Trade in CSIO

Table of Contents

• 1. Introduction
• 2. Method
  • 2.1. A Star is born, then dies, is reborn, and finally discarded
  • 2.2. Drunken Sailors, not Bee-lines
  • 2.3. Plains, Boats, and Roads
  • 2.4. Hard Border
  • 2.5. Moving House
  • 2.6. Here Don’t Be Dragons
• 3. Other Factors
  • 3.1. Race
  • 3.2. Alignment
  • 3.3. Civilisation Level
• 4. The Results
  • 4.1. Trade Routes
  • 4.2. Trade Volumes
    • 4.2.1. CSIO
    • 4.2.2. Warwik
    • 4.2.3. Modron
    • 4.2.4. Ossary
    • 4.2.5. Darkfield
    • 4.2.6. Haghill
    • 4.2.7. Sticklestead
• 5. So what?

1 Introduction

I’ve been wanting to do this for a very long time indeed. The City State of the Invincible Overlord (CSIO) came out in the 70’s and a revised edition in 1980 or thereabouts. Over the course of several publications and 18 maps of about 38,000m² each, Judges Guild mapped out and gave basic population statistics for an area (“The Wilderlands”) about the size of modern Libya, or the UK, France, Germany, Italy, Poland, and a bit of Belgium put together.

One thing that was never clear to me was just how wild the “wilderlands” were. There’s not much in the way of roads, and the basic terrain is not open plains but light woods, with some plains interspersed. Did the towns marked trade with each other much? How dominant were the handful of city stats and how dependant on their satellite towns where they in turn? Where would a pirate or bandit lurk and by extension where would patrols be placed to protect the trade routes?

Around 1989 or so I bought a book - Settlements by Meyer and Huggett - in a Cancer Research charity shop which introduced me to Reilly’s theory of retail gravitation (from 1929). The idea was that trade between two settlements could be modelled based on the product of the populations divided by the distance between them raised to some power. Add in some constant multiplier to scale it to specific units and the resulting formula looks a lot like the standard Newtonian one for gravity, hence the name.

The specific reason I bought the book was that I thought that I could use it to somehow model the trade pattern in the Wilderlands of the CSIO. Nearly 30 years have passed since that day and finally I’ve gotten around to it! What can I say? I had a long list of things to do.

The catalyst for finally doing this was two-fold: firstly I had been nursing an idea for a path-finding algorithm for a couple of years which I had hopes for, not only for geographic path finding but for broader applications in code optimisation. Secondly, I stumbled across von Thünen’s model of the “Isolated City State” and how he showed a city state’s surrounding economic area could be seen to diverge from a theoretical model under the influence of terrain and transport factors, something I was seeing in my reading around mediaeval London and its trade patterns. I even found a couple of websites that demonstrated this with some limited path-finding algorithms.

One attractive aspect of von Thünen’s model was that it is essentially pre-industrial and focused on agricultural questions which were broadly applicable back into the mists of time.

2 Method

So, Reilly used distance, but what is “distance”? Straight line distance is clearly nonsensical; as von Thünen emphasised, the real distance should be measured in time. Since we’re dealing with D&D here, that meant looking at movement rates. But underlying this was a deeper question about the nature of trade. In the von Thünen model, different sorts of produce are segregated into bands around the city state, primarily by their perishability and this maps directly into Reilly’s theory where the power distance is raised to relates to the urgency of delivery. The obvious example is that fresh milk, especially before refrigeration, simply has to be produced near or in the city.

By extension of this idea, it’s clear that on the scale of the Wilderlands maps very little trade is done in such products and if the CSIO needs milk it must be producing it itself in the surrounding hex of farmland or getting from very close-by up the Twilight Road or down the Old South Road. In any case, such things would not make up a huge amount of the trade on the map as published, where roads are very rare indeed.

For the sort of bulk non-perishable goods I was thinking about - timber, grain, metals, wine etc. - the value drops of linearly. Which is to say that the value of the product is constant, at least on a time scale of a season or two, and profit is eroded as much by the costs of the fifth day on the road as it is on the first. For milk, the second day erodes profit much faster than the first, and by the fifth you’ve got nothing left to sell.

