rough guide to inbreeding and outbreeding

this is obviously an over-simplification, but i wanted to outline some of the effects that inbreeding and outbreeding have on social behaviors. so, it’s way, waaaay over-simplified. it’s just me thinking out loud.

these are just a few of my favorite things i thought of off the top of my head. there’ve been many others discussed around here lately — like harmonious jim suggested that exogamy lead to humanism. i think that’s prolly pretty right. we wouldn’t have gotten here without strong exogamy anyway.

also, there ought to be more gradations here, but i didn’t bother right now — like somewhere in between “no inbreeding” (nuclear families) and “maternal-side inbreeding” (clans) there ought to be extended families — not completely outbred, but not as inbred as clan-based societies. (maternal-side inbreeding = mbd & mzd marriage; paternal-side inbreeding = fbd & fzd marriage.)

anyway — here is a rough guide to some of the apparent effects of inbreeding and outbreeding on social behaviors. do, please, suggest some more! (and/or take issue with these!):

(note: comments do not require an email. medieval squirrel.)

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21 Comments

  1. Maybe I should have put a question mark instead of an exclamation after my first comment. Didn’t someone suggest that libertarianism also flourishes under conditions of extreme out-breeding? I remember a writer for Slate, Ashkenazi I think though I forget his name, who casually remarked that he didn’t care about unlimited immigration into the United States from places like Mexico because it was good for the Mexicans, good for him, and he didn’t care if some of his fellow Americans were harmed in the process because he didn’t value them as human beings more than anybody else. I was personally offended that a public intellectual would say such a thing. It was outrageously I thought. Imagine a politician who tried to run on it.

    “One for all and all for one” would seem more typical of ethnically homogeneous societies like Sweden.

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  2. @luke – “Didn’t someone suggest that libertarianism also flourishes under conditions of extreme out-breeding?”

    i think that makes sense. the more outbred a population is, the more all of its individual members are … well … individuals! genetically speaking. you might, then, start looking at the world from a “what’s in it for just me” p.o.v. (and, perhaps, your kids) if you’re not very strongly bonded to anyone else. adding ethnic diversity into the mix would clearly make those sentiments stronger.

    @luke – “‘One for all and all for one’ would seem more typical of ethnically homogeneous societies like Sweden.”

    it is, actually, the national motto of switzerland! now, the swiss, of course, are not a homogeneous group — however, the majority of ethnic swiss people are (prolly) germans — and the french-swiss are really just a bunch o’ burgundians (more germans) — and the italio-swiss just a bunch o’ lombards (germans again).

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  3. For those of us without the benefit of a public school education, what is the significant differences between a state and a nation state?

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  4. It is interesting…the definitions are not exclusive….see: Germanic, German, Bavarian.

    Simon

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  5. “For those of us without the benefit of a public school education, what is the significant differences between a state and a nation state?”

    A lot of this is a question of scale and definition. The Athenian city-state was a small nation-state if you define the nation as limited to Athenians. Out-breeding creates the potential to expand the scale of voluntary co-operation to the wider ethny e.g from Athenians to all Greeks or from Bavarians to all Germans but at the cost of reduced internal cohesion coming simply from close blood-ties. This loss of cohesion leads to idealogical reinforcement to make up the difference hence the development of nationalism and the idea of the nation-state.

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  6. “do, please, suggest some more! (and/or take issue with these!)”

    It is tricky because one of the fundamental features is double-edged, cohesion is zero-sum and directional.

    For an example of what i mean take trust. Making arbitrary scale divisions into clan, tribe and nation, clans are actually very high trust – among themselves – but very low trust to everybody else. Tribes are high trust – among themselves – but low trust to everybody else. Nations are probably actually lower trust among themselves but that set called “themselves” is the whole set. That’s how certain features like “egalitarian”, “democratic”, “independent” etc can appear at all three scales but only within the in-group.

    So take an imaginary nation of ten tribes made up of ten clans made up of ten people each for a total of a thousand people.

