tufts University

    References l.jpg

    • Ao, Ping (2005) Laws in Darwinian evolutionary theory. Physics of Life Reviews 2, 117–156

    • Attard, P. (2006) Theory for non-equilibrium statistical mechanics, Phys. Chem. Chem. Phys. 8: 3585-3611

    • Barton, N.H. and J.B. Coe (2009)On the application of statistical physics to evolutionary biology Journal of Theoretical Biology, 259:317-324

    • Barton, N.H. and de Vladar, H.P. (2009) "Statistical Mechanics and the Evolution of Polygenic Quantitative Traits" Genetics 18(3):997-1011

    • Martyushev, L.M. and V.D. Seleznev (2006) Maximum entropy production principle in physics, chemistry and biology Physics Reports 426:1 – 45

    • Niven, Robert K. (2009) Steady State of a Dissipative Flow-Controlled System and the Maximum Entropy Production Principle Phys. Rev. E 80, 021113

    • Paltridge, G.W. (1975) Quart. J. Royal Meteorol. Soc. 101, 475.

    • Sella, Guy and Aaron E. Hirsh (2005) The application of statistical physics to evolutionary biology PNAS102: 9541–9546

    • Ziegler, H. (1983) An Introduction to Thermomechanics, North-Holland, Amsterdam,