Modeling Long-Term History


First published on March 14, 2019

Modeling Long-Term History


First published on March 14, 2019

This course concerns modeling historical societies beyond single dynasties. In a broadest sense, it follows the timeline of Big History and looks at human history as a continuation of the cosmological processes triggered by the Big Bang. In a more moderate sense, it concerns both the rise and fall of series of dynasties in a single society and the interaction between societies over periods of several hundred to a several thousands of years.

Long-term history also concerns the impact of long-term trends upon history such as advances in technology, increasing population and climate change.

This course concerns modeling historical societies beyond single dynasties. In a broadest sense, it follows the timeline of Big History and looks at human history as a continuation of the cosmological processes triggered by the Big Bang. In a more moderate sense, it concerns both the rise and fall of series of dynasties in a single society and the interaction between societies over periods of several hundred to a several thousands of years.

Long-term history also concerns the impact of long-term trends upon history such as advances in technology, increasing population and climate change.

Table of Contents

  1. 1. Long-Term Trends and the Emergence of Societies

    There are several long-term trends concerning humanity. Although these trends might not be observed every day, and there can even be periods and locations contrary to the trends, they still operate on long periods of time.

    Longterm Trends


    The inclination of the Earth with respect to the Sun changes in 26,000 year cycles (NASA). The Earth is slowly wobbling on its access. This affects regional climates, including wind, rainfall and temperature. This change can be significant when considering periods of more than a few centuries.

    Evolution and Selection

    Modern humans have existed for at least 10,000 years. There may not have been much genetic mutation over the past few millennia, so the scope of human evolution during that period may be limited. However, there has likely been some effects due to selection, that is the ability of people to adapt to particular local and social changes ad well as due to mating preferences. For example, during the 1950s-1970s, there was apparently a tremendous mating preference for those who were able and willing to master the electric guitar, an example of a new technology.

    Human Population Growth

    The human population has grown tremendously in the past 10,000 years (U.S. Census).

    Other Trends

    • Environmental change
    • Climate change
    • Species changes (extinction, domestication, monoculture)
    • Destruction of forests
    • Salinization of soil in irrigated lands
    • Total land area used by humans
    • Technology advancement

    Emergence of Societies

    Due to these trends, and arguably the driving force of the eth Law, human societies formed. Living organisms formed,then multi-cell creatures. Animals formed, then vertebrates, then mammals. Primates became smarter and able to use tools. Homo sapiens developed. Language and agriculture were discovered and adopted, allowing people to form complex societies.

  2. 2. Modeling History as A Series of Dynasties

    Describing A Society As A Series of Regimes

    A society can be modeled as a series of regimes or Hubbert curves. Each curve would typically represent a dynasty for a traditional historic monarchy. Traditional, monarchical, agricultural-based regimes have historically tended to endure for about 300 or so years. This is a rough rule of thumb. Other types of societies will tend to have a governance change in that period of time but may maintain better legal continuity of government.

    Not all regimes last for about 300 years. Where a potential has not restored itself, of those who attempt to rule the regime are not competent (i.e. a defective or inherently inefficient “heat engine”), a regime will be short lived. The other extreme is where the potential is too great. This can happen when neighboring regimes have become weak. In this case, a regime can expand too quickly and become a great, but brief empire. Such appears to have been the case of the first French Empire lead by Napoleon. There are plenty of exception to this 300 Year Rule. Yet, focusing attention on regimes that fit in this pattern can be useful to identify more general principles and constraints that govern humans. This is similar to the case of the development of astronomy, where first the easily observed bodies such as the Moon, Sun and visible planets were modeled first and lead to Newtonian mechanics. Later, smaller, further and more exotic objects were studied and modeled.

    Further, the 300 Year Rule is much less likely to be applicable to most of the regimes in existence when this book is written. Few regimes today are traditional agricultural monarchies. Further, regimes have become much more interdependent with each other, so it can be expected that Hubbert Curves will become more distorted and even more merged than any time in the past, even for the largest regimes in existence today. Also, most of the current regimes are dependent upon non-renewable resources such as petroleum that have never driven regimes before the 19th century. Yet, as mentioned above, a study of historical traditional 300 Year regimes can help to develop generic principles that can be applied to a much broader range of regimes.

    A common error would be to assume that the series of EDEG curves represents a periodic function. It’s not. However, many functions can be expressed as a Fourier series (a combination of sinusoidal functions) so perhaps a series of EDEG curves can be as well.

    Regimes might not follow immediately one after another. Or there could be some overlap between older and newer regimes.

    Historical Dynasties

    Dynasties in major historical civilizations are typically easy to identify. In a sense, dynasties are what fill the pages of historical textbooks. The flowing is a chronological list of French dynasties, along with duration data.