    In a clannish version of that nation say each clan member trusts each other clan member 10 points but 0 points for everyone else outside the clan. Each clan member trusts (9 people x 10 points) + (990 people x 0 points) = 90 points each = 900 for each clan x 100 clans = 90,000 points of trust.

    In the tribal version of that nation say each clan member is now more outbred among their tribe and now trusts each other clan member 8 points and each tribe member 6 points but still 0 for everyone outside the tribe. Each clan member now trusts the 9 other people in their clan x 8 points each and the other 90 people in their tribe x 6 points each = 612 x 100 people in the tribe = 61200 x 10 tribes = 612,000 points of trust.

    So the society as a whole is higher trust than the clan version even though the clan version has the highest levels of individual trust.

    Similarly the national version where the tribes have outbred into each other so that say trust is now 6, 4 and 2 points so each individual trusts the 9 people in their clan at 6 points, the 90 other people in their tribe at 4 points each and the 900 other people in their nation at 2 points each for a total of 2,214 per person x 1000 people or 2,214,000 points of trust.

    Again the society as a whole has much higher total trust even though trust is actually lower at the smaller scales. Hence why some countries with similar average IQs could have warmer families but suckier ambulance services.

    It’s this pushme-pullyou effect that makes it a bit tricky to describe.

    .
    As to your list, the big difference seems to me is scale and going by your other posts i’d say the paternal side in-breeding would be the most clannish with the maternal side variation as an agrarian adaptation for larger scale political units. So i’d maybe have it:

    – nuclear families – linked clans – clans
    – nation states – city states – tribalism

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  7. actually i’m not sure “linked clans” resonates. it’s the circulating of brides among the linked clans that i think would make the difference.

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  8. @g.w. – “Making arbitrary scale divisions into clan, tribe and nation, clans are actually very high trust – among themselves – but very low trust to everybody else. Tribes are high trust – among themselves – but low trust to everybody else. Nations are probably actually lower trust among themselves but that set called ‘themselves’ is the whole set. That’s how certain features like ‘egalitarian’, ‘democratic’, ‘independent’ etc can appear at all three scales but only within the in-group.

    yes — i was struggling with exactly these problems when i was working on this. and then i gave up. (~_^)

    so, how do i get all these nuances into a nice, straight-forward explanatory chart (or, maybe, some other sort of diagramy-thing?)? hmmmm.

    (no need to answer that question just now — it’s the end of my day here. a fully-worked out solution, preferably in a powerpoint presentation, in 7 or 8 hours will be fine. (~_^) seriously — this is something i’d like to work out in the long-run. (^_^) )

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  9. “yes — i was struggling with exactly these problems when i was working on this. and then i gave up. (~_^)”

    yes it’s tricky. i think something visual is needed to express the pushme-pullyou effect but i haven’t had a eureka moment yet.

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  10. “so, how do i get all these nuances into a nice, straight-forward explanatory chart (or, maybe, some other sort of diagramy-thing?)? hmmmm.”

    Good plain old English prose will do nicely. In fact I prefer it.

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  11. @luke – “Good plain old English prose will do nicely. In fact I prefer it.”

    if only i could write some good plain old english prose. (~_^)

    i’m very much a picture person (visual thinker), so i like pie-charts and graphs and tables and such. gimme bullet-points! (^_^)

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  12. “so, how do i get all these nuances into a nice, straight-forward explanatory chart (or, maybe, some other sort of diagramy-thing?)? hmmmm.”

    Thinking aloud.

    I’m thinking the core problem is people not being able to visualize the pushme-pullyou effect as you increase scale. maybe the focus on the word out-breeding on its own increases that by focusing on one half of what’s happening. out-breeding is the active process but the passive result is actually an increase in total relatedness across a nation.

    making sure people get the base concept first and only then applying it to the different mating patterns

    so

    imagined as a set of visuals

    1) Full reductionism. Relatedness = trust and changing the levels of relatedness in your society effects everything.

    1) Chess board, 64 squares, divided into four square regions of 16 squares each, each region divided into four square sub-regions of 4 squares each, each square a village.

    2) Zoom in to a sub-region. 1st case. the people in each village only marry within the village. All four grand-parents from the same village. realtedness within the village at maximum possible. Point made: each village very endogamous.

