We’ve used the economy to distribute fortifications, and used those to locate inns. Now let’s wrap some communities around them.
Part 5 of Chapter 5 is all about Population and its distribution. Most systems that I’ve seen for this purpose start with an overall population and work backwards, and often end up with unreasonable results, like a village every mile-and-a-half.
My system works the other way – a population density model to a population density to a local population. Many local populations give a Zone population, and the total of the Zone populations gives the Kingdom population overall.
Select a model based on the desired ‘look and feel’ of the society within the Kingdom / Zone. The model describes the general distribution of population within the Kingdom / Zone, assuming a fixed unit of area (10.000 km^2), but most zones will be smaller.
The model plus a random roll sets initial village size. Village Frequency is determined by the placement of Inns & Administrative / Military structures, already defined. Together these define the total population density of an entire Kingdom according to the model.
This can then be applied to the size of the actual Kingdom to determine the total population of the Kingdom.
Each village occupies a footprint termed a Locus.
The location within a locus actually occupied by the village or town is generally defined by the content of that locus. The population center will always be in the location within the locus that is most advantageous to growth.
A series of factors increases the size of the village within the locus, sometimes positively and sometimes negatively. Each factor yields a fractional value called a Scale Value. Applicable Scale Values determine the village location because many of them are specific to this place or that, enabling the location to be quickly refined within the locus.
Where there are multiple possible locations of roughly equal value, a community will split into two half-sized populations which will begin growing toward each other.
These Scale Values are totaled. The total Scale Value is applied as exponential growth to the base village size to determine the nominal size of the community.
If this is sufficient to trigger growth into a new size category, it is further adjusted and the new base size is used with the adjusted value to redetermine the size. This process iterates (i.e. gets applied repeatedly) until the final size of the settlement is determined.
Some conditions restrict community size by passing on excess growth to neighboring communities; these are passed from one to another until reaching a community that is no longer restricted. That community is sometimes referred to as the “Gateway” to the region. Becoming a ‘gateway’ is also a growth factor!
This is all achieved by taking the excess part of the Scale Value and applying it as a modifier to the nearest Locus outside the restricted area, reducing the total scaling factor that applies to settlements within the restricted area. Not all the excess can be redirected; growth in restricted areas is slowed, not stopped.
Along the way, various side-issues will be raised and assessed, building up a population profile for the Locus, the administrative division, the Zone overall, and for the Kingdom as a whole. In particular, the political infrastructure of the Kingdom gets determined.
Finally, these various considerations will come together to provide a system whereby a GM can generate a village ‘on the fly’ whenever a group of characters (PCs most of the time) enter a locus or cross a border.
5.8.0.1 Frequency, Size, and Services
The section above does a good job of outlining the process, but I thought it worth taking a moment to explain the philosophy behind it and the reason for this particular approach.
5.8.0.1.1 The traditional approach
The fundamental concepts by which population levels are usually defined come down to two main ones and a boat-load of implications.
The first primary factor is settlement frequency – how many miles or kilometers or day’s march apart they are. The first two options are the ones with which most readers will be familiar and they have the virtue – and penalty – of being absolute measurements. The third option is more abstract, but can also be more practical. It takes account of terrain, for example, and at first that might seem like a good thing – but then you realize that it takes it into account backwards: if the terrain is poor, travel over it will be slower – but a fixed ‘average time apart’ then means that the settlements will cluster more closely together, i.e. there will be less physical distance covered in the same amount of time because of the terrain. What you really want is the opposite – good terrain clustering communities together, bad terrain setting them further apart.
The second primary factor is settlement size – how many families or dwellings make up a ‘typical community’ in the specific zone.
It’s the implications that start to get complicated. Between them, these specify the level of economic and industrial capacity of the typical community, and thus, what services are likely to be available. But that then gets muddied somewhat by demand. Certain services are always going to be in demand and providing those services is an economic opportunity for a practitioner.
And that then gets complicated by the logistics of travel – the ‘footprint’ serviced by a given provider will vary from one occupation to another. A good blacksmith may service several small communities (if they are close together), or just one, while a mill may have a much bigger ‘footprint’.
Add to that the secondary impact of travel capabilities – if travel is easy, and the community is on a trade route, there will be more services geared toward supplying the needs of travelers; if not, the primary driving force will be the needs of the inhabitants.
The more you look into it, the bigger the mess the whole thing becomes. And that’s why I have rejected this traditional approach, at least for the most part.
5.8.0.1.2 The alternative approach
Instead, each settlement starts off at a base size and separation. The ‘tail’ – the implications – then wag the dog. Every location has benefits and drawbacks – the benefits help the settlement grow, the drawbacks cause it to shrink in size. If the demand for a blacksmith is high enough, there will be a blacksmith – who gets added to the base population and causes further population growth. If there’s no local blacksmith, but there is one in the next town over, that makes that town grow at the expense of this community. Taking stock of every relevant factor, the size of the actual settlement is then adjusted.
But there’s one more way of looking at this approach, and for me, it makes this the most compelling possible option – it develops village size to accommodate the needs of the plot! If you need there to be a sage, or a blacksmith, or a tavern with rooms for travelers in the next community, they are there – and the community grows, within the context of the terrain and other factors, to whatever size is needed o justify the presence of these services.
And if you don’t have any specific plot needs, the defaults of terrain and frequency and traffic and trade dictate the size and the services that are available should the PCs decide they need them.
5.8.0.2 Community Sizes: Base, and smaller
The fundamental unit of community size in this system is the Village. It has a certain base population, and that population size supports the provision of a certain number of general services to the community. These are ‘General Services’ and they exist to meet the needs of the inhabitants. A base-sized village also supports a single “Specialist Service” – i.e. a service with a ‘footprint’ larger than just this community. If the distance between communities is large enough, it may add a second ‘Specialist Service”, causing the community to grow – but it’s still within the range of ‘normal’ for the base size.
Various factors shrink communities. If a community shrinks too much, it enters a community scale lower down the size chart. While the real-world terminology is vague in application, in this ‘unified’ view, these are designated Hamlets, and they have a base size 1/8 that of the base community. Hamlets no longer offer any Specialist services, and support fewer ‘General Service’ providers. The model supports Ha-1, 2, and 3 (those terms will make more sense shortly).
Communities smaller than a Hamlet are Thorpes. Officially, this is a variant of a Middle English word meaning hamlet or small village – but I’ve expropriated the term for usage to represent the smallest of settlements. Once again, we can have Th-1, 2, and 3, and the base size of a Thorpe is 1/8 that of a Hamlet.
Except that we can go smaller!
Smaller than a Thorpe is a mining or logging Camp. Actually, the biggest of these overlap with a Thorpe in size, but the typical-and-smaller range of camps starts where a Thorpe leaves off. Such camps exist to enable the residents to perform one function and one function only; they provide only the essentials necessary to achieve that. These are often (usually?) a satellite of a larger community somewhere nearby. Any single-purpose camp comes under this designation.
Camps can be rated Ca-1, -2, -3, -4, or -5. The base size of a camp is 1/4 that of a Thorpe (but they also have a minimum population of 1).
If you’re keeping track, that’s 1/4 of 1/8 of 1/8 of a village, or 1/256th. If your village base size is 256 people or smaller, then the ‘minimum 1’ rule can be said to be in effect.
Technically, you could also describe a Caravan as a Camp – it just happens to be mobile, or semi-mobile.
5.8.0.3 Community Sizes: Larger than Base
Going the other way, we find ourselves buried in adjectives, because there aren’t many terms on offer. Things get even more confusing when you discover that the definition of a city isn’t what we tend to associate with the term – and different countries have different definitions in terms of size.
And, since most adjectives tend to be relative in meaning, and subject to interpretation, I’ve tried to eschew them in favor of suffixes.
So, larger than a Village is a Village-2, Larger than a Village-2 is a Village-3, and Larger than a Village-3 is a Village-4.
A Village-5 is the same size as a Town (leaving off the -1 suffix). The meaning of the term “Town” is also something that can vary widely from one culture to another. The term is used here to designate a community with a municipal authority beyond a singular mayor / burgomaster / whatever. In England, a Town is usually formally defined by a legal Charter issued by the Crown, giving it a specific identity outside of the control of the regional Nobility. In the US, it loosely refers to incorporated communities – i.e. a community that has issued its own Charter, which formally “Incorporates” the community.
Australia and Canada distinguish communities based on population thresholds – but these can vary from state to state. Nevertheless, this is the mindset that this system adopts.
The difference between a Town and a Village is that the town provides, by virtue of its Charter, services restricted to the Town Limits, collecting rates and revenues to fund these services; in a Village, there is no central authority to provide these services, and any that are provided are provided by the broader administrative unit – be it a state government or a Nobleman, paid for from the taxes and fees they are entitled to collect.
‘Town’ is followed by Town-2, Town-3, Town-4, and Town-5.
Towns -6 to -10 follow, but a Town-6 is the same size as a City-1.
A city is distinguished by having a metropolitan area beyond a simple town square, surrounded by residential districts or suburbs. Many of these will possess some singular identifying traits or characteristics (social or economic in nature), or will claim such an identity. Each suburb or district has its own independent retail or services providers. The number of suburbs or districts is roughly equivalent to the city-suffix squared, plus 1, not counting the metropolitan zone. So City-1 has 2 residential zones, City-2 has 5, City-3 has 10, and so on. These residential zones are all still administered by the central metropolitan zone.
City-5 (with its 26 residential zones) is the same size as Metropolis-1. This is the point at which the central metropolitan area and surrounding suburbs are excerpted from the larger community to form a smaller City (usually City-1 or -2), while the remaining suburbs or districts collectively organize into a separate but contiguous City (usually City-2 or City-3 in size) with an authority independent of that of the central hub. Collectively, these form “Greater [name]”. For example, Greater Sydney consists of the City Of Sydney and 32 surrounding Cities, each of which contains and administers a number of smaller Suburbs. My residence is in the suburb of Panania, which is one of 41 suburbs within the City of Canterbury-Bankstown.
You can work backwards from such numbers.
Canterbury-Bankstown, with 41 suburbs, would have a suffix = sqr root (41-2) = sqr root (39) = 6.245. But this is the result of forced amalgamation between two different cities by the state government, a quite unpopular move at the time. Canterbury used to have 17 suburbs and be a City-3.87, while Bankstown had 10, and was a city-2.8. When they were merged, additional suburbs were also added from surrounding areas. Greater Sydney itself would rank as a City-25.5 if taken collectively – but it instead rates as a Metropolis-5.5 (32 cities, -2, take the square root). But Greater Sydney is a BIG city – 5,356,944 people – or more than five times the population of Imperial Rome at its height (1 Million, according to best estimates).
The justification given for the amalgamation was economy of scale, and for some councils who were struggling to provide services, that was fair enough – but some such mergers were refused by the State Government for political reasons, and others forced through against the wishes of residents even though the parent cities were financially sound. So the whole thing stank of corruption and political manipulation. The leader of the governing party saw his popularity plummet to trump-like figures as a result of this and a couple of other controversies, and was forced to resign so that his successor would stand a shadow of a chance at the next State Election and so that his unpopularity would not impact on the Federal Election due later that year. It was a successful move on the latter front (just barely) but the shadow wasn’t deep enough on the former, and there was a change of state government.
