Dateline 2010: the world-historical situation

In the twilight century of western civilisation, the US, the last resting place of western power, has as its primary purpose the containment of rising China. China has as its primary purpose to put the world 'back to rights'. It is playing a waiting game, and is anxious not to jump the gun.

Dark Age Watch (DAW on hold.)

Issue du jour 1: War with Iran--important to containing China but delayed over two years

Issue du jour 2: The world economy--unbalanced, interwoven, delusional--some predict its unravelling

Issue du jour 3: Somalia--leading the world into a dark age

Issue du jour 4: Pirates exploit the decline of international order

Friday, 4 July 2008

Climate and history

While history should be explained in terms of society's internal dynamic and never purely in terms of external factors, the environment does impose constraints on what can happen. People cannot live on ice sheets, for example, and difficult terrain, such as forest or desert, means low scale and low development.

To understand how people colonised the planet, we therefore need to take account of the environmental context, i.e. the changes in climate, sea level and ice cover over the last 40,000 years.

The methods for reconstructing ancient climates are described by a number of books. The one I used was Global Environments Through the Quaternary by David Anderson, Andrew Goudie and Adrian Parker. The book's Amazon page links to some other titles covering the same area. Another one worth mentioning is Earth's Climate: Past and Future by William F Ruddiman.

The Quaternary Ice Age

Viewed on geological timescales, the 40 thousand years of the human story have occurred during an unusually cold phase of our planet's history. This is the Quaternary Period, which began some 2 million years ago and is regarded by geologists as an ice age.

Geological time is divided into various periods, which can be recognised by differences in the type of rock laid down (e.g. rocks from different periods contain different kinds of fossil). These periods are often named for the regions where the associated rocks were first noticed (e.g. the Jurassic takes its name from the Jura), although some get their names in other ways (e.g. the Cretaceous takes its name from the Latin for chalk, since that is what the rocks consist of).

The Quaternary is the most recent period. Its name comes from a former scheme that divided earth history into Primary, Secondary, Tertiary and Quaternary periods. Of these, only the Tertiary and Quaternary are still recognised (though even this terminology is now coming into question). Conventionally, the Quaternary began 1.8 million years ago, but many geologists now put this back to 2.6 million years. The controversy is irrelevant to us, since we are only interested in the last 40 thousand years - roughly the last 2 percent of the Quaternary - the time for which modern humans have been in existence.

What distinguishes the Quaternary is that during this period the earth has been in its 'ice house' mode, whereby there are ice caps at the north and south poles. It is not usual for the earth to have such ice caps. There were none during the great age of the dinosaurs, 100 million years ago, for example. However, from time to time, the earth's climate goes through a noticeably cooler phase. No one knows why this occurs, even though there are many theories. Whatever the reason, over the last 3 billion years, there have been some half dozen such glacial episodes, each lasting up to 100 million years. In the Quaternary, we are currently in the early stages (if past durations are anything to go by) of the latest of these ice ages.

The current cooling of the earth's climate began as long as 65 million years ago, at the end of the Cretaceous (when the dinosaurs died out). The cooling trend continued throughout the Tertiary, the period immediately before our own. In fact, ice sheets were already appearing in the late Tertiary. It is when these became persistent, and temperatures reached their current low, that is taken to mark the start of the Quaternary.

Climate chaos

While the Quaternary as a whole has been cold, temperatures have by no means remained constant during this time. In fact, not only have temperatures fluctuated but there have been smaller fluctuations within larger fluctuations. The more evidence climatologists gather, and the closer they look, the more ups and downs become apparent. Within the Quaternary, there have been warmer and colder millennia; within a given millennium, there have been warmer and colder centuries; within a given century, there have been warmer and colder decades; within a given decade, there have been warmer and colder years. Climate fluctuates on all scales.

The fact that climate fluctuates on all scales suggests it is a chaotic system. External inputs--such as variations in solar activity, continental drift, the passage of the solar system through interplanetary clouds, or gases released by volcanoes and living organisms--energise the system. They do not directly drive change, except possibly large, long-term change, but rather prevent the atmosphere from reaching equilibrium. The system remains unstable in itself, and is bound to change in an essentially patternless, unpredictable manner, on account of complex, multi-level feedbacks between climatic variables.

One of the earliest pieces of work in chaos theory was, in fact, the research of Edward Lorenz on computer models of the atmosphere. He found his simulated weather systems wandered all over the place in a way that had no simple relationship to the input conditions. Given that the weather seemed to vary continuously and unpredictably, he questioned whether it even makes sense to speak of the earth's 'normal' climate.

The diagram below shows some of the differing climatic regimes of the Quaternary, which will be explained in the following sections. Red represents warmer phases, and blue colder ones.

Glacials and interglacials

The grossest temperature fluctuations within the Quaternary are the glacials (or glaciations), when ice sheets grew massively around the world, and interglacials, when the ice sheets retreated and in some cases disappeared.

The most recent glacial is known as the Würm glacial in Alpine Europe or as the Wisconsin in North America (and has other names in other regions). This began around 75,000 years ago, was at a point of maximum coldness around 20,000 years ago (the Last Glacial Maximum or LGM), and came to an end around 11,500 years ago. We are currently in an interglacial.