    3) Zoom in to a sub-region. 2nd case. the people in the four villages of the sub-region inter-marry. each village provides one grand-parent. arrows going between the four villages. relatedness *decreases* within the village. Point made: inter-marriage within the four villages makes each village becomes less endogamous..

    (I think it’s neccessary to make sure people get that as you increase the total pool of people the average relatedness must go down simply because the number of ancestors is fixed: 2 parents, 4 grand-parents, 16 great grand-parents etc. there may be a better way to express it.)

    4) Zoom into a sub-region. 2nd case. Part 2. Inter-marrying among the villages *increases* each village’s relatedness to the other three villages. Point made: pushme-pullyou effect.

    5) Numerically.
    Case 1. Number in each village, 400. Average relatedness in each village, 12. Total relatedness village: 400 x 12 = 4800.
    Number in other three villages, 1200. Average relatedness to other villages, 0. Total relatedness in the sub-region, 4*((400*12) + (1200*0)) = 19,200. Average relatedness sub-region 19,200/1600 = 12.

    Case 2. Number in each village, 400. Average relatedness, 10. Total relatedness in each village: 400 x 10 = 4000.
    Number in other three villages, 1200. Average relatedness to other villages, 2. Total relatedness in the sub-region, 4*((400*10) + (1200*2)) = 25,600. Average relatedness sub-region 25,600/1600 = 16.

    Point made: reduced relatedness to a small number of people outweighed by increased relatedness to a larger group of people.

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  13. @g.w. – “5) Numerically.
    Case 1. Number in each village, 400. Average relatedness in each village, 12. Total relatedness village: 400 x 12 = 4800.
    Number in other three villages, 1200. Average relatedness to other villages, 0. Total relatedness in the sub-region, 4*((400*12) + (1200*0)) = 19,200. Average relatedness sub-region 19,200/1600 = 12.

    Case 2. Number in each village, 400. Average relatedness, 10. Total relatedness in each village: 400 x 10 = 4000.
    Number in other three villages, 1200. Average relatedness to other villages, 2. Total relatedness in the sub-region, 4*((400*10) + (1200*2)) = 25,600. Average relatedness sub-region 25,600/1600 = 16.

    Point made: reduced relatedness to a small number of people outweighed by increased relatedness to a larger group of people.”

    this is very good. you are awesome! i’m gonna sleep on this for a few nights to see what my brain makes of it. thank you! thank you so much! (^_^) (^_^) (^_^)

    Reply

  14. thinking aloud, draft #2

    (just realised something)

    1) same principle as previous post i.e i think the way to go with this is to explain the general concept first without relation to anything else i.e simply how marriage customs change relatedness patterns in human groups. particularly the pushme-pullyou effect

    2) just realised where my urge for trying to explain this using certain forms of numerical expression came from: it’s the fixed numbers of ancestors
    – 2 parents
    – 4 grandparents
    – 8 great grandparents
    – 16 ggreat grandparents

    so the idea can be expressed numerically and possibly more clearly using this explicitly

    if you arbitrarily divide the conceptual space as village/subregion/region/country then for purposes of explaining the general concept you can express endogamy as number of great-grandparents from each of the sub-divisions

    so if all 8 came from the same village then it’s 8/0/0/0
    if 6 came from the same village and 2 from the same subregion then it’s 6/2/0/0
    if 4 come from the same village, 2 from other villages in the same subregion, and 2 from other subregions of the same region then it’s 4/2/2/0
    If 2 come from the village, 2 from the village’s subregion, 2 from the village’s region but outside the subregion, 2 from the other regions then it’s 2/2/2/2

    If you take it back to ggrand-parents you have 16 slots for more granularity but the principle is the same.

    so again imagined as a set of visuals

    1) Square piece of terriotory like a chess board representing a nation. 64 squares divided into 4 regions of 16 squares divided into 4 subregions of 4 squares each. each square contains a village.

