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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Sensory Information and Encounter Rates of Interacting Species

Sensory Information and Encounter Rates of Interacting Species | Social Foraging | Scoop.it

Most motile organisms use sensory cues when searching for resources, mates, or prey. The searcher measures sensory data and adjusts its search behavior based on those data. Yet, classical models of species encounter rates assume that searchers move independently of their targets. This assumption leads to the familiar mass action-like encounter rate kinetics typically used in modeling species interactions. Here we show that this common approach can mischaracterize encounter rate kinetics if searchers use sensory information to search actively for targets. We use the example of predator-prey interactions to illustrate that predators capable of long-distance directional sensing can encounter prey at a rate proportional to prey density to the power (where is the dimension of the environment) when prey density is low. Similar anomalous encounter rate functions emerge even when predators pursue prey using only noisy, directionless signals. Thus, in both the high-information extreme of long-distance directional sensing, and the low-information extreme of noisy non-directional sensing, encounter rate kinetics differ qualitatively from those derived by classic theory of species interactions. Using a standard model of predator-prey population dynamics, we show that the new encounter rate kinetics derived here can change the outcome of species interactions. Our results demonstrate how the use of sensory information can alter the rates and outcomes of physical interactions in biological systems.

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Stochasticity, Bistability and the Wisdom of Crowds: A Model for Associative Learning in Genetic Regulatory Networks

Stochasticity, Bistability and the Wisdom of Crowds: A Model for Associative Learning in Genetic Regulatory Networks | Social Foraging | Scoop.it

It is generally believed that associative memory in the brain depends on multistable synaptic dynamics, which enable the synapses to maintain their value for extended periods of time. However, multistable dynamics are not restricted to synapses. In particular, the dynamics of some genetic regulatory networks are multistable, raising the possibility that even single cells, in the absence of a nervous system, are capable of learning associations. Here we study a standard genetic regulatory network model with bistable elements and stochastic dynamics. We demonstrate that such a genetic regulatory network model is capable of learning multiple, general, overlapping associations. The capacity of the network, defined as the number of associations that can be simultaneously stored and retrieved, is proportional to the square root of the number of bistable elements in the genetic regulatory network. Moreover, we compute the capacity of a clonal population of cells, such as in a colony of bacteria or a tissue, to store associations. We show that even if the cells do not interact, the capacity of the population to store associations substantially exceeds that of a single cell and is proportional to the number of bistable elements. Thus, we show that even single cells are endowed with the computational power to learn associations, a power that is substantially enhanced when these cells form a population.

 

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Why Are Product Prices in Online Markets Not Converging?

Why Are Product Prices in Online Markets Not Converging? | Social Foraging | Scoop.it

Why are product prices in online markets dispersed in spite of very small search costs? To address this question, we construct a unique dataset from a Japanese price comparison site, which records price quotes offered by e-retailers as well as customers’ clicks on products, which occur when they proceed to purchase the product. The novelty of our approach is that we seek to extract useful information on the source of price dispersion from the shape of price distributions rather than focusing merely on the standard deviation or the coefficient of variation of prices, as previous studies have done. We find that the distribution of prices retailers quote for a particular product at a particular point in time (divided by the lowest price) follows an exponential distribution, showing the presence of substantial price dispersion. For example, 20 percent of all retailers quote prices that are more than 50 percent higher than the lowest price. Next, comparing the probability that customers click on a retailer with a particular rank and the probability that retailers post prices at a particular rank, we show that both decline exponentially with price rank and that the exponents associated with the probabilities are quite close. This suggests that the reason why some retailers set prices at a level substantially higher than the lowest price is that they know that some customers will choose them even at that high price. Based on these findings, we hypothesize that price dispersion in online markets stems from heterogeneity in customers’ preferences over retailers; that is, customers choose a set of candidate retailers based on their preferences, which are heterogeneous across customers, and then pick a particular retailer among the candidates based on the price ranking.

 

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Neural and Behavioral Evidence for an Intrinsic Cost of Self-Control

Neural and Behavioral Evidence for an Intrinsic Cost of Self-Control | Social Foraging | Scoop.it

The capacity for self-control is critical to adaptive functioning, yet our knowledge of the underlying processes and mechanisms is presently only inchoate. Theoretical work in economics has suggested a model of self-control centering on two key assumptions: (1) a division within the decision-maker between two ‘selves’ with differing preferences; (2) the idea that self-control is intrinsically costly. Neuroscience has recently generated findings supporting the ‘dual-self’ assumption. The idea of self-control costs, in contrast, has remained speculative. We report the first independent evidence for self-control costs. Through a neuroimaging meta-analysis, we establish an anatomical link between self-control and the registration of cognitive effort costs. This link predicts that individuals who strongly avoid cognitive demand should also display poor self-control. To test this, we conducted a behavioral experiment leveraging a measure of demand avoidance along with two measures of self-control. The results obtained provide clear support for the idea of self-control costs.

