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Social Simulation
News about social simulation, social networks dynamics and complex systems
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Rescooped by Frédéric Amblard from Network Science
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Networks thrive in complexity

Networks thrive in complexity | Social Simulation | Scoop.it

In complex environments, weak hierarchies and strong networks are the best organizing principle. One good example of complexity that we can try to fathom is nature itself. Networks thrive in nature. As Howard Bloom stated in a speech at Yale University


Via Ashish Umre, David Rodrigues
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Tim Williamson's curator insight, December 30, 2012 1:39 PM

Excellent work.  I would add that to properly understand complex systems on the global scale requires a holistic approach.  You can somewhat understand the mechanics of the system only generally since these systems are emergent by definition, but to 'influence' the outcomes of an emergent system requires a holistic understanding.

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Runaway Events Dominate the Heavy Tail of Citation Distributions

Michael Golosovsky, Sorin Solomon
(Submitted on 10 Jun 2012)
Statistical distributions with heavy tails are ubiquitous in natural and social phenomena. Since the entries in heavy tail have disproportional significance, the knowledge of its exact shape is very important. Citations of scientific papers form one of the best-known heavy tail distributions. Even in this case there is a considerable debate whether citation distribution follows the log-normal or power-law fit. The goal of our study is to solve this debate by measuring citation distribution for a very large and homogeneous data. We measured citation distribution for 418,438 Physics papers published in 1980-1989 and cited by 2008. While the log-normal fit deviates too strong from the data, the discrete power-law function with the exponent $\gamma=3.15$ does better and fits 99.955% of the data. However, the extreme tail of the distribution deviates upward even from the power-law fit and exhibits a dramatic "runaway" behavior. The onset of the runaway regime is revealed macroscopically as the paper garners 1000-1500 citations, however the microscopic measurements of autocorrelation in citation rates are able to predict this behavior in advance.

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Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity

Sebastian Grauwin (ENS / LIP Laboratoire de l'Informatique du Parallélisme / INRIA Grenoble Rhône-Alpes, IXXI), Guillaume Beslon (Insa Lyon / INRIA Grenoble Rhône-Alpes / UCBL), Eric Fleury (ENS / LIP Laboratoire de l'Informatique du Parallélisme / INRIA Grenoble Rhône-Alpes, IXXI, LIP), Sara Franceschelli (RNSC), Céline Robardet (LIRIS), Jean-Baptiste Rouquier (ISC-PIF), Pablo Jensen (IXXI, Phys-ENS)
(Submitted on 11 Jun 2012)
Using a large database (~ 215 000 records) of relevant articles, we empirically study the "complex systems" field and its claims to find universal principles applying to systems in general. The study of references shared by the papers allows us to obtain a global point of view on the structure of this highly interdisciplinary field. We show that its overall coherence does not arise from a universal theory but instead from computational techniques and fruitful adaptations of the idea of self-organization to specific systems. We also find that communication between different disciplines goes through specific "trading zones", ie sub-communities that create an interface around specific tools (a DNA microchip) or concepts (a network).

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Friendship networks and social status

Brian Ball, M. E. J. Newman
(Submitted on 30 May 2012)
In empirical studies of friendship networks participants are typically asked, in interviews or questionnaires, to identify some or all of their close friends, resulting in a directed network in which friendships can, and often do, run in only one direction between a pair of individuals. Here we analyze a large collection of such networks representing friendships among students at US high and junior-high schools and show that the pattern of unreciprocated friendships is far from random. In every network, without exception, we find that there exists a ranking of participants, from low to high, such that almost all unreciprocated friendships consist of a lower-ranked individual claiming friendship with a higher-ranked one. We present a maximum-likelihood method for deducing such rankings from observed network data and conjecture that the rankings produced reflect a measure of social status. We note in particular that reciprocated and unreciprocated friendships obey different statistics, suggesting different formation processes, and that rankings are correlated with other characteristics of the participants that are traditionally associated with status, such as age and overall popularity as measured by total number of friends.

