Social Foraging
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# Identifying news clusters using Q-analysis and Modularity

With online publication and social media taking the main role in dissemination of news, and with the decline of traditional printed media, it has become necessary to devise ways to automatically extract meaningful information from the plethora of sources available and to make that informationreadily available to interested parties. In this paper we present a method of automated analysis of theunderlying structure of online newspapers based on Q-analysis and modularity. We show how thecombination of the two strategies allows for the identiﬁcation of well deﬁned news clusters that arefree of noise (unrelated stories) and provide automated clustering of information on trending topicson news published online.

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# Social Foraging

Dynamics of Social Interaction
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## Modeling the evolution of programming languages

Darwin’s notion of natural selection is undoubtedly one of the most important ideas in the history of science. Powerful as it is, explaining a great deal of the complexities intrinsic to biology, many wonder if the same or some similarly elegant idea could be used to explain cultural change as well. Darwin himself was interested in the relation between natural and human-driven change. Although many ideas from evolutionary biology have been applied in studying cultural change, there’s still a lot of debate as to whether natural and cultural phenomena evolve similarly. One of the reasons why the debate persists is that it is not easy to compare and tell definitely where and how the two differ, because cultural evolution and technological innovation have not been modelled as extensively as biological change. This is mainly because cultural phenomena lack a “genome” that would serve as a change measure, with the exception of natural language for which researchers in formal linguistics have devised more than several metrics accounting for grammatical, pragmatic, phonetic, orthographic and semantic changes. Other cultural phenomena, such as technological innovation, are much more difficult to quantify and measure adequately. That is why Sergi Valverde and Ricard Solé of Santa
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## The dilemma of statistics: Rigorous mathematical methods cannot compensate messy interpretations and lousy data

Statistics, although being indispensable in present day science and society has a bad reputation, in particular, in public. This can hardly be expressed in a better way than in the famous well-known quotation:
There are three kinds of lies: Lies, damned lies, and statistics. 1
It would be unfair not to make an attempt to restore the image of statistics and I try to do this in part by means of another citation.
While it is easy to lie with statistics, it is even easier to lie without them.
This quote is attributed to Frederick Mosteller [1]. Both citations are built undoubtedly on the association of statistics with telling lies and it is worth asking why statisticians have such a bad image. I feel there are two main reasons for it: (...)

The dilemma of statistics: Rigorous mathematical methods cannot compensate messy interpretations and lousy data
Peter Schuster
http://dx.doi.org/10.1002/cplx.21553

Complexity
Volume 20, Issue 1, pages 11–15, September/October 2014

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## Symbolic regression of generative network models

Networks are a powerful abstraction with applicability to a variety of scientific fields. Models explaining their morphology and growth processes permit a wide range of phenomena to be more systematically analysed and understood. At the same time, creating such models is often challenging and requires insights that may be counter-intuitive. Yet there currently exists no general method to arrive at better models. We have developed an approach to automatically detect realistic decentralised network growth models from empirical data, employing a machine learning technique inspired by natural selection and defining a unified formalism to describe such models as computer programs. As the proposed method is completely general and does not assume any pre-existing models, it can be applied “out of the box” to any given network. To validate our approach empirically, we systematically rediscover pre-defined growth laws underlying several canonical network generation models and credible laws for diverse real-world networks. We were able to find programs that are simple enough to lead to an actual understanding of the mechanisms proposed, namely for a simple brain and a social network.

Symbolic regression of generative network models
• Telmo Menezes & Camille Roth

Scientific Reports 4, Article number: 6284 http://dx.doi.org/10.1038/srep06284

Via Complexity Digest
Flora Moon's curator insight,

Big data meets systems and can potentially shines a light on system dynamics....

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## Self-organizing Intelligent Network of UAVs

This video explains our research on autonomous unmanned aerial vehicles (UAVs). The research team at the Alpen-Adria University and Lakeside Labs developing a multi-UAV system by four key components: - the multiple UAV platforms,

http://youtu.be/QX2UPkd6yIc

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## A/B Testing the Untestable in Checkout

92% of merchants participating in the e-tailing group’s 16th Annual Merchant Survey ranked A/B, multi-variate or other usability testing as most or somewhat important merchandising tactic for customer retention (second only to web analytics).