 

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Hidden Markov Models: The Best Models for Forager Movements?

Hidden Markov Models: The Best Models for Forager Movements? | Social Foraging | Scoop.it

One major challenge in the emerging field of movement ecology is the inference of behavioural modes from movement patterns. This has been mainly addressed through Hidden Markov models (HMMs). We propose here to evaluate two sets of alternative and state-of-the-art modelling approaches. First, we consider hidden semi-Markov models (HSMMs). They may better represent the behavioural dynamics of foragers since they explicitly model the duration of the behavioural modes. Second, we consider discriminative models which state the inference of behavioural modes as a classification issue, and may take better advantage of multivariate and non linear combinations of movement pattern descriptors. For this work, we use a dataset of >200 trips from human foragers, Peruvian fishermen targeting anchovy. Their movements were recorded through a Vessel Monitoring System (~1 record per hour), while their behavioural modes (fishing, searching and cruising) were reported by on-board observers. We compare the efficiency of hidden Markov, hidden semi-Markov, and three discriminative models (random forests, artificial neural networks and support vector machines) for inferring the fishermen behavioural modes, using a cross-validation procedure. HSMMs show the highest accuracy (80%), significantly outperforming HMMs and discriminative models. Simulations show that data with higher temporal resolution, HSMMs reach nearly 100% of accuracy. Our results demonstrate to what extent the sequential nature of movement is critical for accurately inferring behavioural modes from a trajectory and we strongly recommend the use of HSMMs for such purpose. In addition, this work opens perspectives on the use of hybrid HSMM-discriminative models, where a discriminative setting for the observation process of HSMMs could greatly improve inference performance.

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DistMap: A Toolkit for Distributed Short Read Mapping on a Hadoop Cluster

DistMap: A Toolkit for Distributed Short Read Mapping on a Hadoop Cluster | Social Foraging | Scoop.it

With the rapid and steady increase of next generation sequencing data output, the mapping of short reads has become a major data analysis bottleneck. On a single computer, it can take several days to map the vast quantity of reads produced from a single Illumina HiSeq lane. In an attempt to ameliorate this bottleneck we present a new tool, DistMap - a modular, scalable and integrated workflow to map reads in the Hadoop distributed computing framework. DistMap is easy to use, currently supports nine different short read mapping tools and can be run on all Unix-based operating systems. It accepts reads in FASTQ format as input and provides mapped reads in a SAM/BAM format. DistMap supports both paired-end and single-end reads thereby allowing the mapping of read data produced by different sequencing platforms. DistMap is available from http://code.google.com/p/distmap/

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The Biomimicry Manual: What Can a Thorny Devil Teach Us About Water Harvesting?

The Biomimicry Manual: What Can a Thorny Devil Teach Us About Water Harvesting? | Social Foraging | Scoop.it

"One of Australia’s more bizarre creatures is the thorny devil or dragon, also known as the moloch. The devil is named for the ancient god Moloch, a hideous demon smeared with the blood of child sacrifice, but in reality, she is five inches long and lives entirely on ants. The thorny devil is, of course, covered in fearsome thorns, presumably to warn off would-be predators, but the spiky scales also serve another ingenious function. They form an incredibly efficient water harvesting system. What can the thorny devil teach us about water management in our own increasingly parched world?"


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Computer program uses Twitter to 'map mood of nation'

Computer program uses Twitter to 'map mood of nation' | Social Foraging | Scoop.it

British scientists have developed a computer program they say can map the mood of the nation using Twitter.

 

Named Emotive, it works by accessing the emotional content of postings on the social networking site.

 

The team, from Loughborough University, say it can scan up to 2,000 tweets a second and rate them for expressions of one of eight human emotions.

 

They claim Emotive could help calm civil unrest and identify early threats to public safety.

 

More than 500 million people across the world use Twitter, and more than 340 million tweets are posted daily.

 

The team, from the university's new Centre for Information Management, say the system can extract a direct expression of anger, disgust, fear, happiness, sadness, surprise, shame and confusion from each tweet.

