Kin selection, which can lead organisms to behave altruistically to their genetic relatives, works differently when—as is often the case in human societies—altruism can be boosted by social pressure. Here I present a model of social norms enforced by indirect reciprocity. In the model there are many alternative stable allocations of rewards (“distributional norms”); a stable norm is stable in the sense that each player is best off following the norm if other players do the same. Stable norms vary widely in how equally they reward players with unequal abilities. In a population of mixed groups (some group members follow one norm, some follow another, and some compromise) with modest within-group coefficients of relatedness, selection within groups favors those who compromise, and selection between groups favors generous generalized reciprocity rather than balanced reciprocity. Thus evolved social norms can amplify kin altruism, giving rise to a uniquely human mode of kin-based sociality distinct from spontaneous altruism among close kin, or cooperation among non-kin.
Arjen ten Have's insight:
Group selection is nonsensical but that does not mean that social processes cannot affect evolution
Complex social behaviors lie at the heart of many of the challenges facing evolutionary biology, sociology, economics, and beyond.
Arjen ten Have's insight:
Small groups and good memory promote cooperation. No surprise here, still it has to be shown adequately. Moreove, the authors combined groupsize and memory evolution and this results in coevolution. Ver cool.
(Phys.org)—Standard evolutionary theories of aging and mortality, being based on mean-field assumptions – which analyze the behavior of large and complex stochastic models by studying a simpler model – conclude that programmed mortality resulting from natural selection is impossible. Recently, however, scientists at the New England Complex Systems Institute, and the Wyss Institute for Biologically Inspired Engineering at Harvard University, both in Cambridge, Massachusetts, using spatial models with local rather than globally-uniform reproduction, demonstrated that programmed deaths strongly result in long-term benefit to an organismal lineage by reducing local environmental resource depletion over many generations. (In spatial models, variables are distributed in space such that actions can affect the local environment without affecting the global environment.) Moreover, the researchers found that these results continued to be favored when a large number of variations related to different real-world factors were applied to the spatial model, which they say supports their approach being applicable to a wide range of biological systems, and therefore that direct selection for shorter life span may be quite widespread in nature.
In recent years, the surprising idea that we’ll one day merge with our technology has warily made its way into the mainstream. Often it’s couched in a combination of snark and fear. Why in the world would we want to do that? It’s so inhuman.
That the idea is distasteful isn’t shocking. The imagination rapidly conjures images of Star Trek’s Borg, a nightmarish future when humans and machines melt into a monstrosity of flesh and wires, forever and irrevocably leaving “nature” behind.
But let’s not fool ourselves with such dark fantasies. Humans are already technological animals; tight integration with our inventions is in our nature; and further increasing that integration won’t take place in some distant future—it’s happening now.
To observe our technological attachment, we need simply walk out the door. It’s everywhere, all around us—on the bus or train, at work, at home, in the bathroom, in bed—people gazing into screens, living digital lives right next to their ordinary ones.
In the Matrix, the experience is involuntary, a tool of control and oppression. In our world, it’s voluntary, and mostly about freedom, expansion, and expression. As Jason Silva recently noted, our devices augment our brains, like cognitive prosthetics.
Network methods have had profound influence in many domains and disciplines in the past decade. Community structure is a very important property of complex networks, but the accurate definition of a community remains an open problem. Here we defined community based on three properties, and then propose a simple and novel framework to detect communities based on network topology. We analyzed 16 different types of networks, and compared our partitions with Infomap, LPA, Fastgreedy and Walktrap, which are popular algorithms for community detection. Most of the partitions generated using our approach compare favorably to those generated by these other algorithms. Furthermore, we define overlapping nodes that combine community structure with shortest paths. We also analyzed the E. Coli. transcriptional regulatory network in detail, and identified modules with strong functional coherence.
