* The original article was published in the INOMICS Customer Newsletter. Click here to subscribe.
Are academics too smart and busy to become active participants in the share-and-like race on social media platforms? To answer this question exactly one year ago we surveyed 340 economists who visited the INOMICS platform. The majority of economists who responded to the survey (87%) were employed in Universities, Research Institutes/Think Tanks or Government. Given the global nature of internet space and keeping in mind that the use of the social media platforms may vary from country to country, we ensured a fair representation of all regions among the respondents, with 63% of respondents still being based in Europe.
To better understand the professional and demographic profile of the respondents, have a look at the charts below:
a. Age Groups of the Respondents b. Completed Academic Degrees of the Respondents
What did economists share with us about their use of social networks? Twitter was reported to be the least used by the economists with only 21% of the respondents having an account. 62% of those economists who do use Twitter use it for professional networking, while 38% use it solely for private purposes. Google+ had a slightly higher use (33% of respondents) and is used by equal numbers of economists for professional and private purposes. Not surprisingly, LinkedIn, used by 60% of survey participants, was mostly kept for professional networking. Surprisingly, none of the economists surveyed mentioned any professional platforms created specifically for academics.
Facebook is an obvious exception in the types of social media used by economists. Despite continuous predictions of the coming fall of the “like button” monopolist, which are supported by the declining numbers of Facebook users, it remains the most used social media platform regardless of professional profile of users. Moreover, as online communication methods are increasingly becoming the norm, user behavior is changing. According to last year’s survey, 60% of economists still used Facebook for private purposes only, but the growing number of jobs and academic conferences shared suggests that private and professional spheres in the online world are currently not that isolated from each other. And while from the privacy point of view it is perceived by many as a negative side effect of social networks, it has its obvious advantages, too. An opportunity to connect with people in your professional community regardless of their geographical location and school of thought can have an enhancing effect on both career and personal development, whereas trust and accountability increase proportionally to the number of shared connections. If it has to be done on another platforms, created for these professional communities and networking only, is another question – especially if predictions about Facebook fall come true.
Being a platform for economists, here at INOMICS we have the privilege to watch these trends in the context of the economics community ourselves. Since April last year the INOMICS Facebook page has grown by 100%, and also demonstrated a disproportional increase in fans activity. Users are engaging significantly more actively in the professional discussions and are sharing related information with their private networks.
The trends in web platforms use are changing with amazing speed, as do the platforms themselves. Given the changing landscape of professional networks for academics, we have added extra dimensions to our survey this year: please take part in our survey here and help us to understand trends of the changing internet landscape in 2013.
Con una ley débil e insuficiente, en 2012 se cuadruplicaron las denuncias por delitos informáticos. Por el uso que hacen de Internet, los chicos son los más vulnerables frente a pedófilos y abusadores. Clarín
Alguien te está mirando. Aunque el uso de las redes sociales parezca privado, es muy vulnerable al fisgoneo y la intervención de acosadores./ CARLOS SARRAF.
Un chico de 19, en un evento público, encuentra a una chica de 15, que está con un grupo de amigas. Lleva chateando seis meses por Facebook con ella. “Mai”, le grita, contento de haberla encontrado por primera vez. Ella lo mira sin saber quién es ni por qué la llama “Mai”.
El chico trata de que ella lo reconozca: toma su celular y se conecta a Facebook. Le muestra los diálogos que mantuvieron en los últimos tiempos, incluso el de anoche. En el avatar, sobre su foto real, ella se llama “Mai”. Y en el celular del desconocido, en el falso perfil, ve sus fotos, las de sus amigas, sus “asistiré” a distintos eventos, los “me gusta” de sus contactos. Su identidad digital le fue robada. No sólo en Facebook. Esa misma noche, en su casa, se enterará que también tiene un perfil en la red Ask.fm, en el que cualquier usuario puede responder preguntas de otro usuario anónimo. Allí, se entera de que durante varios meses, con su foto real en el avatar y su identidad de “Mai”, mantuvo diálogos sexuales, relató su pérdida de virginidad, habló de novios, de sus gustos personales, de su grupo de amigos, y en los “me gusta” de cada una de sus respuestas aparecían sus contactos de Facebook, compañeros de colegio con sus fotos personales, que el creador de su perfil –el nuevo dueño de su identidad digital– había capturado y reproducido.