Adding to the size of Sydney is the fact that it’s a State Capital – and our present National Capital only exists as a compromise between Sydney and Melbourne, neither of whom were willing to let the other be the political Big Dog.
5.8.0.3 Demographic Research
Although the models will abstract things greatly, and not adhere to historical reality if it’s inconvenient, reality has to be the underpinning of the Demographic Models that are available.
You don’t have to dig very deep into the history of various townships in Arkansas to discover the effects, both economic and social, or gaining or losing County leadership; I can only project up to the effect of being named a State Capital, and then scale up again for a National Capital.
But it is worth noting that in 33 out of 50 US States, the largest city in the state is Not the State Capital. I put this down to everyone else in the state not wanting to be dominated by that largest city, just as Melbourne would not accept Sydney as the capital of Australia as well as of the state of New South Wales.
Before moving on from this discussion, some historical context is worth highlighting.
According to this graph…
Excerpted from “Mortality, migration and epidemiological change in English cities, 1600–1870” by Romola Davenport, University of Cambridge, CC BY 4.0, courtesy of Researchgate (image scaled by me)
…in 1600, the population of England was 5 million, and about 10% – half a million – lived in an Urban setting. In about 1650, the general population peaked and only slow growth could be seen until about 1775. At that time, the urban population was about 25%, or 1.25 million – and half of them lived in London.
This graph…
Excerpted from “When Bioterrorism Was No Big Deal” by
Patricia Beeson & Werner Troesken (both from the University of Pittsburgh), Copyright unstated, courtesy of Researchgate (left caption moved and image cropped and scaled by me).
…is harder to read, but shows that the trend given in the first continues back another 50 years and then flattens – so in 1550 it would have been about 6% of 5 million (i.e. 300,000) and in 1500, it might only have been 5% (250,000). And almost all of them would have resided in London.
(That paper, downloadable from the link “Researchgate”, has a bunch of others for comparison at the back – Western Europe, Scandinavia, Eastern Europe. Worth grabbing for reference if one of those resembles the Kingdom “tone” that you’re going for.)
This graph…
…shows the historical population of France, which provides additional context.
Below, I’ve isolated the part that matches the 1500-1950 range of the England Graphs:
Extract From Historical_population_of_France.svg
Creative Commons CC-BY-3.0 as above, Cropped and Enlarged by Mike
In 1500, there were about 15 million in France, rising to 18 million by 1600. 1550 would therefore have been about 16.5 million.
In 1500, it can be estimated that 5.6% of the French population lived in towns of 10,000 or more. In 1550, that was 6.3%; and in 1600, 8%, according to one source (and there aren’t many to pick from).
In 1500, Paris had a population of about 150,000, or just 16.1% of the urban population.
In 1550, that was somewhere between 300 and 350,000 people, and 25.2-29.4% of the urban population.
In 1600, we’re talking between 300 and 400,000 people, and 18.8-25% of the urban population – so other cities grew faster than Paris in the 1550-1600 period.
Which other cities? The only one with more than 60,000 on all three dates was Paris. In 1600, Lyon or Ruen may have hit that number. We need to go to one-sixth the size of Paris or less for the next biggest population center, Toulouse, but it might also be in the vicinity of Lyon and Ruen. Estimates of the population in those cities at the time vary from about 40-60,000 in 1500, and 70-80,000 in 1600. But when you compare that with England, you see a stark difference.
Here are some estimated population densities and population levels from the year 1300:
▪ France – 36 to 40 people per sqr km – 18 to 20 million total population.
▪ England and Wales – 33 to 40 people per sqr km – 5-6 million total population.
▪ Germany (then core of the Holy Roman Empire) – 24 to 28 people per square km, 12 to 14 million total population.
▪ Scotland – 6-13 people per sqr km – 0.5 to 1 million total population.
Some other relevant Demographic research:
France
▪ Largest Regional Cities (Excluding Capital): Milan, Venice, Florence (in broader Western Europe) were over 100,000. In France, cities like Ruen or Bordeaux may have reached 25,000?40,000.
▪ Major Towns (5,000?10,000+): Numerous. The median major town size in this range may have been around 12,000?15,000.
▪ Minor Towns/Large Boroughs (1,000?5,000): The backbone of the French urban network; perhaps a few hundred such towns scattered across the kingdom.
▪ Very Small Boroughs (Below 1,000): Most settlements below 1,000 people were agricultural villages.
England (and Wales)
▪ Largest Regional Cities (Excluding Capital): York and Bristol were the undisputed next-largest, likely reaching 15,000?25,000 at their peak before the Black Death.
▪ Major Towns (5,000?10,000+): Only a handful of towns (eg., Norwich, Coventry, King’s Lynn) were in this tier, perhaps 8-10 total.
▪ Minor Towns/Large Boroughs (1,000?5,000): This was the most numerous class of true urban centers in England. The average was likely around 2,000?3,500 people.
▪ Very Small Boroughs (Below 1,000): Many hundreds of market settlements were under 1,000 people, functioning as local market centers but not true urban areas.
Germany (Holy Roman Empire Core)
▪ Largest Regional Cities (Excluding Capital): Cities like Cologne and Prague were major international centers, likely with 30,000?40,000 inhabitants.
▪ Major Towns (5,000?10,000+): Cities like Lübeck, Nuremberg, and Augsburg were regional powers, mostly in the 10,000?25,000 range.
▪ Minor Towns/Large Boroughs (1,000?5,000): There were hundreds of walled, independent towns across the Empire, with many falling into this category. The average would be difficult to pin down but was lower than England.
▪ Very Small Boroughs (Below 1,000): A very large number of minor market towns and Minderstädte (small towns) were below 1,000.
Scotland
▪ Largest Regional Cities (Excluding Capital): Edinburgh was the only city approaching major European size, perhaps 10,000?12,000 at its peak.
▪ Major Towns (5,000?10,000+): None. The scale of Scottish urbanization was significantly smaller than its neighbors.
▪ Minor Towns/Large Boroughs (1,000?5,000): The largest burghs, such as Aberdeen and Perth, were likely only around 3,000 people.
▪ Very Small Boroughs (Below 1,000): Most Scottish burghs (towns) throughout the Middle Ages are believed to have had populations below 1,000.
Those four models emerge as the most robust to choose from. But I’m going to expand the list further with some bigger-population models and one or two even smaller ones, and abstract the ones that have already been identified so that it doesn’t matter if the results of the generation model aren’t quite 100%.in line with History.
This is clearly a village in Switzerland. The buildings are bigger and much closer together, but there’s still a lot of empty landscape. Image by ?Christel? from Pixabay
5.8.0.4 The reality-based Demographic Models r
&squarf: France: Demonstrated a more distributed urban network with many cities (especially in the Low Countries/Italy) capable of sustaining populations of 25,000+.
Urban Population: 5.6% (1500) – 8% (1600)
Hierarchy Slope: Flat but rising sharply
Regional Cities: 0.2-0.3 / 10,000 sqr km
Major Towns: 0.5-1 / 10,000 sqr km
Minor Towns: 5-7 / 10,000 sqr km
Base Village: 320-480
&squarf: Germany: Akin to France but with a significant amount of Forests and Mountains which were relatively lightly populated while occupying great swathes of land.
Urban Population: 10%
Hierarchy Slope: Flat
Regional Cities: 0.4-0.5 / 10,000 sqr km
Major Towns: 1-2 / 10,000 sqr km
Minor Towns: 8-12 / 10,000 sqr km
Base Village: 400-600
&squarf: England: Had a relatively high urban density for its size, but a steep hierarchy. The difference between London and the next tier (York/Bristol) was large, and the gap between those and the average town was also significant.
Urban Population: 5-6%
Hierarchy Slope: Steep
Regional Cities: 0.15 / 10,000 sqr km
Major Towns: 0.4-0.5 / 10,000 sqr km
Minor Towns: 3-4 / 10,000 sqr km
Base Village: 240-360
&squarf: Scotland: Was the least urbanized region. Even its major burghs would be considered only medium-sized towns in England or minor towns in France.
Regional Cities: None.
Urban Population: 2-3%
Hierarchy Slope: Very Flat, Slope flattens
Major Towns: 0.1 / 10,000 sqr km
Minor Towns: 0.5-1 / 10,000 sqr km
Base Village: 160-240
5.8.0.5 The Artificial Demographic Models
To those four, I am adding the following:
Imperial Core: A region dominated by a single capital or a handful of enormous cities, like Ancient Rome, Ancient China, or Mamluk, Egypt. It would also apply to any of the others if they have significant improvements over standard medieval technology (including magic) in the fields of agronomy and food transportation.
Urban Population: 15-20%
Hierarchy Slope: Very Steep
Regional Cities: 0.5 – 1 / 10,000 sqr km
Major Towns: 0.1 – 0.3 / 10,000 sqr km
Minor Towns: 1-2 / 10,000 sqr km
Base Village: 480-720
Coastal Mercantile Model: Based on the late medieval and early modern low countries (Flanders./ Holland) and the Italian City States. Power and wealth are distributed among many medium-large communities, trading ports, and other economic centers, but there is no one super-sized city.
Urban Population: 20-30%
Hierarchy Slope: Very flat at low levels, rising sharply from higher town sizes (30,000 people)
Regional Cities: 1 – 2 / 10,000 sqr km
Major Towns: 2 – 4 / 10,000 sqr km
Minor Towns: 4 – 6 / 10,000 sqr km
Base Village: 280-420
Frontier Nation: Somewhere in between Scotland and England, consisting of one part moderately densely settled, one part very sparsely settled (4-4 times as large) and a third part in the middle (2-3 times as large), relative to the densely settled region.
Urban Population: 4-8%
Hierarchy Slope: Moderate, flattens
Regional Cities: 0.05 / 10,000 sqr km
Major Towns: 0.2-0.25 / 10,000 sqr km
Minor Towns: 1-2 / 10,000 sqr km
Base Village: 200-300
Tribal / Clan Model: based on Early Medieval Scandinavia and central Africa. Also useful for an extensive Nomadic Trading Network. Settlements are mainly defensive or seasonal gathering points.
Urban Population: 2-5%%
Hierarchy Slope: Impossibly Steep but capped
Regional Cities: None
Major Towns: 0.001 / 10,000 sqr km
Minor Towns: 0.05 / 10,000 sqr km
Base Village: 80-120
The village is the fundamental unit of the population distribution simulation – everything starts there and flows from it.
5.8.1.1 Village Frequency
I’ve given this section a title that I think everyone will understand, but it’s not actually what it’s all about. The real question to be answered here is, how big is the Locus surrounding a population?
The answer differs from one Demographic Model to another, unsurprisingly.
The area of a given Locus is:
SL = MF x (Pop)^0.5 x k,
where,
SL = Locus Size
MF = Model Factor
Pop is the population of the village
and k = a constant that defines the units of area.