While testing the small stuff like button color, headline copy, can get reasonable lift, often the most impressive gains come from bigger changes – new approaches to layout, radical redesigns that fix multiple conversion speed bumps and, of more recent popularity, responsive design.

And it’s often recommended to start A/B testing your checkout process before tackling other areas of your site, as visitors that get as far as

your checkout are closest to conversion.

The trouble is, many marketers face limitations in fully testing the

checkout process.

Both IT requirements and technology can be roadblocks. Even if you enjoy the luxury of dedicated IT, the time it takes to make such changes is costly.

Your testing tool and ecommerce platforms may only support simple changes like button colors, headlines, images, and relatively simple layout adjustments. Test that typically yield bigger results require more effort.

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## 3 strategies for developing the data-driven retail store

Tracking customers through today’s data-driven shopping journey provides retailers a fresh take on doing business and the chance to deliver the best customer experience and build loyalty.

The retail store has traditionally been a destination inviting footfalls and converting them to transactions. Today, however, consumers do not engage with stores in this type of linear fashion, as they are always connected to the store. Many see the constantly distracted consumer and the abundance of choice across channels as threats to traditional retail. However, tracking customers on their journey provides new opportunities to retailers to engage with customers and achieve their business goals.

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## Miller experiments in atomistic computer simulations

In 1953, Stanley Miller reported on the spontaneous formation of glycine when applying an electric discharge on a mixture of simple molecules, giving birth to modern research on the origins of life. The effect of electric fields on mixtures of simple molecules is presently studied in computer simulations at the quantum level, and Miller results are reproduced for the first time, to our knowledge, in atomistic simulations, as glycine forms spontaneously only in the presence of electric fields. However, this occurs through reaction pathways more complex than believed, identifying formamide as a key compound in prebiotic chemistry. Moreover, electric fields are naturally present at mineral surfaces, suggesting a potentially crucial role in the biogeochemistry of both the primordial and the modern Earth.

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## Complex network theory and the brain

We have known for at least 100 years that a brain is organized as a network of connections between nerve cells. But in the last 10 years there has been a rapid growth in our capacity to quantify the complex topological pattern of brain connectivity, using mathematical tools drawn from graph theory.
Here we bring together articles and reviews from some of the world’s leading experts in contemporary brain network analysis by graph theory. The contributions are focused on three big questions that seem important at this stage in the scientific evolution of the field: How does the topology of a brain network relate to its physical embedding in anatomical space and its biological costs? How does brain network topology constrain brain dynamics and function? And what seem likely to be important future methodological developments in the application of graph theory to analysis of brain networks?
Clearer understanding of the principles of brain network organization is fundamental to understanding many aspects of cognitive function, brain development and clinical brain disorders. We hope this issue provides a forward-looking window on this fast moving field and captures some of the excitement of recent progress in applying the concepts of graph theory to measuring and modeling the complexity of brain networks.

Complex network theory and the brain
Issue compiled and edited by David Papo, Javier M. Buldú, Stefano Boccaletti and Edward T. Bullmore

http://rstb.royalsocietypublishing.org/site/2014/network.xhtml

Via Complexity Digest
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## The Merits of Unconscious Thought in Rule Detection - Unconscious Decision Making

According to unconscious thought theory (UTT), unconscious thought is more adept at complex decision-making than is conscious thought. Related research has mainly focused on the complexity of decision-making tasks as determined by the amount of information provided. However, the complexity of the rules generating this information also influences decision making. Therefore, we examined whether unconscious thought facilitates the detection of rules during a complex decision-making task. Participants were presented with two types of letter strings. One type matched a grammatical rule, while the other did not. Participants were then divided into three groups according to whether they made decisions using conscious thought, unconscious thought, or immediate decision. The results demonstrated that the unconscious thought group was more accurate in identifying letter strings that conformed to the grammatical rule than were the conscious thought and immediate decision groups. Moreover, performance of the conscious thought and immediate decision groups was similar. We conclude that unconscious thought facilitates the detection of complex rules, which is consistent with UTT.