This makes Reilly’s power simply equal to one. Since I’m only looking at relative levels of trade I don’t need a constant to convert into units (yet), so the final version of the formula is just

trade=pop1*pop2/travelTime

and all I had to do was calculate the travel time between every city on the map (all 98 of them).

I looked at how to do this using the hex map. I tried a lot of things but eventually I had to admit that the hex map simply wasn’t good enough. Forests and especially rivers did not follow hex sides and trying to encode them as side data ballooned. Eventually I realised that “hexpath” would have to be come “pixepath”, and the map would have to be some sort of bitmap image where each pixel was given one and only one terrain value.

2.1 A Star is born, then dies, is reborn, and finally discarded

I had been mulling this path finding idea for a while. I have written a few chess playing programs over the years, including a Dragon Chess written in M68000 assembly, and had been struck by the whole idea of quickly finding a move, and then progressively trying to find a better move. If the first “guess” move is sensibly chosen then the search time is drastically improved as candidates can be discarded early.

What, I thought, if we took a direct line from Point A to Point B over a map as a first guess, and then tried to refine it from there?

I constructed a test map and gave it a go. Well, to cut a long story short, it didn’t work. Looking back, I was gradually approaching the correct answer but my biggest problem was actually the direction of travel. Going from start to finish, my algorithm was constantly tripping over itself because the square it had “come from” at each step was not itself in a resolved and final state so it ended up looping over and over again. Adding flags didn’t help as that left the previous square as an unknown, preventing any objective early cutting out of paths by comparing them to the first guess.

I gradually started peeking at the Internet for help and found the A* algorithm. This is based on Dijkstra’s algorithm and like his it starts at the end and works backwards. Since the search starts with an objectively completed square (the destination, who’s cost is by definition known to be zero), it avoids the problem of tripping over its own tail and can always complete.

So I redid the code to use A* and things were good. For a while.

As it turns out, A* is a very fast way to find the distance between two points. But to find the distance between 97 points and a 98th, it’s not so hot. In my earlier code I had cached a lot of data in the hope of re-suing it, but A* doesn’t do that, so each run through starts from nothing again. For my code, once there were about 7 cities on the map, A* was getting slow.

Once I switched from a small test map to the map included in the code, which is 1807x1349 (8px = 1 mile), with lots of terrains, A* died a death. Going back to Dijkstra’s original algorithm, I realised that it in fact caches the whole map. Once Dijkstra’s has run once the output is the cost of moving from any point on the map to your chosen city. This means that finding the route from Thunderhold to CSIO takes 3 minutes - much longer than A* - but those three minutes also get you the distance from every other city and point on the map. Over all, this is a huge advance over A*.

2.2 Drunken Sailors, not Bee-lines

The first runs with the code generated trade routes all right. But they didn’t look very realistic. Partly this is because Dijkstra’s algorithm is known to behave slightly odd on a grid like a bitmapped image, but also because the transition between two pixels of light wood, or between light wood and open plain, always costs the same. So the trade routes were all made of horizontal, vertical, or 45° lines.

Adding a small pseudo-random factor to the cost of each square had a much larger effect than I had expected and the routes now look quite good, although not perfect.

2.3 Plains, Boats, and Roads

The next problem was that everything went by boat as far as possible. It’s certainly true that river and sea trade were extraordinarily important for mediaeval trade and one thing that modern people really don’t appreciate as they fly along the motorway at 70+mph is the humble bridge.

Until the 13th century, Britain had a massive shortage of bridges over rivers. If you have a one-ton cart being pulled by a couple of horses, even quite a small stream was an obstacle. If you were using oxen it was even worse as their short legs meant they would drown in a stream that a man could probably wade across.

So, yes, rivers should be important but they were insanely so when I first started to add significant numbers of towns to the map. The solution was to think more about what was being modelled: bulk goods.

One does not simply throw a ton of goods onto a barge and demand to be taken to Modor Modron. You generally have to wait for the tide, the ship to be ready, perhaps for other goods to be loaded up etc. So there’s a delay. There’s also a delay at the other end, albeit a smaller one.

Similarly, where an adventuring party might simply build a raft and enter a river or even the sea almost anywhere, if they needed to, 10cwt of barley needs something more sophisticated. So, rivers became a barrier again, except at settlements. And at settlements, the assumption is that on average goods set off on the boat/ship/barge, the day after they arrive, and 6 hours are needed to get unloaded and underway at the other end.