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Egocentric Social Network Structure, Health, and Pro-Social Behaviors in a National Panel Study of Americans

Egocentric Social Network Structure, Health, and Pro-Social Behaviors in a National Panel Study of Americans | Social Simulation | Scoop.it

A. James O’Malley, Samuel Arbesman, Darby Miller Steiger, James H. Fowler, Nicholas A. Christakis

Using a population-based, panel survey, we study how egocentric social networks change over time, and the relationship between egocentric network properties and health and pro-social behaviors. We find that the number of prosocial activities is strongly positively associated with having more friends, or an increase in degree, with approximately 0.04 more prosocial behaviors expected for every friend added. Moreover, having more friends is associated with an improvement in health, while being healthy and prosocial is associated with closer relationships. Specifically, a unit increase in health is associated with an expected 0.45 percentage-point increase in average closeness, while adding a prosocial activity is associated with a 0.46 percentage-point increase in the closeness of one’s relationships. Furthermore, a tradeoff between degree and closeness of social contacts was observed. As the number of close social contacts increases by one, the estimated average closeness of each individual contact decreases by approximately three percentage-points. The increased awareness of the importance of spillover effects in health and health care makes the ascertainment of egocentric social networks a valuable complement to investigations of the relationship between socioeconomic factors and health.Using a population-based, panel survey, we study how egocentric social networks change over time, and the relationship between egocentric network properties and health and pro-social behaviors. We find that the number of prosocial activities is strongly positively associated with having more friends, or an increase in degree, with approximately 0.04 more prosocial behaviors expected for every friend added. Moreover, having more friends is associated with an improvement in health, while being healthy and prosocial is associated with closer relationships. Specifically, a unit increase in health is associated with an expected 0.45 percentage-point increase in average closeness, while adding a prosocial activity is associated with a 0.46 percentage-point increase in the closeness of one’s relationships. Furthermore, a tradeoff between degree and closeness of social contacts was observed. As the number of close social contacts increases by one, the estimated average closeness of each individual contact decreases by approximately three percentage-points. The increased awareness of the importance of spillover effects in health and health care makes the ascertainment of egocentric social networks a valuable complement to investigations of the relationship between socioeconomic factors and health.Using a population-based, panel survey, we study how egocentric social networks change over time, and the relationship between egocentric network properties and health and pro-social behaviors. We find that the number of prosocial activities is strongly positively associated with having more friends, or an increase in degree, with approximately 0.04 more prosocial behaviors expected for every friend added. Moreover, having more friends is associated with an improvement in health, while being healthy and prosocial is associated with closer relationships. Specifically, a unit increase in health is associated with an expected 0.45 percentage-point increase in average closeness, while adding a prosocial activity is associated with a 0.46 percentage-point increase in the closeness of one’s relationships. Furthermore, a tradeoff between degree and closeness of social contacts was observed. As the number of close social contacts increases by one, the estimated average closeness of each individual contact decreases by approximately three percentage-points. The increased awareness of the importance of spillover effects in health and health care makes the ascertainment of egocentric social networks a valuable complement to investigations of the relationship between socioeconomic factors and health.

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Rescooped by Frédéric Amblard from Artificial life
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Algorithms Take Control of Wall Street

Algorithms Take Control of Wall Street | Social Simulation | Scoop.it

Last spring, Dow Jones launched a new service called Lexicon, which sends real-time financial news to professional investors. This in itself is not surprising. The company behind The Wall Street Journal and Dow Jones Newswires made its name by publishing the kind of news that moves the stock market. But many of the professional investors subscribing to Lexicon aren’t human—they’re algorithms, the lines of code that govern an increasing amount of global trading activity—and they don’t read news the way humans do. They don’t need their information delivered in the form of a story or even in sentences. They just want data—the hard, actionable information that those words represent.

Lexicon packages the news in a way that its robo-clients can understand. It scans every Dow Jones story in real time, looking for textual clues that might indicate how investors should feel about a stock. It then sends that information in machine-readable form to its algorithmic subscribers, which can parse it further, using the resulting data to inform their own investing decisions. Lexicon has helped automate the process of reading the news, drawing insight from it, and using that information to buy or sell a stock. The machines aren’t there just to crunch numbers anymore; they’re now making the decisions.