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## Time Travel Simulation Resolves “Grandfather Paradox”

What would happen to you if you went back in time and killed your grandfather? A model using photons reveals that quantum mechanics can solve the quandary—and even foil quantum cryptography

Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

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## Estimation of Instantaneous Complex Dynamics through Lyapunov Exponents: A Study on Heartbeat Dynamics

Measures of nonlinearity and complexity, and in particular the study of Lyapunov exponents, have been increasingly used to characterize dynamical properties of a wide range of biological nonlinear systems, including cardiovascular control. In this work, we present a novel methodology able to effectively estimate the Lyapunov spectrum of a series of stochastic events in an instantaneous fashion. The paradigm relies on a novel point-process high-order nonlinear model of the event series dynamics. The long-term information is taken into account by expanding the linear, quadratic, and cubic Wiener-Volterra kernels with the orthonormal Laguerre basis functions. Applications to synthetic data such as the Hénon map and Rössler attractor, as well as two experimental heartbeat interval datasets (i.e., healthy subjects undergoing postural changes and patients with severe cardiac heart failure), focus on estimation and tracking of the Instantaneous Dominant Lyapunov Exponent (IDLE). The novel cardiovascular assessment demonstrates that our method is able to effectively and instantaneously track the nonlinear autonomic control dynamics, allowing for complexity variability estimations.

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## Evolution Characteristics of the Network Core in the Facebook

Statistical properties of the static networks have been extensively studied. However, online social networks are evolving dynamically, understanding the evolving characteristics of the core is one of major concerns in online social networks. In this paper, we empirically investigate the evolving characteristics of the Facebook core. Firstly, we separate the Facebook-link(FL) and Facebook-wall(FW) datasets into 28 snapshots in terms of timestamps. By employing the k-core decomposition method to identify the core of each snapshot, we find that the core sizes of the FL and FW networks approximately contain about 672 and 373 nodes regardless of the exponential growth of the network sizes. Secondly, we analyze evolving topological properties of the core, including the k-core value, assortative coefficient, clustering coefficient and the average shortest path length. Empirical results show that nodes in the core are getting more interconnected in the evolving process. Thirdly, we investigate the life span of nodes belonging to the core. More than 50% nodes stay in the core for more than one year, and 19% nodes always stay in the core from the first snapshot. Finally, we analyze the connections between the core and the whole network, and find that nodes belonging to the core prefer to connect nodes with high k-core values, rather than the high degrees ones. This work could provide new insights into the online social network analysis.

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## Inferring on the Intentions of Others by Hierarchical Bayesian Learning

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## Knowledge Sharing #Tools and #Methods Toolkit - #SNA

"Social network analysis is the mapping and measuring of relationships and flows between people, groups, organisations, computers or other information/knowledge processing entities." (Valdis Krebs, 2002). Social Network Analysis (SNA) is a method for visualizing our people and connection power, leading us to identify how we can best interact to share knowledge.

Via jean lievens, Nevermore Sithole, João Greno Brogueira, luiy
luiy's curator insight,

When to use:Visualize relationships within and outside of the organization.Facilitate identification of who knows who and who might know what - teams and individuals playing central roles - thought leaders, key knowledge brokers, experts, etc.Identify isolated teams or individuals and knowledge bottlenecks.Strategically work to improve knowledge flows.Accelerate the flow of knowledge and information across functional and organisational boundaries.Improve the effectiveness of formal and informal communication channels.Raise awareness of the importance of informal networks.

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## Dynamic Homeostasis in Packet Switching Networks

In this study, we investigate the adaptation and robustness of a packet switching network (PSN), the fundamental architecture of the Internet. We claim that the adaptation introduced by a transmission control protocol (TCP) congestion control mechanism is interpretable as the self-organization of multiple attractors and stability to switch from one attractor to another. To discuss this argument quantitatively, we study the adaptation of the Internet by simulating a PSN using ns-2. Our hypothesis is that the robustness and fragility of the Internet can be attributed to the inherent dynamics of the PSN feedback mechanism called the congestion window size, or \textit{cwnd}. By varying the data input into the PSN system, we investigate the possible self-organization of attractors in cwnd temporal dynamics and discuss the adaptability and robustness of PSNs. The present study provides an example of Ashby's Law of Requisite Variety in action.