Cobbling together transport is still possible but it’s madly expensive. Similarly, switching from or to river transport at a ford can be done but it takes a week.

Finally, there is an assumption that the boats in question are quite small and do not handle the larger swell of the open sea as well as they do the more sheltered areas near the coast, so movement rates for the former are reduced. At the moment, I’ve defined (using the map) “open sea” to be anywhere more than 5 miles from any coast.

Movement aside from the special delays for transshipping is defined using D&D scaled inches so that it’s easy to relate to. Normal overland movement in D&D is 2xMv. So, a move rate of 12“ translates to 24 miles per day. I’ve pre-calculated the 2x part in order to allow some finer-grained rates as shown in the code snippet below:

\ These values are effectively 2 x the normal D&D move rates
+terrain impassible
infinite impassible self!
32 " opensea  \ slightly more risky, so avoid if possible
18 " city
48 " coastal
18 " plains
 9 " woods
24 " roads
12 " ltwoods
 9 " hills
 6 " wdhills
 1 " mts
 1 " swamp
 6 " desert
 4 " scrub
 6 " rough
30 " river
12 " ford
1 " jungle
4 " snow

I’ve not used snow yet. Note that the order of these terrains matches, and defines, the code used in the bitmap, so impassible is zero, open-sea is 1, city is 2 etc. When the program starts up it displays the combined movement table, which looks like this at the moment (probably best in the print-friendly version):

		opensea	city	coastal	plains	woods	roads	ltwoods	hills	wdhills	mts	swamp	desert	scrub	rough	river	ford	jungle	snow
1	opensea	15	960	12	53760	53760	53760	53760	53760	53760	53760	53760	53760	53760	53760	15	53760	53760	53760
2	city	3840	26	3840	26	39	23	33	39	53	253	253	53	73	53	3840	33	253	73
3	coastal	12	960	10	115200	115200	115200	115200	115200	115200	115200	115200	115200	115200	115200	13	115200	115200	115200
4	plains	53760	26	115200	26	39	23	33	39	53	253	253	53	73	53	115200	33	253	73
5	woods	53760	39	115200	39	53	36	46	53	66	266	266	66	86	66	115200	46	266	86
6	roads	53760	23	115200	160	36	20	30	36	50	250	250	50	70	50	115200	30	250	70
7	ltwoods	53760	33	115200	33	46	30	40	46	60	260	260	60	80	60	115200	40	260	80
8	hills	53760	39	115200	39	53	36	46	53	66	266	266	66	86	66	115200	46	266	86
9	wdhills	53760	53	115200	53	66	50	60	66	80	280	280	80	100	80	115200	60	280	100
10	mts	53760	253	115200	253	266	250	260	266	280	480	480	280	300	280	115200	260	480	300
11	swamp	53760	253	115200	253	266	250	260	266	280	480	480	280	300	280	115200	260	480	300
12	desert	53760	53	115200	53	66	50	60	66	80	280	280	80	100	80	115200	60	280	100
13	scrub	53760	73	115200	73	86	70	80	86	100	300	300	100	120	100	115200	80	300	120
14	rough	53760	53	115200	53	66	50	60	66	80	280	280	80	100	80	115200	60	280	100
15	river	15	640	13	115200	115200	115200	115200	115200	115200	115200	115200	115200	115200	115200	16	26880	115200	115200
16	ford	53760	33	115200	33	46	30	40	46	60	260	260	60	80	60	26880	40	260	80
17	jungle	53760	253	115200	253	266	250	260	266	280	480	480	280	300	280	115200	260	480	300
18	snow	53760	73	115200	73	86	70	80	86	100	300	300	100	120	100	115200	80	300	120

These costs are scaled to the 1px=1furlong of the bitmap. Notice that it’s not symmetrical: going from city to river costs 3840 movement points, and from river to city costs 640.

When considering these movement rates bear in mind that they’re for a horse-drawn wagon when on land.

2.4 Hard Border

The biggest limitation of what I’ve done so far is, of course, the map edge. The results below all assume that there is nothing beyond the map, including the western edge where of course the City State of the World Emperor lies.