    TABLE: Dynasty Series for France

    Dates (CE) Regime Duration
    481–751 CE Merovingian dynasty 270 years
    754–987 CE Carolingian dynasty 233 years
    987–1328 CE Capetian dynasty 341 years
    1429–1588 CE Period of relative discontinuity
    1589–1791*/1848 CE Bourbon dynasty 202/259 years

    *1791 represents the French Revolution that interrupted the regime that was restored for awhile after the fall of Napoleon until 1848.

    It is clear that the dynasties are not exactly periodic (exactly the same length in years as each other).

    Rise and fall profile for four dynasties in series, with discontinuity between third and fourth

    French dynasties 500-1850 CE

    Below is a plot of the power progressions of several major West African dynasties, from 750 CE to 1591 CE. The lack of periodicity is more obvious. Although the geographic locations were all in West Africa, the exact locations varied. There was less territorial overlap than in the French dynasties plotted above.
    Three rise-fall power progressions in series.

    Selected major West African dynasties, 750-1591 CE

    Thermodynamic Approaches to Model a Series of Dynasties

    An exciting next step it to attempt to model a series of past dynasties. You could simply do this by superimposing a best fit set of distributions over time. This is fairly simple to do any might provide some utility and satisfaction.

    However, the above series of French dynasties has been modeled.  In this case, each regime was modeled individually using the simple thermodynamic method described. An important point about applying fast entropy to real life situations is keeping the expression “it takes two to tango” in mind. Fast entropy only creates a potential. There must also be the equivalent of an engine (or conductor) to bridge the potential to observe fast entropy in action. In history, that engine may be produced by a new royal family replacing the prior family, or a group of organized invaders. Sometimes that engine comes along immediately after the end of the prior regime, or sometimes it may take a few hundred years before a new major regime takes root.

    However, there is a more powerful approach. If you can determine past potential profiles for past regimes, and if they seem consistent or to follow some pattern, then you can create a “boiler” program to literally boil a series of regimes. In that sort of program, potential builds up until it reaches a trigger threshold, then a regime forms and goes through its lifecycle and exhausts the built-up potential and dies. Then the potential starts building up again and eventually another regime forms. If the pattern varies from actual history, it may be possible to identify catastrophes, unexpected events, and interference from more powerful regimes.

    If you have appropriate software, you can literally reverse-simulate a past regime to determine a past potential at each point of the regime as well as the total quantity of the CCR. Such parameters can be sometimes useful for forecasts. The function that expresses the potential in terms of time (age of regime) is its potential profile.

    Rise fall repeats itself for series of dynasties for three dynasties.

    Series of Russian dynasties

    Dynasty series Involving Nonrenewable Physical Resource

    Below is a series of EDEG models for a series of Spanish dynasties where significant extraction of gold and solver occurs. The presence of tremendous metal extraction may have shortened the life of the Habsburg dynasty. Model for extraction with blasting is based on one data point, and is largely conjecture. Bourbon dynasty is considered to have ended with takeover by Francisco Franco, regardless of present monarchy.

    Plots of Habsburg and first Bourbon dynasty superimposed on metal data

    Spanish dynasties 1531-1930s with some metal extraction data


    • For gold and silver data, see Gibson, C., Spain in America. Harper and Row, 1966.

  3. 3. A GIS Approach to World History

    Graphical Information Systems (GIS) can be used to analyze spatial aspects of societies, as well as their progression and interaction with concurrent societies over time.

    The Colossus world history grid was superimposed on an image of the Europe, Africa and Asia. GIS was utilized to better understand the relations between societies. Spatial connections between adjacent or nearby societies were identified. Each connection was discounted for distance and terrain factors. It is possible to study correlations found in the Colossus model with such factors.

    A map of the old world with major regions shaded and lines connecting them to neighbors

    Dynasty-producing regions and connections

  4. 4. Modern Times and the Near Future

    Modeling the near future involves two major sources of “fuzziness”. First is the uncertainty in our future ability to make accurate measurements. Second is due to uncertainty associated with extremal events. A large meteor could hit the Earth and wipe out civilization, or at least a large part of it. A really large volcanic eruption could occur or extreme space weather could disrupt the Earth’s environment. There is also uncertainty due to the  effects of complexity and possibly free well. It must be done using different approaches for different time windows and levels of accuracy.

    Weather provides a good example for understanding. Forecasters can predict the temperatures and precipitation over the next seven days reasonable well, but not beyond that. However, for the next year, forecasters can predict average temperatures and precipitation type and quantity reasonably well. Meteorologists have also identified patterns that repeat over several years (such as El Nino and La Nina), but they cannot predict the exact timing or strength. It is also possible to predict the general climate by location and for the entire Earth for the next hundred or so years, assuming there are no catastrophic changes such as a large meteor hitting the Earth.

    There is often much short term “noise”. In weather, there might be a local tornado which deviates the local wind without changing the overall mean wind. One should use probabilistic methods when modeling the future to overcome noise effects.