Via Ashish Umre, Bronwen Evans
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Structure and Overlaps of Communities in Networks

Jaewon Yang, Jure Leskovec
(Submitted on 28 May 2012)
One of the main organizing principles in real-world social, information and technological networks is that of network communities, where sets of nodes organize into densely linked clusters. Even though detection of such communities is of great interest, understanding the structure communities in large networks remains relatively limited. Due to unavailability of labeled ground-truth data it is practically impossible to evaluate and compare different models and notions of communities on a large scale.
In this paper we identify 6 large social, collaboration, and information networks where nodes explicitly state their community memberships. We define ground-truth communities by using these explicit memberships. We then empirically study how such ground-truth communities emerge in networks and how they overlap. We observe some surprising phenomena. First, ground-truth communities contain high-degree hub nodes that reside in community overlaps and link to most of the members of the community. Second, the overlaps of communities are more densely connected than the non-overlapping parts of communities, in contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves.
Existing models of network communities do not capture dense community overlaps. We present the Community-Affiliation Graph Model (AGM), a conceptual model of network community structure, which reliably captures the overall structure of networks as well as the overlapping nature of network communities.

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A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats’ neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.

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A review of High Performance Computing foundations for scientists

The increase of existing computational capabilities has made simulation emerge as a third discipline of Science, lying midway between experimental and purely theoretical branches [1, 2]. Simulation enables the evaluation of quantities which otherwise would not be accessible, helps to improve experiments and provides new insights on systems which are analysed [3-6]. Knowing the fundamentals of computation can be very useful for scientists, for it can help them to improve the performance of their theoretical models and simulations. This review includes some technical essentials that can be useful to this end, and it is devised as a complement for researchers whose education is focused on scientific issues and not on technological respects. In this document we attempt to discuss the fundamentals of High Performance Computing (HPC) [7] in a way which is easy to understand without much previous background. We sketch the way standard computers and supercomputers work, as well as discuss distributed computing and discuss essential aspects to take into account when running scientific calculations in computers.

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Long trend dynamics in social media

Long trend dynamics in social media | Social Simulation | Scoop.it

A main characteristic of social media is that its diverse content, copiously generated by both standard outlets and general users, constantly competes for the scarce attention of large audiences. Out of this flood of information some topics manage to get enough attention to become the most popular ones and thus to be prominently displayed as trends. Equally important, some of these trends persist long enough so as to shape part of the social agenda. How this happens is the focus of this paper. By introducing a stochastic dynamical model that takes into account the user’s repeated involvement with given topics, we can predict the distribution of trend durations as well as the thresholds in popularity that lead to their emergence within social media. Detailed measurements of datasets from Twitter confirm the validity of the model and its predictions.

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Autopoiesis and how hyper-connectivity is literally bringing the networks to life | Trends in the Living Networks

Autopoiesis and how hyper-connectivity is literally bringing the networks to life | Trends in the Living Networks | Social Simulation | Scoop.it

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Rescooped by Frédéric Amblard from CxAnnouncements
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Global Survey of Complex Systems and Social Simulation

English version:

https://www.surveymonkey.com/s/survey_complex-systems_social-simulation ;

Spanish version:
https://www.surveymonkey.com/s/encuesta_sistemas-complejos_simulacion-social

French version:
https://www.surveymonkey.com/s/enquete_systemes-complexes_simulation-sociale ;


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Exploring Statistical and Population Aspects of Network Complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities.

 

In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples.


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Mathematical Model Developed to Predict Malaria Outbreaks: Open Malaria Warning (OMaWa)

Ethiopian and Norwegian researchers have developed a mathematical model that can identify conditions that increase the likelihood of a malaria outbreak up to two months ahead of its occurrence.The computer model, Open Malaria Warning (OMaWa), incorporates hydrological, meteorological, mosquito-breeding and land-use data to determine when and where outbreaks are likely to occur.

 

Torleif Markussen Lunde, one of the model's developers and a researcher at Norway's University of Bergen, told SciDev.Net that the model made direct use of the limited real-time information available in typical rural areas.

 

"The model also reproduces observed mosquito species composition in Africa. It is the first time this has been done with a biophysical model. We are now looking at which areas in Africa the model can be applied," he said.

 

Lunde said that past attempts at predicting malaria epidemics have had limited success because "some models [were] oversimplifications of the reality, and might have led to problematically high or low sensitivity to changes in the environment".

 

Predictions made by the model compared favourably with observations from field trials and health clinics, the researchers said.

However the model needs to be tested during a significant malaria outbreak, and its outputs compared with case studies and field observations, according to Bernt Lindtjørn, professor of international health at the University of Bergen and a co-author of the paper.


