Social Simulation and Explaining Religion

1. Social Simulation and Explaining Religion • Agent Behaviour and Simulation Results • 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. • Other Simulations Relevant to Religion • 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. Joanna Bryson Email: j.j.bryson@bath.ac.uk 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. 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. 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. For any particular amount of food-types 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 Biology7:1–52. 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, Science324(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.