The Human
Demographic Regulator*
Christopher Chase-Dunn, Peter
Turchin, Robert A. Hanneman,
Hiroko Inoue and Kirk Lawrence
Institute for Research on
World-Systems,
[4687 words, v. 3-21-08]
* This work is supported by National Science
Foundation grant NSF-HSD
SES-0527720 Award type: HSD-AOC
Keywords: human socio-cultural evolution, demographic
cycles, migration, population pressure, settlement growth, polity growth,
modeling and forecasting global state formation
Institute for Research on
World-Systems
University of
California-Riverside
To be presented at the International Studies Association
annual meeting,
Session FB15 on Global Forecasting: Data-Based
Computational Models and Precursors: Short and Long Time-Horizon Modeling. Session
Organizer: Paul Williamson, Discussants: Myron S. Karasik and Solomon W.
Polachek
This is
IROWS Working Paper #41 available at http://irows.ucr.edu/papers/irows41/irows41.htm
This paper describes the initial construction of a
dynamical model of the human population regulator as it operated during the
“human state of nature” and it continues to operate when complex institutional
systems fail. This is a module within a
larger model of human socio-cultural evolution and is part of a project that
will examine the prospects for global state formation in the next centuries. In
that larger project we use quantitative estimates of the sizes of settlements
and the territorial sizes of polities to help parameterize causal models of the
growth and decline of large polities. This paper focusses on the basic elements
that cause human populations to grow or decline in both nomadic and early
sedentary world-systems. It is alleged
that the basic human demographic regulator returns to operate when complex
social systems fail – the so-called Malthusian corrections – and so the “nasty
bottom” should be retained as part of the overall model even though its logic
may be temporarily transcended as human institutions articulate population
pressures with technological and organizational change. Collapses send us back
to the “nasty bottom,” and this will continue to be the case as long as
population growth is an automatic response to the availability of food and
other resources. One can imagine a future transformation in which humans get
off of the Malthusian treadmill entirely. The demographic transition that has
occurred in the industrial societies of the core and that seems to be spreading
to the non-core may signal a new day in which the demographic regulator is
transcended, but this transition will not be complete until well after the
human population of the Earth ceases to rise and population pressures cease to
operate. Until then we need to have a good representation of how the nasty
bottom of the process works and we also need this in order to explain what
happened in human history and prehistory.
This paper presents an overview of our modeling project
and a new model of the human demographic regulator that incorporates insights
from anthropologists and archaeologists who have sought to explain demographic
processes in relatively simple human societies. As with our other work, the
unit of analysis is the world-system, not single societies or polities. We
employ the comparative world-systems approach developed by Chase-Dunn and Hall
(1997) in which human interaction networks define the boundaries of
world-systems [for a brief overview see Hall and Chase-Dunn (2006)]. Early
world-systems were not global. They were regional affairs because of the nature
of transportation and communications. We employ an anthropological scale of
comparison that includes world-systems composed of small nomadic
hunter-gatherer bands as well as the modern global system. We abstract from
scale in order to compare the structures and processes of small and large
interactive systems.
Human
Socio-cultural Evolution
Formal evolutionary models of the long-term and
emergent causes of upward sweeps in polity sizes and settlement sizes are being
developed and empirically tested with long-term estimates of settlement
population sizes and the territorial sizes of states (Chase-Dunn, Anderson and
Turchin 2005; Chase-Dunn et al 2008).
These models will be used to forecast
future global state formation under specified ecological, technological, social
and demographic conditions.
The “human state of nature” refers to the period between
the emergence of the use of symbolic language and the emergence of sedentism
(Gat 2006). In this long period humans primarily lived in small nomadic
hunter-gatherer bands. Sedentism and diversified foraging emerged only since
the last Ice Age. So human prehistory, the paleolithic, lasted a very long
time. It was during this period that humanity migrated to all the continents
except
The first version of the iteration model of world-systems
evolution was presented in Chapter 6 of Chase-Dunn and Hall (1997). It is called an interation model because its
overall structure is a positive feedback loop that explains the growing scale
of human societies and world-systems since the stone age. But within the
overall positive feedback loop there is a smaller negative feedback loop, the
“nasty bottom” that comprises the human demographic regulator. A revised
version of the overall iteration model is depicted in Figure 1.