Pleistocene and Holocene

The relatively warm period that began 11,500 years ago is considered to be a distinct sub-division (or epoch) of the Quaternary, called the Holocene. All the rest of the Quaternary, before this, is called the Pleistocence. Pleistocene means 'very recent'. Holocene means 'completely recent'.

Some geologists dispute the notion of the Holocene as a special epoch. They have a point, as it is only the last of a series of interglacials. The idea that it marks a new epoch seems to be exaggerating the importance of a relatively minor change simply because of our closeness to the event. That said, the Holocene provides a useful label for the current warm phase, whatever its geological status.

Marine isotope stages

The broad pattern of glacials and interglacials was first identified in the nineteenth century by the characteristic valleys and deposits of debris left by ancient glaciers. However, a much more detailed record of our planet's changing ice cover is now available from studies of ocean sediments.

Seawater molecules (H2O) contain two isotopes of oxygen, 16O, the lighter of the two, and 18O, the heavier. When water is evaporated from the oceans, to fall as rain or snow over the continents, the lighter water molecules, containing 16O, evaporate a little more easily. If the earth is going through a cold spell, and ice sheets are growing, these lighter water molecules become locked up in the ice, leaving the oceans with a higher concentration of the heavier molecules. Later, when the earth warms and the ice sheets melt, the lighter water molecules return to the oceans, reducing the concentration of heavier molecules there. This means that the concentration of 18O in seawater reflects the size of the earth's ice sheets, with higher concentrations of 18O when the ice volume is larger.

We can reconstruct past variation in 18O concentrations because the shells of tiny animals that live in the oceans reflect the chemical composition of the water that surrounds them. If 18O is more concentrated, these shells also contain a higher concentration of 18O. When the animals die, their shells fall to the bottom of the oceans and build up as layers of sediment, creating a record of the changing concentration of 18O and hence of the changing size of the earth's ice sheets.

Although this 'marine isotope' record shows fluctuations of all sizes and durations, geologists recognise in it an overarching pattern of swings between warmer (less ice) and colder (more ice) stages. The current warm swing is designated marine isotope stage (MIS) 1. The previous cold swing is MIS 2. The warm swing before that is MIS 3, and so on. Odd numbers correspond to warmer stages and even numbers to colder stages.

The marine isotope record does not tie up exactly with the more traditional division into glacials and interglacials, but rather reveals the complexity of climatic fluctuations. The current warming (MIS 1) had its beginnings in the last few millennia of the Würm glacial. The previous cold phase (MIS 2) was an exceptionally cold part of the Würm glacial, spanning the LGM. Before that, MIS 3 was a less cold phase, but still within the Würm glacial and not as warm as today.

The ending of the last glacial

The overlap between the nominal end of the last glacial and the beginning of MIS 1 reflects the fact that the colder climate did not terminate in a once-and-for-all manner. Within the warming, there were setbacks, involving a temporary return to colder conditions. First there was a warming lasting a little under 2000 years (the Bølling), then a short cold snap of about 3 centuries (the Older Dryas), then another warming of a little under 1000 years (the Allerød), then a longer cold snap of nearly 1500 years (the Younger Dryas). The end of the Younger Dryas marks the end of the glacial.

It should be apparent from all this that identifying the termination of the glacial is somewhat arbitrary, and requires a degree of hindsight we do not currently possess. Whether the present warming should be seen as part of a longer-term warm phase or just as a warmer interval in a longer-term cold phase depends on what happens in the future.

Climatic variation in the Holocene

During the Holocene, climates have continued to fluctuate. In Europe, the first 1500 years (the PreBoreal) were relatively cool. There then followed 7500 years of relative warmth, ending 2500 years ago (i.e. around 500 BC). The beginning and end of this phase (the Boreal and SubBoreal) were both warm and dry, while the middle part (the Atlantic), lasting about 3000 years, was warm and wet. Finally, the last 2500 years (the SubAtlantic) have been relatively cool again.

generally deteriorating climate with cooler and wetter conditions
600 BC to present
climatic optimum with warmer and drier conditions
3800 BC to 600 BC
climatic optimum with warmer and wetter conditions
6900 BC to 3800 BC
climatic amelioration, warmer and drier
8100 BC to 6900 BC
subarctic conditions
9600 BC to 8100 BC

Adapted from: D Anderson et al. Global environments through the Quaternary (Oxford 2007) p. 11.

Again, within the current (SubAtlantic) phase, there have been shorter term fluctuations. The heyday of the Roman Empire was relatively warm. The end of the Empire and the early medieval period (the 'Dark Ages') was colder. The high middle ages, the time of the monasteries and crusades, was warm, with grapes being grown in Britain, and the Vikings settling Greenland. The early modern period, the time of Shakespeare, Elizabeth I and Philip II, up to the Victorian period was colder (the 'Little Ice Age').