The base calculation, with a k of 1, is measured in days of travel. That works for a lot of things, but comparison to a base area of 10,000 km^2 isn’t one of them. For that, we need a different K – one based on the Travel Ranges defined in previous parts of this series.
Section 5.7.1.14.5.1 gives answers based on travel speed, more as a side-issue than anything else, based on the number of miles that can be traversed in a day:
(Very) Low d = 10 miles / day
Low d = 20 miles / day
Reasonable d = 25 miles / day
Doable d = 30 miles / day
Close To Max (High) d = 40 miles / day
Max d = 50 miles / day
( x 1.61 = km).
— but these are the values for Infantry Marching, and that’s a whole other thing.
Infantry march faster than people walk or ride in wagons. The amount varies depending on terrain (that’s the main variable in the above values), but – depending on who you ask – it’s 1 2/3 or 2 or 2.5 times.
But, because they travel in numbers, they can march for less time in a day. Some say 6 hours, some 7, some 8. Ordinary travelers may be slower, but they can operate for all but an hour or two of daylight. That might be 8-2=6 or 7 hours in winter, but it’s more like 12-2=10 or 11 hours in summer.
And it has to be borne in mind that the basis for these values assumes travel in Summer – at least in medieval times. But we want to take the seasons out of the equation entirely and set a baseline from which to adjust the list given earlier.
One could argue that summer is when the crops are growing, and therefore that should be the basis of measurement, given that we’re looking for the size of a community’s reach.
So let’s take the summer values, and average them to 10.5 hours. When you take the various factors into account and generate a table (I used 6, 6.5, 7, 7.5, and 8 for army marching times per day, and the various figures for speed cited plus 2.25 as an additional intermediate value, and work out all the values that it might be, and average them, you get 1.04. That’s so small a change as to be negligible – 1.04 x 50 = 52. We will have far bigger approximations than that!
So we can use the existing table as our baseline. Isn’t that convenient?
But which value from amongst those listed to choose? Overall, unless there’s some reason not to, you have to assume that terrain is going to average out when you’re talking about a baseline unit of 10,000 sqr kilometers. So, let’s use the “Reasonable” value unless there’s reason to change it.
And that gives a conversion rate of 1 day’s travel = roughly 25 miles, or 40 km. And those are nice round numbers.
Now, a locus is roughly circular in shape, so is that going to be a radius or a diameter? Well, a “market day” is how far a peasant or farmer can travel with their goods and return. in a day, so I think we’re dealing with a radius of 1/2 the measurement, so that measurement must be the diameter of the locus.
Which means that the base radius of a locus is 12.5 miles or 20 km.
In an area where the terrain is friendly in terms of travel, this could inflate to twice as much; in an area where terrain makes travel difficult, it could be 1/2 as much or less. But if we’re looking for a baseline, that works.
12.5 miles radius = area roughly 500 sqr miles = area 1270 sqr km. So in 10,000 sqr km, we would expect to find, on average, 7.9 locuses.. But that’s without looking at the population levels and the required Model Factors.
The minimum size for an English Village is 240 people. The Square Root of 240 is 15.5.
So the formula is now 1270 = 15.5 x 20 x Model Factor, and the Model Factor for England conditions and demographics is 4.1. Under this demographic model, there will be 4.1 Village Loci – which is the same thing as 4.1 villages – in 10,000 sqr km.
Having worked one example out to show you how it’s done, here are the Model Factors for all the Demographic Models:
▪ Imperial Core: 480^0.5 = 21.9, and 21.9 x 20 x Model Factor = 1270, so MF = 2.9
▪ Germany (HRE): 400^0.5=20, and 20 x 20 x MF = 1270, so MF = 3.175
▪ France: 320^0.5 = 17.9, and 17.9 x 20 x MF = 1270, so MF = 3.55
▪ Coastal Mercantile Model: 280^0.5 = 16.733, and 16.733 x 20 x MF = 1270, so MF = 3.8
▪ England: 4.1
▪ Frontier Nation: 200^0.5 = 14.14, and 14.14 x 20 x MF = 1270, so MF = 4.5
▪ Scotland: 160^0.5 = 12.65, and 12.65 x 20 x MF = 1270, so MF = 5.02
▪ Tribal / Clan Model: 80^0.5 = 8.95, and 8.95 x 20 x MF = 1270, so MF = 8.95
So, why didn’t I simply state the number of loci (i.e. the number of villages) in an area?
It’s because that’s a base number. When we get to working on actual loci or zones, these can shrink, or grow; according to other factors. This is a guideline – but to define an actual village and it’s surrounds, we will need to use the MF. Besides, you might want to generate a specific model for a specific Kingdom in your game.
You may be wondering, then, why it should be brought up at all, or especially at this stage? The answer to those questions is that the area calculated is a generic base number which may have only passing resemblance to the actual size of the locus.
A locus will continue to expand until it hits a natural boundary, a border, or equidistance to another population center. Very few of them will actually be round in shape – some of them not even approximately.
The ratio between ACTUAL area and BASE area is an important factor in calculating the size of a specific village.
An example of the ‘real borders’ of a Locus
To create the above map, I made a copy of the base map (shown to the left). At the middle top and bottom, i placed a dot representing the Locus ‘radius’.
At the left top, another dot marked the half-way point to the next town (top left), where it intersected a change of terrain – in this case, a river.
At the top right, doing the same thing would have made the town at top right a bit of a mixed bag – it already has forests and hills and probably mountains. I didn’t want it to have a lot of farmland as well. So I deliberately let the current locus stretch up that way. The point below it is also slightly closer to the top right town than it would normally be, but that’s whee there is a change of terrain – the road. I tossed up whether the locus in question should include the intersection and road, but decided against it.
And so on. Once I had the main intersection points plotted, I thought about intermediate points – I didn’t want terrain features to be split between two towns, they had to belong to one or the other. You can see the results in the “bites” that are taken out of the borders of the locus at the bottom.
If you use your fingers, one pointing at the town in the center and the other at the top-middle intersection point, and then rotate them to get an idea of the ‘circular’ shape of the locus, you can see that it’s missing about 1/6 of it’s theoretical area to the east, another 1/6 to the south, and a third 1/6th to the west. It’s literally 1/2 of the standard size. That’s going to drive the population down – but it’s fertile farmland, which will push it up. But that’s getting ahead of ourselves.
As an exercise, though, imagine that the town lower right wasn’t there. The one that’s on the edge of the swamp. Instead of ending at a point at the bottom, the border would probably have continued, including in the locus that small stand of trees and then following the rivers emerging from the swamp, and so including the really small stand of trees. The Locus wouldn’t stop until it got to the swamp itself. The locus would have extended east to the next river, in fact, encompassing forest and hills until reaching the East-road, which it would follow inwards until ii joined the existing boundary. It would still have lost maybe 1/12th in the east, but it would have gained at least that much and probably more in the south, instead of losing 1/3. The locus would be 1 – 1/12 + 1/3 – 1/12 – 1/3 = 10/12 of normal instead of 1/2 of normal.
5.8.1.2 Village Base Size
If you look at the models, you will notice “Base Village” and a population count, and might be fooled into thinking that everything in that range is equally likely. It’s not.
Take the French model – it lists the village size as 320-480.
First, what’s the difference, high minus low? In this case, it’s 160. We need to divide that by 8 as a first step – which in this case is a nice, even, 20.
Half of 20 is 10, and three times 10 is 30. Always round these UP.
With that, we can construct a table:
01-30 = 320
31-40 = 321-350 (up by 30)
41-50 = 351-380 (up by 30)
51-60 = 381-400 (up by 20)
61-70 = 401-420 (up by 20)
71-75 = 421-430 (up by 10)
76-80 = 431-440 (up by 10)
81-85 = 441-450 (up by 10)
86-90 = 451-460 (up by 10)
91-95 = 461-470 (up by 10)
96-00 = 470-480 (up by 10)
I used Gemini to assist in validating various elements of this section, and it thought the “up by 30” was confusing and the terminology be replaced with something more formal.
I disagree. I think the more colloquial vernacular will get the point across more clearly.
It was also concerned – and this is a more important point – that GMs couldn’t implement this roll and the subsequent sub-table quickly. I disagree, once again – I’ve seen far more complicated constructions for getting precise population numbers than two d% rolls, especially since the same tables will apply to all areas within the Kingdom that are similar in constituents. Everywhere within a given zone, in fact, unless you deliberately choose to complicate that in search of precision.
In general, you construct one set of tables for the entire zone – and can often copy those as-is for other similar zones as well. Maybe even for a whole Kingdom.
The d% breakdown is always the same percentages, and there are always 2 “up by “3 x 1/2″s, 2 “up by 2 x 1/2″s, and 5 “up by 1/2″‘s – with the final one absorbing any rounding errors; in this example there aren’t any.
We then construct a set of secondary tables by dividing our three (or four) increments by 10. In this case, 30 -> 3, 20 -> 2, 10 -> 1. And we apply the same d% breakdown in exactly the same way, but from a relative position:
So:
1/2 x 3 = 1.5, rounds to 2; 3 x 1.5 = 4.5, rounds to 5.
1/2 x 2 = 1; 3 x 1 = 3.
1/2 z 1 = 0.5, rounds to 1; 3 x 1 = 3.
The “Up By 30” Sub-table reads:
01-30 = +0
31-40 = +5
41-50 = +5+5 = +10
51-60 = +10+3=+13
61-70 = +13+3=+16
71-75 = +16+2 = +18
76-80 = +18+2 = +20
81-85 = +20+2 = +22
86-90 = +20+2 = +24
91-95 = +24+2 = +26
96-00 = +30 (up by whatever’s left).
The “Up By 20” Sub-table:
01-30 = +0
31-40 = +3
41-50 = +3+3 = +6
51-60 = +6+2 =+8
61-70 = +8+2=+10
71-75 = +10+1 = +11
76-80 = +11+1 = +12
81-85 = +12+1 = +13
86-90 = +13+1 = +14
91-95 = +14+1 = +15
96-00 = +20 (up by whatever’s left).
The “Up By 10” Sub-table:
01-30 = +0
31-40 = +3
41-50 = +3+3 = +6
51-60 = +6+1 =+7
61-70 = +7+1=+8
71-75 = +8+1 = +9
76-80 = +9+1 = +10
81-85 = +0-1 = -1
86-90 = -1-1 = -2
91-95 = -2-1 = -3
96-00 = -3-1 = -4
Notice what happened when I ran out of room in the “+10”? The values stopped going up, and starting from +0, started going DOWN.
It takes just two rolls to determine the Base Population of a specific village with sufficient accuracy for our needs within a zone..
EG: Roll of 43: Main Table = 380, in an up-by-30 result. So we use the “Up By 30” Sub-table and roll again: 72, which gives a +18 result. So the Base population is 380+18=398.
These results are intentionally non-linear.
Optional:
If you want more precise figures, apply -3+d3.
Or -6+d6.
Or anything similar – though I don’t really think you should go any larger than -10+d10 – and I’d consider -8+2d6 first.