2.5 Moving House

Beyond adding in a “wooded hills” terrain in a couple of places, the main changes I made to the map was to move some of the towns to more logical places. I never moved any outside of their own hex but, for example, Seasteadholm was moved to the coast instead of being a couple of miles inland. I assume the cartographer had to fit the symbols into the map and there wasn’t always room when there were also wood’s rivers, and coastal cliffs to show. Similarly, if a settlement is elven and there are woods in the hex, I moved them into the woods at least a bit.

2.6 Here Don’t Be Dragons

The biggest lack of this iteration of the code is the lack of monsters and castles. In some way, both of these should affect trade. Both are given in the guidebooks but their effect must be based at least partially on movement - a group of ogres three miles away across water is unimportant, while the same group by the side of a road is going to have an impact on trade. Castles may reduce the effect of monsters, but may also impose their own tolls.

Given that the 98 towns require something like 5hrs to analyse at the moment, this is going to have to wait.

3 Other Factors

Once the raw data was calculated, giving a basic figure for the trade between two settlements, a couple of other factors were added in which Reilly probably didn’t spend much time on: race, alignment, and civ (tech) level.

3.1 Race

The race relations were based on the tables in PHB and DMG with some fiddling to join them together, as well as the addition of balrogs - a feature of the wider CSIO. The letters used in the game (P, G, T etc.) were mapped to specific multipliers (100% for prefer-ed,; 32% for hated; 10% for feared, a new category).

100 constant p
95 constant g
86 constant t
71 constant n
55 constant a
32 constant h
10 constant f

\ dw el gn  he  hb  ho  hu  gl  go  og  or  ba
p , a , g , n , g , h , n , h , h , h , h , f , \ dw dwarf
a , p , t , g , t , a , n , a , a , h , h , f , \ el elf
g , t , p , t , g , h , n , h , h , f , h , f , \ gn gnoll
n , p , t , p , n , a , t , a , a , h , h , f , \ he half-elf
g , g , t , n , p , n , n , f , h , f , f , f , \ hb hobbit
h , a , h , a , n , p , t , n , t , g , h , g , \ ho half orc
n , n , n , t , n , n , p , a , a , h , h , f , \ hu human
h , h , h , h , h , h , a , p , a , g , t , g , \ gl gnoll
h , a , h , a , h , t , a , a , p , h , n , g , \ go golin
f , f , h , f , a , t , t , t , a , p , t , n , \ og ogre
f , f , h , h , a , g , t , n , t , g , h , g , \ or orc
h , f , h , f , n , g , t , n , g , n , g , p , \ ba balrog

Since the race relations are not symmetrical, they’re calculated for each side of the trade and combined, so that if trade occurs between orcs and goblins the overall modifier is .95*.71=67.45% of normal.

3.2 Alignment

Alignment is handled the same way but the percentages were loosely based on the loyalty modifiers in the DMG. On that note, I resolved the question about “1 step, 2 steps, 3 steps” by ruling that changing any letter to N was one step, but that someone at N could move to any alignment in a single step. So LG->LE is two steps (LG-LN->LE) but LG to CE is three (LG->NG->N->CE). Fudge-a-rama.

create aligntradetab
\ N    LG  NG   CG   CN   CE   NE   LE   LN
85 , 100 , 85 , 80 , 75 , 65 , 70 , 90 , 95 , \ N
70 , 100 , 85 , 65 , 50 , 30 , 35 , 75 , 95 , \ LG
85 , 100 , 85 , 80 , 60 , 50 , 55 , 75 , 80 , \ NG
70 ,  85 , 85 , 80 , 75 , 50 , 35 , 55 , 60 , \ CG
85 ,  85 , 70 , 80 , 75 , 65 , 55 , 75 , 80 , \ CN
70 ,  65 , 50 , 65 , 75 , 65 , 70 , 75 , 60 , \ CE
85 ,  85 , 70 , 65 , 60 , 65 , 70 , 90 , 80 , \ NE
70 ,  85 , 50 , 45 , 40 , 50 , 70 , 90 , 95 , \ LE
85 , 100 , 70 , 65 , 60 , 50 , 55 , 90 , 95 , \ LN

Generally, people trust Lawful and Good -types most, and Neutrals are suspected a bit by everyone.