The last hundred years or so have been relatively warm, but still by no means uniformly so. The first half of the twentieth century was warm, and scientists spoke of global warming as a boon to humanity, bringing not just better weather but better growing conditions for crops. The late 1940s to 1970s were cooler, leading to talk of a renewed ice age, with soaring energy costs for heating, and the threat of famine; in the 1970s, British harvests were on average 11 days later than they had been in the mid-twentieth century. The 1980s and especially the 1990s were warm again, so that talk was once more of global warming, though now as a source of concern and even fear. Finally, temperatures in the first decade of the twenty-first century have shown little trend either way.

To repeat, climatic fluctuations occur on all scales, and the closer one looks, the more variation one sees. This is variation not just in time but also in space. Episodes like the medieval warm period and subsequent little ice age do not appear to have occurred in other regions the same way they occurred in Europe, and temperature changes in the southern hemisphere seem sometimes to have been in the opposite direction to those in the northern hemisphere.

Sea level changes

Changes in global ice cover cause corresponding changes in the global sea level. More ice means less water in the oceans and larger areas of dry land.

This would have affected people's ability to get from A to B, and is important for how they migrated around the world.

The chart at right (source: Wikipedia) shows changes in sea level through the late Quaternary. The light and dark shaded bands indicate the marine isotope stages (note that MIS 5 is subdivided into 5a, 5b etc.).

For most of human existence, sea level has been lower than today, reaching a minimum at the LGM, when it was more than 100 metres below the present level. Around 5000 years ago, however, sea level was some 10 metres higher than it is today.

We can translate the above chart into maps of how the continents would have looked at different times, courtesy of an applet developed by Sebastien Merkel at the University of Lille. You enter a given sea level (metres above or below the present) and the applet draws the land as it would then appear. (There are actually several applets, for the world as a whole and for different regions.)

I have used Sebastien's applet to create a slideshow of changing sea level, spaced at 5000-year intervals, from 40,000 years ago to the present.

Here is a Youtube version:

I have also created a set of Google Earth layers showing the ancient coastlines. (This does not include a layer for the present, since you can get that from Google Earth itself.)

Below, is an animated version, which requires the Google Earth plug-in to see it. Move the slider to change the date. (If you do not want to install the plug-in, but have a standalone Google Earth browser, you can download the animated coastlines here.)

Lower sea levels meant that the world's land surface was more connected in the past than it is today. The British Isles were joined to continental Europe. There was a land bridge, known as Beringia, between Asia and North America. The islands of modern Indonesia were mostly joined to each other and to the mainland. Australia was the only separate continent, cut off by a sea crossing of about 100 miles, but was joined to New Guinea. The entrance to the Black Sea was dry land, so the Black Sea was then a lake. However, the Gibraltar Strait remained submerged, so there was a short sea crossing between Africa and Spain, while the Mediterranean still opened into the Atlantic Ocean.

Early human migrants would have followed the coasts in spreading around the continents, and followed the rivers into the interior. The early settlement of Australia shows they could also cross the sea. Evidently, they had boats, which would have served them for both fishing and transport. They could thus have crossed between Africa and Spain, and reached offshore islands, such as those of the Mediterranean, Caribbean and South China Sea.

The rise in sea levels means that most of the sites occupied by human migrants 40-50,000 years ago are now beneath the waves. Future advances in underwater archaeology can be expected to reveal much more about this time, and give a clearer picture of the colonisation of our planet.

Climate maps

The pattern of coastlines and land connections represents only part of the information we need for thinking about how early humans moved around the planet. We also need to know the type of terrain that confronted them.

For example, while the low sea levels of the LGM produced the Beringia land bridge between Asia and North America, the heavy glaciation of that time meant that Beringia was blocked from the rest of the North American continent by an ice sheet. Given the traditional belief that humans only reached the Americas after the LGM, the moment at which the ice sheet had retreated enough to leave an ice-free corridor from Alaska to the Great Plains provides an important constraint on the timing of their arrival. (For an animation of the retreat of the North American ice sheet, see this site.)

My view is that the earth, including America, was colonised in essentially one great movement, at the start of the Upper Paleolithic, i.e. 20-30,000 years before the LGM. Beringia existed at that time, while the North American ice sheet extended only a little way beyond Hudson's Bay, and did not block movement via the west coast. That said, we should not discount the possibility that humans arrived in America via the Atlantic or Pacific. It may be a long voyage, but the Americas present a huge target.

I have prepared a set of maps, in Google Earth, of global climate/environment at 10,000 year intervals, from 40,000 years ago to the present. They are derived from those produced by the Quaternary Environments Network plus a certain amount of guesswork (the QEN maps are quite patchy in their chronological and regional coverage, and I have had to fill in the gaps to create a consistent set of maps at regular intervals).

Terrain is classified into eight types, using the following colour scheme. In a nutshell, the lighter the green, the drier and more open the terrain (plus yellow for desert, including polar desert, and white for ice).

You can see these maps either with the Google Earth plug-in below, or by accessing the Google Earth files directly here. Move the slider to change the date.

Note that these maps take into account the different coastlines at different periods, which is why they may show grassland etc. in what is now sea. The map for the present shows potential vegetation (i.e. as it would be in the absence of human influence). Actual vegetation can be very different due to the effects of industry and agriculture, the main thing being the widespread clearance of forests.

