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Brain blast: DIY attempts at electrical brain stimulation to improve cognition are to get easier

Buyer beware. For US$249 a company in the United States is promising to send curious and competitive players of computer games an unusual headset. The device, the company claims, will convert electronic gamers into electronic-gamers. At the touch of a button, the headset will send a surge of electricity through their prefrontal cortex. It promises to increase brain plasticity and make synapses fire faster, to help gamers repel more space invaders and raid more tombs. And, according to the publicity shots on the website, it comes in a choice of red or black.

 

The company is accepting orders, but says that it will not ship its first headsets to customers until next month. Some are unwilling to wait. Videos on the Internet already show people who have cobbled together their own version with a 9-volt battery and some electrical wire. If you are not fussy about the colour scheme, other online firms already promise to supply the components and instructions you need to make your own. Or you could rummage around in the garage.

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A Reappraisal of How to Build Modular, Reusable Models of Biological Systems

A Reappraisal of How to Build Modular, Reusable Models of Biological Systems | Social Foraging | Scoop.it
Biological researchers increasingly rely on computational models to integrate biological systems knowledge, test hypotheses, and forecast system behavior. The expanding size of these models requires solutions for managing their complexity. Modularity, a time-tested design principle for managing complexity, can be applied within the biological modeling field to parallelize work, automate composition, and promote effective model sharing. As modelers of complex biological systems, we aim to apply modular production to accelerate our efforts and have therefore investigated several currently available approaches for modular modeling. We argue that some traditional features of modularity, in particular the isolation of a module's contents from the rest of the system, can impede model sharing and composition when applied within the context of biological simulation. Alternative approaches that can automatically interface model components based on the biological meaning of their contents (their semantics) avoid these limitations. Our conclusions have strategic implications for the design of systems biology, synthetic biology, and integrated physiological modeling technologies, as well as community-level model curation efforts.
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Earl Miller on Biology of Consciousness: Bridging the Mind-Body Gap?

Earl Miller on Biology of Consciousness: Bridging the Mind-Body Gap? | Social Foraging | Scoop.it
The mind-body problem was first raised, rather circuitously, as a non-problem by Aristotle more than 2,000 years ago. He postulated that, "It is not necessary to ask whether soul and body are one, just as it is not necessary to ask whether the wax and its shape are one." Plato disagreed, as he thought that souls could transmigrate during reincarnation. But a more radical opponent to Aristotle's theory arose about two millenniums later in the person of René Descartes. He tried to rip Aristotle's theory apart. His philosophy introduced the notion of dualism, opposing "the surrounding spirits" that directed thoughts to the mechanism of the human body. Can today's advances in neurobiology help us make a decision on the matter?

A quick scroll through the names of biology laboratories worldwide -- from Connecticut to London, to Sataima, Japan -- might throw up names like "Molecular Psychiatry" or "Receptors and Cognition" suggesting they have begun to bridge the mind-body gap, long the preserve of philosophers. "What language does the brain use to generate consciousness?" Francis Crick, the scientist who discovered DNA, once asked. But have scientists really understood the real nature of consciousness? And if it's a language, why are its grammar and syntax not better understood? Scientists are looking to be the first to propose an unified theory of consciousness.

Research in the 1950s led scientists to call the brain, like the heart, a "battery" and a "hive." The examination of the brain has generally taken place along these lines. Electrophysiology (the discipline that examines the brain as a "battery") studies the electrical waves that flow between neurons, or ensembles of neurons, and Biochemistry (the "hive") approaches the brain's function through measurements of the interactions between brain molecules as they catalyze, regulate, replicate and destroy. Unfortunately no biochemical substrate have been proposed to correlate with consciousness yet. Only electrophysiological studies have proposed such correlates.

