Societal growth depends on IT

Read an interesting article the other day in SciencDaily (IT played a key role in growth of ancient civilizations) and a Phys.Org article (Information drove development of early states) both of which were reporting on a Nature article (Scale and information processing thresholds in Holocene social evolution) which discussed how the growth of society during ancient times was directly correlated to the information processing capabilities they possessed. In these articles IT meant writing, accounting, currency, etc., relatively primitive forms of IT but IT nonetheless.

Seshat: Global History Databank

What the researchers were able to do was to use the Seshat: Global History Databank which “systematically collects what is currently known about the social and political organization of human societies and how civilizations have evolved over time” and use the data to analyze the use of IT by societies.

We have talked about Seschat before (See our Data Analysis of History post)

The Seshat databank holds information on 30 (natural) geographical areas (NGA), ~400 societies and, their history from 4000 BCE to 1900CE.

Seschat has a ~100 page Code Book that identifies what kinds of information to collect on each society, how it is to be estimated, identified, listed, etc. to normalize the data in their databank. Their Code Book provides essential guidelines on how to gather the ~1500 variables collected on societies.

IT drives society growth

The researchers used the Seshat DB and ran a statistical principal component analysis (PCA) of the data to try to ascertain what drove society’s growth.

PCA (see wikipedia Principal Component Analysis article) essentially produces a list of variables and their inter-relationships. Their combined inter-relationships is essentially a percentage (%Var) of explanatory power in how much those variables explains the variance of all variables. PCA can be one, two, three or N-dimensional.

The researchers took Seshat 51 society variables and combined them into 9 (societal) complexity characteristics (CC)s and did a PCA of those variables across all the (285) society’s information available at the time.

Fig, 2 says that the average PC1 component of all societies is driven by the changes (increases and decreases) in PC2 components. Decreases of PC2 depend on those elements of PC2 which are negative and increases in PC2 depend on those elements of PC2 which are negative.

The elements in PC2 that provide the largest positive impacts are writing (.31), texts (.24), money (.28), infrastructure (.12) and gvrnmnt (.06). The elements in PC2 that provide the largest negative impacts are PolTerr (polity area, -0.35), CapPop (capital population, -0.27), PolPop (polity population, -0.25) and levels (?, -0.15). Below is another way to look at this data.

The positive PC2 CC’s are tracked with the red line and the negative PC2 CC’s are tracked with the blue line. The black line is the summation of the blue and red lines and is effectively equal to the blue line in Fig 2 above.

The researchers suggest that the inflection points in Fig 2 and the black line in Fig 3),represent societal information processing thresholds. Once these IT thresholds have passed they change the direction that PC2 takes on after that point

In Fig4 they have disaggregated the information averaged in Fig. 2 & 3 and show PC2 and PC1 trajectories for all 285 societies tracked in the Seshat DB. Over time as PC1 goes more positive, societie, start to converge on effectively the same level of PC2 . At earlier times, societies tend to be more heterogeneous with varying PC2 (and PC1) values.

Essentially, societies IT processing characteristics tend to start out highly differentiated but over time as societies grow, IT processing capabilities tend to converge and lead to the same levels of societal growth

Classifying societies by I

The Kadashev scale (see wikipedia Kardashev scale article) identifes levels or types of civilizations using their energy consumption. For example, The Kardashev scale lists the types of civilizations as follows:

  • Type I Civilization can use and control all the energy available on its planet,
  • Type II Civilization can use and control all the energy available in its planetary system (its star and all the planets/other objects in orbit around it).
  • Type III Civilization can use and control all the energy available in its galaxy

I can’t help but think that a more accurate scale for civilization, society or a polity’s level would a scale based on its information processing power.

We could call this the Shin scale (named after the primary author of the Nature paper or the Shin-Price-Wolpert-Shimao-Tracy-Kohler scale). The Shin scale would list societies based on their IT levels.

  • Type A Societies have non-existant IT (writing, money, texts, money & infrastructure) which severely limits their population and territorial size
  • Type B Societies have primitive forms of IT (writing, money, texts, money & infrastructure, ~MB (10**6) of data) which allows these societies to expand to their natural boundaries (with a pop of ~10M).
  • Type C Societies have normal (2020) levels of IT (world wide Internet with billions of connected smart phones, millions of servers, ZB (10**21) of data, etc.) which allows societies to expand beyond their natural boundaries across the whole planet (pop of ~10B).
  • Type D Societies have high levels of IT (speculation here but quintillion connected smart dust devices, trillion (10**12) servers, 10**36 bytes of data) which allows societies to expand beyond their home planet (pop of ~10T).
  • Type E Societies have high levels of IT (more speculation here, 10**36 smart molecules, quintillion (10**18) servers, 10**51 bytes of data ) which allows societies to expand beyond their home planetary system (pop of ~10Q).

I’d list Type F societies here but a can’t think of anything smaller than a molecule that could potentially be smart — perhaps this signifies a lack of imagination on my part.


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