The evolutionary emergence of the egalitarian syndrome is one of the most intriguing unsolved puzzles related to the origins of modern humans. Standard explanations and models for cooperation and altruism—reciprocity, kin and group selection, and punishment—are not directly applicable to the emergence of egalitarian behavior in hierarchically organized groups that characterized the social life of our ancestors. Here I study an evolutionary model of group-living individuals competing for resources and reproductive success. In the model, the differences in fighting abilities lead to the emergence of hierarchies where stronger individuals take away resources from weaker individuals and, as a result, have higher reproductive success. (...)
On the evolutionary origins of the egalitarian syndrome Sergey Gavrilets
The emerging field of computational social science is attracting mathematically inclined scientists in ever-increasing numbers. This, in turn, is spurring the creation of academic departments and prompting companies such as the social-network giant Facebook, (...) to establish research teams to understand the structure of their networks and how information spreads across them.
Researchers are exploring links between evolution and ecology in a number of different settings, documenting interconnections that extend down to genetic changes. One notable example focuses on how the alewife, a fish that lives in lakes in eastern North America, shapes and is shaped by its freshwater ecosystem. The researchers have shown how these so-called eco-evo effects can ripple across a food web in unexpected ways.
Eco-Evo Effects Up and Down the Food Chain Elizabeth Pennisi
Whether or not to change strategy depends not only on the personal success of each individual, but also on the success of others. Using this as motivation, we study the evolution of cooperation in games that describe social dilemmas, where the propensity to adopt a different strategy depends both on individual fitness as well as on the strategies of neighbors. Regardless of whether the evolutionary process is governed by pairwise or group interactions, we show that plugging into the "wisdom of groups" strongly promotes cooperative behavior.(...)
Wisdom of groups promotes cooperation in evolutionary social dilemmas
The uncanny valley—the unnerving nature of humanlike robots—is an intriguing idea, but both its existence and its underlying cause are debated. We propose that humanlike robots are not only unnerving, but are so because their appearance prompts attributions of mind. In particular, we suggest that machines become unnerving when people ascribe to them experience (the capacity to feel and sense), rather than agency (the capacity to act and do). Experiment 1 examined whether a machine’s humanlike appearance prompts both ascriptions of experience and feelings of unease. Experiment 2 tested whether a machine capable of experience remains unnerving, even without a humanlike appearance. Experiment 3 investigated whether the perceived lack of experience can also help explain the creepiness of unfeeling humans and philosophical zombies. These experiments demonstrate that feelings of uncanniness are tied to perceptions of experience, and also suggest that experience—but not agency—is seen as fundamental to humans, and fundamentally lacking in machines.
Feeling robots and human zombies: Mind perception and the uncanny valley Kurt Gray, Daniel M. Wegner
Cognition Volume 125, Issue 1, October 2012, Pages 125–130
Russian scientists are closer than they have ever been to creating artificial intelligence. The program called “Eugene” has almost passed the famous Turing test, which checks a machine’s ability to exhibit intelligent behavior.
The program-emulating a personality of a 13-year old boy was exhibited at an international science contest in the United Kingdom along with four other programs.
Even with the exacting criteria, “Eugene” has left all its competitors far behind.
The test was designed by mathematician and computer scientist, Alan Turing over 60 years ago. During the examination a human judge engages in a text conversation with a machine and an actual human being without seeing them. If the judge fails to tell the machine from the human in at least 30 percent of the answers, the program passes.
So far no program has managed to pass successfully but Russia’s “Eugene” has come strikingly close. It deceived human judges in 29,2 percent of the answers.
A total of 29 judges took part in the test with some 150 dialogues taking place.
Scientists at the Max Planck Institute for Dynamics and Self-Organization in Göttingen have developed an entirely new principle for information processing. The complex network computer now stands as an alternative to the other possibilities in data processing - such as the conventional computer or the quantum computer. The fundamental requirement is a system, for instance a laser, with oscillating elements that can interact with one another. The researchers were able to demonstrate that the characteristic dynamics of such a system can be cleverly harnessed to perform the full range of logical operations. The complex network computer can even perform some tasks, such as the coarse sorting of numbers, considerably faster than conventional computers. Furthermore, the researchers have managed to take a first step in programming a robot according to the new principle.
