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Econometrics by Simulation: MSimulate Command

Econometrics by Simulation: MSimulate Command | Social Foraging | Scoop.it
Stata has a wonderfully effective simulate function that allows users to easily simulate data and analysis in a very rapid fashion. * The only drawback is that when you run it, it will replace the data in memory with the simulated ...
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Hybrid Epidemics—A Case Study on Computer Worm Conficker

Hybrid Epidemics—A Case Study on Computer Worm Conficker | Social Foraging | Scoop.it
Conficker is a computer worm that erupted on the Internet in 2008. It is unique in combining three different spreading strategies: local probing, neighbourhood probing, and global probing. We propose a mathematical model that combines three modes of spreading: local, neighbourhood, and global, to capture the worm’s spreading behaviour. The parameters of the model are inferred directly from network data obtained during the first day of the Conficker epidemic. The model is then used to explore the tradeoff between spreading modes in determining the worm’s effectiveness. Our results show that the Conficker epidemic is an example of a critically hybrid epidemic, in which the different modes of spreading in isolation do not lead to successful epidemics. Such hybrid spreading strategies may be used beneficially to provide the most effective strategies for promulgating information across a large population. When used maliciously, however, they can present a dangerous challenge to current internet security protocols.
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A Wandering Mind Does Not Stray Far from Home: The Value of Metacognition in Distant Search

A Wandering Mind Does Not Stray Far from Home: The Value of Metacognition in Distant Search | Social Foraging | Scoop.it
When faced with a problem, how do individuals search for potential solutions? In this article, we explore the cognitive processes that lead to local search (i.e., identifying options closest to existing solutions) and distant search (i.e., identifying options of a qualitatively different nature than existing solutions). We suggest that mind wandering is likely to lead to local search because it operates by spreading activation from initial ideas to closely associated ideas. This reduces the likelihood of accessing a qualitatively different solution. However, instead of getting lost in thought, individuals can also step back and monitor their thoughts from a detached perspective. Such mindful metacognition, we suggest, is likely to lead to distant search because it redistributes activation away from initial ideas to other, less strongly associated, ideas. This hypothesis was confirmed across two studies. Thus, getting lost in thoughts is helpful when one is on the right track and needs only a local search whereas stepping back from thoughts is helpful when one needs distant search to produce a change in perspective.
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Evolutionary Dynamics for Persistent Cooperation in Structured Populations

The emergence and maintenance of cooperative behavior is a fascinating topic in evolutionary biology and social science. The public goods game (PGG) is a paradigm for exploring cooperative behavior. In PGG, the total resulting payoff is divided equally among all participants. This feature still leads to the dominance of defection without substantially magnifying the public good by a multiplying factor. Much effort has been made to explain the evolution of cooperative strategies, including a recent model in which only a portion of the total benefit is shared by all the players through introducing a new strategy named persistent cooperation. A persistent cooperator is a contributor who is willing to pay a second cost to retrieve the remaining portion of the payoff contributed by themselves. In a previous study, this model was analyzed in the framework of well-mixed populations. This paper focuses on discussing the persistent cooperation in lattice-structured populations. The evolutionary dynamics of the structured populations consisting of three types of competing players (pure cooperators, defectors and persistent cooperators) are revealed by theoretical analysis and numerical simulations. In particular, the approximate expressions of fixation probabilities for strategies are derived on one-dimensional lattices. The phase diagrams of stationary states, the evolution of frequencies and spatial patterns for strategies are illustrated on both one-dimensional and square lattices by simulations. Our results are consistent with the general observation that, at least in most situations, a structured population facilitates the evolution of cooperation. Specifically, here we find that the existence of persistent cooperators greatly suppresses the spreading of defectors under more relaxed conditions in structured populations compared to that obtained in well-mixed population.
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You Asked: Are My Devices Messing With My Brain?

You Asked: Are My Devices Messing With My Brain? | Social Foraging | Scoop.it
Yes—and you're probably suffering from phantom text syndrome, too.