Dynamic Homeostasis in Packet Switching Networks
Mizuki Oka, Hirotake Abe, Takashi Ikegami

http://arxiv.org/abs/1409.1533

Via Complexity Digest
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Business mergers and acquisitions bring about significant imbalances in the functioning of economic systems, and the threat of monopoly looms large, according to a new analysis of economic data published in Proceedings of the Royal Society A.

Drawing approaches from complexity and evolutionary biology -- and analyzing historical business data from a variety of industries and geographies and from the 1830s to the present -- SFI Distinguished Professor Geoffrey West and colleagues from Imperial College London and PricewaterhouseCoopers show that the cumulative history of mergers and acquisitions of companies (i.e. ancestry) is a key characteristic underpinning the dynamics of business ecosystems.

They conclude that a universal mechanism leads to imbalanced business ecosystems in which a few very large but sluggish “too big to fail” entities, and very small niche entities prevail.

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## The Leverage Effect on Wealth Distribution in a Controllable Laboratory Stock Market

Wealth distribution has always been an important issue in our economic and social life, since it affects the harmony and stabilization of the society. Under the background of widely used financial tools to raise leverage these years, we studied the leverage effect on wealth distribution of a population in a controllable laboratory market in which we have conducted several human experiments, and drawn the conclusion that higher leverage leads to a higher Gini coefficient in the market. A higher Gini coefficient means the wealth distribution among a population becomes more unequal. This is a result of the ascending risk with growing leverage level in the market plus the diversified trading abilities and risk preference of the participants. This work sheds light on the effects of leverage and its related regulations, especially its impact on wealth distribution. It also shows the capability of the method of controllable laboratory markets which could be helpful in several fields of study such as economics, econophysics and sociology.

The Leverage Effect on Wealth Distribution in a Controllable Laboratory Stock Market

Zhu C, Yang G, An K, Huang J

PLoS ONE 9(6): e100681. (2014)

http://dx.doi.org/10.1371/journal.pone.0100681

Via Complexity Digest
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## Consumers Spent 75% Of The Time On Top 4 Apps [bigMobility]

There was a time when desktop was considered to be the most used medium for the world to connect online. But with increasing adoption of smartphones in recent times, this trend has changed and now mobile phones and tablets dominate the digital connectivity. Last year US became a multi-platform majority with user digital consumption being mainly through mobile phones and tablets every month. The same year, mobile usage crossed desktop usage in terms of total digital media engagement. This year saw a new development – APP MAJORITY – where now the majority of all digital media time spent occurs on mobile apps.

This was an expected phenomenon as apps have allowed users to attain a great deal of real life fulfillment such as hailing a cab, checking weather, purchase of commodities etc or for digital fulfillment to post an update on Facebook, stream videos or watch movies.. These apps drive the mobile digital consumption and is where most of the device’s utility comes from.

Although engagement is higher on mobile, advertising on this platform has not made it big yet. But as seen in every previous development, money follows eyeballs and hence mobile app economy  will have a bright future.

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## Synchronization in human musical rhythms and mutually interacting complex systems

Though the statistical properties of musical compositions have been widely studied, little is known about the statistical nature of musical interaction—a foundation of musical communication. The goal of this study was to uncover the general statistical properties underlying musical interaction by observing two individuals synchronizing rhythms. We found that the interbeat intervals between individuals exhibit scale-free cross-correlations, i.e., the next beat played by an individual is dependent on the entire history (up to several minutes) of their partner’s interbeat intervals. To explain this surprising observation, we introduce a general stochastic model that can also be used to study synchronization phenomena in econophysics and physiology. The scaling laws found in musical interaction are directly applicable to audio production.