I have to make it clear, this is relating to the population of a specific village in a specific zone not a generic one. For anything of the latter kind, continue to use the minimum base population. I just thought that it bookended the ‘real locus’ discussion. We had to have the former because it affects what terrain influences the town size and how much of it there is; the latter is just a bonus that seemed to fit..
5.8.1.3 Village Demographics
Let’s start by talking Demographics, both real-world and Fantasy-world.
The raw population numbers are not as useful as numbers of families would be. But that’s incredibly complicated to calculate and there’s no good data – the best that I could get was a broad statement that medieval times had a child mortality rate (deaths before age 15) of 40-50%, an infant mortality rate (deaths before age 1) of 25-35%, and an average family size of 5-7 children.
If look at modern data, we get this chart:
Source: Our World In Data, cc-by, based on data from the United Nations. Click the image to open a larger version (3400 x 3003 px) in a new tab.
I did a very rough-and-ready curve fitting in an attempt to exclude social and cultural factors and derive a basic relationship for what is clearly a straight band of results:
Derivative work (see above), cc-by, extrapolating a relationship curve in the data
…from which I extracted two data points: (0%,1.8) and (10%,5.6), which in turn gave me: Y = 0.38 X + 1.8, which can be restated, X = 2.63Y – 4.74. And that’s really more precision than this analysis can justify, but it gives a readout of child mortality for integer family sizes.
Yes, I’m aware that the real relationship isn’t linear. But this simplified approximation is good enough for our purposes.
That, in turn, gives me the following:
Y = Typical Number Of Children,
X = Overall Child Mortality Rate
Y, X:
1, -3%
2, 0%
3, 3%
4, 5%
5, 8%
6, 11%
7, 13%
8, 16%
9, 18%
10, 21%
11, 24%
12, 26%
…so far, so good.
Next, I need to adjust everything for the rough data points that we have for medieval times, when bearing children was itself a mortality risk for the mothers.
5-7 children, 40-50%
so that gives me (5, 8, 40) and (7, 13, 50) – more useful in this case as (8, 40) and (13,50) – which works out to Z = 2 Y + 24.
Z=Child Mortality, Medieval-adjusted
Y, X, Z:
1, -3%, 18%
2, 0%, 24%
3, 3%, 30%
4, 5%, 34%
5, 8%, 40%
6, 11%, 46%
7, 13%, 50%
8, 16%, 56%
9, 18%, 60%
10, 21%, 66%
11, 24%, 72%
12, 26%, 76%
But here’s the thing: realism and being all grim and gritty might work for some campaigns, but for most of us – no. What we need to do now is apply a “Fantasy Conversion” which contains just enough realism to be plausible and replaces the balance with optimism.
I think Division of Z (the medieval-adjusted child mortality rate) by 3 sounds about right – YMMV. That gives me the F values below – but I also checked on a ratio of 2.5, which gives me the F2 values.
Gemini suggested using 3.5 or 4 for an even ‘softer’ mortality rate, and 2.25 or 2 for a grittier one.
In principle, I don’t have a problem with that – and part of the reason why I’m not just throwing the mechanics at you, but explaining how they have been derived, is so that GMs can use alternate values if they think them appropriate to their specific campaigns.
I don’t just want to feed the hungry, I want to teach them to fish, to paraphrase the biblical parable.
F= Fantasy Adjusted Child Mortality Rate
F2 = more extreme Child Mortality Rate
Y, X, Z, F, F2:
1, -3%, 18%, 6%, 7%
2, 0%, 24%, 8%, 10%
3, 3%, 30%, 10%, 12%
4, 5%, 34%, 11%, 14%
5, 8%, 40%, 13%, 16%
6, 11%, 46%, 15%, 18%
7, 13%, 50%, 17%, 20%
8, 16%, 56%, 19%, 22%
9, 18%, 60%, 20%, 24%
10, 21%, 66%, 22%, 26%
11, 24%, 72%, 24%, 29%
12, 26%, 76%, 25%, 30%
I think the F values are probably more appropriate for High Fantasy, while the F2 are better for more typical fantasy – but you’re free to use this information any way you like, the better to suit your campaign world.
You might decide, for example, that averaging the Medieval Adjusted Values with the F2 is ‘right’ – so that 5 children would indicate (40+16)/2 = 28% mortality.
Social values can also adjust these values – traditionally, that means valuing male children more than females. But in Fantasy / Medieval game settings, I think that would be more than counterbalanced, IF it were a factor, by the implied increased risks from youthful adventuring. In a society that practices such gender-bias, it would not surprise me if the ultimate gender ratio was 60-40 or even 70-30 – in favor of Girls.
5.8.1.3.1 Maternal Survival
The next element to consider is the risk of maternal death in childbirth. That’s even harder to pin down data on, but 1-3% per child is probably close to historically accurate. Balanced around that is the greater risks from adventuring, and the availability of clerical healing. So I’m extending the table to cover 4, 5, and 6%, but you are most likely to want the values in the first columns. To help distinguish these extreme possibilities from the usual ones, they have been presented in Italics.
We’re not interested so much in the number of cases where it happens as I am the number of cases where it doesn’t – the % of families with living mothers, relative to the number of children.
Y, @1, @2, @3, @4, @5, @6:
1, 99%, 98%, 97%, 96%, 95%, 94%
2, 98.0%, 96.0%, 94.1%, 92.2%, 90.3%, 88.4%
3, 97.0%, 94.1%, 91.3%, 88.5%, 85.7%, 83.1%
4, 96.1%, 92.2%, 88.5%, 84.9%, 81.5%, 78.1%
5, 95.1%, 90.4%, 85.9%, 81.5%, 77.4%, 73.4%
6, 94.1%, 88.6%, 83.3%, 78.3%, 73.5%, 69.0%
7, 93.2%, 86.8%, 80.8%, 75.1%, 69.5%, 64.8%
8, 92.3%, 85.1%, 78.4%, 72.1%, 66.3%, 61.0%
9, 91.4%, 83.4%, 76.0%, 69.3%, 63.0%, 57.3%
10, 90.4%, 81.7%, 73.7%, 66.5%, 59.9%, 53.9%
11, 89.5%, 80.1%, 71.5%, 63.8%, 56.9%, 50.6%
12, 88.6%, 78.5%, 69.4%, 61.3%, 54.0%, 47.6%
The method of calculation is 100 x ( 1- [D/100] ) ^ Y. Just in case you want to use different rates than these.
There does come a point at which the likelihood of maternal death begins to limit the size of the average family, though, and I think the 6% values are getting awfully close to that mark.
Let’s say that a couple have 6 children, right in the middle of the historical average. If the mother falls pregnant a 7th time, at 6%, she has roughly a 1 in 3 chance of dying (and a fair risk of the child perishing with her). Which means that she HAS no more children. But if she beats those odds to have 7 children, her chances are even worse when it comes to child #8, and so on.
Of all the cases with a mother who survived childbirth, we then need to factor in death from all other causes – monsters and adventuring and mischance and so on. Fantasy worlds tend to be dangerous, so this could be quite high – maybe as much as 5% or 10% or 20%. So multiply the living mothers by 0.8. Or 0.7 Or 0.9 – whatever you consider appropriate – to allow for this.
This rural community is obviously alongside a major river or coastline – the proximity of the mountains suggests the first, but isn’t definitive. The name offers a clue: ‘hallstatt’, which to me sounds Germanic, and suggests that the waterway may be the Rhine. Or not, if I’ve misinterpreted. Image by Leonhard Niederwimmer from Pixabay
5.8.1.3.2 Paternal Survival
The result is the % of families with a surviving mother. So how many surviving fathers are there per surviving mother? Estimates here vary all over the shop, and more strongly reflect social values. But if I’m suggesting 5% – 20% mortality for mothers from other sources, the same would probably be reasonably true of fathers – if those social values don’t get in the way.
0.95 x 0.95 = 90.25%.
0.9 x 0.9 = 81%.
0.85 x 0.85 = 72.25%
0.8 x 0.8 = 64%.
Those values give the percentages in which both parents have survived to the birth of the average number of children.
If you’re using 10% mortality from other causes, then in 90% of cases in which the mother has died, the father has survived. But in 10% of the cases in which the mother has succumbed, the children are orphaned by the loss of the other parent.
The higher this percentage, the higher the rate of survivors remarrying and potentially doubling the size of their households at a stroke. And that will distort the average family size far more quickly than the actual mortality percentages, unless there is some social factor involved – maybe it’s expected that parents with children will only marry single adults without children, for example.
The problem with this approach is that if it’s the mother who is remarrying, this puts her right back on that path to mortality through childbirth; the child-count ‘clock’ does not get reset. If it’s a surviving father marrying a new and childless wife, it DOES reset, because the new mother has not had children previously.
In a society that permits such actions, there is a profound dichotomy at its heart that favors larger families for husbands who survive while placing mothers who survive at far greater risk of the family becoming a burden to the community – which is likely to change that social acceptance. Paradoxically, a double standard is what’s needed to give both parents a more equal risk of death, and a more equal chance of surviving.
5.8.1.3.3 Childless Couples
Next, let’s think about the incidence of Childless Couples. We can state that there’s a given chance of pregnancy in any given year of marriage; but once it happens, there is just under a full year before that chance re-emerges.
Year 1: A% -> 1 child born
Year 2: (100-A) x A% -> 1 child born, A%^2 -> 2 children born
Year 3: (100-A^2) x A% -> 1 child born, (100-A) x A% -> 2 children born, A^3% -> 3 children born
… and so on.
This quickly becomes difficult to calculate, because each row adds 1 to the number of columns, and its easy to lose track.
But here’s the interesting part: we don’t care. To answer this question, there’s a far simpler calculation.
In any given year, there will be B couples married. (100-A%) of them will not have children in the course of that year. If we specify B as the average, rather than as a value specific to a given year, then the year before we will also have B couples marry, and (100-A%) of them without children at the end of that year – which means that in the course of the second year of marriage, A% will have children and stop being counted in this category, and (100-A)% will not, and will still count.
Adding these up, we get (100-A)% + (100-A)%^2 + …. and so on. And these additions will get progressively and very rapidly smaller.
Let’s pick a number, by way of example – let’s try A=80%, just for the sake of argument.
We then get 20% + 4% + 0.8 % + 0.16% + 0.032% + 0.0064% … and I don’t think you’d really need to go much further, the increases become so small. I pushed on one more term (0.000128%) and got a total of 24.998528%. I pushed further with a spreadsheet, and not even 12 years was enough to cross the 25% mark – but it was getting ever closer to it. Close enough to say that for A=80, there would be 25 childless couples for every… how many?
The answer to that question comes back to the definition of A: It the number of couples out of 100 who have a child in any given year. So, over 12 years, that’s a total of 1200 couples. And 25 / 1200 = 2.08%.
I did the math – cheating, I used a spreadsheet – and got the following, all out of 1200 couples:
A%, C, [C rounded]
80%, 25,
75%, 33.33, 33
70%, 42.86, 43
65%, 53.85, 54
60%, 66.67, 67
55%, 81.81, 82
50%, 99.98, 100
45%, 122.13, 122
40%, 149.67, 150
35%, 184.66, 185
30%, 230.10, 230
25%, 290.50, 291
20%, 372.51, 373
But that has to mean that the rest of those 1200 couples have to have children – and the number of children will approach the average number that you chose.