First it was radio. Then it was television. Now doomsayers are offering scary predictions about the consequences of smartphones and all the other digital devices to which we’ve all grown so attached. So why should you pay any attention to the warnings this time?

Apart from portability, the big difference between something like a traditional TV and your tablet is the social component, says Dr. David Strayer, a professor of cognition and neural science at the University of Utah. “Through Twitter or Facebook or email, someone in your social network is contacting you in some way all the time,” Strayer says.
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Optimal Census by Quorum Sensing

Optimal Census by Quorum Sensing | Social Foraging | Scoop.it
Quorum sensing is the regulation of gene expression in response to changes in cell density. To measure their cell density, bacterial populations produce and detect diffusible molecules called autoinducers. Individual bacteria internally represent the external concentration of autoinducers via the level of monitor proteins. In turn, these monitor proteins typically regulate both their own production and the production of autoinducers, thereby establishing internal and external feedbacks. Here, we ask whether feedbacks can increase the information available to cells about their local density. We quantify available information as the mutual information between the abundance of a monitor protein and the local cell density for biologically relevant models of quorum sensing. Using variational methods, we demonstrate that feedbacks can increase information transmission, allowing bacteria to resolve up to two additional ranges of cell density when compared with bistable quorum-sensing systems. Our analysis is relevant to multi-agent systems that track an external driver implicitly via an endogenously generated signal.
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Inside the Wonderful World of Bee Cognition – How it All Began

Inside the Wonderful World of Bee Cognition – How it All Began | Social Foraging | Scoop.it
One of the first things I get asked when I tell people that I work on bee cognition (apart from ‘do you get stung a lot?’) is ‘bees have cognition?’. I usually assume that this question shouldn’t be taken literally otherwise it would mean that whoever was asking me this thought that there was a possibility that bees didn’t have cognition and I had just been making a terrible mistake for the past two years. Instead I guess this question actually means ‘please tell me more about the kind of cognitive abilities bees have, as I am very much surprised to hear that bees can do more than just mindlessly sting people’. So, here it is: a summary of some of the more remarkable things that bees can do with their little brains. In the first part of two articles on this topic, I introduce the history and basics of bee learning. In the second article, I go on to discuss the more advanced cognitive abilities of bees.
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gathersord's curator insight, April 28, 5:03 AM

nice

firesolid's curator insight, May 2, 1:45 AM

Its fascinating

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Temporal dynamics in fMRI resting-state activity

Temporal dynamics in fMRI resting-state activity | Social Foraging | Scoop.it
In a significant new study, Mitra et al. (1) demonstrate the existence of reproducible temporal patterns of spontaneous activity from human functional magnetic resonance imaging (fMRI) recordings. This finding and the novel methods used to demonstrate it bring the question of the role of temporally patterned activity into the domain of human cognition.

The Brain as a Dynamical Machine What the brain does is ultimately simple: it takes in sensory information, transforms it into an abstract code of spikes, and uses it to generate motor patterns. This spike code thus constitutes a mental representation of the world, which interacts with memories, expectations, motivations, and other internal states of the animal to generate a series of behaviors that are adaptive and intelligent, and maximize the survival of the individual and the spread of its genes.
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Can the Intellectual Processes in Science Also Be Simulated? The Anticipation and Visualization of Possible Future States

Socio-cognitive action reproduces and changes both social and cognitive structures. The analytical distinction between these dimensions of structure provides us with richer models of scientific development. In this study, I assume that (i) social structures organize expectations into belief structures that can be attributed to individuals and communities; (ii) expectations are specified in scholarly literature; and (iii) intellectually the sciences (disciplines, specialties) tend to self-organize as systems of rationalized expectations. Whereas social organizations remain localized, academic writings can circulate, and expectations can be stabilized and globalized using symbolically generalized codes of communication. The intellectual restructuring, however, remains latent as a second-order dynamics that can be accessed by participants only reflexively. Yet, the emerging "horizons of meaning" provide feedback to the historically developing organizations by constraining the possible future states as boundary conditions. I propose to model these possible future states using incursive and hyper-incursive equations from the computation of anticipatory systems. Simulations of these equations enable us to visualize the couplings among the historical--i.e., recursive--progression of social structures along trajectories, the evolutionary--i.e., hyper-incursive--development of systems of expectations at the regime level, and the incursive instantiations of expectations in actions, organizations, and texts.
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Still Not Significant

Still Not Significant | Social Foraging | Scoop.it
What to do if your p-value is just over the arbitrary threshold for ‘significance’ of p=0.05?