Current advances in Electrophysiology and Consciousness trace their way back to research done by Crick himself in the 1990s, his late research period. He and his colleague, Christof Koch, were the first to propose that visual awareness correlates with certain brain regions electrical waves. Using electroencephalography, a way of recording electrical activity along the scalp, they worked out that these waves were being fired at 40 Hertz (1). In other words, from all the electrical activity generated by the brain, they isolated a frequency -- 40 Hertz -- of the electrical waves involved in attention. These ways were called gamma waves.
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Google's Secretive DeepMind Start-up Unveils A "Neural Turing Machine"

Google's Secretive DeepMind Start-up Unveils A "Neural Turing Machine" | Social Foraging | Scoop.it
DeepMind has built a neural network that can access an external memory like a conventional Turing machine. The result is a computer that mimics the short-term memory of the human brain.

 

One of the great challenges of neuroscience is to understand the short-term working memory in the human brain. At the same time, computer scientists would dearly love to reproduce the same kind of memory in silico.

 

Today, Google’s secretive DeepMind start-up, which it bought for $400 million earlier this year, unveils a prototype computer that attempts to mimic some of the properties of the human brain’s short-term working memory. The new computer is a type of neural network that has been adapted to work with an external memory. The result is a computer that learns as it stores memories and can later retrieve them to perform logical tasks beyond those it has been trained to do.

 

DeepMind’s breakthrough follows a long history of work on short-term memory. In the 1950s, the American cognitive psychologist George Miller carried out one of the more famous experiments in the history of brain science. Miller was interested in the capacity of the human brain’s working memory and set out to measure it with the help of a large number of students who he asked to carry out simple memory tasks.

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Imitative Learning as a Connector of Collective Brains

Imitative Learning as a Connector of Collective Brains | Social Foraging | Scoop.it
The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent in computer science and business circles. Here we consider a primordial form of cooperation – imitative learning – that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain. In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem. An agent can either perform local random moves to explore the solution space of the problem or imitate a model agent – the best performing agent in its influence network. There is a trade-off between the number of agents and the imitation probability , and for the optimal balance between these parameters we observe a thirtyfold diminution in the computational cost to find the solution of the cryptarithmetic problem as compared with the independent search. If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group.
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A Low Dimensional Approach on Network Characterization

A Low Dimensional Approach on Network Characterization | Social Foraging | Scoop.it
In many applications, one may need to characterize a given network among a large set of base networks, and these networks are large in size and diverse in structure over the search space. In addition, the characterization algorithms are required to have low volatility and with a small circle of uncertainty. For large datasets, these algorithms are computationally intensive and inefficient. However, under the context of network mining, a major concern of some applications is speed. Hence, we are motivated to develop a fast characterization algorithm, which can be used to quickly construct a graph space for analysis purpose. Our approach is to transform a network characterization measure, commonly formulated based on similarity matrices, into simple vector form signatures. We shall show that the similarity matrix can be represented by a dyadic product of two N-dimensional signature vectors; thus the network alignment process, which is usually solved as an assignment problem, can be reduced into a simple alignment problem based on separate signature vectors.
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Wild chimpanzees plan their breakfast time, type, and location

Wild chimpanzees plan their breakfast time, type, and location | Social Foraging | Scoop.it

How do large-brained primates maintain high rates of energy intake when times are lean? By analyzing early-morning departure times and sleeping nest positioning of female chimpanzees as a function of the ephemerality of next day’s breakfast fruit and its location, we found evidence that wild chimpanzees flexibly plan when and where they will have breakfast after weighing multiple factors, such as the time of day, their egocentric distance to, and the type of food to be eaten. To our knowledge, our findings reveal the first clear example of a future-oriented cognitive mechanism by which hominoids, like great apes, can buffer the effect of seasonal declines in food availability and increased interspecific competition to facilitate first access to nutritious food.