 

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Spectral community detection in sparse networks

Spectral methods based on the eigenvectors of matrices are widely used in the analysis of network data, particularly for community detection and graph partitioning. Standard methods based on the adjacency matrix and related matrices, however, break down for very sparse networks, which includes many networks of practical interest. As a solution to this problem it has been recently proposed that we focus instead on the spectrum of the non-backtracking matrix, an alternative matrix representation of a network that shows better behavior in the sparse limit. Inspired by this suggestion, we here make use of a relaxation method to derive a spectral community detection algorithm that works well even in the sparse regime where other methods break down. Interestingly, however, the matrix at the heart of the method, it turns out, is not exactly the non-backtracking matrix, but a variant of it with a somewhat different definition. We study the behavior of this variant matrix for both artificial and real-world networks and find it to have desirable properties, especially in the common case of networks with broad degree distributions, for which it appears to have a better behaved spectrum and eigenvectors than the original non-backtracking matrix.

 

Spectral community detection in sparse networks
M. E. J. Newman

http://arxiv.org/abs/1308.6494


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Who Watches the Watchers?

Who Watches the Watchers? | Social Foraging | Scoop.it

In The Watchers, the creative geniuses at Studio Smack picture a world where surveillance systems don’t just watch us – they actively judge.  Are you a green-coded Conformist or a red-alert Intellectual? The tone is paranoid, but it’s a vivid reminder that our technological systems make us as much as we make them. Autonomous algorithms already control our economy, our internet, and our vacuum cleaners. It’s not a stretch to imagine that autonomous cameras will control our security and social spaces. Make sure to wait for the twist ending.

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Is the Voter Model a model for voters?

The voter model has been studied extensively as a paradigmatic opinion dynamics' model. However, its ability for modeling real opinion dynamics has not been addressed. We introduce a noisy voter model (accounting for social influence) with agents' recurrent mobility (as a proxy for social context), where the spatial and population diversity are taken as inputs to the model. We show that the dynamics can be described as a noisy diffusive process that contains the proper anysotropic coupling topology given by population and mobility heterogeneity. The model captures statistical features of the US presidential elections as the stationary vote-share fluctuations across counties, and the long-range spatial correlations that decay logarithmically with the distance. Furthermore, it recovers the behavior of these properties when a real-space renormalization is performed by coarse-graining the geographical scale from county level through congressional districts and up to states. Finally, we analyze the role of the mobility range and the randomness in decision making which are consistent with the empirical observations.

 

Is the Voter Model a model for voters?
Juan Fernández-Gracia, Krzysztof Suchecki, José J. Ramasco, Maxi San Miguel, Víctor M. Eguíluz

http://arxiv.org/abs/1309.1131


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Festo BionicOpter: Robot Flies Like A Real Dragonfly

Festo BionicOpter: Robot Flies Like A Real Dragonfly | Social Foraging | Scoop.it

With the BionicOpter, Festo has technically mastered the highly complex flight characteristics of the dragonfly. Just like its model in nature, this ultralight flying object can fly in all directions, hover in mid-air and glide without beating its wings.

 

Thirteen degrees of freedom for unique flight manoeuvres: In addition to control of the shared flapping frequency and twisting of the individual wings, each of the four wings also features an amplitude controller. The tilt of the wings determines the direction of thrust. Amplitude control allows the intensity of the thrust to be regulated. When combined, the remote-controlled dragonfly can assume almost any position in space.

 

This unique way of flying is made possible by the lightweight construction and the integration of functions: components such as sensors, actuators and mechanical components as well as open- and closed-loop control systems are installed in a very tight space and adapted to one another.

 

With the remote-controlled dragonfly, Festo demonstrates wireless real-time communication, a continuous exchange of information, as well as the ability to combine different sensor evaluations and identify complex events and critical states.

 


Via Dr. Stefan Gruenwald
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IT's comment, September 6, 2013 5:04 AM
Real Dragon fly mě přivádí k šílenství
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Personality counts in bug 'cage match'

Personality counts in bug 'cage match' | Social Foraging | Scoop.it

Looking at the personalities of one type of animal doesn’t predict predator-prey interactions as well as looking at the personalities of multiple species.

“If we’re interested in really understanding how individual personalities influence ecology, then we also have to acknowledge and accept that the personalities of many species or groups are also important,” says Jonathan Pruitt, assistant professor of behavioral ecology in the biological sciences department at the University of Pittsburgh.