So if you pick a value for A, you can calculate exactly how many childless couples there are relative to the number of families with children:
A=45%, C=122:
1200-122 = 1078
1078 families with children, 122 childless couples
1078 / 122 = 8.836
8.836 + 1 = 9.863
so 1 in 9.863 families will be childless couples.
5.8.1.3.4 Unwed Singles
The social pressure to marry has varied considerably through the ages, but the greater the dangers faced by the community, the greater this pressure is going to be. And the fitter and healthier you are, the greater this pressure is going to be amplified.
This is inescapable logic – the first duty of any given generation in a growing society is to replace the population who have passed away, and it takes a long time to turn children into adults.
You could calculate the average lifespan, deduct the age of social maturity, and state that society frowns heavily on unwed singles above that age, and as every year passed with the individual approaching that age, the greater the social pressure would become – and that would be a true approach.
The problem is that the average lifespan is complicated by those high rates of childhood death, and trying to extract that factor becomes really complicated and messy. And then you throw in curveballs like Elves and Dwarves, with their radically different lifespans and the whole thing ends up in a tangled mess.
So, I either have to pull a mathematical rabbit out of my hat, or I do the sensible thing and get the GM to pick a social practice and do my best to make it an informed choice.
While a purely mathematical approach is possible, the more that I looked at the question, the more difficult it became to factor every variable into the equation.
Want the bare bones? Okay, here goes.
For a given population, P, there are B marriages a year, removing B x 2 unwed individuals from the population. We can already extract the count of those who are ineligible for marriage due to age, because they are all designated as children.
We can subtract the quantity of childless couples who are already wed in a similar fashion to the calculations of the previous subsection.
The end result is the number of unwed singles of marriageable age who have not married. Setting P at a fixed value – say 100 people – we can then quickly determine the number of unmarried singles.
What ultimately killed this approach was that it was – in the final analysis – using a GM estimate of B as a surrogate for getting the GM to estimate the % of singles in their community – and doing so in a manner that was less conducive to an informed choice, and requiring a lot of calculations to end up with the number that they could have directly estimated in the first place.
Nope. Not gonna work in any practical sense.
So, instead, let’s talk about the life of the social scene – singles culture. There is still going to be all that social pressure to marry and contribute to the population, especially if you are an even half-successful adventurer, because that makes you the healthiest, wealthiest, and most prosperous members of the community.
It can be argued that instead of using the average lifespan (with all its attendant problems) and deducting the age of maturity (i.e. the age at which a child becomes an adult) to determine at what age a couple have to have children in order to keep the population at least stable (you need two children for that, since there are two adults involved, and you need to take that child mortality rate into consideration, dividing those 2 by the mortality rate and rounding up), you should use add age of the mother as a factor in the rise of the mother’s mortality during childbirth, and work back from that age. In modern times, that’s generally somewhere in the thirties, maybe up to 40. That doesn’t mean that older women can’t have children, just that under these circumstances, the risks of dying before you have enough offspring are considered too high by the general culture.
But what does that really get you? There’s always going to be some age at which the pressure to wed starts to grow. Shifting it this way or that by a couple of years won’t change much.
Looking at it from the reverse angle – how much single life will society tolerate – can be far more useful.
I would suggest a base value of a decade. Ten years to be an adventurer and live life on the edge.
In high-danger societies, especially with a high mortality rate, that might come back 2 or 3 years, At it’s most extreme, 5. That’s all the time you have to focus on becoming a professional who is able to support a family, or at least to setting your feet firmly on that path.
In low-danger societies, especially those with a lower mortality rate, it might get pushed out a few years, maybe even another 5. That’s enough time that you can spread some wild oats and still settle down into someone respectable within the community.
How long is the typical apprenticeship? In medieval times? In your fantasy game-world? From the real world, I could bandy about numbers like 4 years, or 5 years, or 5 years and 5 more learning on the job, or repaying debts to the master that trained you. And you end up with the same basic range – 5-15 years.
What is the age of maturity in your world? Again, I could throw numbers around – 18 or 21 seem to be the most common in modern society, but 16 (even 15) has its place in the discussion – that’s how old you had to be back when I was younger before you could leave school and pursue a trade, i.e. becoming an apprentice. But I have played in a number of games where apprenticeships started at eight, or twelve, and lasted a decade – and THEN you got to start repaying your mentor for the investment that he’s made in you. With interest.
Does there come a point where people are deemed anti-social because they have not married, and find their prospects of attracting a husband or wife diminishing as a result? Don’t say it doesn’t happen, because there is plenty of real-life evidence that it’s there as a social undercurrent – one that shifts, and sometimes intensifies or weakens, without real understanding of the factors that drive the phenomenon – instead, forget the real world and think about the game-world.
How optimistic / positive is the society? How grim and gritty?
Think about all these questions, because they all provide context to the basic question: What percentage of the population are unwed with no (official) children?
Here’s how I would proceed: Pick a base percentage. For every factor you’ve identified that gives greater scope for personal liberty, add 2%. For every factor that demands the sacrifice of some of that liberty, from society’s point of view, subtract 2%. In any given society, there are likely to be a blend of factors, some pushing the percentage up, and some down – but in more extreme circumstances, they might all factor up or down. If you identify a factor as especially weak, only adjust by 1%; if you judge a factor as especially strong, adjust by 3 or even 4%.
In the end, you will have a number.
Let me close out this section with some advice on setting that base percentage.
There are two competing and mutually-exclusive trains of thought when it comes to these base values. Here’s one:
▪ In positive societies, low child mortality means fewer young widows/widowers. The society is more stable, allowing for strong family formation and early marriage. Base rate is low.
▪ In moderate societies, dangers still disrupt family units, leading to a moderate rate of single, adult households. Base rate is moderate.
▪ In dangerous societies, high death rates mean many broken families, orphans, and single parents. The number of adult individuals living outside a stable family unit is maximized. Base rate is high.
Here’s the alternative perspective:
▪ Positive societies produce less social pressure and greater levels of personal freedom, reducing the rate of marriage and increasing the capacity for unwed singles. Base rate is high.
▪ Moderate societies have a positive social pressure toward marriage at a younger adult age, and less capacity for personal liberty. Base rate is moderate.
▪ Societies that swarm with danger have a higher death rate, and there would be more social pressure to marry very young to create population stability. The alternative leads to social collapse and dead civilizations.
What’s the attitude in your game world? They are all reasonable points of view.
In a high-fantasy / positive social setting, I would start with a base percentage of 22%. Most factors will tend to be positive, so you might end up with a final value of 32% – but there can be strains beneath the surface, which could lead to a result of 12% in extreme cases.
In a mid-range, fairly typical society, I would employ a base of 27%. If there are lots of factors contributing to a high singles rate, this might get as high as 37%, and if there are lots of negatives, it might come down to 17% – but for the most part, it will be somewhere close to the middle.
In an especially grim and dark world, I would employ a base of 33%, in the expectation that most factors will be negative, and lead to totals more in the 23-28% range. But if social norms have begun to break down, social institutions like marriage can fall by the wayside, and you can end up with an unsustainable total of 40-something percent.
Anything outside 20-35 should be considered unsustainable over the long run. Whatever negative impacts can apply will be rife.
5.8.1.3.5 Population Breakdown
That’s the final piece of the puzzle – with that information, you can assess the four types of ‘typical families’ and their relative frequency:
# Children with no parents,
# Children with mothers but no fathers,
# Children with fathers but no mothers, and
# Children with two parents.
# Childless Couples
# Unwed Singles
Get the total size of each of these family units / households* in number of individuals, multiply that size by the frequency of occurrence, add up all the results, and convert them to a percentage and you have a total population breakdown. Average the first five and you have the average family size in this particular region and all similar ones.
Multiply each frequency of occurrence by the village population total (rounding as you see fit), and you get the constituents of that village.
I have never liked the use of the term ‘households” in a demographic context, even though that seems to be the most commonly preferred term these days. I’ve lived in a number of shared accommodations as a single. over the years, and that experience muddies what’s intended to be a clearer understanding of the results. If you have 50 or 100 singles living in a youth hostel, are they one household or 50-100? Families – nuclear or non-nuclear – for me, at least, is the clearer, more meaningful, term.
5.8.1.3.6 The Economics Of The Demographics
In modern times, it’s not unusual for two adults and even multiple children all to have different occupations for different businesses all at the same time. Some kids start as paper boys and girls at a very young age. Even five year olds with Lemonade stands count in this context.
Go back about 100 years and that all changes. There is typically only one breadwinner – with exceptions that I’ll get to in a moment – and while some of them will have their own business (be it retail or in a service industry), most will be working for someone else.
There will be a percentage who have no fixed employment and operate as day labor.
Going into Victorian times, we have the workhouses and poorhouses, where brutal labor practices earn enough for survival but little more. While some were profitable for the owners, most earned less than they cost, and relied on charitable ‘sponsorship’ from other public institutions – sometimes governments, more often religious congregations. These are the exceptions that I mentioned. This is especially true where the father has deserted the family or died (often in war) leaving the mother to raise the children but unable to do so because of the gender biases built into the societies of the time.
Go back still further, and it was a matter of public shame for a woman to work – with but a few exceptions such as midwifery. Nevertheless, they often earned supplemental income for the families with craft skills such as sewing, knitting, and needlework.
The concept that the male was the breadwinner only gets stronger as you pass backwards through history.
Fantasy games are usually not like that. They do see the world from the modern perspective and force the historical reality to conform to that perspective. In particular, gender bias is frequently and firmly excluded from fantasy societies.
The core reasoning is that characters and players can be of either gender (or any of the supplementary gender identifications) and the makers of the games don’t wish to exclude potential markets with discomforting historical reality.
There are a few GMs out there who intentionally try to find an ‘equal but distinct’ role for females and others within their fantasy societies; it’s difficult, but it can be done – and it usually happens by excluding common males from segments of the economy within the society. If there are occupations that are only open to women, and occupations of equal merit (NOT greater merit) that are only open to men, you construct a bilateral society in which two distinct halves come together to form a whole.
But it would still be unusual for a single household to have multiple significant breadwinners; you had one principal earner and zero or more supplemental incomes ‘on the side’.
Businesses were family operations in which the whole family were expected to contribute in some way, subject to needs and ability.
And that’s the fundamental economic ‘brick’ of a community – one income per family, whether that income derives as profits from a business or from labor in someone else’s business.
You can use this as a touchstone, a window into understanding the societies of history, all the way back into classical times – who earned the money and how? In early times, it might be that you need to equate coin-based wealth with an equivalent value in goods, but once you start thinking of farm produce or refined ore as money, not as goods, the economic similarities quickly reveal themselves.
So that is also the foundation of economics in this system. One family, one income (plus possible supplements). In fact, there were periods in relatively recent history in which the supplementary income itself was justification for marriage and children.