For most of human existence, the climate has been colder and drier than today, resulting in more desert and less woodland. However, around 10,000 years ago, climate was generally moister than today, and the Sahara desert was converted to grassland and steppe. That said, the global environment did not vary as one. Climatic change could, for example, mean a shift in wind patterns, carrying moisture away from one region and towards another, so that the first region became drier and the second one wetter.


This post has provided a narrative of climatic variation over the period of human existence, plus some relevant resources in terms of maps of changing coastlines and terrestrial environments. It has not reached any particular conclusions but is intended to provide the high-level background for subsequent discussion of humans' discovery and conquest of their world.

Sunday, 20 April 2008

Specific coastline and development

Sea transport was more efficient than land transport throughout most of the development of civilisation - from the late neolithic or early bronze age until today. Coastal areas had higher scale, and were more populous and more advanced than inland regions.

Therefore, other things being equal, regions with a large amount of coastline for a given landmass developed faster and further. Europe benefited from this, since it is a relatively small continent with a long, convoluted coastline. By contrast, Africa and Asia, with smoother, more rounded shapes that encompassed a much bigger area, were at a developmental disadvantage.

We call the ratio of a region's coastline to its surface area, the specific coastline. Values of the specific coastline for various regions are as follows:

Specific coastline (km-1)
4.1 x 10-3
1.7 x 10-3
1.0 x 10-3
Western Europe
6 x 10-3

Source: N Rashevsky Looking at history through mathematics (Cambridge, MA 1968) pp. 132-3.

The advantage of Europe, especially western Europe (excluding Russia and Poland), is clear. Its high specific coastline helped it to develop faster than other regions during the last half-millennium of ocean-going transport. This advantage has diminished with the growth of land and air transport, and will all but disappear as humanity transitions to a space-based economy.

Europe - specific coastline = 4.6 x 10-3

Asia - specific coastline = 1.7 x 10-3

Africa - specific coastline = 1.0 x 10-3

Europe appears even more advantaged when we consider the specific river coastline (ratio of total river length to area). For Europe, this is 9 x 10-3 km-1, compared with 1 x 10-3 km-1 for China, and 5 x 10-4 km-1 for India. Europe's total coastline (river and sea) is nearly ten times that of China or India (Rashevsky Looking at history through mathematics p. 133).

Sunday, 2 March 2008

The Mediterranean and development

I will now discuss the pattern of world development with respect to the model introduced in my last post. You can use the program supplied in that post to reproduce the following discussion and experiment for yourself.

Set up a simple model of the Mediterranean region as shown below.

This uses the default terrain types, representing land, river, coast and sea, as well as the surrounding ocean.

The layout depicts the Mediterranean sea, surrounded by Europe, Asia and North Africa. The regions bordering the sea are designated as coast. The Nile and Tigris-Euphrates river valleys are also represented. However, the main body of Africa is not included, leaving North Africa and the Nile valley as an isolated strip. This represents North Africa being isolated from the rest of the continent by the Sahara desert. (One could include some 'desert' terrain to represent this, but it is easier just to leave the southerly regions as impassable 'ocean'.)

See the diagram below, with red labels showing the terrain types, and white labels showing the geography.

The aim of this experiment is to demonstrate two aspects of historical development:

1. The Nile valley benefited from both its central location and the ease of movement afforded by the river.
2. As (marine) technology improved, the advantage shifted from the river valleys to the regions bordering the sea, especially the Italian peninsula sticking out into the middle of the sea.

We need to make sure the various terrain types have the appropriate properties of habitability and traversibility. The ones we will use are supplied as the default values in the program.

Land, river and coast are assumed to have the same habitabilities. The only difference between them is that coast and river have traversibilities that are respectively 20 and 40 percent higher than that of land. Sea has a habitability of zero, and initially a traversibility of zero. However, when technology reaches a high enough value, the traversibility of sea switches to a value much higher than that of land. The traversibilities (and habitabilities) of land/river/coast do not change at all with technology. This reflects the notion that, over the period we are interested in, roughly 3000 BC to 1 BC, although movement on land improved somewhat, the really significant change was the opening up of sea transport.

Having set up the topography as above, click the button to populate all regions.

Check the Verbose box, to get a display of the status of each region (you may need to move the land to the middle of the map display, so you can see it in verbose mode).

Press Step, to calculate the scale of each region. Compare 'Italy' with the 'Nile delta' (the region of river adjacent to the 'Mediterranean'). You should find that the Nile delta has a higher scale and consequently higher potential technology than Italy (specifically, 0.41 for the Nile delta, 0.142 for Italy, see below).

Now press the Run button and allow the simulation to run till the values for each region have pretty much stopped changing. You should find that Italy has overtaken the Nile delta (potential technologies of 1.739 for Italy, 1.489 for the delta, with both regions probably having reached their potential). The diagram below shows how the development levels of the different regions (Italy, Egypt, Mesopotamia [i.e. Tigris-Euphrates], Levant [i.e. coastal strip at east end of Mediterranean]) change during the run.