3.3 Civilisation Level

Each town is given a civ rating in the guidebooks and I’ve modelled this by simply them together, adding 1, and then multiplying trade volume by that. So a civ 8 trading with a civ 5 gets a 14x multiplier while a civ 9 trading with civ 9 gets an 19x multiplier.

The median civ rating seems to be about 5, so x11 (5+5+1) is the “normal” rating.

4 The Results

4.1 Trade Routes

This was the original target question: what are the trade routes? The current base map is (note: these maps are not opening when clicked on but if you select "Open in new tab" you should get the full-res versions):

https://raw.githubusercontent.com/tworthington/csiotrade/master/map1.bmp

Overlaying trade routes, with the boldness of the route being proportional to the amount of trade, gives this (open sea removed for clarity:

Figure 1: Graduated Trade Map

Here’s the same map but with the routes shown at the same boldness regardless of the amount of traffic:

Figure 2: Un-Graduated Trade Map

The next map is one that relates mostly to von Thünen’s model a bit more. This is a graduated tint overlaid on the map and centred on the CSIO itself, each step from light to dark indicating one day’s travel to the city.

I’ve pushed the bounds of copyright here to allow some of the original map to show through under my coloured version and the tint.

Figure 3: Days Travel To CSIO

It’s worth looking at this map for a while and visualising how much you know about places that are 5½ days travel away from you - the distance that Thunderhold is from CSIO. Assuming that you can think of anywhere that would take 5½days to get to in the modern world!

The final map is just for interest and superimposes a map of Kent on Wilderlands map 1 to show the scale. Basically, each Wilderlands map covers an area of 24.3 million acres (38088m²), which is about ¾ of the size of England.

Figure 4: Kent for scale

4.2 Trade Volumes

Trade is measured in arbitrary units for now and the program captures two main numbers for each settlement: the amount of external trade it engages in itself, and the amount of trade which passes through it on the way to somewhere else.

Since each town has a listed population we can translate these numbers into a measurement of wealth, even if we don’t have a specific ratio to convert it into gps. Another obstacle to making too much of these numbers - creating a measure of GDP - is that these numbers are all for external trade and don’t capture anything at all about internal trading within each town.

Taking tax on passing trade at a rate of 5% (1 shilling per pound) and adding it to the main trade figure gives the following table ordered by total income per person:

City	Trade	Per Person	Via	+tax
Darkfield	21199	121	117863	149
Haghill	7150	47	54507	63
Sea Hill	11342	38	153155	62
Modron	59829	48	59973	48
Bier	7187	42	7187	42
Thelamie	5778	35	21603	40
Croy	18179	39	18179	39
Brushwood	4614	34	4614	34
Wolfstone	13457	33	13457	33
Sticklestead	81295	32	82366	32
Woe	8431	31	8431	31
Seastrand	12553	30	12553	30
Ottersgild	5460	29	5460	29
Dearthmead	9288	29	9288	29
Zarthstone	12170	29	12823	29
Tain	11034	27	16507	28
Armagh	7741	28	7741	28
Fireside	9373	28	9373	28
Luckstone	6270	27	6270	27
Warwik	259121	27	259121	27
Anvil	4846	26	4846	26
Goodhap	5726	26	5726	26
Smitten	10539	22	25241	24
Tegal	5340	23	10672	24
Byrny	10923	23	22016	24
Muskholm	8985	23	8985	23
Rockhollow	4821	22	4821	22
Gaehill	9021	22	9021	22
Oakenbridge	6847	21	14383	22
Swarin’s Cairn	3211	20	5226	21
Flint	6264	21	6264	21
CSIO	435882	21	435939	21
Ryefield	2773	21	2994	21
Omen	4500	19	15445	21
Mill Haven	3713	19	5869	20
Shewolf	3276	20	3276	20
Sea Rune	2591	19	2742	19
Troth	3523	19	3796	19
Dorn	4259	18	4259	18
Thunderhold	36340	18	36340	18
Orlage	2894	17	7012	18
Foremost	5957	17	5957	17
Bernost	8304	17	8304	17
Carmage	3931	16	3931	16
Greybeard	7277	16	7277	16
Sunlitten	7429	16	7440	16
Limerick	2230	14	4288	15
Finmark	4510	15	4510	15
Lakenheath	8126	15	9226	15
Wormingford	1751	10	20486	15
Heatherbush	2883	14	2883	14
Bulwark	4267	14	4267	14
Hlymadle	4887	14	4887	14
Varin’s Firth	6304	14	6331	14
Ossary	47768	14	47777	14
Benobles	3859	14	4775	14
Irungsway	2501	13	2501	13
Springle	5081	13	5081	13
Landmarch	5904	13	5904	13
Red Cliffe	1568	12	1599	12
Ashenshaft	5372	12	5445	12
Charnock	5795	12	5795	12
Seasteadholm	1889	12	2037	12
Hankam	2031	11	2031	11
Hel	5131	11	5131	11
Bondmaid	1953	11	2094	11
Atwan	3266	10	3266	10
Hindfell	3362	10	3365	10
Elixer	4188	10	4188	10
Boughrune	2154	9	2154	9
Guilding	2809	9	2809	9
Doom	3028	9	3028	9
Caelam	4432	9	4432	9
Catalan	4604	9	4604	9
Shavenoar	1192	8	1192	8
Serpeant Little	1209	8	1209	8
Havocia	1313	8	1313	8
Jasonyria	2110	8	2110	8
Karn	2296	8	2296	8
Goblin Hill	3817	8	3817	8
Grita Heath	810	7	810	7
Smite	2404	7	2404	7
Anguikan	2975	7	3031	7
Lightelf	3057	7	3057	7
Elf-burn	975	6	5122	7
Forecastle	939	6	939	6
Adderwood	1873	6	1873	6
Palewood	571	5	571	5
Sunfells	2280	5	2280	5
Hunwood	2615	5	2615	5
Wildwood	741	4	741	4
Ered Chimera	1032	4	1032	4
Crucible	521	3	521	3
Hledra	799	3	799	3
Trollslore	486	2	486	2
Wenlock	608	2	608	2
Skaney	742	2	742	2
Dart	293	1	293	1