In modern times, we evaluate based on the reduction of expenses; this is because most of our utilities don’t rise in usage as fast as the number of people using them (which goes back to the muddying concept of ‘households’; if two people are sharing the costs, both have more economic leftover to spend because the costs per person have gone down; if they are NOT sharing expenses, each providing fully for themselves, then they are two ‘households’, not one. It also helps to think of rent as a ‘utility’ within this context).
But that’s a very modern perspective, and one that only works with the modern concept of ‘utilities’ – electricity, gas, and so on. Go back before that, into the pre-industrial ages, and the perspective changes from one of diminishing liabilities into one of growth of potential advantages. And having daughters who could supplement the household income by working as maids or providing craft services gave a household an economic advantage.
5.8.1.3.7 An Economic Village Model
8 a^2 = b^2 – c^2.
Looks simple, doesn’t it? In fact, it is oversimplified – the reality would be
a^d = (b^e – c^f ) / g,
but that’s beyond my ability to model, and too fiddly for game use.
a = the village’s profitability. Some part of this may show up as public amenities; most of it will end up in the pockets of the broader social administration, in whatever form that takes.
b = the village’s productivity, which can be simplified to the number of economic producers in the village. You could refine the model by contemplating unemployment rates, but the existence of day laborers whose average income automatically takes into account days when there’s no work to be found, means that we don’t have to.
c = the village’s internal demand for services and products. While usually less than production, it doesn’t have to be so. But it’s usually close to b in value.
To demonstrate the model, let’s throw out figures of 60 and 58 for b and c.
8 a^2 = 60^2 – 58^2 = 3600 – 3364 = 236.
a = (236 / 8)^0.5 = 29.5^0.5 = 5.43
The village grows. b rises to 62. c rises to 59.
8 a^2 = 62^2 – 59^2 = 3844 – 3481 = 363.
a = (363 / 8)^0.5 = 45.375^0.5 = 6.736.
It has risen – but not by very much.
Things become clearer if you can define c as a percentage of b:
a^2 = b^2 – (D x b^2) / 100
100 a^2 = 100 b^2 – D x b^2 = b^2 x (100-D)
If 98% of the village’s production goes to maintaining and supporting the village, then only 2% is left for economic growth. If the village adds more incomes, demand rises by the normal proportion as well – so economic growth rises, but quite slowly. In the above example calculations, 59/62 = 95.16% going to support the village – and 95% is about as low as it’s ever going to realistically go. In exceptionally productive years, it might be as low as 66.7%, but most years it’s going to be much higher than that.
Side-bar: 5.8.1.3.7.1 Good Times
You can actually model how often an exceptional year comes along, by making a couple of assumptions. First, if 66.7 is as good as they get, and 95 is as bad as an exceptionally good year gets, then the average ‘exceptional year’ will be 80.85%.
Second, if 95% is as good as a typical year gets, and 102% is as bad as a typical year gets, then the average ‘normal’ year will be 98.5%.
Third, if the long term average is 95.16%, then what we need is the number of typical years needed to raise the overall average (including one exceptional year) to 95.16%.
95.16 x (n+1) = 80.85 + (n x 98.5)
95.16 x n + 95.16 = 80.85 + 98.5 x n
(95.16 – 98.5) x n = 80.85 – 95.16
3.34 n = 14.31
n = 14.31 / 3.34 = 4.284.
4-and-a-quarter normal years to every 1 good year.
You can go further, with this as a basis, and make the good years better or worse so that you end up with a whole number of years.
95.16 x (5 +1) = g + 5 x 98.5
g = 95.16 x 6 – 98.5 x 5
g = 570.96 – 492.5 = 78.46.
That’s a six-year cycle with one good year averaging 78.46% of productivity sustaining the village and five typical years in which 98.5% of productivity is needed for the purpose.
I grew up on the land, and I can tell you that an industry is thriving if one year out of 10 is really good; an industry is marking time if one year out of 20 is good, and in trouble if one year in 25 or less is really profitable. One year in six is a boom.
So to close out this sidebar, let’s look at what those numbers equate to in overall economic productivity for the rural population that depend on them:
Boom: (1 x 78.46 + 5 x 98.5) / 6
= (78.46 + 492.5) / 6
= 570.96 / 6
= 95.16%
(we already knew this but it’s included for comparison)
Thriving: (1 x 78.46 + 9 x 98.5) / 10
= (78.46 + 886.5) / 10
= 964.96 / 10
= 96.496
Stable, Marking Time: (1 x 78.46 + 19 x 98.5) / 20
= (78.46 + 1871.5) / 20
= 1949.96 / 20
= 97.498
In trouble / in economic decline: (1 x 78.46 + 24 x 98.5) / 25
= (78.46 + 2364) / 25
= 2442.46 / 25
= 97.6984
Look at the differences, and how thin the lines are between growth and stagnation.
Stable to In Decline: 0.2004% change.
Stable to Thriving: 1.002% change.
Thriving to Booming: 1.336% change.
Booming to In Decline: 2.5384% change.
The whole boom-bust cycle – and it can be a cyclic phenomenon – is contained within 2.54% difference in economic activity.
An aside within an aside shows why:
Boom: 95.16% = 0.9516;
0.9516 ^ 6 = 0.74255;
so 25.74% productivity goes into growth.
Thriving: 96.496% = 0.96496;
0.96496 ^ 6 = 0.8073;
so 19.27% productivity goes into growth over the same six-year period.
Stable: 97.498% = 0.97498;
0.97498 ^ 6 = 0.859;
14.1% of productivity goes into growth over the same six-year period.
Declining: 97.6984% = 0.976984;
0.976984 ^ 6 = 0.8696;
13.04% of productivity goes into growth.
Every homeowner sweats a 0.25% change in interest rates because they compound, snowballing into huge differences. This is exactly the same thing.
5.8.1.4 The Generic Village
The generic village is perpetually dancing on a knife-edge, but the margins are so small that it’s trivially easy to overcome a bad year with a better one. Even a boom year doesn’t incite a lot of growth, but a lot of factors pulled together over a very long time, can.
Some villages won’t manage to escape the slippery slope long enough and will decline into Hamlets, but find stability at this smaller size. Given time, disused buildings will be torn down and ‘robbed’ of any useful construction material because that’s close to free, and that alone can make enough of a difference economically. With the land reclaimed, after a while you could never tell that it once was a village.
Some won’t be able to arrest their decline – whatever led to their establishment in the first place either isn’t profitable enough, or too much of the profits are being taken in fees, tithes, greed, and taxes. They decline into Thorpes.
In some cases, communities exist for a single purpose; they never grew large enough to even have permanent structures. They are strictly temporary in nature (though one may persist for dozens of years or more); they are forever categorized as Mining or Logging Camps.
Other villages have more factors pushing them to growth, and once they reach a certain size, they can organize and be recognized as a town. And some towns become cities, and some cities become a great metropolis.
With each change of scale, the services on offer to the townsfolk, and the services on offer to the traveler passing through, increase.
The fewer such services there are, the more general and generic they have to become, just to earn enough to stay in operations.
The general view of a generic village is that most services exist purely for the benefit of the locals, but a small number of operations will offer services aimed at a temporary target market, the traveler. These services are often more profitable but less reliable in terms of income, more vulnerable to changes in markets. They don’t tend to be set up by existing residents; instead, they are founded by a traveler who settles down and joins a community because they see an economic opportunity.
That means that the number of such services on offer is very strongly tied to both the growth of the village, and to the overall economic situation of the Kingdom as a whole and to the local Region of which this village is a part.
Here’s another way to look at it: The reason so much of the village’s economic potential goes into maintaining the village is because of all those tithes and taxes and so on. Some of those will be based on the land in and around the village; some on the productivity of that land; and some of it on the size and economic activity of the village. The rest provides what the village needs to sustain its population and keep everything going. There’s not a lot left – but any addition to the bottom line that isn’t eroded away by those demands makes the village and the region more profitable, creating more opportunities for sustained growth. Again, there is a snowball effect.
Some villages – and this is a social thing – don’t want the headaches and complications of growth; they like things just the way they are. They will have local rules and regulations designed to limit growth by making growth-producing business opportunities less attractive or compelling. Others desperately want growth, and will try to make themselves more attractive to operations that encourage it.
That divides villages into two main categories and a number of subcategories.
Main Category: Villages that encourage growth
Subcategory: Villages that are growing
Subcategory: Villages that are not growing
Subcategory: Villages that are being left behind, and declining.
Ratios: 40:40:20, respectively.
Main Category: Villages that are discouraging growth despite the risk of decline
Subcategory: Villages that are growing and can only slow that growth
Subcategory: Villages that have achieved stability
Subcategory: Villages that have or are declining.
Ratios: 20:40:40, respectively.
5.8.1.5 Blended Models
In general, the rule is one zone, one model. In fact, as a general rule, your goal should be one Kingdom, one model – that way, if you choose “England” as your model, your capital city will resemble London in size and characteristics, and not, say, Imperial Rome.
But, if you can think of a compelling enough reason, there’s no reason not to blend models. There are lots of ways to do this.
The simplest is to designate one model for part of a zone, and another to apply to the rest.
Example, if your capital city were much older than the rest of the Kingdom, you might decide that for IT ALONE, the Imperial model might be more appropriate, while the rest of the Kingdom is England-like. Or you might decide that because of its size, it has sucked up resources that would otherwise grow surrounding communities more strongly, and declare a three-model structure: Imperial Capital, France for all zones except zone 1, and England for the rest of Zone 1.
Example: A zone contains both swamp and typical agricultural land. You decide that those parts that are Swamp are German or Frontier in nature, while the rest are whatever else you are using.
An alternative approach to the problem that works in the case of the latter example is to actually average the two models’ characteristics and apply the result either to just the swamp areas, or to the zone overall.
When you get right down to it, the models are recommendations and guidelines, describing a particular demographic pattern seen in Earth’s history. There’s absolutely nothing to prevent you from inventing a unique one for a Kingdom in your world – except for it being a lot of work, that is.
5.8.1.6 Zomania – An Example
I don’t really think that a fully-worked example is actually necessary at this point, but I need to have one up-to-date and ready to go for later in the article. So it’s time for another deep-dive into the Kingdom of Zomania.
5.8.1.6.1 Zone Selection
I’ll start by picking a couple of Zones that look interesting, and distinctive compared to each other.
Zone 7 is bounded by a major road, but doesn’t actually contain that road; it DOES have capacity for a lot of fishing, though. And I note that there are cliffs in the zones to either side of it, so they WON’T support fishing – in fact, those cliffs appear to denote the limits of the zone..Zone 7 adds up to 167.8 units in area, and features 26 units of pristine beaches.
Zone 30 has an international border, and a major road, lots of forest and foothills becoming mountainous. It’s larger than one 7, at 251.45 units.
Because I haven’t detailed these areas at all, the place that I have to start is back in 5.7.1.13. But first…
5.8.1.6.7.1.1 Sidebar: Anatomy Of A Fishing Locus
I was going to bring this up a little later, but realized that readers need to know it, now.
Coastal Loci are a little different to the normal. To explain those differences, I threw together the diagram below.