Not all tropical fruits are equally desired by rainforest foragers and some fruit trees get depleted more quickly and carry fruit for shorter periods than others. We investigated whether a ripe-fruit specialist, the chimpanzee (Pan troglodytes verus), arrived earlier at breakfast sites with very ephemeral and highly sought-after fruit, like figs, than sites with less ephemeral fruit that can be more predictably obtained throughout the entire day. We recorded when and where five adult female chimpanzees spent the night and acquired food for a total of 275 full days during three fruit-scarce periods in a West African tropical rainforest. We found that chimpanzees left their sleeping nests earlier (often before sunrise when the forest is still dark) when breakfasting on very ephemeral fruits, especially when they were farther away. Moreover, the females positioned their sleeping nests more in the direction of the next day’s breakfast sites with ephemeral fruit compared with breakfast sites with other fruit. By analyzing departure times and nest positioning as a function of fruit type and location, while controlling for more parsimonious explanations, such as temperature, we found evidence that wild chimpanzees flexibly plan their breakfast time, type, and location after weighing multiple disparate pieces of information. Our study reveals a cognitive mechanism by which large-brained primates can buffer the effects of seasonal declines in food availability and increased interspecific competition to facilitate first access to nutritious food. We discuss the implications for theories on hominoid brain-size evolution.

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Algorithms, complexity, and the sciences

Algorithms, complexity, and the sciences | Social Foraging | Scoop.it
Algorithms, perhaps together with Moore’s law, compose the engine of the information technology revolution, whereas complexity—the antithesis of algorithms—is one of the deepest realms of mathematical investigation. After introducing the basic concepts of algorithms and complexity, and the fundamental complexity classes P (polynomial time) and NP (nondeterministic polynomial time, or search problems), we discuss briefly the P vs. NP problem. We then focus on certain classes between P and NP which capture important phenomena in the social and life sciences, namely the Nash equlibrium and other equilibria in economics and game theory, and certain processes in population genetics and evolution. Finally, an algorithm known as multiplicative weights update (MWU) provides an algorithmic interpretation of the evolution of allele frequencies in a population under sex and weak selection. All three of these equivalences are rife with domain-specific implications: The concept of Nash equilibrium may be less universal—and therefore less compelling—than has been presumed; selection on gene interactions may entail the maintenance of genetic variation for longer periods than selection on single alleles predicts; whereas MWU can be shown to maximize, for each gene, a convex combination of the gene’s cumulative fitness in the population and the entropy of the allele distribution, an insight that may be pertinent to the maintenance of variation in evolution.
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Dynamic population mapping using mobile phone data

Dynamic population mapping using mobile phone data | Social Foraging | Scoop.it
During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.
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The Three Breakthroughs That Have Finally Unleashed AI on the World

The Three Breakthroughs That Have Finally Unleashed AI on the World | Social Foraging | Scoop.it
A few months ago I made the trek to the sylvan campus of the IBM research labs in Yorktown Heights, New York, to catch an early glimpse of the fast-arriving, long-overdue future of artificial intelligence. This was the home of Watson, the electronic genius that conquered Jeopardy! in 2011. The original Watson is still here—it's about the size of a bedroom, with 10 upright, refrigerator-shaped machines forming the four walls. The tiny interior cavity gives technicians access to the jumble of wires and cables on the machines' backs. It is surprisingly warm inside, as if the cluster were alive.

Today's Watson is very different. It no longer exists solely within a wall of cabinets but is spread across a cloud of open-standard servers that run several hundred “instances” of the AI at once. Like all things cloudy, Watson is served to simultaneous customers anywhere in the world, who can access it using their phones, their desktops, or their own data servers. This kind of AI can be scaled up or down on demand. Because AI improves as people use it, Watson is always getting smarter; anything it learns in one instance can be immediately transferred to the others. And instead of one single program, it's an aggregation of diverse software engines—its logic-deduction engine and its language-parsing engine might operate on different code, on different chips, in different locations—all cleverly integrated into a unified stream of intelligence.
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Google Teaming up with Oxford University on Artificial Intelligence

Google Teaming up with Oxford University on Artificial Intelligence | Social Foraging | Scoop.it

It is a really exciting time for Artificial Intelligence research these days, and progress is being made on many fronts including image recognition and natural language understanding. Today we are delighted to announce a partnership with Oxford University to accelerate Google’s research efforts in these areas. 