Re-arranging the table to give the top 20 cities by raw trade gives us:

City	Trade	PP	+tax
CSIO	435882	21	21
Warwik	259121	27	27
Sticklestead	81295	32	32
Modron	59829	48	48
Ossary	47768	14	14
Thunderhold	36340	18	18
Darkfield	21199	121	149
Croy	18179	39	39
Wolfstone	13457	33	33
Seastrand	12553	30	30
Zarthstone	12170	29	29
Sea Hill	11342	38	62
Tain	11034	27	28
Byrny	10923	23	24
Smitten	10539	22	24
Fireside	9373	28	28
Dearthmead	9288	29	29
Gaehill	9021	22	22
Muskholm	8985	23	23
Woe	8431	31	31

4.2.1 CSIO

There’s not much to say about the City State itself. As expected, it is the largest economy and it draws in some trade from basically everywhere on the map. The biggest trading partner is Warwik at 26.2% of all trade. Here’s the whole list, down to those with 0.1% share:

City	% CSIOTrade
Warwik	26.2
Sticklestead	7.6
Modron	6.0
Darkfield	4.3
Ossary	4.2
Thunderhold	3.8
Tain	1.6
Woe	1.5
Croy	1.5
Haghill	1.4
Dearthmead	1.4
Bier	1.4
Gaehill	1.3
Byrny	1.1
Luckstone	1.1
Seastrand	1.1
Sea Hill	1.0
Fireside	1.0
Lakenheath	0.9
Flint	0.9
Goodhap	0.9
Wolfstone	0.9
Foremost	0.8
Varin’s Firth	0.8
Smitten	0.7
Zarthstone	0.7
Oakenbridge	0.7
Charnock	0.6
Springle	0.6
Landmarch	0.6
Bernost	0.6
Bulwark	0.6
Omen	0.6
Muskholm	0.6
Rockhollow	0.6
Sunlitten	0.6
Armagh	0.6
Greybeard	0.5
Hlymadle	0.5
Goblin Hill	0.5
Hel	0.5
Dorn	0.5
Ashenshaft	0.5
Caelam	0.4
Shewolf	0.4
Anvil	0.4
Atwan	0.4
Carmage	0.4
Ottersgild	0.4
Brushwood	0.4
Tegal	0.4
Finmark	0.3
Elixer	0.3
Hindfell	0.3
Catalan	0.3
Anguikan	0.3
Hunwood	0.3
Troth	0.3
Thelamie	0.3
Limerick	0.3
Ryefield	0.3
Guilding	0.3
Benobles	0.3
Mill Haven	0.3
Jasonyria	0.2
Irungsway	0.2
Hankam	0.2
Doom	0.2
Bondmaid	0.2
Smite	0.2
Sunfells	0.2
Orlage	0.2
Adderwood	0.2
Boughrune	0.2
Wormingford	0.2
Heatherbush	0.2
Lightelf	0.2
Swarin’s Cairn	0.2
Sea Rune	0.2
Havocia	0.1
Shavenoar	0.1
Wildwood	0.1
Red Cliffe	0.1
Karn	0.1
Elf-burn	0.1
Seasteadholm	0.1
Serpeant Little	0.1