Access to clean, affordable and reliable energy has been a cornerstone of the world's increasing prosperity and economic growth since the beginning of the industrial revolution. Our use of energy in the twenty–first century must also be sustainable. Solar and water–based energy generation, and engineering of microbes to produce biofuels are a few examples of the alternatives. This Perspective puts these opportunities into a larger context by relating them to a number of aspects in the transportation and electricity generation sectors. It also provides a snapshot of the current energy landscape and discusses several research and development opportunities and pathways that could lead to a prosperous, sustainable and secure energy future for the world.
Opportunities and challenges for a sustainable energy future
Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.
A Whole-Cell Computational Model Predicts Phenotype from Genotype Jonathan R. Karr, Jayodita C. Sanghvi, Derek N. Macklin, Miriam V. Gutschow, Jared M. Jacobs, Benjamin Bolival Jr., Nacyra Assad-Garcia, John I. Glass, Markus W. Covert
Cell Volume 150, Issue 2, 20 July 2012, Pages 389–401
Complex software systems are among most sophisticated human-made systems, yet only little is known about the actual structure of 'good' software. We here study different software systems developed in Java from the perspective of network science. The study reveals that network theory can provide a prominent set of techniques for the exploratory analysis of large complex software system. We further identify several applications in software engineering, and propose different network-based quality indicators that address software design, efficiency, reusability, vulnerability, controllability and other. We also highlight various interesting findings, e.g., software systems are highly vulnerable to processes like bug propagation, however, they are not easily controllable.
Software systems through complex networks science: Review, analysis and applications
When the full stock of a new product is quickly sold in a few days or weeks, one has the impression that new technologies develop and conquer the market in a very easy way. This may be true for some new technologies, for example the cell phone, but not for others, like the blue-ray. Novelty, usefulness, advertising, price, and fashion are the driving forces behind the adoption of a new product. But, what are the key factors that lead to adopt a new technology? In this paper we propose and investigate a simple model for the adoption of an innovation which depends mainly on three elements: the appeal of the novelty, the inertia or resistance to adopt it, and the interaction with other agents. Social interactions are taken into account in two ways: by imitation and by differentiation, i.e., some agents will be inclined to adopt an innovation if many people do the same, but other will act in the opposite direction, trying to differentiate from the "herd". We determine the conditions for a successful implantation of the new technology, by considering the strength of advertising and the effect of social interactions. We find a balance between the advertising and the number of anti-herding agents that may block the adoption of a new product. We also compare the effect of social interactions, when agents take into account the behavior of the whole society or just a part of it. In a nutshell, the present model reproduces qualitatively the available data on adoption of innovation.
A collaboration between a Stanford ant biologist and a computer scientist has revealed that the behavior of harvester ants as they forage for food mirrors the protocols that control traffic on the Internet. By Bjorn Carey On the surface, ants and the Internet don't seem to have much in common. But two Stanford researchers have discovered that a species of harvester ants determine how many foragers to send out of the nest in much the same way that Internet protocols discover how much bandwidth is available for the transfer of data. The researchers are calling it the "anternet."
Deborah Gordon, a biology professor at Stanford, has been studying ants for more than 20 years. When she figured out how the harvester ant colonies she had been observing in Arizona decided when to send out more ants to get food, she called across campus to Balaji Prabhakar, a professor of computer science at Stanford and an expert on how files are transferred on a computer network. At first he didn't see any overlap between his and Gordon's work, but inspiration would soon strike.
Sasai has impressed many researchers with his green-fingered talent for coaxing neural stem cells to grow into elaborate structures. As well as the optic cup, he has cultivated the delicate tissue layers of the cerebral cortex and a rudimentary, hormone-making pituitary gland. He is now well on the way to growing a cerebellum — the brain structure that coordinates movement and balance.
(...) a study published in Nature finds that the age at which a father sires children determines how many mutations those offspring inherit2. By starting families in their thirties, forties and beyond, men could be increasing the chances that their children will develop autism, schizophrenia and other diseases often linked to new mutations.
Fathers bequeath more mutations as they age Genome study may explain links between paternal age and conditions such as autism.