You don’t need to play the significance testing game – there are better methods, like quoting the effect size with a confidence interval – but if you do, the rules are simple: the result is either significant or it isn’t.

So if your p-value remains stubbornly higher than 0.05, you should call it ‘non-significant’ and write it up as such. The problem for many authors is that this just isn’t the answer they were looking for: publishing so-called ‘negative results’ is harder than ‘positive results’.
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Computational Models of Consumer Confidence from Large-Scale Online Attention Data: Crowd-Sourcing Econometrics

Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.

 

Dong X, Bollen J (2015) Computational Models of Consumer Confidence from Large-Scale Online Attention Data: Crowd-Sourcing Econometrics. PLoS ONE 10(3): e0120039. http://dx.doi.org/10.1371/journal.pone.0120039 ;


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Responding to complexity in socio-economic systems: How to build a smart and resilient society?

The world is changing at an ever-increasing pace. And it has changed in a much more fundamental way than one would think, primarily because it has become more connected and interdependent than in our entire history. Every new product, every new invention can be combined with those that existed before, thereby creating an explosion of complexity: structural complexity, dynamic complexity, functional complexity, and algorithmic complexity. How to respond to this challenge? And what are the costs?

 

Responding to complexity in socio-economic systems: How to build a smart and resilient society?
Dirk Helbing

http://arxiv.org/abs/1504.03750


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Flexibility of collective decision making during house hunting in Temnothorax ants - Springer

Flexibility of collective decision making during house hunting in Temnothorax ants - Springer | Social Foraging | Scoop.it

Many social animals cooperatively process information during decision making, allowing them to concentrate on the best of several options. However, positive feedback created by information sharing can also lock the group into a suboptimal outcome if option quality changes over time. This creates a trade-off between consensus and flexibility, whose resolution depends on the information-sharing mechanisms groups employ. We investigated the influence of communication behavior on decision flexibility in nest site choice by colonies of the ant Temnothorax rugatulus. These ants divide their emigration into two distinct phases separated by a quorum rule. In the first phase, scouts recruit nestmates to promising sites using the slow method of tandem running. Once a site's population surpasses a quorum, they switch to the faster method of social transport. We gave colonies a choice between two sites of different quality, and then switched site quality at different points during the emigration. Before the quorum was met, colonies were able to switch their choice to the newly superior site, but once they began to transport, their flexibility dropped significantly. Close observation of single ants revealed that transporters were more likely than tandem leaders to continue recruiting to a site even after its quality was diminished. That is, tandem leaders continued to monitor the quality of the site, while transporters instead fully committed to the site without further assessment. We discuss how this change in commitment with quorum attainment may enhance the rapid achievement of consensus needed for nest site selection, but at a cost in flexibility once the quorum is met.

 

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Macroscopic description of complex adaptive networks co-evolving with dynamic node states

In many real-world complex systems, the time-evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here, we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the co-evolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we show that in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability play a crucial role for the sustainability of the system's equilibrium state. We derive a macroscopic description of the system which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network and is applicable to many fields of study, such as epidemic spreading or social modeling.
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Percolation on Networks with Conditional Dependence Group

Percolation on Networks with Conditional Dependence Group | Social Foraging | Scoop.it
Recently, the dependence group has been proposed to study the robustness of networks with interdependent nodes. A dependence group means that a failed node in the group can lead to the failures of the whole group. Considering the situation of real networks that one failed node may not always break the functionality of a dependence group, we study a cascading failure model that a dependence group fails only when more than a fraction β of nodes of the group fail. We find that the network becomes more robust with the increasing of the parameter β. However, the type of percolation transition is always first order unless the model reduces to the classical network percolation model, which is independent of the degree distribution of the network. Furthermore, we find that a larger dependence group size does not always make the networks more fragile. We also present exact solutions to the size of the giant component and the critical point, which are in agreement with the simulations well.
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Measuring the signal-to-noise ratio of a neuron