Google DeepMind will be working with two of Oxford’s cutting edge Artificial Intelligence research teams. Prof Nando de Freitas, Prof Phil Blunsom, Dr Edward Grefenstette and Dr Karl Moritz Hermann, who teamed up earlier this year to co-found Dark Blue Labs, are four world leading experts in the use of deep learning for natural language understanding. They will be spearheading efforts to enable machines to better understand what users are saying to them.

Also joining the DeepMind team will be Dr Karen Simonyan, Max Jaderberg and Prof Andrew Zisserman, one of the world’s foremost experts on computer vision systems, a Fellow of the Royal Society, and the only person to have been awarded the prestigious Marr Prize three times. As co-founders of Vision Factory, their aim was to improve visual recognition systems using deep learning. Dr Simonyan and Prof Zisserman developed one of the winning systems at the recent 2014 ImageNet competition, which is regarded as the most competitive and prestigious image recognition contest in the world.

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Time-variant clustering model for understanding cell fate decisions

Time-variant clustering model for understanding cell fate decisions | Social Foraging | Scoop.it
Both spatial characteristics and temporal features are often the subjects of concern in physical, social, and biological studies. This work tackles the clustering problems for time course data in which the cluster number and clustering structure change with respect to time, dubbed time-variant clustering. We developed a hierarchical model that simultaneously clusters the objects at every time point and describes the relationships of the clusters between time points. The hidden layer of this model is a generalized form of branching processes. A reversible-jump Markov Chain Monte Carlo method was implemented for model inference, and a feature selection procedure was developed. We applied this method to explore an open question in preimplantation embryonic development. Our analyses using single-cell gene expression data suggested that the earliest cell fate decision could start at the 4-cell stage in mice, earlier than the commonly thought 8- to 16-cell stage. These results together with independent experimental data from single-cell RNA-seq provided support against a prevailing hypothesis in mammalian development.
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Local and global epidemic outbreaks in populations moving in inhomogeneous environments

Local and global epidemic outbreaks in populations moving in inhomogeneous environments | Social Foraging | Scoop.it
We study disease spreading in a system of agents moving in a space where the force of infection is not homogeneous. Agents are random walkers that additionally execute long-distance jumps, and the plane in which they move is divided into two regions where the force of infection takes different values. We show the onset of a local epidemic threshold and a global one and explain them in terms of mean-field approximations. We also elucidate the critical role of the agent velocity, jump probability, and density parameters in achieving the conditions for local and global outbreaks. Finally, we show that the results are independent of the specific microscopic rules adopted for agent motion, since a similar behavior is also observed for the distribution of agent velocity based on a truncated power law, which is a model often used to fit real data on motion patterns of animals and humans.
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With Mindware Upgrades and Cognitive Prosthetics, Humans Are Already Technological Animals

With Mindware Upgrades and Cognitive Prosthetics, Humans Are Already Technological Animals | Social Foraging | Scoop.it
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.
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Using synchronous Boolean networks to model several phenomena of collective behavior

In this paper, we propose an approach for modeling and analysis of a number of phenomena of collective behavior. By collectives we mean multi-agent systems that transition from one state to another at discrete moments of time. The behavior of a member of a collective (agent) is called conforming if the opinion of this agent at current time moment conforms to the opinion of some other agents at the previous time moment. We presume that at each moment of time every agent makes a decision by choosing from the set {0,1} (where 1-decision corresponds to action and 0-decision corresponds to inaction). In our approach we model collective behavior with synchronous Boolean networks. We presume that in a network there can be agents that act at every moment of time. Such agents are called instigators. Also there can be agents that never act. Such agents are called loyalists. Agents that are neither instigators nor loyalists are called simple agents. We study two combinatorial problems. The first problem is to find a disposition of instigators that in several time moments transforms a network from a state where a majority of simple agents are inactive to a state with a majority of active agents. The second problem is to find a disposition of loyalists that returns the network to a state with a majority of inactive agents. Similar problems are studied for networks in which simple agents demonstrate the contrary to conforming behavior that we call anticonforming. We obtained several theoretical results regarding the behavior of collectives of agents with conforming or anticonforming behavior. In computational experiments we solved the described problems for randomly generated networks with several hundred vertices. We reduced corresponding combinatorial problems to the Boolean satisfiability problem (SAT) and used modern SAT solvers to solve the instances obtained.
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Ant Behavior Might Shed Insight on Problems Facing Electronics Design