Obviously, this model is very crude in terms of the values assumed and the way we have laid out the topography. However, it demonstrates the basic points referred to above: that, with respect to development, Egypt had an initially favoured position and that this advantage shifted elsewhere as technological growth changed the sea from an insulator to a conductor of human interaction.

What happens in the simulation is that, as technology grows (in Egypt and elsewhere), it passes the level that opens the sea up to marine transport. At this point, the regions bordering the Mediterranean receive a boost in scale and in technological potential. For Italy, being surrounded by sea was previously a disadvantage but now becomes an advantage.

If you want to follow the detailed steps by which this change comes about, pause the simulation, click on Clear Population then Populate All again, and just press Step repeatedly.

  • You may like to create more realistic representations of world topography, including additional terrain types, and experiment with different values for traversibility and habitability.
  • You could also extend this simulation to model the later shift of advantage from the Mediterranean to the Atlantic rim, once technology growth opened up the ocean.

Italy is not the only land sticking out into the Mediterranean. Greece also does so, and the development of civilisation there preceded that on the Italian peninsula. This reflects its greater proximity to the original centres of civilisation in the near east. See the map below.

View Larger Map

Friday, 22 February 2008

Experimentation with development

This program is for exploring some of the ideas in my last post. (Scroll down for explanation.)

(No program? See only a red X? You need to install the Java Runtime Environment (JRE). Click here.)

The above program allows you to investigate how scale and development depend on topography.

The idea is that you can create a land-mass with a particular shape and give it a certain population. This allows you to see how scale varies at different points of the land-mass. Higher scale results in greater development of technology, which permits a larger population and greater ease of movement. Since scale depends on population size and ease of movement, this creates a positive feedback. Having set up your land-mass and initial population, you can run a simulation to watch how things develop.

In the program, the world is divided into a grid of square regions. You create your land-mass by 'painting' different shapes (e.g. oval, rectangle) and different types of terrain (e.g. land, river) on the grid. Similarly, you 'paint' the population on the regions. You can add your own terrain types, and, for each terrain type, define the habitability (maximum population) and traversibility (ease of movement) as a function of technological level. Initially, all regions are set to 'ocean' (pale blue colour).

The traversibility of terrain depends on the technology of the source region. Suppose a region X has a given population. This population contributes to the scale of surrounding regions. To work out X's contribution to the scale at region Y, we have to take into account the difficulty of movement from X to Y, which means taking into account the traversibility of each region between X and Y. In performing this calculation, we use the technological level of X to determine all the traversibilities (i.e. not the local technological level of each region). Conversely, when working out Y's contribution to the scale at X, we use the technological level of Y to work out the traversibilities of the intervening regions. The idea behind this is that a region's ability to project itself depends on its own technology not on the technology of the receiving regions. E.g. America's ability to influence, say, Nigeria depends on American technology (internet, TV, airlines) not on Nigerian technology.

To familiarise yourself with the program, try the following (you may want to open a copy of this window so you can follow these instructions and view the program at the same time):

1. First, we need to activate the applet (program). Click anywhere in the program box to do this. The program has controls at the top, and a display area where you can set up land-masses by painting terrain onto the different regions.

2. To see the regions clearly, click on the check box labelled Grid (top right). You can move the display around by dragging with your mouse, and zoom in/out with your scroll wheel.

3. To create an island in the shape of a rectangle divided into 9 regions with terrain type 'land', do the following:
a. Click on the radio button labelled Rectangle (top left).
b. Click on the button labelled Paint (top centre).
c. Make sure the adjacent drop-down box says Land. If not, select it from the list.
d. Draw a 3x3 square in the middle of the display area by clicking and dragging the mouse. If you make a mistake, click the button Clear Terrain (top right) and try again.

4. To set up each region with an initial population of 100, do the following.
a. Edit the field labelled Population to read 100 instead of 1000 (left hand side of control panel).
b. Click the button labelled Populate All (left of control panel). Dots should appear on the square island. The size of the dot in each region represents the size of the population in that region. (If you want, you can add population to one region at a time, by clicking the Paint button next to the population field, then clicking on the relevant region.)

5. To see the status of a given region of the island, right click on it, and select Region Information from the popup menu. A dialog box will appear with the relevant information. 'Population' is self-explanatory. 'Potential' represents the potential scale generated by all the populations in surrounding regions; the potential generated by each population depends on its size, how far away it is, and the ease of movement between the two regions. 'Scale' is the potential scale multiplied by the population of the region in question; e.g. a large population in region B generates a large potential scale in region A, but, if there is nobody in region A, the actual scale of region A is zero. 'Technology' represents the technological and institutional sophistication of the region; this can reach a maximum level that is a function of the region's scale. 'Habitability' represents the maximum number of people the region can support, given its technological level. Finally, 'traversibility' represents the ease of movement across the given type of terrain. In this case, you should find that the region's population is 100, the habitability is 1000, the traversibility is 0.1, and everything else is 0. Click ok to get rid of the dialog box. If you check the other regions, you will find that they are the same.