1: is a coast of some kind. It might not be an actual beach, but it’s flat and meets the water.
2: It’s normal, especially if there’s a beach, for the ends to be ‘capped’ with some sort of headland. This is often rocky in nature. This is the natural location for expensive seaside homes and lighthouses.
3. Fishing villages.
4. Water. It could be a lake, or the sea, or even a river if it’s wide enough.
5. Non-coastal land, usually suitable for agriculture.
6. A fishing village’s locus is compressed along the line of the coast and bulging out into the water. This territory produces a great deal more food than the equivalent land area – anywhere from 2-5 times as much. Some cultures can go beyond coastal fishing, doubling this area – though what’s further out than shown is generally considered open to anyone from this Kingdom. Beyond that, some cultures can Deep-Sea fish (if this is the sea), which quadruples the effective area again. If you’re keeping track, that’s 2-5 x 2 x 4 = 16-40 times the land area equivalent. The axis of the locus is always as perpendicular to the coast as possible.
7. The bottoms of the lobes are lopped off…
8. And the land equivalent is then found ‘squaring up’ the locuses…
9. …which means that these are the real boundaries of the locus. The area stays roughly the same, though.
The key point is this: you don’t have to choose “Coastal Mercantile” to simulate living on the coast and fishing for food. There are mechanisms already built into the system for handling that – it’s all done with Terrain and a more generous interpretation of “Arable Land”.
Save the “Coastal Mercantile” Model for islands and coastal cultures whose primary endeavor is water-based trade.
Zone 7, then, should have the same Model as all the other farmland within the Kingdom. I think France is the right model to choose.
Zone 30 is a slightly more complicated story. For a start, don’t worry about the road – like coastal villages, that gets taken care of later. For that matter, so is the heavy forestation, and the local geography – hills and mountains. But this is an area under siege from the wilderness, as explained in an earlier post. Which changes the fundamental parameters of how people live, and that should be reflected in a change of model. In this case, I think the Germany / Holy Roman Empire model of lots of small, walled, communities is the most appropriate.
But this does raise the question of where the change in profile takes place. I have three real options: The Zone in it’s entirety may be HRE-derived; or the HRE model might only apply to the forests; or might take hold in the hills and mountains, only.
My real inclination would be to choose one of the first two options, but in this case I’m going to choose door number 3m simply because it will contrast he HRE model with the base French version of the hills and forests. In fact, for that specific purpose, I’m going to set the boundary midway through the range of hills:
5.8.1.6.1.2 Sidebar: Elevation Classification
Which means, I guess, that I should talk about how such things are classified in this system. There are eight elevation categories, but the categories themselves are based on the differences between peak elevation and base elevation.
I tried, but couldn’t quite get this to be fully legible at CM-scale. Click on the image above to open a larger copy in a new tab.
To get the typical feature size – the horizontal diameter of hills or mountains – divide 5 x the average of the Average Peak Elevation range by the average Relief range and multiply by the elevation category number, squared for mountains, or twice the previous category’s value, whichever is higher. Note that the latter is usually the dominant calculation! The results are also shown below. Actual cases can be 2-3 times this value – or 1/2 of it.
1. Undulating Hillocks – Average Peak Elevation 10-150m, Local Relief <50m; Features 16m (see below).
2. Gentle Hills – Average Peak Elevation 150-300m, Local Relief 50-150m; Features 32m.
3. Rolling Hills – Average Peak Elevation 300-600m, Local Relief 150-300m; Features 64m
-> □ Zone 30 Treeline from the start of this category
-> □ Normal Treeline is midway through the range
4. Big Hills – Average Peak Elevation 600-1000m, Local Relief 300-600m; Features 128m
5. Shallow Mountains – Average Peak Elevation 1000-2500m, Local Relief 600-1500m; Features 417m
6. Medium Mountains – Average Peak Elevation 2500-4500m, Local Relief 1000-3000m; Features 834 m
7. Steep Mountains – Average Peak Elevation 4500-7000m, Local Relief 3000-5000m; Features 1668m
8. Impassable Mountains, permanent snow-caps regardless of climate – Average Peak Elevation 7000m+, Local Relief 5000m+; Features 3336m.
Undulating Hillocks (also known as Rolling Hillocks or Rolling Foothills) are basically a blend of scraped-away geography and boulders deposited by glaciers. If the boulders have any sort of faults (and most do), they will quickly become more flat than round and start to tumble within the Glacier. When they come to rest, several will be stacked, on on top of another, generally in long waves. There will be gaps in between, which get filled with earth and mud and weathered rock over time, unless the rocks are less resistant to weathering than soil, in which case the rocks get slowly eaten away. In a few tens of thousands of years, you end up with undulating hillocks, or their big brothers. The flatter the terrain, the more opportunity there is for floodwaters to cover everything with topsoil, smoothing out the bumps. The diagram above shows how this ‘stacking and filling’ can produce structures many times the size of individual hillocks.
A very similar phenomenon – wind instead of glaciers, and sand instead of boulders – creates sandy dunes in deserts prone to that sort of thing. Over time, great corridors get carved out before and after each dune, generally at right angles to the prevailing winds. It can help you picture it if you think of the wind “rolling” across the dunes – when they come to a spot where the sand is a little less held together, it starts to carve out a trench, and before long, you have wave-shaped sand-dunes.
5.8.1.6.3 Area Adjustments – from 5.7.1.13
Zone 7 has a measured area of 167.8 units, but that needs to be adjusted for terrain. Instead of the slow way, estimating relative proportions, let’s use the faster homogenized approach:
Hostile Factors:
Coast 1.1 + Farmland 0.9 + Scrub 1.1 = 3.1; average 1.03333.
Coast +0.25 + Beaches -0.05 + Civilized -0.1 = +0.1
Towns -0.1
Net total: 1.03333
167.8 x 1.0333 = 173.4 units^2.
Benign Factors:
Town 0.1 + Coast 0.15 + Beaches 0.15 + Civilized 0.2
Subtotal +0.6
Square Root = 0.7746
173.4 x 0.7746 = 134.3 units^2.
Zone 30 is… messier. Base Area 251.45 units^2.
Hostile Factors:
Mining 1.5 +
Average (Mountains 1.4 + Forest 1.25 + Hills 1.2 = 3.85) = 1.28
Town -0.1 + Foreign Town 0.1 + River 0.2 + Caves 0.05 + Ruins 0.4 + “Wild” 0.1 = +0.75
Net total = 1.5 + 1.28 + 0.75 = 3.53
251.45 x 3.53 = 887.6 units^2.
Benign Factors:
Town 0.1 + Foreign Town -0.1 + River +0.1 + Caves 0.05 + Ruin 0.4 + Major Road 0.2
Subtotal 0.75
“Wild” = average subtotal with 1 = 0.875
Sqr Root = 0.935
887.6 x 0.935 = 829.9 units^2.
To me, this looks very Greek – but it’s actually ‘Gordes’ in England, which the photographer describes as a village. One glance is enough to show that it’s bigger than the town depicted previously. Image by Neil Gibbons from Pixabay
5.8.1.6.4 Defensive Pattern – from 5.7.1.14
Zone 7 is pretty secure, the biggest threat being local insurrection or maybe pirate raids. A 4-lobe structure of 2½,5 looks about right.
When I measure out the area protected by a single fort and 4 satellites, I get 47.2 days^2. That takes into account overlapping areas where this one structure shares the burden 50% with a neighboring structure, and the additional areas that have to be protected by cavalry units.
That means that in Zone 7, there should be S x 134.3 / 47.2 = 2.845 x S of them, depending on the size of a “unit” on the map is, measured in days’ march for infantry.
S is going to be the same for all zones I’ve avoided making that decision for as long as I can – the question is, how large is Zomania?
5.8.1.6.5 Sidebar: The Size of Zomania, revisited
16,000 square miles – at least, that’s the total that I threw out in 5.7.1.3.
That’s about the same size as the Netherlands.
It’s a lot smaller than the Zomania that I’m picturing in my head when I look at the map. It IS the right size if the units shown are miles. But if they aren’t?
There are two reasons for regularly offering up Zomania as an example. The first is to provide a consistent foundation and demonstration of the principles discussed coming together into a cohesive whole. And the second is for me to check on the validity of the logic and techniques that I’ve described.
Feeling ‘wrong’ is keeping my subconscious radar from achieving purpose #2. And the Zomania being described being too small, which is the cause of that ‘wrong’ feeling, means that it isn’t going to adequately perform function #1, either.
There can be only one solution – Zomania has to grow, has to be scaled up. I want Zone 7 to be comparable to the size of the Netherlands, not the entire Kingdom, which should be comparable to France, or Germany, or England, or Spain.
A factor of 10? Where would 160,000 sqr miles place Zomania amongst the European Nations that I’ve named?
UK: 94,356. Germany: 138,063. Spain: 192,466. France: 233,032. So 160,000 would be smack-dab in the middle, and absolutely perfect for both purposes.
So Zomania is now 160,000 square miles, and the ‘units’ on all the maps are 10 miles each.
It wasn’t easy sorting this out – it’s been a road-block in my thinking for a couple of days now – triggered by results that seemed to show Zone 7 to be about 0.08 defensive structures in size.
And that is due to a second scaling problem that was getting in the way of my thinking:
How much is that in day’s marching?
In 5.7.1.14.3, I offered up:
If d=10 miles (low), that’s 103,923 square miles.
If d=20 miles (still low), that’s 415,692 square miles.
If d=25 miles (reasonable), that’s 649, 519 square miles.
If d=30 miles (doable), 935,307 square miles.
If d=40 miles (close to max), 1.66 million square miles.
If d=50 miles (max), 2.6 million square miles.
But that was in reference to a theoretical 6 x 4, 12 + 12 pattern. Nevertheless, the scales are there. And they are way bigger than I thought they would be, and way to big to be useful as examples. Yet the logic that led to them seemed air-tight. Clearly, there was an assumption that had been made that wasn’t correct, but this problem was getting in the way of solving the first one.
Once I had separated the two, answers started falling into place. The numbers shown above are how far infantry can march in 24 solid hours, such as they might do in a dire emergency. But defensive structures would not be built and arranged on that basis.
If infantry march for 8 hours, they have just about enough daylight left to break camp in the morning (after being fed) and set up camp in the evening (digging latrines and getting fed). That’s the scale that would be used in establishing fortifications, not the epic scale listed. In effect, then, those areas of protection are nine times the size they should be.
So, let’s redo them on that basis:
If d=10 miles (low), that’s 11,547 square miles.
If d=20 miles (still low), that’s 46,188 square miles.
If d=25 miles (reasonable), that’s 72,169 square miles.
If d=30 miles (doable), 103,923 square miles.
If d=40 miles (close to max), 184,444 square miles.
If d=50 miles (max), 288,889 square miles.
And those are still misleading, because mentally, I’m thinking of this as the area protected by the central stronghold, and ignoring the satellites. To get the area per fortification,, we should divide by the total number of fortifications in the pattern – in the case of the numbers cited, that’s 6×4+12=36.