Ant Behavior Might Shed Insight on Problems Facing Electronics Design | Social Foraging | Scoop.it
Michael Hsiao plans to harness swarm intelligence based on the efficient behavior of ants.
Why would this matter?
Ant colonies are known for their efficiency in finding the best route to food sources. So Hsiao, professor of electrical and computer engineering at Virginia Tech, and an expert in design verification has tackled one of the major problems facing electronics design in a novel way.
He has developed mathematical formulas that simulate the methods used by the ants when they are seeking nourishment.
Hsiao plans to use these algorithms to improve the accuracy in electronics design when one needs to validate and verify that the design meets the spec.
The National Science Foundation has awarded him a grant of $418,345 to continue exploring his ideas.
Hsiao explained that as electronics designers add more features and capabilities into ever-smaller electronics hardware, such as the latest versions of cell phones, they are increasing the difficulty of verifying that their designs perform as planned.
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Network Theory Reveals The Hidden Link Between Trade And Military Alliances That Leads to Conflict-Free Stability

Network Theory Reveals The Hidden Link Between Trade And Military Alliances That Leads to Conflict-Free Stability | Social Foraging | Scoop.it
The study of modern history is currently undergoing a revolution. That is largely because historians are beginning to apply the ideas in network theory to the complex interactions that have forged our past.

There was a time when historians focused largely on events as the be all and end all of history. But in recent years, there has been a growing understanding that a complex network of links, alliances, trade agreements and so on play a hugely important role in creating an environment in which conflict (or peace) can spread.

An interesting open question in this regard is whether certain kinds of networks exist that are stable against the outbreak of war. Today, we get an answer thanks to the work of Matthew Jackson and Stephen Nei at Stanford University in California. These guys combine network theory and game theory to study the stability of different kinds of networks based on real-world data.

In particular, Jackson and Nai study the theoretical properties of networks consisting of countries that have military links and compared them to the properties of networks in which countries have both military and trade links.

Finally, they apply real data to their model. They combine international trade data with the well-known “Correlates of War” database to see how closely their predictions match those of real networks.
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Reputation and Competition in a Hidden Action Model

Reputation and Competition in a Hidden Action Model | Social Foraging | Scoop.it

The economics models of reputation and quality in markets can be classified in three categories. (i) Pure hidden action, where only one type of seller is present who can provide goods of different quality. (ii) Pure hidden information, where sellers of different types have no control over product quality. (iii) Mixed frameworks, which include both hidden action and hidden information. In this paper we develop a pure hidden action model of reputation and Bertrand competition, where consumers and firms interact repeatedly in a market with free entry. The price of the good produced by the firms is contractible, whilst the quality is noncontractible, hence it is promised by the firms when a contract is signed. Consumers infer future quality from all available information, i.e., both from what they know about past quality and from current prices. According to early contributions, competition should make reputation unable to induce the production of high-quality goods. We provide a simple solution to this problem by showing that high quality levels are sustained as an outcome of a stationary symmetric equilibrium.

 
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Effects of Infection on Honey Bee Population Dynamics: A Model

Effects of Infection on Honey Bee Population Dynamics: A Model | Social Foraging | Scoop.it
We propose a model that combines the dynamics of the spread of disease within a bee colony with the underlying demographic dynamics of the colony to determine the ultimate fate of the colony under different scenarios. The model suggests that key factors in the survival or collapse of a honey bee colony in the face of an infection are the rate of transmission of the infection and the disease-induced death rate. An increase in the disease-induced death rate, which can be thought of as an increase in the severity of the disease, may actually help the colony overcome the disease and survive through winter. By contrast, an increase in the transmission rate, which means that bees are being infected at an earlier age, has a drastic deleterious effect. Another important finding relates to the timing of infection in relation to the onset of winter, indicating that in a time interval of approximately 20 days before the onset of winter the colony is most affected by the onset of infection. The results suggest further that the age of recruitment of hive bees to foraging duties is a good early marker for the survival or collapse of a honey bee colony in the face of infection, which is consistent with experimental evidence but the model provides insight into the underlying mechanisms. The most important result of the study is a clear distinction between an exposure of the honey bee colony to an environmental hazard such as pesticides or insecticides, or an exposure to an infectious disease. The results indicate unequivocally that in the scenarios that we have examined, and perhaps more generally, an infectious disease is far more hazardous to the survival of a bee colony than an environmental hazard that causes an equal death rate in foraging bees.
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Cooperation and control in multiplayer social dilemmas