One important question for science and society is how to best promote scientific progress. Inspired by the great success of Hilbert's famous set of problems, the FuturICT project tries to stimulate and focus the efforts of many scientists by formulating Grand Challenges, i.e. a set of fundamental, relevant and hardly solvable scientific questions.
Accelerating Scientific Discovery by Formulating Grand Scientific Challenges
In this paper, the study of epidemic spreading of mobile individuals on networks focuses on the system in which each node of the network may be occupied by either one individual or a void, and each individual could move to a neighbour void node. It is found that for the susceptible-infected-susceptible (SIS) model, the diffusion increases the epidemic threshold for arbitrary heterogeneous networks having the degree fluctuations, and the diffusion doesn’t affect the epidemic threshold for regular random networks. In the SI model, the diffusion suppresses the epidemic spread at the early outbreak stage, which indicates that the growth time scale of outbreaks is monotonically increasing with diffusion rate d. The heterogeneous mean-field analysis is in good agreement with the numerical simulations on annealed networks.
Role of diffusion in an epidemic model of mobile individuals on networks
Digital information is accumulating at an astounding rate, straining our ability to store and archive it. DNA is among the most dense and stable information media known. The development of new technologies in both DNA synthesis and sequencing make DNA an increasingly feasible digital storage medium. Here, we develop a strategy to encode arbitrary digital information in DNA, write a 5.27-megabit book using DNA microchips, and read the book using next-generation DNA sequencing.
Next-Generation Digital Information Storage in DNA George M. Church, Yuan Gao, Sriram Kosuri
Synthetic systems cannot easily mimic the color-changing abilities of animals such as cephalopods. Soft machines—machines fabricated from soft polymers and flexible reinforcing sheets—are rapidly increasing in functionality. This manuscript describes simple microfluidic networks that can change the color, contrast, pattern, apparent shape, luminescence, and surface temperature of soft machines for camouflage and display. The color of these microfluidic networks can be changed simultaneously in the visible and infrared—a capability that organisms do not have. These strategies begin to imitate the functions, although not the anatomies, of color-changing animals.
Camouflage and Display for Soft Machines Stephen A. Morin, Robert F. Shepherd, Sen Wai Kwok, Adam A. Stokes, Alex Nemiroski, George M. Whitesides
Human communication in social networks is dominated by emergent statistical laws such as non-trivial correlations and temporal clustering. Recently, we found long-term correlations in the user's activity in social communities. Here, we extend this work to study the collective behavior of the whole community with the goal of understanding the origin of clustering and long-term persistence. At the individual level, we find that the correlations in activity are a byproduct of the clustering expressed in the power-law distribution of inter-event times of single users, i.e. short periods of many events are separated by long periods of no events. On the contrary, the activity of the whole community presents long-term correlations that are a true emergent property of the system, i.e. they are not related to the distribution of inter-event times. This result suggests the existence of collective behavior, possibly arising from nontrivial communication patterns through the embedding social network.
Communication activity in a social network: relation between long-term correlations and inter-event clustering
Diego Rybski, Sergey V. Buldyrev, Shlomo Havlin, Fredrik Liljeros & Hernán A. Makse
We conduct a brief survey on Wolfram's classification, in particular related to the computing capabilities of Cellular Automata (CA) in Wolfram's classes III and IV. We formulate and shed light on the question of whether Class III systems are capable of Turing universality or may turn out to be "too hot" in practice to be controlled and programmed. We show that systems in Class III are indeed capable of computation and that there is no reason to believe that they are unable, in principle, to reach Turing-completness.
Wolfram's Classification and Computation in Cellular Automata Classes III and IV
Genaro J. Martinez, J. C. Seck-Touh-Mora, Hector Zenil
Network structure is a product of both its topology and interactions between its nodes. We explore this claim using the paradigm of distributed synchronization in a network of coupled oscillators. As the network evolves to a global steady state, nodes synchronize in stages, revealing the network's underlying community structure. Traditional models of synchronization assume that interactions between nodes are mediated by a conservative process similar to diffusion. However, social and biological processes are often nonconservative. We propose a model of synchronization in a network of oscillators coupled via nonconservative processes. We study the dynamics of synchronization of a synthetic and real-world networks and show that the traditional and nonconservative models of synchronization reveal different structures within the same network.
"Network structure, topology, and dynamics in generalized models of synchronization"