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## Using Cellular Automata for Parking Recommendations in Smart Environments

In this work, we propose an innovative adaptive recommendation mechanism for smart parking. The cognitive RF module will transmit the vehicle location information and the parking space requirements to the parking congestion computing center (PCCC) when the driver must find a parking space. Moreover, for the parking spaces, we use a cellular automata (CA) model mechanism that can adjust to full and not full parking lot situations. Here, the PCCC can compute the nearest parking lot, the parking lot status and the current or opposite driving direction with the vehicle location information. By considering the driving direction, we can determine when the vehicles must turn around and thus reduce road congestion and speed up finding a parking space. The recommendation will be sent to the drivers through a wireless communication cognitive radio (CR) model after the computation and analysis by the PCCC. The current study evaluates the performance of this approach by conducting computer simulations. The simulation results show the strengths of the proposed smart parking mechanism in terms of avoiding increased congestion and decreasing the time to find a parking space.

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## Evaluating sentiment in financial news articles

Can the choice of words and tone used by the authors of financial news articles correlate to measurable stock price movements? If so, can the magnitude of price movement be predicted using these same variables? We investigate these questions using the Arizona Financial Text (AZFinText) system, a financial news article prediction system, and pair it with a sentiment analysis tool. Through our analysis, we found that subjective news articles were easier to predict in price direction (59.0% versus 50.0% of chance alone) and using a simple trading engine, subjective articles garnered a 3.30% return. Looking further into the role of author tone in financial news articles, we found that articles with a negative sentiment were easiest to predict in price direction (50.9% versus 50.0% of chance alone) and a 3.04% trading return. Investigating negative sentiment further, we found that our system was able to predict price decreases in articles of a positive sentiment 53.5% of the time, and price increases in articles of a negative sentiment 52.4% of the time. We believe that perhaps this result can be attributable to market traders behaving in a contrarian manner, e.g., see good news, sell; see bad news, buy.

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## How to Assess the Existence of Competing Strategies in Cognitive Tasks: A Primer on the Fixed-Point Property

When multiple strategies can be used to solve a type of problem, the observed response time distributions are often mixtures of multiple underlying base distributions each representing one of these strategies. For the case of two possible strategies, the observed response time distributions obey the fixed-point property. That is, there exists one reaction time that has the same probability of being observed irrespective of the actual mixture proportion of each strategy. In this paper we discuss how to compute this fixed-point, and how to statistically assess the probability that indeed the observed response times are generated by two competing strategies. Accompanying this paper is a free R package that can be used to compute and test the presence or absence of the fixed-point property in response time data, allowing for easy to use tests of strategic behavior.

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## Identification of Important Nodes in Directed Biological Networks: A Network Motif Approach

Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.

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## Logistics Startup Delhivery: The next big bet in Indian Ecommerce Industry

Logistics is the next big bet in Indian ecommerce industry. Players like DHL are piloting their logistics play in India and Delhi based Delhivery has raised $35mn in Series C round led by Multiples Alternate Asset Management, with participation from existing investors Nexus Venture Partners and Times Internet. Delhivery provides logistics services such as inventory management, cash on delivery, warehousing, last mile delivery and tracking. Delhivery earlier raised$5mn from Nexus Venture Partners in Series B in 2013. Started in 2011, the company now handles 51,000 products per day.

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## Study shows ‘less is more’ for kids learning new words

Toddlers are much more successful at learning new words when the learning environment stays the same, according to new University of Sussex research.

Much recent research has shown that less is more as far as children’s learning is concerned. Now Sussex psychologists Dr Jessica Horst and post-doctoral fellow Dr Emma Axelsson have shown that this is also true for learning individual words.

Their study, published online this week (3 September) in the journal Acta Psychologica, involved 48 three-year-old children. Simulating a game on a touchscreen computer, the children were introduced to names for unusual toys – such as a clacker (noisemaker) – by being asked to touch different toys to help an alien ‘tidy up his room’.

They heard the name of a new toy and then had to choose it from two other objects displayed on the screen. Children saw familiar things such as toy cars and plush animals alongside the unusual toys.

All children heard the new words the same number of times, but sometimes the familiar objects were always different and sometimes they were always the same. For example, some children always saw the clacker with the elephant and boat and some children always saw that toy with different things.

The children who had less distraction - those who always saw things with the same other objects - learned the words. The children who encountered more distraction did not learn the words.

Dr Horst says: "Across developmental psychology we are seeing two big shifts in the research on how children learn. Several studies—including ours—are finding that less is more when it comes to learning.

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