(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.
We investigate the emergence and persistence of communities through a recently proposed mechanism of adaptive rewiring in coevolutionary networks. We characterize the topological structures arising in a coevolutionary network subject to an adaptive rewiring process and a node dynamics given by a simple voterlike rule. We find that, for some values of the parameters describing the adaptive rewiring process, a community structure emerges on a connected network. We show that the emergence of communities is associated to a decrease in the number of active links in the system, i.e. links that connect two nodes in different states. The lifetime of the community structure state scales exponentially with the size of the system. Additionally, we find that a small noise in the node dynamics can sustain a diversity of states and a community structure in time in a finite size system. Thus, large system size and/or local noise can explain the persistence of communities and diversity in many real systems.
Emergence and persistence of communities in coevolutionary networks J. C. González-Avella, M. G. Cosenza, J. L. Herrera, K. Tucci
Evolutionary biologists have long thought that lying ought to destroy societies. Now computational anthropologists have shown that nothing could be further from the truth.
Everybody learns as a child that lying is wrong. We all learn something else too—that some kinds of lies are worse than others. What’s more, certain kinds of fibs—so-called white lies– are actually quite acceptable, even necessary at times.
Consequently, humans become sophisticated liars. Indeed, various studies have shown that we lie all the time, perhaps as often as twice a day on average.
It’s easy to see how lying reduces the level of trust between individuals and so threatens the stability of societies. So how do societies survive all this lying?
That’s something of a puzzle for evolutionary biologists. The very fact that lying is so prevalent in human society suggests that it might offer some kind of evolutionary advantage. In other words, we all benefit from lying in some way. But how?
Today, we get an answer thanks to the work of Gerardo Iñiguez at Aalto University in Finland and a few pals (including Robin Dunbar, an anthropologist from the University of Oxford of Dunbar’s number fame). These guys have simulated the effect that lies have on the strength of connections that exist within a social network.
But they’ve added fascinating twist. These guys have made a clear distinction between lies that benefit the person being lied to versus lies that benefit the person doing the lying. In other words, their model captures the difference between “white” lies, which are prosocial, and “black” lies, which are antisocial.
Interesting although it holds no real surprises, this is one of the things that just needed confirmation. Obviously white and black lies both have their explanation in evolution. A white lie helps you because you help the other, the black lie because you don't. Is to so hard or strange to understand? Fun stuff though!
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.
Overexploitation of renewable resources today has a high cost on the welfare of future generations. Unlike in other public goods games, however, future generations cannot reciprocate actions made today. What mechanisms can maintain cooperation with the future? To answer this question, we devise a new experimental paradigm, the /`Intergenerational Goods Game/'. A line-up of successive groups (generations) can each either extract a resource to exhaustion or leave something for the next group. Exhausting the resource maximizes the payoff for the present generation, but leaves all future generations empty-handed. Here we show that the resource is almost always destroyed if extraction decisions are made individually. This failure to cooperate with the future is driven primarily by a minority of individuals who extract far more than what is sustainable. In contrast, when extractions are democratically decided by vote, the resource is consistently sustained. Voting is effective for two reasons. First, it allows a majority of cooperators to restrain defectors. Second, it reassures conditional cooperators that their efforts are not futile. Voting, however, only promotes sustainability if it is binding for all involved. Our results have implications for policy interventions designed to sustain intergenerational public goods.
Cooperating with the future Oliver P. Hauser, David G. Rand, Alexander Peysakhovich & Martin A. Nowak
Evolutionary Robotics is a field that “aims to apply evolutionary computation techniques to evolve the overall design or controllers, or both, for real and simulated autonomous robots” (Vargas et al., 2014). This approach is “useful both for investigating the design space of robotic applications and for testing scientific hypotheses of biological mechanisms and processes” (Floreano et al., 2008). However, as noted in Bongard (2013) “the use of metaheuristics (i.e., evolution) sets this subfield of robotics apart from the mainstream of robotics research,” which “aims to continuously generate better behavior for a given robot, while the long-term goal of Evolutionary Robotics is to create general, robot-generating algorithms.”
Many hostile scenarios exist in real-life situations, where cooperation is disfavored and the collective behavior needs intervention for system efficiency improvement. Towards this end, the framework of soft control provides a powerful tool by introducing controllable agents called shills, who are allowed to follow well-designed updating rules for varying missions. Inspired by swarm intelligence emerging from flocks of birds, we explore here the dependence of the evolution of cooperation on soft control by an evolutionary iterated prisoner's dilemma (IPD) game staged on square lattices, where the shills adopt a particle swarm optimization (PSO) mechanism for strategy updating. We demonstrate that not only can cooperation be promoted by shills effectively seeking for potentially better strategies and spreading them to others, but also the frequency of cooperation could be arbitrarily controlled by choosing appropriate parameter settings. Moreover, we show that adding more shills does not contribute to further cooperation promotion, while assigning higher weights to the collective knowledge for strategy updating proves a efficient way to induce cooperative behavior. Our research provides insights into cooperation evolution in the presence of PSO-inspired shills and we hope it will be inspirational for future studies focusing on swarm intelligence based soft control.
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