    2) zoom into a subregion. case one. all marriages are internal to each village. all 8 ggrandparents from same village. one big arrow. very endogamous. defining relatedness as where the ggrandparents came from the average relatedness in the village is 8 and the average relatedness to the wider subregion is 0

    3) zoom into same subregion. case two. the four villages intermarry to an extent. 6 ggrandparents from same village, 2 from the other villages. big arrow at village. smaller arrows from outside. average relatedness by the ggrandparent definition is 6/2

    point to make: as relatedness goes down from 8 to 6 within the village it goes *up* from 0 to 2 in the subregion. pushme-pullyou.

    4) numerically
    say 400 people per village, total 1600 in the subregion
    case one:
    village scale: 400 people x relatedness 8
    subregion scale: (400 people x relatedness 8) + (1200 people x relatedness 0)
    individual’s relatedness score = 400*8 +1200*0 = 3200

    case two:
    village scale: 400 people x relatedness 6
    subregion scale: (400 people x relatedness 6) + (1200 people x relatedness 2)
    individual’s relatedness score = 400*6 +1200*2 = 4800

    relatedness at the village level goes down but relatedness at the subregion level goes up. each individual at the same time becomes less related to a smaller group of people but more related to a larger group so the total relatedness expressed numerically increases.

    5) repeat the process at the region level. each village gets 4 ggrandparents from the village, 2 from other villages in the same subregion, 2 from other subregions in the same region. relatedness therefore goes to 4/2/2/0. same process as before. relatedness within the subregion goes from 8->6 but relatedness within the region goes 0->2. as the total number of people at the regional scale is greater then the total number at the subregional scale then the increased relatedness to a larger group of people outweighs the reduced relatedness to a smaller group. total relatedness goes up.

    6) repeat the process at the national level and a relatedness of 2/2/2/2. same result. total relatedness goes up.

    .
    In this frame national consanquinity rates would act as a proxy for how high the first number in our ggrandparents vs region division sequence is. A consanquinity rate of 50% might be translatable to the abstract case of Chesstasia as 6/2/0/0 and a Dutch rate of 0.1% as 2/2/2/2.

    .
    Once people have got the basic concept down then i think slotting the various marriage customs into the testing environment of Chesstasia might become reasonably straightforward.

    e.g it might turn out that FBD in the Chesstasia model leads to a case where the 4 ggrandfathers are from the same village and the 4 ggrandmothers are from outside whereas MZD leads to 2 ggrandfathers from within the village while the other 2 and the 4 ggrandmothers are from outside etc.

    Also if you make the strong reductionist case that total relatedness = total trust then the fully nationally exogamous version of Chesstasia will have the maximum amount of relatedness therefore maximum trust etc which if you accept the separately arguable trust -> synergy -> wealth premise makes the main point about the root of the western explosion from c1600 onwards.

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  15. “i’m gonna sleep on this for a few nights to see what my brain makes of it”

    cool. when i try and explain it i immediately jump into what i think some of the causes and consequences are and so i muddy the argument up.

    also now i realise where i was getting those weird number sequences from i think using the fixed number of grandparents, ggrandparents etc to illustrate the zero sum, pushme-pullyou aspect may help people get the basic concept more easily.

    so now i’m thinking maybe a simple proof of concept first in an abstract Chesstasia without reference to possible causes or consequences might be the best first step.

    once a person gets that picture in their mind i think the causes and effects you’ve been teasing out all start to fall pretty neatly into place.

    anyway, more thinking aloud…

    .
    in terms of Chesstasia divided into village/subregion/region/nation with 8 ggrandparents to allocate to each division in the form a/b/c/d and a village size of 400 that means
    – 400 in each village
    – 1200 in the other villages in same subregion
    – 4800 in the other subregions of the same region
    – 19200 in the other regions of the same nation

    which means (in very simple terms) the total relatedness (using source of ggrandparents a/b/c/d as the proxy for genes) is sort of (over time)
    400a + 1200b + 4800c + 19200d

    so within a homogenous nation maximal exogamy (counter-intuitively maybe) gives low individual relatedness but maximum totalrelatedness.

    (obviously i don’t think this simple equation proves anything, merely illustrates a point, but i’d bet there’s an integral somewhere, somehow that does prove it.)