Measuring the signal-to-noise ratio of a neuron | Social Foraging | Scoop.it
The signal-to-noise ratio (SNR), a commonly used measure of fidelity in physical systems, is defined as the ratio of the squared amplitude or variance of a signal relative to the variance of the noise. This definition is not appropriate for neural systems in which spiking activity is more accurately represented as point processes. We show that the SNR estimates a ratio of expected prediction errors and extend the standard definition to one appropriate for single neurons by representing neural spiking activity using point process generalized linear models (PP-GLM). We estimate the prediction errors using the residual deviances from the PP-GLM fits. Because the deviance is an approximate χ2 random variable, we compute a bias-corrected SNR estimate appropriate for single-neuron analysis and use the bootstrap to assess its uncertainty. In the analyses of four systems neuroscience experiments, we show that the SNRs are −10 dB to −3 dB for guinea pig auditory cortex neurons, −18 dB to −7 dB for rat thalamic neurons, −28 dB to −14 dB for monkey hippocampal neurons, and −29 dB to −20 dB for human subthalamic neurons. The new SNR definition makes explicit in the measure commonly used for physical systems the often-quoted observation that single neurons have low SNRs. The neuron’s spiking history is frequently a more informative covariate for predicting spiking propensity than the applied stimulus. Our new SNR definition extends to any GLM system in which the factors modulating the response can be expressed as separate components of a likelihood function.
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Ambiguity and nonidentifiability in the statistical analysis of neural codes

Ambiguity and nonidentifiability in the statistical analysis of neural codes | Social Foraging | Scoop.it
Many experimental studies of neural coding rely on a statistical interpretation of the theoretical notion of the rate at which a neuron fires spikes. For example, neuroscientists often ask, “Does a population of neurons exhibit more synchronous spiking than one would expect from the covariability of their instantaneous firing rates?” For another example, “How much of a neuron’s observed spiking variability is caused by the variability of its instantaneous firing rate, and how much is caused by spike timing variability?” However, a neuron’s theoretical firing rate is not necessarily well-defined. Consequently, neuroscientific questions involving the theoretical firing rate do not have a meaning in isolation but can only be interpreted in light of additional statistical modeling choices. Ignoring this ambiguity can lead to inconsistent reasoning or wayward conclusions. We illustrate these issues with examples drawn from the neural-coding literature.
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The Effect of Incentives and Meta-incentives on the Evolution of Cooperation

The Effect of Incentives and Meta-incentives on the Evolution of Cooperation | Social Foraging | Scoop.it
Although positive incentives for cooperators and/or negative incentives for free-riders in social dilemmas play an important role in maintaining cooperation, there is still the outstanding issue of who should pay the cost of incentives. The second-order free-rider problem, in which players who do not provide the incentives dominate in a game, is a well-known academic challenge. In order to meet this challenge, we devise and analyze a meta-incentive game that integrates positive incentives (rewards) and negative incentives (punishments) with second-order incentives, which are incentives for other players’ incentives. The critical assumption of our model is that players who tend to provide incentives to other players for their cooperative or non-cooperative behavior also tend to provide incentives to their incentive behaviors. In this paper, we solve the replicator dynamics for a simple version of the game and analytically categorize the game types into four groups. We find that the second-order free-rider problem is completely resolved without any third-order or higher (meta) incentive under the assumption. To do so, a second-order costly incentive, which is given individually (peer-to-peer) after playing donation games, is needed. The paper concludes that (1) second-order incentives for first-order reward are necessary for cooperative regimes, (2) a system without first-order rewards cannot maintain a cooperative regime, (3) a system with first-order rewards and no incentives for rewards is the worst because it never reaches cooperation, and (4) a system with rewards for incentives is more likely to be a cooperative regime than a system with punishments for incentives when the cost-effect ratio of incentives is sufficiently large. This solution is general and strong in the sense that the game does not need any centralized institution or proactive system for incentives.
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Understanding Brains: Details, Intuition, and Big Data