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The extreme vulnerability of interdependent spatially embedded networks

Amir Bashan, Yehiel Berezin, Sergey V. Buldyrev, Shlomo Havlin
(Submitted on 10 Jun 2012)
Recent studies show that in interdependent networks a very small failure in one network may lead to catastrophic consequences. Above a critical fraction of interdependent nodes, even a single node failure can invoke cascading failures that may abruptly fragment the system, while below this "critical dependency" (CD) a failure of few nodes leads only to small damage to the system. So far, the research has been focused on interdependent random networks without space limitations. However, many real systems, such as power grids and the Internet, are not random but are spatially embedded. Here we analytically and numerically analyze the stability of systems consisting of interdependent spatially embedded networks modeled as lattice networks. Surprisingly, we find that in lattice systems, in contrast to non-embedded systems, there is no CD and \textit{any} small fraction of interdependent nodes leads to an abrupt collapse. We show that this extreme vulnerability of very weakly coupled lattices is a consequence of the critical exponent describing the percolation transition of a single lattice. Our results are important for understanding the vulnerabilities and for designing robust interdependent spatial embedded networks.

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Bursty communication patterns facilitate spreading in a threshold-based epidemic dynamics

Taro Takaguchi, Naoki Masuda, Petter Holme
(Submitted on 11 Jun 2012)
Records of social interactions provide us with new sources of data for understanding how interaction patterns affect collective dynamics. Such human activity patterns are often bursty, i.e., they consist of short periods of intense activity followed by long periods of silence. This burstiness has been shown to affect spreading phenomena; it accelerates epidemic spreading in some cases and slows it down in other cases. We investigate a model of history-dependent contagion. In our model, repeated interactions between susceptible and infected individuals in a short period of time is needed for a susceptible individual to contract infection. We carry out numerical simulations on real temporal network data to find that bursty activity patterns facilitate epidemic spreading in our model.

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Social Networks Over Time and the Invariants of Interaction

Social Networks Over Time and the Invariants of Interaction | Social Simulation | Scoop.it

We are all embedded within social networks. Who we interact with can affect the choices we each make as individuals. Therefore, if we can quantitatively study social networks, we can better understand human behavior.

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"Social networks that balance themselves" by Steven Strogatz

"Social networks that balance themselves" by Steven Strogatz | Social Simulation | Scoop.it
The second of Steven Strogatz's Simons Lectures, given in the MIT Department of Mathematics in April, 2011.

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The Code S01E03 "Prediction"

This video is part of the InternsUK Open Source Academy selection.
We select and share funny and instructive videos, to allow everyone to access useful information and stimulate an ongoing personal development.
This is for an educational purpose only.

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Defining and Evaluating Network Communities based on Ground-truth

Jaewon Yang, Jure Leskovec
(Submitted on 28 May 2012)
Nodes in real-world networks, such as social, information or technological networks, organize into communities where edges appear with high concentration among the members of the community. Identifying communities in networks has proven to be a challenging task mainly due to a plethora of definitions of a community, intractability of algorithms, issues with evaluation and the lack of a reliable gold-standard ground-truth.
We study a set of 230 large social, collaboration and information networks where nodes explicitly define group memberships. We use these groups to define the notion of ground-truth communities. We then propose a methodology which allows us to compare and quantitatively evaluate different definitions of network communities on a large scale. We choose 13 commonly used definitions of network communities and examine their quality, sensitivity and robustness. We show that the 13 definitions naturally group into four classes. We find that two of these definitions, Conductance and Triad-participation-ratio, consistently give the best performance in identifying ground-truth communities.

