Transcript Social simulation and Explaining Religion

Social Simulation and
Explaining Religion
Joanna Bryson
Agent Behaviour and Simulation Results
Email: [email protected]
Institute of Cognitive and Evolutionary Anthropology,
University of Oxford;
Artificial Models of Natural Intelligence,
University of Bath
The Science of Simulation
Science is a process for producing increasingly likely explanations of the
natural world. This is often done by proposing competing theories and
then seeking evidence to favour one theory over another. Computer
modelling can be used to check whether the components of a theory can
actually produce the sort of phenomena or dynamics that theory is
intended to explain. Their role in science is to demonstrate whether a
particular theory could account for the data. A simulation allows for the
testing of theories too complex to be matched against data real-world data
purely on the basis of human reasoning. The predictions of the model can
be generated by running the simulation, then these predictions can be
tested against real data through standard methods. Social simulation is a
form of computer modelling that focuses on how the behaviour of
individuals affects societies. Individual’s behaviour is described as a set
of actions that will be taken in a given environment, then the environment
itself is also modelled. When the simulation runs we can observe the
consequences for the society of the individuals’ actions.
Altruism goes to fixation – evolution
selects for it (top curve | purple).
The average knowledge increases
as it does (bottom | green).
Altruists (top | red) have more
energy on average than free-riders
(bottom | blue), thus higher
reproduction.
Other Simulations Relevant to Religion
Example: Will Evolution Favour the Altruistic
Communication that Underlies Culture?
In biology, altruism is defined as performing a costly behaviour to the
benefit of others. For example, when a mother feeds her offspring she
sacrifices her own nutrition on their behalf. Many researchers (including
Darwin) have wondered how more general altruistic behaviour can evolve
given that evolution favours promotion of one’s own descendents. This
conundrum was solved by Hamilton (1964) who observed that individuals
that live near each other will generally share more genetic material than
those further away, and that evolution will favour a sacrifice so long as its
cost provides sufficient benefit to those sufficiently related to the altruist.
However, over the years there has been a great deal of debate about
whether such behaviour is stable or can emerge where there is already
selfishness (Sober & Wilson 2010 for review).
To test whether altruistic agents could survive and create a culture even in
the face of cheaters they cannot recognise as such, we create an
ecosystem with a variety of food-types which, if eaten, grown back at a
fixed rate. Most types of food require special knowledge to eat. We then
create two types of agents acting in this world. The two types are
generally identical. Both have a 5% chance of discovering how to eat a
new food, thus without altruism and culture we would expect the average
level of knowledge in the society to be 0.05.
We begin the simulation with only selfish (free riding) agents. If an agent
is able to eat enough food and gain enough energy, then it will have a
child. The child will normally be just like the parent, but on very rare
occasions children mutate between types. The other type of agent is
altruistic. At regular intervals, altruistic agents will tell all the nearby
agents about how to eat any food it happens to know about. This costs
the altruist opportunities to eat that food, since the nearby agents will now
eat all the nearby food.
Nevertheless, altruistic agents always wind up driving selfish agents to
extinction. This is because altruistic agents tend to be near other altruistic
agents, so while they lose some of the advantage of their own knowledge,
they also gain the advantage of their neighbours’. This would lead to a
symmetry with no evolutionary advantage, except that the altruists’ bank
of knowledge – their culture – allows more of them to survive in a given
area, because there is more food they know how to eat. Now that we
have the simulation, we can explore what factors lead to strong cultures.
For example, living longer, talking faster, and attending to older or
otherwise more-successful individuals all help increase the size of culture.
For any particular amount of foodtypes they know how to process,
altruists (red | right) have less
energy than free-riders (blue | left),
because altruists tell their neighbors
how to process the same food.
However, altruists are more likely to
know more types of food processing
(numbers at base of columns).
Screenshot of the simulation
running in NetLogo. Written by
Čače and Bryson.
References
Čače, I. and Bryson, J. J. (2007). Agent based modelling of communication costs: Why information can be free. In Lyon, C., Nehaniv, C. L., and Cangelosi, A., editors, Emergence
and Evolution of Linguistic Communication, pages 305–322. Springer, London.
Hamilton WD (1964). The genetical evolution of social behaviour. Journal of Theoretical Biology 7:1–52.
Nogueira, T. and Rankin, D.J. and Touchon, M. and Taddei, F. and Brown, S.P. and Rocha, E.P.C.(2009) Horizontal Gene Transfer of the Secretome Drives the Evolution of Bacterial
Cooperation and Virulence, Current Biology 19 (1683–1691)
Powell, Shennan & Thomas (2008) Late Pleistocene Demography andthe Appearance of ModernHuman Behavior, Science 324(1298-1301).
Sozou PD (2009) Individual and social discounting in a viscous population Proc. R. Soc. B 276,(2955-2962)
Whitehouse H (2002) Modes of religiosity: Towards a cognitive explanation of the sociopolitical dynamics of religion, Method & Theory in the Study of Religion 14(293-315)
Will Lowe (left | Maastricht), Ivana
Čače (centre | Utrecht) and Avri
Bilovich (right | Bath, funded by
Nuffield; now UCL) assisted with
modeling and analysis.
•Powell, Shennan & Thomas (2008) show that the appearance of
Paleolithic culture around the world (associated with art and imagistic
religion) is correlated with increased population size. Thus the complex
religion that may be necessary for certain political organisations
(Whitehouse 2002) may itself depend on cultural innovations supporting
larger populations.
•Sozou (2009) shows the impact of various weightings for the value of
future outcomes on the communal good. The need for individual’s to
weigh future generations in order for society to be stable may explain the
prevalence of ancestor worship.
•Simulations do not have to happen only on computers. Many have
criticised memetics (the theorythat culture evolves analogously to
genetics) as implying that all culture evolves to exploit human minds like
diseases exploit our bodies, but virulance is not the only biological model.
Nogueira, Rankin, Touchon, Taddei, Brown & Rocha (2009) demonstrate
a system where information transmitted from agent to agent is known to
evolve independently, but this actually benefits the host agents and
increases their altruism because it increases their effective relatedness.
Here the models are not computer programs but bacteria, and the
information they share are even smaller organisms known as phages and
plasmids. These evolve to insert new behaviour in their host, protecting
them from environmental threats like mercury. Such systems may help us
understand how religion coevolves with human societies.