La vida personal de la adolescente estaba replicada en dos perfiles –uno en Facebook, otro en Ask.fm– que la chica jamás había creado.
El chico que se le había acercado era uno más de sus “amigos de la red” que ella desconocía.
El “robo de identidad” en la red, además de la transmisión de pornografía infantil, estafas digitales, acosos y hostigamientos, es una de las tendencias del cibercrimen que crece exponencialmente frente al desamparo legal.
“Esto es el iceberg de lo que viene.
La tecnología y los ataques informáticos van evolucionando, se fortalecen sobre las aplicaciones tecnológicas y luego atacan al factor humano, que es el más débil, y sobre el que más van golpeando”, indica el comisionado Carlos Rojas, jefe del Area de Investigaciones Telemáticas de la Policía Metropolitana.
Cuando su área se habilitó en 2009, las denuncias eran esporádicas. Ahora ya lleva judicializados 450 casos en el primer trimestre de este año. “Los delitos en la red se multiplican por diez”, afirma.
Hoy en día, cada vez es más difícil discernir cuándo un “perfil” de Facebook es real o inventado. Y cuando se roba, o se replica, un “perfil” desde el que se difama u hostiga, las posibilidades de “levantarlo” de la red son escasas o demoran mucho. Además, la usurpación de identidad, si el daño no es malicioso, es una contravención, pero no tiene alcance penal.
Facebook no tiene representación legal en la Argentina. Para que atiendan un reclamo hay que librar un exhorto internacional, via judicial, con intervención de la Cancillería, y luego la justicia de Estados Unidos resolverá si se vulneraron o no las leyes de Estados Unidos, de modo que se justifique ordenarle a Facebook la baja de un perfil falso. El trámite puede llevar más de seis meses. Twitter presenta los mismos obstáculos legales, e incluso la empresa establece que le envíen los requerimientos por fax (sí, por fax) para las denuncias. Para solicitar los diálogos vía messenger del BlackBerry, hay que reclamarlo en Canadá.
Sin embargo, en casos de pornografía infantil o de menores secuestrados, ambas redes sociales tienen una suerte de “botón de alerta” para las fuerzas de seguridad, con el que pueden pedir el resguardo de la información y adelantar, vía Interpol, la gravedad del caso para actuar rápido y detectar la identidad del usuario. Pero en casos de estafas digitales, acosos sexuales o amenazas quedan exentos de procedimientos veloces.
Por el “robo de identidad” citado a principio del texto, en el que se trasladó información verdadera a un perfil falso, la red social Ask.fm respondió al Área de Investigaciones Telemáticas que iniciarán el –lento– camino judicial por vía internacional. De modo que el perfil falso, con la foto real de la damnificada, sigue activo.
El domicilio legal de Ask.fm es Letonia –ex Unión Soviética–, país que adhirió a la Unión Europea en 2004. Tiene casi 30 millones de usuarios. Esa red social es muy popular en América Latina. Permite crear un perfil, enlazado con el de Facebook, con datos básicos, nombre y apellido, correo electrónico, clave. Su modalidad es que un usuario pueda dar respuestas a otro usuario anónimo, que puede llegar a convertirse en una plataforma digital de “ciberacoso”, con diálogos sexuales, intimidaciones y amenazas de violencia. Según especialistas británicos en seguridad informática, en declaraciones publicadas en el diario Daily Mail el 13 de enero de este año, Ask.fm “está asociada a algunas de las peores formas de ciberintimidación y fue vinculada a varios suicidios en Irlanda y Estados Unidos” Los medios tecnológicos están abiertos. Mensajes de texto, salas de chat, celulares con cámara incorporada, Facebook, WhatsApp representan un universo virtual por el que chicos de 8 años en adelante se relacionan sin restricciones. Como “nativos digitales”, su vida pasa por la tecnología, por la comunicación en las redes. También los riesgos: ese universo en la red circula sin control, y los menores están expuestos al “grooming”, al “bullying” o promueven el “sexting ”, tendencias de la red que atraviesan el aula (ver recuadro “Los mayores ...”).