    .
    in a way this is looking at a reverse process. if you imagine Chesstasia initially empty then the process in abstract might be
    – small group of 400 moves in
    – group grows to 1600 and divides four ways into the four regions
    – groups in each region grow to 1600 and divide four ways into the four subregions
    – groups in each subregion grow to 1600 and split into four villages

    so a common genepool moves into a terriotory sequentially subdividing and inbreeding within the subdivisions eventually forming 64 village branchs off the initial root. the out-breeding process then shuffles it all back together to create a more unified whole.

    (there’s something missing from the simple equation described above buried in the premise that the 64 villages make up a nation.)

    .
    another visual way of looking at it. imagine the village in the top left square
    8/0/0/0 -> all the ggrandparents from same village. no arrows
    6/2/0/0 -> 400 villagers so (400*2)=800 ggrandparents from the other three villages in the subregion i.e c266 arrows from the test village to each of those other three villages.
    4/2/2/0 -> same c266 lines to the other villages in the subregion but now also another 800 arrows divided between the other 12 villages in the region but outside the subregion so c66 arrows to each of those 12 squares.
    2/2/2/2 -> same c266 arrows to three villages in subregion, same c66 lines to the other 12 squares in the region, plus now 800 ggrandparent lines divided between the 48 villages in the other regions or say c16 each

    so the topleft square would have c266 lines to each of the 3 adjacent squares, c66 lines to each of the other 12 squares in the topleft quarter of the board and a further
    12 lines to each of the other 48 squares.

    if you then repeat that process for each of the 64 villages and overlay the results you see the spiderweb of inter-relatedness that creates a nation.

    .
    one obvious aspect of immigration when looked at in these terms is it increases the denominator without increasing the numerator. the effect is negative by default except when outweighed on an individual basis where the individual involved increases the numerator by a larger amount.

    .

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  16. Last bit…

    taking the empty Chesstasia, genes and the reverse nation premise

    (assuming chess board divisions as before numbering leftright, topbottom so whole board is A, regions B1-B4, subregions C1-C16, squares D1-D64)

    stage 1: group arrives. define as gene pool A. all the group are A.

    stage 2: space to expand. group grows and splits four ways to settle each of four regions. regional level mutations Bn. population groups become
    A + B1
    A + B2
    A + B3
    A + B4.

    stage 3: space to expand so regional groups grow and subdivide into the subregions. subregional level mutations Cn. subregional groups now genetically
    A + B1 + C1
    A + B1 + C2
    A + B1 + C3
    A + B1 + C4

    in the topleft corner, down to

    A + B4 + C13
    A + B4 + C14
    A + B4 + C15
    A + B4 + C16

    in the bottom right corner.

    stage 4: space to expand so subregional groups grow and divide into four villages. village level mutations Dn. village groups now genetically
    A + B1 + C1 + D1
    A + B1 + C1 + D2
    A + B1 + C1 + D3
    A + B1 + C1 + D4

    down to

    A + B4 + C16 + D61
    A + B4 + C16 + D62
    A + B4 + C16 + D63
    A + B4 + C16 + D64

    at this point the originally identical population are different in (abstractly) three degrees at the national level, two degrees at the regional level and one at the subregional level

    stage 5: hit the geographic buffers. no room to expand. conflict. attempts to resolve conflict.

    villages in the subregions inter-marry so over time the 16 subregional populations become
    A + B1 + C1 + D(1-4)
    A + B1 + C2 + D(5-8)
    etc

    down to

    A + B4 + C15 + D(57-60)
    A + B4 + C16 + D(61-64)

    they were already the same in three parts and merging the D part makes it four

    then if the subregions combine within the four regions

    A + B1 + C(1-4) + D(1-16)
    A + B2 + C(5-8) + D(17-32)
    A + B3 + C(9-12) + D(33-48)
    A + B4 + C(13-16) + D(49-64)

    then finally A + B(1-4) + C(1-16) + D(1-64)

    so you have an identical population becoming more dissimilar as it spreads out and then recombining again.

    .

    Reply

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