Understanding Brains: Details, Intuition, and Big Data | Social Foraging | Scoop.it
Understanding how the brain works requires a delicate balance between the appreciation of the importance of a multitude of biological details and the ability to see beyond those details to general principles. As technological innovations vastly increase the amount of data we collect, the importance of intuition into how to analyze and treat these data may, paradoxically, become more important.
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Insect-Inspired Navigation Algorithm for an Aerial Agent Using Satellite Imagery

Insect-Inspired Navigation Algorithm for an Aerial Agent Using Satellite Imagery | Social Foraging | Scoop.it
Humans have long marveled at the ability of animals to navigate swiftly, accurately, and across long distances. Many mechanisms have been proposed for how animals acquire, store, and retrace learned routes, yet many of these hypotheses appear incongruent with behavioral observations and the animals’ neural constraints. The “Navigation by Scene Familiarity Hypothesis” proposed originally for insect navigation offers an elegantly simple solution for retracing previously experienced routes without the need for complex neural architectures and memory retrieval mechanisms. This hypothesis proposes that an animal can return to a target location by simply moving toward the most familiar scene at any given point. Proof of concept simulations have used computer-generated ant’s-eye views of the world, but here we test the ability of scene familiarity algorithms to navigate training routes across satellite images extracted from Google Maps. We find that Google satellite images are so rich in visual information that familiarity algorithms can be used to retrace even tortuous routes with low-resolution sensors. We discuss the implications of these findings not only for animal navigation but also for the potential development of visual augmentation systems and robot guidance algorithms.
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Species fluctuations sustained by a cyclic succession at the edge of chaos

Species fluctuations sustained by a cyclic succession at the edge of chaos | Social Foraging | Scoop.it
Although mathematical models and laboratory experiments have shown that species interactions can generate chaos, field evidence of chaos in natural ecosystems is rare. We report on a pristine rocky intertidal community located in one of the world’s oldest marine reserves that has displayed a complex cyclic succession for more than 20 y. Bare rock was colonized by barnacles and crustose algae, they were overgrown by mussels, and the subsequent detachment of the mussels returned bare rock again. These processes generated irregular species fluctuations, such that the species coexisted over many generations without ever approaching a stable equilibrium state. Analysis of the species fluctuations revealed a dominant periodicity of about 2 y, a global Lyapunov exponent statistically indistinguishable from zero, and local Lyapunov exponents that alternated systematically between negative and positive values. This pattern indicates that the community moved back and forth between stabilizing and chaotic dynamics during the cyclic succession. The results are supported by a patch-occupancy model predicting similar patterns when the species interactions were exposed to seasonal variation. Our findings show that natural ecosystems can sustain continued changes in species abundances and that seasonal forcing may push these nonequilibrium dynamics to the edge of chaos.
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Cascading Walks Model for Human Mobility Patterns

Cascading Walks Model for Human Mobility Patterns | Social Foraging | Scoop.it
Abstract

Background

Uncovering the mechanism behind the scaling laws and series of anomalies in human trajectories is of fundamental significance in understanding many spatio-temporal phenomena. Recently, several models, e.g. the explorations-returns model (Song et al., 2010) and the radiation model for intercity travels (Simini et al., 2012), have been proposed to study the origin of these anomalies and the prediction of human movements. However, an agent-based model that could reproduce most of empirical observations without priori is still lacking.

Methodology/Principal Findings

In this paper, considering the empirical findings on the correlations of move-lengths and staying time in human trips, we propose a simple model which is mainly based on the cascading processes to capture the human mobility patterns. In this model, each long-range movement activates series of shorter movements that are organized by the law of localized explorations and preferential returns in prescribed region.