El “grooming” se inicia con un engaño, una identidad falsa, un usuario adulto de la red que se hace pasar por alguien con el posible objetivo final de abusar de un menor. Incluso existe un programa, a través de una cámara web, que permite transmitir una imagen en la que un menor saluda y sonríe, y su uso permite dar mayor credibilidad a su falsa identidad. El acercamiento es a través de algo en común, el deporte, la institución educativa, y de ese modo el pedófilo va concentrando a las potenciales víctimas, agrupadas por edad, para luego producir el ataque sexual, por la cámara de video o el contacto físico. En ese trabajo de acercamiento digital, el pedófilo no tiene impedimentos legales.
“La ley penaliza el delito, pero la persuasión del mayor sobre el menor no está tipificada. Inicia una relación en la que hace sentir cómoda a su futura víctima, obtiene información familiar y luego busca que se saque la ropa delante de la cámara para filmarlo. Si no lo hace, comienza la extorsión moral, la amenaza de relatárselo a sus padres. Lo mismo sucede si no acepta un encuentro personal”, dice Belén Rey, de la ONG Argentina Cibersegura, que inició una campaña por la sanción parlamentaria de una ley que condene el “grooming”, como un delito preparatorio hacia otro de carácter sexual más grave. El “grooming” es considerado delito en las recientes reformas de la legislación penal de España, Alemania o Estados Unidos, entre otros países. En Australia, el uso de medios electromagnéticos para involucrar, tentar o inducir a menores de 16 años a actividades sexuales alcanza a penas de 16 años de prisión. (ver recuadro “La ley ...) .
Según una investigación de la ONG Argentina Cibersegura, casi 53% de los casos de “grooming” denunciados en el país en los últimos meses se ejerció sobre menores de 11 a 15 años; el 33,7%, entre chicos de 7 a 10 años, 10,2% entre adolescentes de 16 a 18 años y un 3,2% en menores de 6 años. En el 75% de los casos, el “grooming” se consumó a través de redes sociales (Twitter, Facebook, etc), el 49,8% por chats (con una franja que utilizó las dos plataformas) y luego, en forma decreciente, a través de videojuegos en línea, correo electrónico, o mensajes vía celular.
Si bien juegos online como el “Call of Duty” tienen márgenes de interacción muy limitados, también se utilizan como punto de contacto de menores, para luego invitarlos a interactuar por una red social y desde allí consumar el “grooming”.
A la Argentina llegan muchas investigaciones que impulsa el FBI para detectar redes de prostitución infantil. Pero el problema es que ambos países tienen legislaciones incompatibles sobre delitos sexuales. En Estados Unidos, la nueva ley de cibercrímenes sanciona a quienes contacten menores por Internet a fin de abusar de ellos. La ley permite la actuación del “agente encubierto”, que actúa en la red con la simple sospecha de la tenencia o el tráfico de pornografía. Argentina no tiene esa figura. Desde el área de Investigaciones Telemáticas presentan un estado de indefensión legal como obstáculo para las investigaciones. La policía llega con el delito consumado.
“En la Argentina no hay agente encubierto para la pornografía infantil. El policía está expectante, sólo puede esperar a ver si el acoso se concreta personalmente. A partir de allí puede actuar. La ley debería permitir que un ciberpolicía previniera delitos”, requiere el comisionado Rojas.