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Research on Mechanism of Cooperative in Peer-to-Peer Networks

Currently, Peer-to-Peer Network(P2P network), as a newly developed Internet application architecture, are developing rapidly and attracting increasing attention from industries and academic researchers since the great growth of Internet. Meantime, its application expands and goes deeply. However, as the researches and study of this field going further, the deficiencies of P2P, such as the autonomy feature of the peers has negative impact on system operation, are revealed. The autonomy feature of the peers results in its self-interest maximization at the cost of ignoring the autonomy feature of the peers in network protocols which gives rise to free rider and finally damages the fairness among peers. What’s worse, it would affect the system operation. The cooperation mechanism among peers was studied to resolve the problem introduced by the autonomy feature of peers. The study about enforce cooperation among pees in order to achieve the object of the system and promote the cooperation among peers in order to improve the efficiency of the system on premise that protect the fairness of the peers. In this work, we present the mechanism of cooperation of peers in P2P network and the influence on the performance of P2P network.The study in this work includes:(1)The survey and analysis of present findings of the research of the cooperation mechanism of the peers in P2P networks, find out the usual method and analysis the essence of the cooperation mechanism as the foundation of our study.(2)Our research focuses on the cooperation mechanism of peers in P2P network based vertical search engine system. We proposal a new data distribution policy base called LRS on the cooperation of peers and LRS based index syncopation. LRS has characteristics of high data available, less bandwidth consuming, load balance among peers and LRS based index syncopation can improve the efficiency of index search.(3)We also propose a versatile cooperation mechanism, Hermes, which peer could ask for cooperative service according to their own requirement. It is based on the management of credit and avoids free rider successfully. Hermes mechanism enhances the efficiency among cooperative peers by meeting the autonomy feature of the peers for better cooperation.(4)Our research study the BitTorrent system and we simulate the BitTorrent system and implement Hermes mechanism in the simulated BitTorrent system. We study the influence of cooperation mechanism by experiment result.


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Diffusion Adaptation over Networks

Adaptive networks are well-suited to perform decentralized information processing and optimization tasks and to model various types of self organized and complex behavior encountered in nature. Adaptive networks consist of a collection of agents with processing and learning abilities. The agents are linked together through a connection topology, and they cooperate with each other through local interactions to solve distributed inference problems in real-time. The continuous diffusion of information across the network enables agents to adapt their performance in relation to changing data and network conditions; it also results in improved adaptation and learning performance relative to non-cooperative networks. This article provides an overview of diffusion strategies for adaptation and learning over networks. 

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Effects of time window size and placement on the structure of an aggregated communication network

Effects of time window size and placement on the structure of an aggregated communication network | Social Simulation | Scoop.it

Complex networks are often constructed by aggregating empirical data over time, such that a link represents the existence of interactions between the endpoint nodes and the link weight represents the intensity of such interactions within the aggregation time window. The resulting networks are then often considered static. More often than not, the aggregation time window is dictated by the availability of data, and the effects of its length on the resulting networks are rarely considered. Here, we address this question by studying the structural features of networks emerging from aggregating empirical data over different time intervals, focussing on networks derived from time-stamped, anonymized mobile telephone call records. Our results show that short aggregation intervals yield networks where strong links associated with dense clusters dominate; the seeds of such clusters or communities become already visible for intervals of around one week. The degree and weight distributions are seen to become stationary around a few days and a few weeks, respectively. An aggregation interval of around 30 days results in the stablest similar networks when consecutive windows are compared. For longer intervals, the effects of weak or random links become increasingly stronger, and the average degree of the network keeps growing even for intervals up to 180 days. The placement of the time window is also seen to affect the outcome: for short windows, different behavioural patterns play a role during weekends and weekdays, and for longer windows it is seen that networks aggregated during holiday periods are significantly different.

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Rescooped by Frédéric Amblard from Papers
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Simulations of the social organization of large schools of fish whose perception is obstructed

Individual-based models have shown that simple interactions among moving individuals (repulsion, attraction and alignment) result in travelling schools that resemble those of real fish. In most models individuals interact with all neighbours within sensory range which usually includes almost all the individuals of the school. Thus, it implies (almost) global perception. However, in reality in large groups, individuals will only interact with their neighbours close by, because they cannot perceive those farther away, since they are masked by closer ones. Here, we have developed a new model to investigate how such obstruction of perception influences aspects of social organization in schools of up to 10,000 individuals.

 

Applied Animal Behaviour Science, Volume 138, Issue 3, Pages 142-151, May 2012, Authors:Hanspeter Kunz; Charlotte K. Hemelrijk

http://dx.doi.org/10.1016/j.applanim.2012.02.002

 


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Jean-Baptiste Michel: The mathematics of history

What can mathematics say about history? According to TED Fellow Jean-Baptiste Michel, quite a lot. From changes to language to the deadliness of wars, he shows how digitized history is just starting to reveal deep underlying patterns.


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