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Host Mobility Drives Pathogen Competition in Spatially Structured Populations

Host Mobility Drives Pathogen Competition in Spatially Structured Populations | Social Foraging | Scoop.it

Interactions among multiple infectious agents are increasingly recognized as a fundamental issue in the understanding of key questions in public health regarding pathogen emergence, maintenance, and evolution. The full description of host-multipathogen systems is, however, challenged by the multiplicity of factors affecting the interaction dynamics and the resulting competition that may occur at different scales, from the within-host scale to the spatial structure and mobility of the host population. Here we study the dynamics of two competing pathogens in a structured host population and assess the impact of the mobility pattern of hosts on the pathogen competition. We model the spatial structure of the host population in terms of a metapopulation network and focus on two strains imported locally in the system and having the same transmission potential but different infectious periods. We find different scenarios leading to competitive success of either one of the strain or to the codominance of both strains in the system. The dominance of the strain characterized by the shorter or longer infectious period depends exclusively on the structure of the population and on the the mobility of hosts across patches. The proposed modeling framework allows the integration of other relevant epidemiological, environmental and demographic factors, opening the path to further mathematical and computational studies of the dynamics of multipathogen systems.

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Visual Nonclassical Receptive Field Effects Emerge from Sparse Coding in a Dynamical System

Visual Nonclassical Receptive Field Effects Emerge from Sparse Coding in a Dynamical System | Social Foraging | Scoop.it

Extensive electrophysiology studies have shown that many V1 simple cells have nonlinear response properties to stimuli within their classical receptive field (CRF) and receive contextual influence from stimuli outside the CRF modulating the cell's response. Models seeking to explain these non-classical receptive field (nCRF) effects in terms of circuit mechanisms, input-output descriptions, or individual visual tasks provide limited insight into the functional significance of these response properties, because they do not connect the full range of nCRF effects to optimal sensory coding strategies. The (population) sparse coding hypothesis conjectures an optimal sensory coding approach where a neural population uses as few active units as possible to represent a stimulus. We demonstrate that a wide variety of nCRF effects are emergent properties of a single sparse coding model implemented in a neurally plausible network structure (requiring no parameter tuning to produce different effects). Specifically, we replicate a wide variety of nCRF electrophysiology experiments (e.g., end-stopping, surround suppression, contrast invariance of orientation tuning, cross-orientation suppression, etc.) on a dynamical system implementing sparse coding, showing that this model produces individual units that reproduce the canonical nCRF effects. Furthermore, when the population diversity of an nCRF effect has also been reported in the literature, we show that this model produces many of the same population characteristics. These results show that the sparse coding hypothesis, when coupled with a biophysically plausible implementation, can provide a unified high-level functional interpretation to many response properties that have generally been viewed through distinct mechanistic or phenomenological models.

 

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Robots with Display Screens: A Robot with a More Humanlike Face Display Is Perceived To Have More Mind and a Better Personality

Robots with Display Screens: A Robot with a More Humanlike Face Display Is Perceived To Have More Mind and a Better Personality | Social Foraging | Scoop.it

It is important for robot designers to know how to make robots that interact effectively with humans. One key dimension is robot appearance and in particular how humanlike the robot should be. Uncanny Valley theory suggests that robots look uncanny when their appearance approaches, but is not absolutely, human. An underlying mechanism may be that appearance affects users’ perceptions of the robot’s personality and mind. This study aimed to investigate how robot facial appearance affected perceptions of the robot’s mind, personality and eeriness. A repeated measures experiment was conducted. 30 participants (14 females and 16 males, mean age 22.5 years) interacted with a Peoplebot healthcare robot under three conditions in a randomized order: the robot had either a humanlike face, silver face, or no-face on its display screen. Each time, the robot assisted the participant to take his/her blood pressure. Participants rated the robot’s mind, personality, and eeriness in each condition. The robot with the humanlike face display was most preferred, rated as having most mind, being most humanlike, alive, sociable and amiable. The robot with the silver face display was least preferred, rated most eerie, moderate in mind, humanlikeness and amiability. The robot with the no-face display was rated least sociable and amiable. There was no difference in blood pressure readings between the robots with different face displays. Higher ratings of eeriness were related to impressions of the robot with the humanlike face display being less amiable, less sociable and less trustworthy. These results suggest that the more humanlike a healthcare robot’s face display is, the more people attribute mind and positive personality characteristics to it. Eeriness was related to negative impressions of the robot’s personality. Designers should be aware that the face on a robot’s display screen can affect both the perceived mind and personality of the robot.