4.2.2 Warwik

Yes, so. Warwik. No one ever talks about Warwik, but it’s one of the biggest cities in the whole setting, not just Map 1. Bigger than Tarantis, bigger than Valon by miles. It dominates the north coast and the only reason it is not more important as a trading partner is that the map ends just north of it.

As it is, it has more trade per person than CSIO and has a higher civ rating too.

4.2.3 Modron

Much smaller than Warwik, or even Ossary, the model used doesn’t, I think, give Modron due consideration for potential tax collection from passing vessels but since they don’t literally pass through the town the program doesn’t catch them. Even so, it has the second highest ratio of direct trade to population, after Darkfield, and on that measure the citizens are more than twice as wealthy as those of the CSIO.

City	% Modron Trade
CSIO	44.4
Warwik	18.5
Sticklestead	12.7
Ossary	2.8
Croy	1.3
Seastrand	1.0
Thunderhold	0.9
Brushwood	0.8
Wolfstone	0.7
Byrny	0.5
Zarthstone	0.5
Fireside	0.5
Sunlitten	0.5
Armagh	0.5
Smitten	0.4
Tain	0.4
Varin’s Firth	0.4
Ryefield	0.4
Oakenbridge	0.4
Greybeard	0.3
Flint	0.3
Muskholm	0.3
Sea Hill	0.3
Hel	0.3
Rockhollow	0.3
Ottersgild	0.3
Ashenshaft	0.3
Tegal	0.3
Charnock	0.2
Finmark	0.2
Lakenheath	0.2
Bernost	0.2
Gaehill	0.2
Darkfield	0.2
Anvil	0.2
Dearthmead	0.2
Woe	0.2
Troth	0.2
Thelamie	0.2
Dorn	0.2
Benobles	0.2
Caelam	0.1
Springle	0.1
Hlymadle	0.1
Elixer	0.1
Hindfell	0.1
Landmarch	0.1
Catalan	0.1
Foremost	0.1
Smite	0.1
Omen	0.1
Orlage	0.1
Shewolf	0.1
Haghill	0.1
Goodhap	0.1
Luckstone	0.1
Ered Chimera	0.1
Atwan	0.1
Bier	0.1
Karn	0.1
Carmage	0.1
Limerick	0.1
Seasteadholm	0.1
Boughrune	0.1
Guilding	0.1
Wormingford	0.1
Heatherbush	0.1
Mill Haven	0.1
Lightelf	0.1
Swarin’s Cairn	0.1
Sea Rune	0.1

1. Pareto Curves
   

   The question naturally arises from the above observation as to how the uppermost members of society compare between the CSIO and Modron, given that the (external trading) economy of the former is 7¼ times the latter.

   If we use an 80/20 split (80% of wealth is owned by 20% of people) then we get the top 20% of Modronites are sharing 47863 between them, which is 129.7 units each. The same calculation for CSIO gives 348705 to the top 20%, which is 87. When looking at percentages this disparity holds, but for the CSIO the number of people sharing that wealth is much higher and the true picture comes out when looking at it from that PoV.

   So when we look at the top 20 people in each place, the difference between the two cities’ elites is clearer. For Modron these people represent 1.6%, whereas for the CSIO they represent 0.1%. The 20 Modronites have 1695 units of wealth each, the City Staters average 8405 units each.

   Ultimately, the top guy in Modron can expect to control 22424 units of trade while the Overlord grabs 111204 (assuming that the Overlord is as content to share as Anoethin is).