Figure 1: Revised Iteration Model of World-Systems Evolution
This version relabels some of the processes
depicted in the original model and it adds trade, epidemics and
non-anthropogenic climate worsening. Trade was put in because, after reading
the world historians who emphasize a network node theory of innovation (e.g
McNeill and McNeill 2003; Christian 2003), we realized that we had used
interaction networks to bound world-systems but had left them out of the causal
model that explains socio-cultural evolution. So we put trade back in. Thompson
(2008), and the large literature that he reviews, convinced us that epidemics
and non-anthropogenic climate change are not merely epiphenomenal and so should
be included in the model. We note that anthropogenic climate change e.g. due to
deforestation, etc.) has always been included under “Environmental
Degradation.”
The Human
Demographic Regulator
Figure 2: The Bottom Half of the Iteration Model : Human Demographic Regulator
Figure 2 depicts the nasty bottom of the iteration
model of human socio-cultural evolution, which is the part below the line in
Figure 1. The nasty bottom is the demographic regulator that humans share with
other animals. One of the most exciting things that has happened at the
Institute for Research on World-Systems in the last few years is the discovery
of animal societies and world-systems of animals. Inspired by a remark by
Douglas Massey at the American Sociological Association meetings in
What
we have learned is that animal social structures can vary over relatively short
periods of time in response to changing environmental conditions. We had always
thought that animal societies were tightly structured by inherited instincts,
which would mean that their social relations would change only slowly based on
genetic changes responding to changes in the environment.
We have been reading recent books on the evolution of
human warfare (Thompson and Levy, forthcoming; Gat 2006) so we started paying
attention to
In the comparative human world-systems theory this is
what we call the “nasty bottom” of the iteration model. Actually there are two vicious circles, one based on
resource depletion that acts directly back on human population, and another
larger negative feedback that goes through human to human conflict. As human
populations rise natural resources are depleted. In the absence of other action
this eventually causes shortages. Resources that are non-renewable decline and
do not recover even when population goes down because people live shorter lives
and have fewer babies when resources are scarce. Renewable resources recover
when population goes down, though at different rates and with different time
lags.[3]
This is analogous to the famous Lotka-Volterra predator-prey model that causes
populations to oscillate. Peter Turchin (2003:13) applies the logic of this
second order non-linear dynamic model to human populations and resource use.
This is the first nasty bottom. But there is a second nasty bottom that
operates through migration, circumscription and conflict.
Population
pressure leads to emigration unless the land is already occupied. If the land
is full (circumscription) this causes higher levels of within-society and
between-society conflict. This reduces population pressure by killing off users
of scarce resources, which reduces population pressure. It is like flour beetles
in a jar. When the food supply goes down they eat each other. Over the long run
there is oscillation around an equilibrium ratio between the population and the
amount of food. This works for some human world-systems that get stuck in the
nasty bottom. Patrick Kirch (1991) shows this cycle as revealed in
archaeological evidence for the
But some human systems break out of one or both of the
nested nasty bottoms by developing new technologies that allow more resources
to be produced in a given area (diversified foraging, gardening, agriculture,
industry) or by erecting a new hierarchy that regulates access to scarce
resources (chiefdoms, states).
So animal and human patterns overlap considerably, at
least at the level of the nasty bottom. It does not require complex symbolic
systems to run a simple demographic regulator of this kind. This is recognized
by the many computational social scientists who use predator-prey models as the
beginning of the job of modeling the relationship between human population and
conflict (e.g. Turchin and Korotayev 2006).
Our new model shows what intervenes between population
growth and conflict. As population grows people use more resources because
there are more mouths to feed, more dwellings to build, etc. This puts pressure
on resources and they become depleted. The low-hanging fruit or the deer close
to the hamlet are taken and it takes more effort to get the same return. This
is the general idea of “population pressure” as theorized by Ester Boserup (1981) and many others. The cost of
obtaining resources starts going up long before the population carrying
capacity of the natural environment is reached. Natural resources do not need
to be completely exhausted for population pressures to be working strongly on
humans or other forms of life. Population pressure derives from depletion of
natural resources, but also from pollution of the local environment and from
anthropogenic climate worsening. A water hole may become unclean. Cutting down
trees may decrease rainfall or the ability of the soil to hold water. All these
elements are part of population pressure. Humans react to depletion first by
increasing their efforts, but the costs of this lead to a search for
alternatives, and either some or all of the members of a group are more likely
to migrate to greener pastures when the local pastures have become relatively
depleted. By this process did humans move to all the continents.