6. Now let's look at scale.
a. Click the button labelled Step. This performs one iteration of the simulation and results in the scale of each region being calculated as a function of the populations of surrounding regions.
b. Now click on different regions of the square island and view the Region Information again. Where is scale highest? Where is it lowest? Why is this? You should find that scale is highest in the centre, lowest at the four corners, and intermediate in the remaining regions. This reflects the fact that the central region is overall closest to the other regions, while the corner regions are overall furthest away.

7. Next let's simulate the growth of population and technology. The assumption is that population grows asymptotically towards the level set by the region's habitability, while technology grows asymptotically towards a level that is equal to the region's scale.
a. Select the checkboxes labelled Population and Technology. These variables will now be updated at each iteration of the model. (This feature means you can choose independently whether or not to simulate the growth of population and/or technology, making it possible to isolate one factor and see how it behaves.)
b. Click Step, then inspect the information for each region. You will find that the population of each region has grown by the same amount, but technology has grown to different extents depending on the scale of each region.
c. Let the simulation continue until the populations have just about reached the maximum habitability, which is currently 1000. Rather than repeatedly clicking the Step button, you can click the Run button to do this automatically. The slider to the right of the button controls the speed. Move the slider to the left for the simulation to run faster. (If the simulation seems to freeze, move the slider right to slow it down.)
d. When the centre region's population is say 999.99 (check the region information, as above), click the Run button (now labelled Pause) to pause the simulation. Check all the regions. You will find that the technology in each region is virtually equal to the region's scale, and is therefore highest in the centre, lowest at the corners. The actual values should be about 0.429 for the centre, 0.217 for the corners, and 0.317 elsewhere. If you click Run again, these values will change little, as population has pretty well reached its maximum everywhere.

8. The next thing to learn is how to set the traversibility and habitability for a given terrain type.
a. Make sure that Land is selected in the drop-down list and look at the table labelled Terrain Types. The first row should read Technology=0, Traversibility=0.1, Habitability=1000. This means that, for a technology level of 0 or above, the traversibility and habitability of 'land' have the values indicated.
b. Click in the second row of the table, in the column labelled Technology, and type 0.4. Then edit the values in the second row under Traversibility and Habitability to be 0.15 and 1000 respectively. This means that, for a technology level above 0.4, the traversibility of land is now 50 percent higher, 0.15 instead of 0.1. This represents the fact that a certain level of technology brings innovations like the road, horse-and-cart or motor-car, which allow people to get around more easily.
c. You can also change the colour assigned to a terrain type, by clicking on the coloured rectangle labelled Colour, and create a completely new type of terrain, by clicking on the button labelled New. We won't do either of these in this case.

9. Now let's see what difference it makes that a higher technology level produces a higher traversibility.
a. Step the simulation once then check the information for each region. You should find that the scale and technology level of the central region are the same as before, around 0.429. However, the scale of the four side regions should be found to be higher, around 0.356, while the technology level has also increased, to around 0.321. Similarly, the scale of the corner regions should have increased to around 0.235 and their technology level to around 0.219.
b. What is going on here? Well, the technology level of the central region is above 0.4, so for this region the traversibility of Land is higher than before, 0.15 instead of 0.1. This means its contribution to the scale of surrounding regions declines more slowly with distance, so their scale is higher than it was. However, the technology of other regions is below 0.4, and for them the traversibility of Land has not changed. Therefore their contributions to the scale of the central region are also unchanged. Overall, the scale of the central region remains the same, but the scale of all the surrounding regions has increased. (Note that a region does not contribute to its own scale.) This increase in scale of the surrounding regions means their technology is beginning to increase towards the level allowed by the new scale.
c. Click the Run button and allow the simulation to run for a while, then check the regions again. You should find that the situation has stabilised with the technologies of the surrounding regions at the new higher level corresponding to their scale.

10. Now we will allow technology to increase habitability as well. This will create the situation where the technology of all regions goes above 0.4.
a. Pause the simulation. Go back to the Terrain Types table, and change the habitability in the second row to 1500. This represents the fact that a higher technology level produces innovations like farming tools, which allow more people to survive on the same area of land.
b. Step the simulation and check the region information. You should find that things are much the same as before, but the population of the central region has increased slightly, to move towards the new, higher habitability.
c. Continue stepping the simulation and keep a careful eye on the scale of all the regions. You should find that their scale is increasing. The growing population of the central region is making a growing contribution to the potential scale of its neighbours, and hence to their actual scale. The potential scale of the central region remains the same, since the neighbouring populations are unchanged, but its actual scale is increasing in line with the increased population. And as scale increases in each region, technology level follows.
d. Run the simulation for a while, until it reaches a new steady state. Check the state of the various regions. You should find that the technology level of the central region is now around 1.480, while that of the side regions is around 1.101 and that of the corner regions is around 0.773. You should also find that the population of all regions is around 1500.
e. What has happened? Well, the growing population of the central region increased the scale of the surrounding regions, until eventually the side regions broke through the 0.4 barrier. This increased their traversibility and habitability so they made a much greater contribution to the scale of the central region, and its technology grew accordingly. Both they and the central regions also increased the scale of the corner regions to the point at which they too broke through the 0.4 barrier, further enhancing the scale and technology of the other regions. The result was a great increase in technology and population all round.