 

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Deep Brain Stimulation Imposes Complex Informational Lesions

Deep Brain Stimulation Imposes Complex Informational Lesions | Social Foraging | Scoop.it

Deep brain stimulation (DBS) therapy has become an essential tool for treating a range of brain disorders. In the resting state, DBS is known to regularize spike activity in and downstream of the stimulated brain target, which in turn has been hypothesized to create informational lesions. Here, we specifically test this hypothesis using repetitive joint articulations in two non-human Primates while recording single-unit activity in the sensorimotor globus pallidus and motor thalamus before, during, and after DBS in the globus pallidus (GP) GP-DBS resulted in: (1) stimulus-entrained firing patterns in globus pallidus, (2) a monophasic stimulus-entrained firing pattern in motor thalamus, and (3) a complete or partial loss of responsiveness to joint position, velocity, or acceleration in globus pallidus (75%, 12/16 cells) and in the pallidal receiving area of motor thalamus (ventralis lateralis pars oralis, VLo) (38%, 21/55 cells). Despite loss of kinematic tuning, cells in the globus pallidus (63%, 10/16 cells) and VLo (84%, 46/55 cells) still responded to one or more aspects of joint movement during GP-DBS. Further, modulated kinematic tuning did not always necessitate modulation in firing patterns (2/12 cells in globus pallidus; 13/23 cells in VLo), and regularized firing patterns did not always correspond to altered responses to joint articulation (3/4 cells in globus pallidus, 11/33 cells in VLo). In this context, DBS therapy appears to function as an amalgam of network modulating and network lesioning therapies.

 

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At Grammatical Faculty of Language, Flies Outsmart Men

At Grammatical Faculty of Language, Flies Outsmart Men | Social Foraging | Scoop.it

Using a symbolic dynamics and a surrogate data approach, we show that the language exhibited by common fruit flies Drosophila (‘D.’) during courtship is as grammatically complex as the most complex human-spoken modern languages. This finding emerges from the study of fifty high-speed courtship videos (generally of several minutes duration) that were visually frame-by-frame dissected into 37 fundamental behavioral elements. From the symbolic dynamics of these elements, the courtship-generating language was determined with extreme confidence (significance level > 0.95). The languages categorization in terms of position in Chomsky’s hierarchical language classification allows to compare Drosophila’s body language not only with computer’s compiler languages, but also with human-spoken languages. Drosophila’s body language emerges to be at least as powerful as the languages spoken by humans.

 

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The Surprising Origins of Life’s Complexity

The Surprising Origins of Life’s Complexity | Social Foraging | Scoop.it

Conventional wisdom holds that complex structures evolve from simpler ones, step-by-step, through a gradual evolutionary process, with Darwinian selection favoring intermediate forms along the way.
But recently some scholars have proposed that complexity can arise by other means—as a side effect, for instance—even without natural selection to promote it.
Studies suggest that random mutations that individually have no effect on an organism can fuel the emergence of complexity in a process known as constructive neutral evolution.

 

https://www.simonsfoundation.org/quanta/20130716-the-surprising-origins-of-lifes-complexity/


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What Happens In Your Brain When People Like Your Facebook Status

What Happens In Your Brain When People Like Your Facebook Status | Social Foraging | Scoop.it

When you post something on Facebook, you're usually doing it so people read and appreciate it. So, when people actually do that and "like" your posts, you're going to feel better about yourself. Time breaks down what's going on in your brain when that happens.

 

In research published in Frontiers in Human Neuroscience, researchers found that they could predict people's Facebook use by looking at how their brain reacted to positive social feedback in a scanner.

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Unravelling the origins of life with mathematical chemistry

Unravelling the origins of life with mathematical chemistry | Social Foraging | Scoop.it

How life began is one of the most compelling questions humanity has ever asked. Atoms and molecules, driven by nothing more than unthinking chemical processes, somehow became the complex reproductive organisms that we see roaming the Earth today -- somehow, they became us.

 

Those tiny baby steps at the start of life, when some unknown molecule somehow became self-replicating, for example, hold the key to understanding how life began and how likely it is to have sprouted throughout the Universe.

Martin Hanczyc, from the University of Southern Denmark, has dedicated his professional life to this area of study. His popular TED Talk from 2011 is a great discussion of the blurred line between life and non-life.