Cooperation and control in multiplayer social dilemmas | Social Foraging | Scoop.it

Many of the world’s most pressing problems, like the prevention of climate change, have the form of a large-scale social dilemma with numerous involved players. Previous results in evolutionary game theory suggest that multiplayer dilemmas make it particularly difficult to achieve mutual cooperation because of the lack of individual control in large groups. Herein, we extend the theory of zero-determinant strategies to multiplayer games to describe which strategies maintain cooperation. Moreover, we propose two simple models of alliances in multiplayer dilemmas. The effect of these alliances is determined by their size, the strategy of the allies, and the properties of the social dilemma. When a single individual’s strategic options are limited, forming an alliance can result in a drastic leverage.

Direct reciprocity and conditional cooperation are important mechanisms to prevent free riding in social dilemmas. However, in large groups, these mechanisms may become ineffective because they require single individuals to have a substantial influence on their peers. However, the recent discovery of zero-determinant strategies in the iterated prisoner’s dilemma suggests that we may have underestimated the degree of control that a single player can exert. Here, we develop a theory for zero-determinant strategies for iterated multiplayer social dilemmas, with any number of involved players. We distinguish several particularly interesting subclasses of strategies: fair strategies ensure that the own payoff matches the average payoff of the group; extortionate strategies allow a player to perform above average; and generous strategies let a player perform below average. We use this theory to describe strategies that sustain cooperation, including generalized variants of Tit-for-Tat and Win-Stay Lose-Shift. Moreover, we explore two models that show how individuals can further enhance their strategic options by coordinating their play with others. Our results highlight the importance of individual control and coordination to succeed in large groups.

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Direct and indirect punishment among strangers in the field

Direct and indirect punishment among strangers in the field | Social Foraging | Scoop.it
Why do humans cooperate in one-time interactions with strangers? The most prominent explanations for this long-standing puzzle rely on punishment of noncooperators, but differ in the form punishment takes. In models of direct punishment, noncooperators are punished directly at personal cost, whereas indirect reciprocity assumes that punishment is indirect by withholding rewards. To resolve the persistent debate on which model better explains cooperation, we conduct the first field experiment, to our knowledge, on direct and indirect punishment among strangers in real-life interactions. We show that many people punish noncooperators directly but prefer punishing indirectly by withholding help when possible. The occurrence of direct and indirect punishment in the field shows that both are key to understanding the evolution of human cooperation.

 

Many interactions in modern human societies are among strangers. Explaining cooperation in such interactions is challenging. The two most prominent explanations critically depend on individuals’ willingness to punish defectors: In models of direct punishment, individuals punish antisocial behavior at a personal cost, whereas in models of indirect reciprocity, they punish indirectly by withholding rewards. We investigate these competing explanations in a field experiment with real-life interactions among strangers. We find clear evidence of both direct and indirect punishment. Direct punishment is not rewarded by strangers and, in line with models of indirect reciprocity, is crowded out by indirect punishment opportunities. The existence of direct and indirect punishment in daily life indicates the importance of both means for understanding the evolution of cooperation.