You should now know the way the program works, and be able to experiment on your own. Some final points to note are:
a. Rather than drawing a rectangle, you can draw an ellipse, line or single cell. Lines have to be drawn from left to right. (Yes, my program is a bit rough, but it does the job, I hope.)
b. If you want to explore the effects of a coastline, it can be tedious to paint the coast manually (changing regions from land to coast). The Outline button can do this for you automatically.
c. The Verbose checkbox causes the grid to be displayed zoomed in, with the details of each region written in. This is convenient if you want to follow the changing numerical values. On the verbose display, P=population, T=technology (maximum possible technology shown in brackets), t=traversibility (at T=0), h=habitability, p=potential scale, s=scale. The pair of numbers at the top of each cell are its co-ordinates (column, row).
d. Each iteration of the simulation involves 3 sub-steps: calculating scale, technology, and population in that order. There is also a sub-step for calculating migration, but this is currently not modelled and does nothing. You can perform one sub-step at a time by clicking the Sub-step button. The next sub-step to be performed is shown in the box to the left of the button.

I will finish with some technical details of the model. In the next post, I will discuss how the above program can be used to illustrate the relative historical roles of Egypt and Rome.

Technical details...

The model involves 3 calculations--scale, technology and population--and a set of constants.

(1) Scale calculation

I take each region (square) in turn and calculate its contribution to the potential scale of all the other regions. Firstly, I set the potential of each region to zero. For each region, the algorithm is then as follows:

a. Call the source region the region now being processed.
b. Set working_potential of the source region to be equal to its population.
c. Set the current region to be the source region.
d. Let current_potential be the working_potential of the current region. Consider each region to north, south, east and west of the current region, in turn, as the target region. If the target region has already had its working_potential set during processing of this particular source region, do nothing (this occurs if potential has been propagated to it by another route, and stops potential being propagated back the way it came). Otherwise, calculate propagated_potential as current_potential x traversibility / (traversibility_threshold + traversibility), where traversibility is the traversibility of the terrain type of the target region given the technology value of the source region (note, not the current region or target region). If propagated_potential exceeds a fixed threshold_potential, set the working_potential of the target region to be equal to propagated_potential.
e. Consider each region whose working_potential was set at the last step, in turn, as the current region, and go back to step d.
f. Once there are no more regions whose working_potential has been set but not propagated to its neighbours, add the working_potential of each region, except the source region, to its potential. Set all working_potentials back to zero.

The above is repeated, taking each region in turn as the source region. The result is a total potential for each region. The scale of each region is then calculated as simply the product of its potential and its population.

(2) Technology calculation

For each region, I calculate delta_technology from the formula, delta_technology = time_step x technology_growth_rate x (technology_constant x scale^technology_exponent - technology). Here, scale^technology_exponent means 'scale raised to the power of technology_exponent'.

The new technology of each region is then calculated as technology + delta_technology.

(3) Population calculation

For each region, I calculate delta_population from the formula, delta_population = time_step x population_growth_rate x population x (habitability - population). In this formula, habitability is the habitability of the region's terrain type given the region's current technology.

The new population of each region is then calculated as population + delta_population.

(4) Constants

I used the following values for the various constants introduced above:

traversibility_threshold = 1
threshold_potential = 10-6
time_step = 0.1
technology_growth_rate = 1
technology_constant = 1
technology_exponent = 1
population_growth_rate = 0.0001

Saturday, 5 January 2008

Introducing development

I wish to discuss development, by which I mean the growth of technology and societal complexity.

The simplest and lowest-tech mode of human existence is that of hunter-gatherers. We can take this as about the zero of development.

There are variations even among hunter-gatherers. The American Indians of the north-west Pacific coast lived in a rich environment, and supported a high population by hunting and gathering. They had villages and chiefs. This contrasts with people like the Kalahari bushmen, who live as isolated, nomadic families and do not produce anything as lasting and ambitious as a totem pole.

Let us therefore propose a zero of development (D) without necessarily being able to point to a society that actually has D=0. Kalahari bushmen and the societies of the Upper Paleolithic must be close, but even they have technology and ways of behaving that an outsider would have to learn and that make their societies at least a little bit 'complex'. Zero development is the 'Adam and Eve' condition, an isolated couple living off the land in complete simplicity and with no technology whatsoever.

While I have characterised development in terms of technology and complexity, these are not independent factors. They are linked in the society's eigenmode. A society with high technology must be complex. Consider the activity involved in a new release of the Windows operating system -- not just the code developers, but the managers, marketers and distributors, the hardware manufacturers, the bankers who process payments between consumers and producers. And while all these people are getting the new software on the streets, they have to be clothed, fed and sheltered, which involves yet more people connected in yet more economic relationships. Conversely, a society that is as complex as that needs high technology, to provide the necessary transport and communications, and to allow a small number of primary food-producers to support the people engaging in all this secondary activity.

We know that a society's complexity is related to its scale (see here for the discussion).