 

Now, using a new computational approach to mapping how simple molecules like hydrogen cyanide become more complex, he's hoping to find those first chemicals that bridged the divide and became living.

are hoping to find those first chemicals that became living

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App Brings Big Data to Birdwatching

App Brings Big Data to Birdwatching | Social Foraging | Scoop.it

Big Data has made it into the bird-watching world. The Cornell Lab of Ornithology’s eBird app allows scientists and citizens to record sightings to a form a vast, real-time map of bird populations and species across the globe. Far from being a niche product, the app is hugely successful in the birding world: Users logged more than 5.6 million observations in May alone, with the number increasing every month.

 

Unlike traditional methods of tallying birds, eBird makes sharing and recording sightings with scientists easy. Its instantaneous aspect addressed the day-to-day distribution of creatures that are, by definition, highly mobile. EBird has already proven its scientific mettle: ornithologists now know that the US has two genetically distinct groups of orchard orioles, and that the population of Eastern meadowlarks is in decline, suggesting that meadow ecosystems themselves are in trouble.

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What Ant Colony Networks Can Tell Us About What’s Next for Digital Networks

What Ant Colony Networks Can Tell Us About What’s Next for Digital Networks | Social Foraging | Scoop.it

Ever notice how ant colonies so successfully explore and exploit resources in the world … to find food at 4th of July picnics, for example? You may find it annoying. But as an ecologist who studies ants and collective behavior, I think it’s intriguing — especially the fact that it’s all done without any central control.

 

What’s especially remarkable: the close parallels between ant colonies’ networks and human-engineered ones. One example is “Anternet”, where we, a group of researchers at Stanford, found that the algorithm desert ants use to regulate foraging is like the Traffic Control Protocol (TCP) used to regulate data traffic on the internet. Both ant and human networks use positive feedback: either from acknowledgements that trigger the transmission of the next data packet, or from food-laden returning foragers that trigger the exit of another outgoing forager.

 

This research led some to marvel at the ingenuity of ants, able to invent systems familiar to us: wow, ants have been using internet algorithms for millions of years!

 

But insect behavior mimicking human networks — another example are the ant-like solutions to the traveling salesman problem provided by the ant colony optimization algorithm — is actually not what’s most interesting about ant networks. What’s far more interesting are the parallels in the other direction: What have the ants worked out that we humans haven’t thought of yet?

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Collective Intelligence 2014

IMPORTANT DATES

Extended abstract submission deadline:  January 15, 2014Notification of acceptance / rejection:  February 15, 2014Conference dates:  June 10-12, 2014

 

This interdisciplinary conference seeks to bring together researchers from a variety of fields relevant to understanding and designing collective intelligence of many types.

 

Topics of interest include but are not limited to:

human computationsocial computingcrowdsourcingwisdom of crowds (e.g., prediction markets)group memory and extended cognitioncollective decision making and problem-solvingparticipatory and deliberative democracyanimal collective behaviororganizational designpublic policy design (e.g., regulatory reform)ethics of collective intelligence (e.g., “digital sweatshops”)computational models of group search and optimizationemergence and evolution of intelligencenew technologies for making groups smarter

CONFERENCE FORMAT

The conference will take place at MIT and consist of:

Invited talks from prominent researchers in different areas related to collective intelligenceOral presentations (see below)Poster/Demo sessions (see below)“Ignite” sessions in which practitioners (e.g. policy makers) connect with researchers around collective-intelligence-based solutions to real-world problems
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An interactive map of more than 800,000 Scientific Papers that have influenced math and physics most

An interactive map of more than 800,000 Scientific Papers that have influenced math and physics most | Social Foraging | Scoop.it

ArXiv is an online archive that stores hundreds of thousands of scientific papers in physics, mathematics, and other fields. The citations in those papers link to one another, forming a web, but you're not going to see those connections just by sifting through the archive.

 

So physicist Damien George and Ph.D student Rob Knegjens took it on themselves to create Paperscape, an interactive infographic that beautifully and intuitively charts the papers.

 

The infographic is a mass of circles. Each circle represents a paper, and the bigger a circle is, the more highly cited it is. The papers are color-coded by discipline--pink for astrophysics, yellow for math, etc.--and papers that share many of the same citations are placed closer together.


Via Lauren Moss, Dr. Stefan Gruenwald
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Jay Ratcliff's curator insight, September 6, 2013 1:35 PM

This is cool!  It is like the map of the Internet done last year sometime.

I lucked out and found the section about SNA in the lower left hand side of the map.  Look for Network under the Quantitative Finance section, go figure.