Con los obstáculos a la vista, la para prevenir el delito en la red la inteligencia policial se realiza “a puertas abiertas”, con especialistas que buscan descifrar los códigos secretos que aparecen en relatos de historias eróticas, en foros donde un usuario indica “busco y tengo SDPA (Sexo Duro Preadolescente) -9”, que da el aviso de la posesión de imágenes pornográficas de menores de esa edad. Pero como la tenencia simple de fotos no implica delito, frente a la presunción de una red de pornografía se hace una denuncia a la justicia. Pero se debe contar con la anuencia de un fiscal que permita continuar la investigación de la cuenta de correo. Son casos contados. Y, mientras que en el delito tecnológico no es sencillo determinar fronteras, las fiscalías suelen rechazar casos por “problemas de jurisdicción”.
Argentina integra el lote de los países más atrasados en la legislación sobre delitos informáticos. Recién en 2008 la ley contempló a la violación del correo electrónico como “violación de correspondencia”. Los servidores de Internet no están obligados a guardar registros horarios de conexiones, que permitan investigar desde dónde se conecta el posible miembro de una red de pornografía infantil, por ejemplo. Su colaboración es voluntaria. Esto hace que las “huellas digitales” que deja el ciberdelito sean siempre difíciles de reconstruir.
La relación entre el usuario y la red se fue transformando con el paso del tiempo. Hasta hace pocos años, el usuario navegaba por Internet para mirar contenidos desde la computadora de su casa. Luego fue al locutorio, después al cibercafé, con su laptop. Empezó a moverse sin conexión fija, con wi-fi. Y después creó sus propios contenidos, publica fotos y videos en la red, cuenta su vida. El intercambio social con conocidos, y desconocidos es vertiginoso. La posibilidad de producir delitos creció con la misma velocidad.
¿Es posible conocer a todos los contactos que tenemos en Facebook?
Un documental registra el intento de un un egresado universitario por ponerse en contacto con los 788 amigos de Facebook y reflejar las historias personales que hay detrás de las actualizaciones diarias en esa red social. La Nación
¿Alguna vez te has preguntado si los cientos de personas con quienes compartes fotografías e historias personales en Facebook son realmente amigos? ¿O se trata sólo de simples conocidos?
Video: Cara a cara con mis amigos en Facebook
Ty Morin , quien acaba de graduarse en Connecticut, Estados Unidos, llegó a la conclusión de que hay una gran diferencia entre comunicarse en masa y establecer una conexión genuina con la gente que es importante para ti.
Así que decidió viajar por el país y a otras partes del mundo para encontrarse cara a cara con sus 788 amigos de Facebook .
Su idea es filmarlos para un documental y realizar una serie fotográfica sobre sus conexiones en internet.
El mural con los contactos que Morin buscará registrar en su documental. Ya contactó a unos 20 de sus amigos en Facebook.
Morin espera que su documental , llamado Friend Request Accepted (Solicitud de Amistad Aceptada), ayude al público a reflexionar más sobre las personas que están detrás de la colección global de perfiles en las redes sociales.
She is currently working on a project calledRevising Ekphrasis, which uses advanced computational tools to explore connections between 4,500 English-language poems. You can find her online at LisaRhody.com and follow her on Twitter at @lmrhody.]
In last week’s post about social network analysis, I introduced NodeXL and its potential use for understanding online social networks. In this post, I want to focus on what we can learn from conference Twitter backchannel conversations, and how we can use software like NodeXL to improve the way we use social media to build computer-mediated scholarly networks.
During this year’s annual meeting of the Modern Language Association, I worked withMarc Smith, co-founder of the Social Media Research Foundation and chief social scientist for Connected Action, to upload several sample datasets that mapped Twitter networks at the conference. On Friday, January 4, 2013, Marc uploaded a social media network graph of tweets that included the hashtag #mla13from this year’s MLA convention to the NodeXL Gallery.
Figure 1 (click to enlarge): A NodeXL network visualization of tweets from January 4, 2013 that include the hashtag #mla13.
The graph uses the Twitter images associated with each user id to represent an account (called an “actor” or “vertex” in the network) that sent a tweet, retweeted, replied to, or was mentioned in another tweet, and each “follows” relationship, tweet, retweet, reply, or mention creates a connection (represented by a green or blue line and called a “tie” or “edge”) between each Twitter user.