   I’ve glossed over many questions here, not least of which are what slope to use for the Pareto calculation and how applicable it is to very small numbers of people in autocratic societies. Not to mention that populations listed in the guides are for able-bodied men of fighting age, not full populations. Perhaps "top families" would be a better way of looking at it.

4.2.4 Ossary

Although Ossary is big and technically advanced (civ 8), its alignment of CE costs it a lot of trade. Still, it dominates the southern area to a degree which can only increase once Map 2 is added. The largest city there, Antil, is just over ⅓ of the size of Ossary and somewhat backward with a civ rating of 4.

City	% Ossary Trade
CSIO	39.0
Warwik	23.5
Sticklestead	5.8
Modron	3.5
Croy	1.6
Swarin’s Cairn	1.4
Sunlitten	1.1
Sea Rune	1.0
Oakenbridge	0.9
Heatherbush	0.9
Mill Haven	0.8
Lightelf	0.7
Smitten	0.6
Armagh	0.6
Varin’s Firth	0.5
Hel	0.5
Seastrand	0.5
Tegal	0.5
Greybeard	0.4
Bernost	0.4
Catalan	0.4
Byrny	0.4
Muskholm	0.4
Tain	0.4
Zarthstone	0.4
Wolfstone	0.4
Guilding	0.4
Ashenshaft	0.4
Caelam	0.3
Lakenheath	0.3
Dearthmead	0.3
Thunderhold	0.3
Woe	0.3
Thelamie	0.3
Rockhollow	0.3
Ottersgild	0.3
Dorn	0.3
Wormingford	0.3
Serpeant Little	0.3
Charnock	0.2
Finmark	0.2
Hlymadle	0.2
Hindfell	0.2
Landmarch	0.2
Gaehill	0.2
Flint	0.2
Darkfield	0.2
Anvil	0.2
Goodhap	0.2
Karn	0.2
Fireside	0.2
Benobles	0.2
Jasonyria	0.1
Irungsway	0.1
Springle	0.1
Elixer	0.1
Doom	0.1
Hunwood	0.1
Foremost	0.1
Smite	0.1
Sunfells	0.1
Orlage	0.1
Shewolf	0.1
Haghill	0.1
Ered Chimera	0.1
Troth	0.1
Atwan	0.1
Bier	0.1
Sea Hill	0.1
Limerick	0.1
Seasteadholm	0.1
Boughrune	0.1
Ryefield	0.1
Forecastle	0.1
Palewood	0.1

4.2.5 Darkfield

Being on the road to CSIO as well as being physically close to it gives Darkfield a huge boost without being too incompatible on race, alignment, or civ.

4.2.6 Haghill

Like Darkfield, Haghill is near the city and gets a bigbump from that despite being small and undeveloped.

4.2.7 Sticklestead

Sticklestead is another large settlement that doesn’t get much of a look in, although an official scenario dealing with the area did come out in 2010(!). Bigger than Modron, it’s important because it allows access to the river and thereby to the sea, CSIO, and Warwik. I’ve also given it a bridge and a ford over the local rivers.

5 So what?

What to do with all this information? Well, the trade route maps show, for example, that Wormshead point is a good place to go a-pirating, and a place to send patrols to prevent it. There’s a line of small islands south of Croy which might make interesting bases for this sort of activity too, if the keyed encounters are not enough to put the players off.

The same goes for the land, with various crossroads apparent where PCs can prey on caravans, hire on to protect them, or where the DM can place inns or similar out-of-town bases.

PCs hired to locate lost cargoes or kidnapped merchants may be able to use their wits to learn of likely places to start a search.

The major cities will have rivalries and intrigues aimed at getting an economic advantage. Guilds from the cities will do likewise.

The information gives some idea of how likely it is to be able to learn something by locating a native traveller. So, finding someone from Warwik won’t pose too much trouble in most locations near the sea but finding people from Tegal to ask about its manor might be hard.

Even Thunderhold has only a 3.8% share of the City State’s trade.

There will be other uses, but for now I need to look at making the code multi-threaded so that I can hope to tackle adding on at least Map 6 and see what that does to the trade routes to the west.

All the Forth code is on Github (https://github.com/tworthington/csiotrade)