Circumscription occurs when there are no good and available
alternative places to move to, or rather when the cost of moving to an
alternative place becomes relatively high. This occurs when only marginal
environments remain unoccupied and when the current inhabitants of adjacent
desireable areas resist immigration. Migrants who bring something that the
current inhabitants want (e.g. new technology) are less likely to face
resistance, and certain attributes make it possible to overcome resistance
(e.g. military superiority based on larger numbers or better weapons or tactics).
Circumscription means that population pressures cannot be lowered by migration.
When this occurs the level of conflict goes up among individuals and among
groups. People more often kill each other within societies and they engage in
more infanticide in order to regulate population. Groups are also more likely
to encounter one another and to fight over scarce resources and so more people
are killed in intergroup conflict. In
both ways the population is reduced and natural resources may recover until
population goes up again. Kirch (1991) shows archaelogical evidence that shows
oscillation in the amount of warfare and cannibalism in the
A number of problems need to be resolved in order to
convert the model as depicted in Figure 2 into a dynamical mathematical model.
The functional form of the relationships need to be specified, and the relevant
time lags over which effects operate need to be estimated. It would also be
desirable to include space in the model because the processes are spatial.
Local resources are depleted but more distant ones are not. Depletion itself is
complex because some resources are more resistant to depletion (e.g. fish)
while others become depleted more quickly and take longer to recover (e.g. big
game, slow-growing trees). Space is also important in migration and
circumscription.
Modeling
Methods
We are using two different approaches to create
dynamic systems models of the human demographic regulator: Stella and APL
(Allied Programming Language). Stella is a systems modeling program that uses
model diagrams to construct a program that will do simulations.
We are not sure yet
whether Stella will allow us to do what we would like to do with regard to
spatial modeling of the demographic process. But Stella is easier for
non-programmers to use, and has features that are not easily replicated with
more tradition simulation methods –like the ability to construct a control
panel that allows parameters to be easily manipulated so that a model user can
see the results of the manipulations.
Figure 3 is a Stella
diagram of the nasty bottom model.
Figure 3: The Human Demographic Regulator (Stella version)
In Stella the boxes represent stocks --
accumulated amounts such as the human population -- and the double-lined arrows represent flows
of material in and out of the stock boxes. The round symbols represent parameters
such as rates that affect the amount of stocks or flows, and flow processes
that affect other variables. The little clouds at the end or beginning of some
of the flow arrows represent untheorized generators of flows or sinks into
which things flow.
Figure 3 is the diagramatic version of the Stella model. It
includes some features that are not made visible in the diagramatic scheme in
Figure 2. In order to make Figure 3 into a simulation model we need to specify
units and initial levels for the stocks and rates, such as the birth rate, etc.
and we need to specify the functional form of the relationships. We know that
human populations tend to cycle, and so we want population to growth and
decline and then grow again in our model. We think that there are two cycles in
the human demographic regulator. One is produced by the direct relationship
between population and natural resources and this should produce a cycle of
population growth and decline. The other
cycle is produced when humans migrate and fill up available niches and then the
rate of conflict goes up and people are killed off, and so the population
declines. So there is a smaller and faster cycle within a larger and longer
cycle. How these two affect one another is something that we are investigating,
but the overall outcome should be cylical.
The cyclical nature of the small or inner
population/resource cycle is produced by the lag time that it takes for natural
resources to recover.
Figure 4: Graph of simulated population and conflict values
Figure 4 shows the simulated values when the model
specified in Figure 3 is run. Population oscillates, but the oscillations are
dampening. This oscillation is mainly caused by lags in the model, but we are
not sure that we have put the lags in the right places. This model needs
further work but we have a beginning.
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