Evolutionary game dynamics are often studied in the context of different population structures. Here we propose a new population structure that is inspired by simple multicellular life forms. In our model, cells reproduce but can stay together after reproduction. They reach complexes of a certain size, n, before producing single cells again. The cells within a complex derive payoff from an evolutionary game by interacting with each other. The reproductive rate of cells is proportional to their payoff. We consider all two-strategy games. We study deterministic evolutionary dynamics with mutations, and derive exact conditions for selection to favor one strategy over another. Our main result has the same symmetry as the well-known sigma condition, which has been proven for stochastic game dynamics and weak selection. For a maximum complex size of n=2 our result holds for any intensity of selection. For n > 2 it holds for weak selection. As specific examples we study the prisoner's dilemma and hawk-dove games. Our model advances theoretical work on multicellularity by allowing for frequency-dependent interactions within groups.
Games of multicellularity Kamran Kaveh, Carl Veller, Martin A. Nowak
Yes, I know I'm writing about two Atlantic pieces in one day, but so be it: such are the laws of physics. The second piece, much better than the article on FGM, is an essay by Stephen Cave, "There's no such thing as free will but we're better off believing it anyway." I'll try to be…
Arjen ten Have's insight:
Jerry Coyne responds to the recent article on free will in The Atlantic. How to deal with it! Very good!
Big-data analysis consists of searching for buried patterns that have some kind of predictive power. But choosing which "features" of the data to analyze usually requires some human intuition. In a database containing, say, the beginning and end dates of various sales promotions and weekly profits, the crucial data may not be the dates themselves but the spans between them, or not the total profits but the averages across those spans.
Obviously it will be important to define Higher social class and I will not be surprised if here it is directly related to income. In that case I am not surprised but still it is good to really demonstrate the obvious.
Group selection may be defined as selection caused by the differential extinction or proliferation of groups. The socially polymorphic spider Anelosimus studiosus exhibits a behavioural polymorphism in which females exhibit either a ‘docile’ or ‘aggressive’ behavioural phenotype. Natural colonies are composed of a mixture of related docile and aggressive individuals, and populations differ in colonies’ characteristic docile:aggressive ratios. Using experimentally constructed colonies of known composition, here we demonstrate that population-level divergence in docile:aggressive ratios is driven by site-specific selection at the group level—certain ratios yield high survivorship at some sites but not others. Our data also indicate that colonies responded to the risk of extinction: perturbed colonies tended to adjust their composition over two generations to match the ratio characteristic of their native site, thus promoting their long-term survival in their natal habitat. However, colonies of displaced individuals continued to shift their compositions towards mixtures that would have promoted their survival had they remained at their home sites, regardless of their contemporary environment. Thus, the regulatory mechanisms that colonies use to adjust their composition appear to be locally adapted. Our data provide experimental evidence of group selection driving collective traits in wild populations.
Site-specific group selection drives locally adapted group compositions • Jonathan N. Pruitt & Charles J. Goodnight
Now the really interesting part would be of course to explain this emergent groups selection by gene selection. How do we define or, if you wish, describe the Evolutionary Stable Strategy that is behind this interesting phenomenon. What can we as human society learn from this?
Artificial intelligence: the next step in evolution? The Age American philosopher Daniel Dennett sums up the feelings of some scientists when suggesting that humans are immensely complex and able computational machines.
Arjen ten Have's insight:
Cool elaboration on AI. Quote:“When we start to design intelligent systems to include motives and the emotional signalling that accompanies them – and to use these as a reference standard against which perceived events and objects can be sorted, evaluated and organised – we’ll have made a major step towards achieving true machine intelligence.”
Research on human social interactions has traditionally relied on self-reports. Despite their widespread use, self-reported accounts of behaviour are prone to biases and necessarily reduce the range of behaviours, and the number of subjects, that may be studied simultaneously. The development of ever smaller sensors makes it possible to study group-level human behaviour in naturalistic settings outside research laboratories. We used such sensors, sociometers, to examine gender, talkativeness and interaction style in two different contexts. Here, we find that in the collaborative context, women were much more likely to be physically proximate to other women and were also significantly more talkative than men, especially in small groups. In contrast, there were no gender-based differences in the non-collaborative setting. Our results highlight the importance of objective measurement in the study of human behaviour, here enabling us to discern context specific, gender-based differences in interaction style.
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