What can NodeXL show us about our conference Tweeting habits?
What we learn from the NodeXL graph is that the MLA Twitter network represents a tightly-bound community. Since the graph includes very few “isolates” (ie. tweets that come from people less closely connected to the group), the MLA network does not expand beyond those members already connected to the network. Marc mentioned this to me when he uploaded the graph. He explained that the MLA Twitter network represents an “in-group bounded community,” meaning that those who tweeted about MLA generally were read and retweeted by others with close ties to the MLA conference themselves. Few “outsiders” were visible in this network, in contrast to the many “isolates” found in more widely-discussed topics like brands or news events.
While it’s perfectly reasonable to assume that the Twitter backchannel during a scholarly convention may serve to form a more tightly-knit social network, the NodeXL graphs of the Twitter conversation at MLA 2013 also suggest that participation in the Twitter exchange did not broaden the community’s outreach. Sociologists distinguish between “bridging” versus “binding forms of social connections. The MLA Twitter network suggests it is used for bonding existing groups more than bridging to new connections. If the purpose of the backchannel conversation had been to strengthen existing ties, then the next step might be reaching out to connect to less-well-connected people, thereby extending the conversation to a larger community.
How can Twitter backchannel networks help us understand our community better?
Measures of betweenness centrality, for example, can show who creates the most connections in the network. In the case of the MLA graph from January 4th, the Twitter handles @MLAConvention, @rgfeal, @HASTAC, and @kfitz are central parts of the network because they act as hubs between disparate Twitter user groups. This is relatively unsurprising because the accounts are either the personal or professional Twitter presences of representatives from larger, pre-existing face-to-face networks, such as MLA and HASTAC. More interesting, though are the betweenness centralities of individuals, such as @briancroxall,@adelinekoh, and @mkirschenbaum. These are individual accounts not associated with the larger conference organizers whose tweeting was central to the interconnectedness of the MLA network as a whole.
Lists of words frequently used in the network of those who mention #mla13 are also detected in many of the MLA graphs. For example, when using the grouping algorithms to cluster Twitter users by their closest affiliations and most commonly used words, we can trace the communities within the network formed by the recurring use of particular vocabularies. In the case of MLA, session tags and workshop titles created smaller communities within the larger network.
By listing the top URLs used in Tweets from the entire #mla13network as well as from each of the smaller groups within the network, we can trace a pattern of interests, values, and sites of engagement that drew the most attention across the network.
What does social network analysis miss?
There are caveats to looking at network graphs. The graphs produced in NodeXL are not direct representations of whole conference networks, as they cannot capture real-time, face-to-face conversations that are not recorded online. Social media analysis is skewed toward those participants who are the most prolific, and more work needs to be done to understand the influence of single actors within the larger community. For example, the graph from January 4 includes the hashtag #wcw as one of the most frequently discussed terms for the day. In fact, what we know from experience of the conference is that there was a large amount of Tweeting going on in William Carlos Williams Society session, which made the #wcw hashtag appear so prominently. Even with more than 400 Twitter network participants, the sampling is still relatively small and so more easily influenced.
Still, social network analysis and visualizations produce much more than hairball graphs and can become a valuable activity to undertake — especially if you are interested in learning how to change, improve, and expand your own online scholarly communities. Future posts will help get you started on using NodeXL yourself: installing NodeXL, importing data from social media applications, and creating visualizations to share.
What networks would you like to better understand? Have you used social network analysis to understand your relationship to your peers online?
In the mid-2000s that lab was, however, one of the only places on earth to do the kind of science Couzin wanted. He didn’t care about locusts, per se—Couzin studies collective behavior. That’s swarms, flocks, schools, colonies … anywhere the actions of individuals turn into the behaviors of a group. Biologists had already teased apart the anatomy of locusts in detail, describing their transition from wingless green loners at birth to flying black-and-yellow adults. But you could dissect one after another and still never figure out why they blacken the sky in mile-wide plagues. Few people had looked at how locusts swarm since the 1960s—it was, frankly, too hard. So no one knew how a small, chaotic group of stupid insects turned into a cloud of millions, united in one purpose.