If d=10 miles (low), that’s 320.75 square miles.
If d=20 miles (still low), that’s 1283 square miles.
If d=25 miles (reasonable), that’s 2,004.7 square miles.
If d=30 miles (doable), 2,886.75 square miles.
If d=40 miles (close to max), 5,123.4 square miles.
If d=50 miles (max), 8024.7 square miles.
Reasonable = 2004.7 square miles, or roughly equal to a 44.8 x 44.8 mile area. For a really tightly packed defensive structure of the one being discussed, that’s entirely reasonable – and it fits the image in my head.
In my error-strewn calculation, my logic went as follows:
▪ In the inner Kingdom, I think that life is easy and lived fairly casually. That points to the lower end of the scale – 10 miles a day or 20 miles a day.
▪ 10^2 = 100, so at 10 mi/day, 16,000 = 160 days march.
▪ 20^2 = 400, so at 20 mi/day, 16,000 = 40 days march.
▪ That’s a BIG difference. 40 is too quick, but 160 sounds a little too slow. Tell you what, let’s pick an intermediate value of convenience and work backwards.
▪ 100 days march to cover anywhere in 16000 square miles gives 160, and the square root of 160 is 12.65 miles per day.
Now, that logic’s not bad. But it doesn’t factor in the ‘working day’ of the infantry march – it needs to be divided by 3. And it DOES factor in my psychological trend toward making the defensive areas smaller, because my instinct was telling me they were too large – but this is the wrong way to correct for that. So this number is getting consigned to the dustbin.
After all, the ‘hostile’ and ‘benign’ factors are supposed to already take into account the threat level that these fortifications are supposed to address, and hence their relative density.
▪ So, let’s start with the “reasonable” 25 miles.
▪ Apply the ‘working day’ to get 8.333 miles.
▪ The measured area of the defensive structure is 47.2 ‘days march’^2.
▪ Each of which is 8.333^2= 69.444 miles^2 in area.
▪ So the defensive unit – stronghold and four satellites – covers 47.2 x 69.444 = 3277.8 sqr miles.
▪ Or 655.56 sqr miles each.
▪ Equivalent to a square 25.6 miles x 25.65 miles.
▪ Or a circle 12.51 miles in radius.
▪ Base Area 173.4 units^2 = 17340 square miles.
▪ Adjusted for threat level, 134.3 units^2 or 13430 square miles. In other words, defensive structures are further apart because there’s less threat than normal.
▪ 13430 / 3277.8 = 4.1 defensive structures, of 1 hub and 4 satellites each.
▪ So that’s 4 hubs and 16 satellites plus an extra half-satellite somewhere.
Those satellites could be anything from a watchtower to a small fort to a hut with a couple of men garrisoned inside, depending on the danger level and what the Kingdom is prepared to spend on securing the region. The stronghold in the heart of the configuration needs to be more substantial.
Okay, so that’s Zone 7. Zone 30 is a whole different kettle of fish.
I wanted to implement a 3-lobed configuration with more overlap than the four-lobed choice made for Zone 7. And it was turning out exactly the way I wanted it to; some every hub was reinforced by three satellites, every satellite reinforced by three hubs. I had the diagrams 75% done and was gearing up to measure the protected area.
Which is when the plan ran aground in the most spectacular way. There were areas where responsibility was shared two ways, and three ways, and four ways, and – at some points – six ways. It was going to take a LONG time to measure and calculate.
If I were creating Zomania as an adventuring location for real, I would have carried on. If I lived in an ideal world, without deadlines (even the very soft ones now in place at Campaign Mastery) I would have continued. I still think that it would have provided a more enlightening example for readers, because I would be doing something a little bit different and having to explain the differences and their significance.
But since neither of those circumstances is the case, and this post is already several days late due to the complications explained earlier, I am going to have to compromise on principle and re-use the configuration established for Zone 7.
Well, at least that will show the impact that the greater threat level will impose on the structure, but it leaves the outer reaches of the Kingdom less well-protected than they should be. If and when I re-edit this series into an e-book, I might well spend the extra time and replace the balance of this section – or even work the problem both ways for readers’ edification.
REMINDER TO SELF – 3 LOBES, 1 DAY EXAMPLE
But, in the meantime…
Zone 30.
▪ Actual area 251.45 square units = 25,145 square miles.
▪ Adjusted for threat level = effective area 829.9 square units = 82,990 sqr miles. (in other words, the defensive structures you would expect to protect 82,990 square miles are so closely packed that they actually protect only 25,145 square miles, a 3.3-to-1 ratio.)
▪ Defensive Structure = 3277.8 square miles (from Zone 7).
▪ 82,990 / 3277.8 = 25.32 defensive structures of 5 fortifications each, or 126.6 fortifications in total. Zone 7 is 69% of the area and had a total of 20.5 fortifications, in comparison.
What does 0.32 defensive structures represent? Well, if I take the basic structure and ‘lop off’ two of the satellites, then it’s 3/5 of a protected area minus the overlaps. By eye, those overlaps look to be a bit more than 2 x 1/4 of one of those 1/5ths, and since 1/4 of 1/5 is 1/20th, that’s roughly 0.6-0.1 = 0.5.
If I take away a third satellite, the structure is down to 2/5 protected area minus overlaps, and those overlaps are now 1 x 1/20th, so 0.4-0.05=0.35. So, somewhere on the border, there’s a spot with one hub and one satellite.
One more point: 3.3 to 1. What does THAT really mean? Well, the defensive structure used has satellites 2.5 days march from the hub. But everything is more compressed, by that 3.3:1 ratio, so the satellites in Zone 30 are actually 2.5 / 3.3 = 0.76 day’s march from the hub. The area each commands is still the same, but there’s a lot more overlap and capacity to reinforce one another.
Another way to look at it is that there are so many fortifications that each only has to protect a smaller area. 3277.8 sqr miles / 3.3 = 993 sqr miles.
5.8.1.6.6 Sidebar: Changes Of Defensive Structure
The point that I’m going to make in this sidebar won’t make a lot of sense unless you’re paying close attention, because the Zone 30 example has the same defensive structure as Zone 7 – it’s just a lot more compressed. But imagine for a moment that there was a completely different defensive structure in Zone 30.
What does that imply for Zone 11, which lies in between the two?
You might think that it should be some sort of half-way compromise or blend between the two, but you would be wrong to do so.
If you look back at the overall zone map for Zomania (reproduced below)
…and recall that the zones are numbered in the order they were established, a pattern emerges. Zone 1 first, then Zone 2, then Zones 3-4-5-6-7, then zones 8-9-10-11-12, and so on. Until Zones 29-32 were established, Zone 11 was the frontier. it would likely have the same defensive structure as Zone 30. Rather than fewer fortifications, it would have them at the same density as Zone 30 – but the manpower in each would be reduced.
If you know how to interpret it, the entire history of the Kingdom should be laid bare by the changes in its fortifications and defenses.
But that’s not as important as the verisimilitude that you create by taking care of little details like this and keeping them consistent. The specifics might never be overtly referenced – but they still add a little to the credibility of the creation.
5.8.1.6.7 Inns in Zone 7 – from 5.7.3
Zone 7 is noteworthy for NOT having a major road – that’s on the Zone 11 / Zone 6 side of the border. Some of the inns along that road, however, may well be over that border – it’s a reasonable expectation that half of them would count. But only that half that is located where the border runs next to the road – there’s a section at the start and another at the end where the border shifts away.
But there’s a second factor – what is the sea, if not another road to travel down? And Zone 7 has quite a lot of beach. The reality, of course, is that these are holiday destinations, and places for health recovery – but it’s a convenient way of placing them.
So that’s two separate calculations. The ‘road that is a road’ first: There are actually two sections. The longer one runs through Zones 6 and 11, as already noted; it measures out at 15 units long, or 150 miles.
The second lies in Zone 15, and it’s got a noticeable bend in it. If I straighten that out and measure it, I get 5 units or 50 miles.
Conditions:
Road condition, terrain, good weather = 3 x 2.
Load = 1 x 1/2.
Everything else is a zero.
Total: 6.5.
6.5 / 16 x 3.1 = 1.26 miles per hour.
1.26 mph x 9 hrs = 11.34 miles.
Here’s the rub: we don’t know exactly where the hubs and satellites are in Zone 7, only how many of them that there are to emplace. But it seems a sure bet that those areas where the road and border part ways, do so because there’s a fortification there that answers to Zone 6 or Zone 11, respectively. And that means that we can treat the entire length of the road as being between two end points.
We know from the defensive structure diagram that the base distance from Satellite to Hub is 2 1/2 days march, and that there’s a scaling of x 1.0333 (hostile) x 0.7746 (benign) = x 0.8 – and that benign factors space fortifications further apart while hostile ones bunch them together, so this is a divided by when calculating distances. We know that 8.333 miles has been defined as a “day’s march”.
If we put all that together, we get 2.5 x 8.333 / 0.8 = 26 miles from satellite to hub.
Armies like their fortifications on roads, it makes it faster to get anywhere. Traders like their trade routes to flow from fortification to fortification, it protects them from bandits. The general public, ditto. If a road doesn’t go to the fortification, people will create a new road and leave the official one to rot. So it can be assumed that the line of fortifications will follow the road, and be spaced every 26 miles along it, alternating between hub and satellite.
150 miles / 26 = 5.77 of them.
It’s an imperfect world; that 0.77 means that you have one of three situations, as shown below:
The first figure shows a hub at the distant end of the road. The first shows a hub at the end of the road closest to the capital. And the third shows the hubs not quite lining up with either position.
But those aren’t the actual ends of the road – this is just the section that parallels the border of Zone 7, or vice-versa. So the last one is probably the most realistic
Now, let’s place Inns – one every 11.34 miles. But we have to do them from both ends – one showing 1 day’s travel for ordinary people headed out, and one showing them heading in. Just because I’m Australian, and we drive on the left, I’ll put outbound on the south side and inbound on the north.
Isn’t that annoying? The don’t quite line up – to my complete lack of surprise. Look at the second in-bound inn – it’s about 20% of a day short of getting to the satellite, and that puts it so close that it’s not worth stopping there; you would keep going.
Well, you can’t make a day longer, but you can make it shorter. And that makes sense, because these are very much average distances.
I’ve shortened the days for the ordinary traveler – including merchants – just a little, so that every 5th inbound Inn is located at a Stronghold, and every 5th outbound inn is located at a satellite. Every half-day’s travel now brings you to somewhere to stop for a meal or for the night.
It’s entirely possible that not all of these Inns will actually be in service, it must be added. Maybe only half of them are actually operating. Maybe it’s only 1/3. But, given it’s position within the Kingdom, there’s probably enough demand to support most of these, so let’s do a simple little table:
1 inn functional
2 inn functional
3 inn functional but 1/4 day closer
4 inn functional but 3/4 day farther away
5 inn not functional
6 inn not functional, and neither is the next one.
Applying this table produces the following (for some reason, my die kept rolling 3s and 6s):
Even here, in this ‘safe’ part of the Kingdom, travelers will be forced to camp by the roadside.