    Current Trends

    • Globalization
    • Resistance to globalization
    • Advancement in automation (robotics, AI)
    • Consolidation of financial institutions
    • Monetary shifts caused by trade imbalanced
    • The 1800s colonia order continues to recede


  5. 5. Modeling The Future

    Modeling The Near Future Of A Single of Regime

    Modeling an existing regime may provide an indication of the magnitude of fast entropy tendencies upon that regime, especially if it is quite similar to a past regime, or is well advanced in age. Yet, the interdependent nature of today’s regimes and the possibility for nuclear or biochemical warfare or catastrophes create greater uncertainties than in the past, so that caveat must always be kept in mind. Further, the existence “unknown unknowns” must not be forgotten.

    General Approaches

    There are two major approaches for modeling a future single regime. The first approach is to guess Gaussian or Maxwell-Boltzmann distributions and adjust the constants involved to produce the best fit for the data that you have so far. The ways to do so could fill whole volumes in themselves, and are better covered in mathematical texts devoted to that subject. For purposes of this text, adjusting the parameters of the proposed function to provide the best visual fit provides a method that anyone who knows how to use a graphing program can utilize. Although this method is easy to implement, make sure to use the proper units for the constants!

    If you do not have any data, this method cannot be used. Also, if you only a small amount of data, or data for only a short time period, be warned that the forecasts could wildly vary from what will actually occur, evenif nothing unexpected happens.

    If you have data that appears to contain a great deal of noise (random variations), is quite inconsistent from period to period or contains a cyclic variation (such as an annual cycle or a regular seven year weather pattern), you may need to smooth out the data. To reduce noise, you can use a moving average smoothing technique. For purposes of this text, you could average the each value with the value immediately before and after it. For cyclical data, you can average over half a cycle before and afterwards. There are much more sophisticated smoothing techniques that can be found in textbooks on various types of forecasting.

    The second approach is to combine the initial characteristics of the data already obtained with the values of parameters of past regimes. This approach is more intelligent, but equally more complicated. A simple way to do this is described as follows. The first step is to try to create a pure exponential growth function that describes the growth seen in the initial data and identify the parameters in that function. Then plug these parameters into a distribution function, and use parameters from past regimes to fill in the remaining parameters. The past regime should be as similar to the present regime as possible, taking into account location, historical point of time, size of regime, type of resource use, and any other characteristics that seem applicable. If you need to alter anyof these parameters to improve the fit, only do so if you have a rational basis for doing so.  It is also possible that old values might give better long-term projections than parameters merely altered to improve the short-term fit.

    Thermodynamic Approach

    The thermodynamic approach is similar to the second approach above. However, an effort should be made to express parameters in terms of a potential and changing efficiency. If the quantity of the most critical conserved resource is known, then the projected total consumption over time should match that quantity. If the present regime uses the same types of resources and does not utilize any major new technologies, then it may be possible to use the potential profile from that past regime

    Modeling The Future As A Series of Regimes

    Future Horizon Challenge

    Modeling a single existing regime may be fairly reliable.  Modeling a series of regimes into the future may be much less reliable. First, all of the reasons that challenge modeling a single present regime apply even more so to modeling a series of regimes. Even worse, human society may face a fundamental change over the next hundred years, if not sooner. This could nearly completely throw off most forecasts. However, once society has made that transition, whatever it may be, then the nature of resource use and technologies of regimes may become more constant over time, so that it may be reasonably reliable to model a series of regimes after that point. If humans are involved and go back to traditional, agricultural technologies, and the same traditional population centers remain, then even the 300 Year Rule and the old potential profiles might be applicable. If robots replace humans in future regimes and they live off of nuclear fusion or some more exotic energy source, than some other potential profile may apply.


    A similar methodology to that described in here may be useful in modeling a future series of regimes, especially if the probable potential and “heat engine” characteristics can be reasonably identified.

    Modeling The Future As A Set of Interacting Regimes

    Many Simultaneous Regimes

    Numerous simultaneous regimes may co-exist. Such was the case of the ancient Greek city-states before the time of the wars between Athens and Sparta.  Each regime has some freedom of action. However, the collection of those states can often be considered aggregately to form a larger “super” regime.

    Interaction Between Two Simultaneous Regimes

    A frequent tale in history is the interaction between two simultaneous regimes, often of apparent equal power. There will typically be oscillatory flow of wealth or military strength back and forth between the two powers as they compete with each other, or there will be the ongoing flow of wealth or military strength from one to the other. The second case represents a potential, and can be modeled by utilizing that potential as a conserved resource.

    Long-term Changes

    There may be long-term changes. For example, the environment may change. Forests may be permanently destroyed. Erosion and increased soil salinity might permanently decrease agricultural productivity. Sheep grazing might result in the desertification of areas. These can often be reasonably estimated through models extrapolating current trends.

    The psychological state of the human population might change. Perhaps people will get smarter or less violent, or less patient in the long-term. These trends are currently difficult to estimate, but improved psychological science may overcome this barrier.

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