Abstract: The study of cultural evolution has drawn much of its momentum from academic areas far removed from human and animal psychology, specially regarding the evolution of cooperation. Game theoretic results and parental investment theory come from economics, kin selection models from biology, and an ever growing amount of models describing the process of cultural evolution in general, and the evolution of altruism in particular come from mathematics. Even from Artificial Intelligence interest has been cast on how to create agents that can communicate, imitate and cooperate. In this article I begin to tackle the 'why?' question. By trying to retrospectively make sense of the convergence of all these fields, I contend that further refinements in these fields should be directed towards understanding how to create environmental incentives fostering cooperation.
We need systems that are wiser than we are. We need institutions and cultural norms that make us better than we tend to be. It seems to me that the greatest challenge we now face is to build them. - Sam Harris, 2013, The Power Of Bad Incentives
1) Introduction
2) Cultures evolve
Culture is perhaps the most remarkable outcome of the evolutionary algorithm (Dennett, 1996) so far. It is the cradle of most things we consider humane - that is, typically human and valuable - and it surrounds our lives to the point that we may be thought of as creatures made of culture even more than creatures of bone and flesh (Hofstadter, 2007; Dennett, 1992). The appearance of our cultural complexity has relied on many associated capacities, among them:
1) The ability to observe, be interested by, and go nearby an individual doing something interesting, an ability we share with norway rats, crows, and even lemurs (Galef & Laland, 2005).
2) Ability to learn from and scrounge the food of whoever knows how to get food, shared by capuchin monkeys (Ottoni et al, 2005).
3) Ability to tolerate learners, to accept learners, and to socially learn, probably shared by animals as diverse as fish, finches and Fins (Galef & Laland, 2005).
4) Understanding and emulating other minds - Theory of Mind- empathizing, relating, perhaps re-framing an experience as one's own, shared by chimpanzees, dogs, and at least some cetaceans (Rendella & Whitehead, 2001).
5) Learning the program level description of the action of others, for which the evidence among other animals is controversial (but see Cantor & Whitehead, 2013). And finally...
6) Sharing intentions. Intricate understanding of how two minds can collaborate with complementary tasks to achieve a mutually agreed goal (Tomasello et al, 2005).
Irrespective of definitional disputes around the true meaning of the word "culture" (which doesn't exist, see e.g. Pinker, 2007 pg115; Yudkowsky 2008A), each of these is more cognitively complex than its predecessor, and even (1) is sufficient for intra-specific non-environmental, non-genetic behavioral variation, which I will call "culture" here, whoever it may harm.
By transitivity, (2-6) allow the development of culture. It is interesting to notice that tool use, frequently but falsely cited as the hallmark of culture, is ubiquitously equiprobable in the animal kingdom. A graph showing, per biological family, which species shows tool use gives us a power law distribution, whose similarity with the universal prior will help in understanding that being from a family where a species uses tools tells us very little about a specie's own tool use (Michael Haslam, personal conversation).
Once some of those abilities are available, and given an amount of environmental facilities, need, and randomness, cultures begin to form. Occasionally, so do more developed traditions. Be it by imitation, program level imitation, goal emulation or intention sharing, information is transmitted between agents giving rise to elements sufficient to constitute a primeval Darwinian soup. That is, entities form such that they exhibit 1)Variation 2)Heredity or replication 3)Differential fitness (Dennett, 1996). In light of the article Five Misunderstandings About Cultural Evolution (Henrich, Boyd & Richerson, 2008) we can improve Dennett's conditions for the evolutionary algorithm as 1)Discrete or continuous variation 2)Heredity, replication, or less faithful replication plus content attractors 3)Differential fitness. Once this set of conditions is met, an evolutionary algorithm, or many, begin to carve their optimizing paws into whatever surpassed the threshold for long enough. Cultures, therefore, evolve.
The intricacies of cultural evolution and mathematical and computational models of how cultures evolve have been the subject of much interdisciplinary research, for an extensive account of human culture see Not By Genes Alone (Richerson & Boyd, 2005). For computational models of social evolution, there is work by Mesoudi, Novak, and others e.g. (Hauert et al, 2007). For mathematical models, the aptly named Mathematical models of social evolution: A guide for the perplexed by McElrath and Rob Boyd (2007) makes the textbook-style walk-through. For animal culture, see (Laland & Galef, 2009).
Cultural evolution satisfies David Deutsch's criterion for existence, it kicks back, it satisfies the evolutionary equivalent of the condition posed by the Quine-Putnam Indispensability argument in mathematics, i.e. it is a sine qua non condition for understanding how the World works nomologically. It is falsifiable to Popperian content, and it inflates the Worlds ontology a little, by inserting a new kind of "replicator", the meme. Contrary to what happened on the internet, the name 'meme' has lost much of it's appeal within cultural evolution theorists, and "memetics" is considered by some to refer only to the study of memes as monolithic atomic high fidelity replicators, which would make the theory obsolete. This has created the following conundrum: the name 'meme' remains by far the most well known one to speak of "that which evolves culturally" within, and specially outside, the specialist arena. Further, the niche occupied by the word 'meme' is so conceptually necessary within the area to communicate and explain that it is frequently put under scare quotes, or some other informal excuse. In fact, as argued by Tim Tyler - who frequently posts here - in the very sharp Memetics (2010), there are nearly no reasons to try to abandon the 'meme' meme, and nearly all reasons (practicality, Qwerty reasons, mnemonics) to keep it. To avoid contradicting the evidence ever since Dawkins first coined the term, I suggest we must redefine Meme as an attractor in cultural evolution (dual-inheritance) whose development over time structurally mimics to a significant extent the discrete behavior of genes, frequently coinciding with the smallest unit of cultural replication. The definition is long, but the idea is simple: Memes are not the best analogues of genes because they are discrete units that replicate just like genes, but because they are continuous conceptual clusters being attracted to a point in conceptual space whose replication is just like that of genes. Even more simply, memes are the mathematically closest things to genes in cultural evolution. So the suggestion here is for researchers of dual-inheritance and cultural evolution to take off the scare quotes of our memes and keep business as usual.