Conclusions/Significance

Based on the numerical simulations and analytical studies, we show more than five statistical characters that are well consistent with the empirical observations, including several types of scaling anomalies and the ultraslow diffusion properties, implying the cascading processes associated with the localized exploration and preferential returns are indeed a key in the understanding of human mobility activities. Moreover, the model shows both of the diverse individual mobility and aggregated scaling displacements, bridging the micro and macro patterns in human mobility. In summary, our model successfully explains most of empirical findings and provides deeper understandings on the emergence of human mobility patterns.
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IBM's Watson Designed The Worst Burrito I've Ever Had

IBM's Watson Designed The Worst Burrito I've Ever Had | Social Foraging | Scoop.it
It’s the worst burrito I’ve ever had.

I don’t know another way to say it. I’m staring at my plate in disbelief. Could burritos be bad? Yes, yes I’d just learned. But that’s not the biggest shocker. The biggest shocker is that this recipe was largely designed by Watson, IBM’s best artificial intelligence—one that had already fed me one of the most uniquely delicious BBQ sauces I’d ever eaten.

I thought through the recipe in my head again. I’d cheated a little, but not enough to ruin a good thing. What went wrong?
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From seconds to months: multi-scale dynamics of mobile telephone calls

Big Data on electronic records of social interactions allow approaching human behaviour and sociality from a quantitative point of view with unforeseen statistical power. Mobile telephone Call Detail Records (CDRs), automatically collected by telecom operators for billing purposes, have proven especially fruitful for understanding one-to-one communication patterns as well as the dynamics of social networks that are reflected in such patterns. We present an overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from mobile telephone calls. We begin with the shortest timescales and fastest dynamics, such as burstiness of call sequences between individuals, and "zoom out" towards longer temporal and larger structural scales, from temporal motifs formed by correlated calls between multiple individuals to long-term dynamics of social groups. We conclude this overview with a future outlook.

 

From seconds to months: multi-scale dynamics of mobile telephone calls
Jari Saramaki, Esteban Moro

http://arxiv.org/abs/1504.01479


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Google open sources a MapReduce framework for C/C++

Google open sources a MapReduce framework for C/C++ | Social Foraging | Scoop.it
Google announced on Wednesday that the company is open sourcing a MapReduce framework that will let users run native C and C++ code in their Hadoop environments. Depending on how much traction MapReduce for C, or MR4C, gets and by whom, it could turn out to be a pretty big deal.

Hadoop is famously, or infamously, written in Java and as such can suffer from performance issues compared with native C++ code. That’s why Google’s original MapReduce system was written in C++, as is the Quantcast File System, that company’s homegrown alternative for the Hadoop Distributed File System. And, as the blog post announcing MR4C notes, “many software companies that deal with large datasets have built proprietary systems to execute native code in MapReduce frameworks.”

This is the same sort of rationale behind Facebook’s HipHop efforts and database startup MemSQL, whose system converts SQL to C++ before executing it.
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Embodied Choice: How Action Influences Perceptual Decision Making

Embodied Choice: How Action Influences Perceptual Decision Making | Social Foraging | Scoop.it

Embodied Choice considers action performance as a proper part of the decision making process rather than merely as a means to report the decision. The central statement of embodied choice is the existence of bidirectional influences between action and decisions. This implies that for a decision expressed by an action, the action dynamics and its constraints (e.g.current trajectory and kinematics) influence the decision making process. Here we use a perceptual decision making task to compare three types of model: a serial decision-then-action model, a parallel decision-and-action model, and an embodied choice model where the action feeds back into the decision making. The embodied model incorporates two key mechanisms that together are lacking in the other models: action preparation and commitment. First, action preparation strategies alleviate delays in enacting a choice but also modify decision termination. Second, action dynamics change the prospects and create a commitment effect to the initially preferred choice. Our results show that these two mechanisms make embodied choice models better suited to combine decision and action appropriately to achieve suitably fast and accurate responses, as usually required in ecologically valid situations. Moreover, embodied choice models with these mechanisms give a better account of trajectory tracking experiments during decision making. In conclusion, the embodied choice framework offers a combined theory of decision and action that gives a clear case that embodied phenomena such as the dynamics of actions can have a causal influence on central cognition.

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