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.
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.
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.
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.