 
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A simple theoretical model goes a long way in explaining complex behavior in protein folding

A simple theoretical model goes a long way in explaining complex behavior in protein folding | Social Foraging | Scoop.it
Understanding how natural proteins fold spontaneously onto their specific, biologically functional 3D structures is both a fascinating fundamental problem in modern biochemistry and a necessary step toward developing technologies for protein engineering and designing protein-based nanodevices. One of the limitations that scientists working in this area have encountered in the past, however, has been the difficulty in connecting analytical theory to experimental results. For a long time experimentalists could not use theory to interpret their results. Theoretical predictions, moreover, were not amenable to experimental testing. Such limitations have been progressively eliminated by the combination of key theoretical concepts, improved simulations, and new experiments and their detailed quantitative analysis with simple statistical mechanical models. The work of Inanami et al. in PNAS (1) provides a remarkable example of how powerful these simple theoretical models can be in explaining the complexities and nuances of protein folding reactions.
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Prediction in R using Ridge Regression

Prediction in R using Ridge Regression | Social Foraging | Scoop.it
Ridge regression is a regularization method, where the coefficients are shrank with the purpose of reducing the variance of the solution and therefore improving prediction accuracy. Below we will implement ridge regression on the longley and prostate data sets using two methods: the lm.ridge() function and  the linearRidge() function. Pay special attention to the scaling of the coefficients and the offseting of the predicted values for the lm.ridge() function.
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A Must-Know for Data Scientists: Hyperloglog Algorithm

A Must-Know for Data Scientists: Hyperloglog Algorithm | Social Foraging | Scoop.it
As mentioned in Dave’s blog post, being able to act on insights from mobile data in real time is key to mobile data management. Before we think about fancy things like building a data pipeline that delivers predictive insights in real time, you need to focus on some basic but critical statistics of your mobile apps. In this blog post, I will review a powerful streaming algorithm, Hyperloglog (HLL), and discuss how it helps mParticle deliver real-time analytics products.
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The psychology of Web design: How colors, typefaces and spacing affect your mood

The psychology of Web design: How colors, typefaces and spacing affect your mood | Social Foraging | Scoop.it

A great website design is so much more than just delivering content and making it look good. When visitors come to your site, they produce a set of feelings about your website and your organization. The type of feelings they produce – positive or negative – are entirely in your hands and should not be overlooked when designing content.

Over the years, there has been a body of knowledge produced to help designers create effective visuals that play into the psychology of their viewers. In order to achieve this, one must understand how different design elements and how we use them affect the mood, attitude and experience the visitor will have while browsing our website.

Below are four major areas of website design and development that have the biggest impacts on the psychology of website visitors. These are the tools you’ll need to create a visually-engaging site that encourages visitors to return.

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Form of an evolutionary tradeoff affects eco-evolutionary dynamics in a predator–prey system

Form of an evolutionary tradeoff affects eco-evolutionary dynamics in a predator–prey system | Social Foraging | Scoop.it
Evolution on a time scale similar to ecological dynamics has been increasingly recognized for the last three decades. Selection mediated by ecological interactions can change heritable phenotypic variation (i.e., evolution), and evolution of traits, in turn, can affect ecological interactions. Hence, ecological and evolutionary dynamics can be tightly linked and important to predict future dynamics, but our understanding of eco-evolutionary dynamics is still in its infancy and there is a significant gap between theoretical predictions and empirical tests. Empirical studies have demonstrated that the presence of genetic variation can dramatically change ecological dynamics, whereas theoretical studies predict that eco-evolutionary dynamics depend on the details of the genetic variation, such as the form of a tradeoff among genotypes, which can be more important than the presence or absence of the genetic variation. Using a predator–prey (rotifer–algal) experimental system in laboratory microcosms, we studied how different forms of a tradeoff between prey defense and growth affect eco-evolutionary dynamics. Our experimental results show for the first time to our knowledge that different forms of the tradeoff produce remarkably divergent eco-evolutionary dynamics, including near fixation, near extinction, and coexistence of algal genotypes, with quantitatively different population dynamics. A mathematical model, parameterized from completely independent experiments, explains the observed dynamics. The results suggest that knowing the details of heritable trait variation and covariation within a population is essential for understanding how evolution and ecology will interact and what form of eco-evolutionary dynamics will result.
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