We can therefore say that development is linked to scale in an eigenmode. That is, each societal eigenmode is associated with a definite scale and level of development. Given the scale, we know the society's development level, and vice versa.

This discussion suggests an issue we will have to come back to once we have a basic understanding of development:

  • Although technology and societal complexity are linked, it is not clear they are in a direct one-to-one relationship. It may be that, within limits, societies with the same technology can vary in complexity, or societies with the same complexity can vary in technology. When, for example, some societies are described as 'underdeveloped', should we understand by this simply 'low in development', or can society's have a development level that is actually below what they ought to have for their given scale/eigenmode'?

What causes development? The case of the Australians suggests that it is not simply an inevitable consequence of time passing.

The eigenmode concept in itself provides little help. It is essentially static, telling us that societies of a particular scale will be developed to a particular extent, but not how societies actually develop and increase in scale. We need to introduce other ideas.

Consider an island that can be divided into five regions: centre, north, east, west and south. And consider three different situations, as follows:

(a) The central region is populated but the rest of the island is empty

(b) All regions are populated, but impassable barriers prevent people in different regions from interacting with each other

(c) The barriers are lifted and interaction between the regions becomes possible

In (a), suppose the population is in an eigenmode (for the moment, do not worry about how it got there, and ignore the possibility of migration ). The population has a certain scale, which corresponds to the amount of interaction among such a population crammed into such an area, and it has the proper development level that both results from and permits that degree of scale. The population is therefore in equilibrium and there is no apparent reason why it should not remain in the same eigenmode for evermore.

In (b), all the regions, being isolated from each other, are identical. The population in each region can be in the same eigenmode as (a). Again, there is no reason why it should not remain in that eigenmode forever.

In (c), the central region experiences interactions with each of the four peripheral regions. (For the moment, assume that the peripheral regions are not in direct contact with each other and remain mutually isolated.) This raises the scale of the central region (remember, scale is a measure of societal interactivity). It also raises the scale of each region with which the central region is in contact. However, the central region experiences four external contributions to its scale, whereas the other regions experience only one external contribution each.

In (c), therefore, the scale of all the regions will increase relative to (b) and the scale of the central region will increase the most. By the logic of eigenmodes, the increase in scale will be associated with an increase in development. And the central region will end up more developed than each of the others.

If we go back to (a) and allow the possibility of migration, the population in each of the peripheral regions will grow, as people move out from the centre. The growth of these populations will raise the scale of the source region. Rather than ending with a situation like (b) where all the regions end up at the same development level as the original, central region, we will actually have the situation described in (c), where all the regions are more developed than the central region was and the central region is developed the most.

In our example, the central region experienced the most development as a consequence of the assumption that the peripheral regions could interact with the centre but not with each other. More realistically, the peripheral regions might be able to interact to some extent. Nevertheless, the north, say, would still tend to interact more with the centre than it would with the south, which is that much further away. In general, if we divide a landmass up into regions, some regions will be better placed than others to interact with their neighbours, and will have higher scale and development.

To summarise, the ability of a population to expand into empty lands can produce an overall increase in development while the highest increases in development will be in the more centrally located regions.

We therefore have a simple understanding of why the ancient spread of humans across the earth would not result in a planet populated entirely at the D=0 condition but would both allow development to take place and lead to a patchy picture with more development in some areas than in others. The development of each region would reflect its centrality and its ease of access to other regions (e.g. regions up and down a river would have more interaction and development than regions with a desert between them).

I want to finish with a question, which takes us more deeply into the issues and which we will have to tackle in later posts.

Let us think back to situation (c). Immediately after the barriers are lifted (if we imagine this hypothetical scenario), the population in each region will have the same technological level as before but can interact with more people (i.e. those in neighbouring regions). Interacting with more people is enough to increase scale. However, that then increases development/technology, and, in general, the increase in technology will itself tend to increase interaction. E.g. development could mean the ability to grow more food and support more people or it could mean better transport allowing people to interact over longer distances. Therefore, the development that results from increased scale will in turn produce an increase in scale. This increase in scale will then generate more development, and so on.

What will happen? Two things are possible. Either increases in scale and development will continue to reinforce each other without limit. Or diminishing returns will set in and the increases in scale and development will peter out, eventually reaching equilibrium in a new eigenmode.

The long term prospects for societal development therefore become a question of the nature of the relationship between scale and development. The case of the Australian aborigines suggests that, at low levels of scale and development, diminishing returns dominate and equilibrium is reached. With the world as a whole, the situation is less clear. There has obviously been positive reinforcement to date, but as global scale and development continue to increase their relationship may move into a region of diminishing returns. After all, one might imagine that there are limits to how many people earth can support, and to transportation speeds and communications bandwidth, all of which would set a cap to scale and development. If we get off this planet into outer space, we could break through those limits into a region of continuing positive reinforcement, but what if diminishing returns set in first, causing us to stagnate before we fully master space travel?

Had Australia been the earth's only landmass, it seems humanity might never have developed beyond the level of the aborigines and never got into space. The earth's landmass is much bigger than that, but the question we would like to answer is, is it big enough?