The evolutionary algorithm has created a new attractor-replicator, the meme, it didn't privilege with it any specific families in the biological trees and it ended up creating a process of cultural-genetic coevolution known as dual-inheritance. This process has been studied in ever more quantified ways by primatologists, behavioral ecologists, population biologists, anthropologists, ethologists, sociologists, neuroscientists and even philosophers. I've shown at least six distinct abilities which helped scaffold our astounding level of cultural intricacy, and some animals who share them with us. We will now take a look at the evolution of cooperation, collaboration, altruism, moral behavior, a sub-area of cultural evolution that saw an explosion of interest and research during the last decade, with publications (most from the last 4 years) such as The Origins of Morality, Supercooperators, Good and Real, The Better Angels of Our Nature, Non-Zero, The Moral Animal, Primates and Philosophers, The Age of Empathy, Origins of Altruism and Cooperation, The Altruism Equation, Altruism in Humans, Cooperation and Its Evolution, Moral Tribes, The Expanding Circle, The Moral Landscape.
3) Cooperation evolves
Shortly describe why and show some inequations under which cooperation is an equelibrium, or at least an Evolutionarily Stable Strategy.
4) The complexity of cultural items doesn't undermine the validity of mathematical models.
4.1) Cognitive attractors and biases substitute for memes discreteness
The math becomes equivalent.
4.2) Despite the Unilateralist Curse and the Tragedy of the Commons, dyadic interaction models help us understand large scale cooperation
Once we know these two failure modes, dyadic iterated (or reputation-sensitive) interaction is close enough.
5) From Monkeys to Apes to Humans to Transhumans to AIs, the ranges of achievable altruistic skill.
Possible modes of being altruistic. Graph like Bostrom's. Second and third order punishment and cooperation. Newcomb-like signaling problems within AI.
6) Unfit for the Future: the need for greater altruism.
We fail and will remain failing in Tragedy of the Commons problems unless we change our nature.
7) From Science, through Philosophy, towards Engineering: the future of studies of altruism.
Philosophy: Existential Risk prevention through global coordination and cooperation prior to technical maturity. Engineering Humans: creating enhancements and changing incentives. Engineering AI's: making them better and realer.
8) A different kind of Moral Landscape
Like Sam Harris's one, except comparing not how much a society approaches The Good Life (Moral Landscape pg15), but how much it fosters altruistic behaviour.
9) Conclusions
I haven't written yet, so I don't have any!
Bibliography (Only of the part already written, obviously):
Cantor, M., & Whitehead, H. (2013). The interplay between social networks and culture: theoretically and among whales and dolphins. Philosophical Transactions of the Royal Society B: Biological Sciences, 368(1618).
Dennett, D. C. (1996). Darwin's dangerous idea: Evolution and the meanings of life (No. 39). Simon & Schuster.
Dennett, D. C. (1992). The self as a center of narrative gravity. Self and consciousness: Multiple perspectives.
Galef Jr, B. G., & Laland, K. N. (2005). Social learning in animals: empirical studies and theoretical models. Bioscience, 55(6), 489-499.
Hauert, C., Traulsen, A., Brandt, H., Nowak, M. A., & Sigmund, K. (2007). Via freedom to coercion: the emergence of costly punishment. science, 316(5833), 1905-1907.
Henrich, J., Boyd, R., & Richerson, P. J. (2008). Five misunderstandings about cultural evolution. Human Nature, 19(2), 119-137.
Hofstadter, D. R. (2007). I am a Strange Loop. Basic Books
McElreath, R., & Boyd, R. (2007). Mathematical models of social evolution: A guide for the perplexed. University of Chicago Press.
Ottoni, E. B., de Resende, B. D., & Izar, P. (2005). Watching the best nutcrackers: what capuchin monkeys (Cebus apella) know about others’ tool-using skills. Animal cognition, 8(4), 215-219.
Persson, I., & Savulescu, J. Unfit for the Future: The Need for Moral Enhancement Oxford: Oxford University Press, 2012 ISBN 978-0199653645 (HB)£ 21.00. 160pp. On the brink of civil war, Abraham Lincoln stood on the steps of the US Capitol and appealed.
Pinker, S. (2007). The stuff of thought: Language as a window into human nature. Viking Adult.
Rendella, L., & Whitehead, H. (2001). Culture in whales and dolphins.Behavioral and Brain Sciences, 24, 309-382.
Richardson, P. J., & Boyd, R. (2005). Not by genes alone. University of Chicago Press.
Tyler, T. (2011). Memetics: Memes and the Science of Cultural Evolution. Tim Tyler.
Tomasello, M., Carpenter, M., Call, J., Behne, T., & Moll, H. (2005). Understanding and sharing intentions: The origins of cultural cognition.Behavioral and brain sciences, 28(5), 675-690.
Yudkowsky, E. (2008A). 37 ways words can be wrong. Available at http://lesswrong.com/lw/od/37_ways_that_words_can_be_wrong/