Couzin would put groups of up to 120 juveniles into a sombrero-shaped arena he called the locust accelerator, letting them walk in circles around the rim for eight hours a day while an overhead camera filmed their movements and software mapped their positions and orientations. He eventually saw what he was looking for: At a certain density, the bugs would shift to cohesive, aligned clusters. And at a second critical point, the clusters would become a single marching army. Haphazard milling became rank-and-file—a prelude to their transformation into black-and-yellow adults.
That’s what happens in nature, but no one had ever induced these shifts in the lab—at least not in animals. In 1995 a Hungarian physicist named Tamás Vicsek and his colleagues devised a model to explain group behavior with a simple—almost rudimentary—condition: Every individual moving at a constant velocity matches its direction to that of its neighbors within a certain radius. As this hypothetical collective becomes bigger, it flips from a disordered throng to an organized swarm, just like Couzin’s locusts. It’s a phase transition, like water turning to ice. The individuals have no plan. They obey no instructions. But with the right if-then rules, order emerges.
Couzin wanted to know what if-then rules produced similar behaviors in living things. “We thought that maybe by being close to each other, they could transfer information,” Couzin says. But they weren’t communicating in a recognizable way. Some other dynamic had to be at work.
Rules that produce majestic cohesion out of local jostling turn up everywhere.
The answer turned out to be quite grisly. Every morning, Couzin would count the number of locusts he placed in the accelerator. In the evening, his colleague Jerome Buhl would count them as he took them out. But Buhl was finding fewer individuals than Couzin said he had started with. “I thought I was going mad,” Couzin says. “My credibility was at stake if I couldn’t even count the right number of locusts.”
When he replayed the video footage and zoomed in, he saw that the locusts were biting each other if they got too close. Some unlucky individuals were completely devoured. That was the key. Cannibalism, not cooperation, was aligning the swarm. Couzin figured out an elegant proof for the theory: “You can cut the nerve in their abdomen that lets them feel bites from behind, and you completely remove their capacity to swarm,” he says.
Couzin’s findings are an example of a phenomenon that has captured the imagination of researchers around the world. For more than a century people have tried to understand how individuals become unified groups. The hints were tantalizing—animals spontaneously generate the same formations that physicists observe in statistical models. There had to be underlying commonalities. The secrets of the swarm hinted at a whole new way of looking at the world.
But those secrets were hidden for decades. Science, in general, is a lot better at breaking complex things into tiny parts than it is at figuring out how tiny parts turn into complex things. When it came to figuring out collectives, nobody had the methods or the math.
A flock of red-winged blackbirds forms and re-forms over California’s Sacramento Valley.
Photo: Lukas Felzmann
The first thing to hit Iain Couzin when he walked into the Oxford lab where he kept his locusts was the smell, like a stale barn full of old hay. The second, third, and fourth things to hit him were locusts. The insects frequently escaped their cages and careened into the faces of scientists and lab techs. The room was hot and humid, and the constant commotion of 20,000 bugs produced a miasma of aerosolized insect exoskeleton. Many of the staff had to wear respirators to avoid developing severe allergies. “It wasn’t the easiest place to do science,” Couzin says.
Now, thanks to new observation technologies, powerful software, and statistical methods, the mechanics of collectives are being revealed. Indeed, enough physicists, biologists, and engineers have gotten involved that the science itself seems to be hitting a density-dependent shift. Without obvious leaders or an overarching plan, this collective of the collective-obsessed is finding that the rules that produce majestic cohesion out of local jostling turn up in everything from neurons to human beings. Behavior that seems impossibly complex can have disarmingly simple foundations. And the rules may explain everything from how cancer spreads to how the brain works and how armadas of robot-driven cars might someday navigate highways. The way individuals work together may actually be more important than the way they work alone.
Blue jack mackerel merge into a bait ball, a torus that confuses predators.
Photo: Christopher Swann/Science Photo Library
Aristotle first posited that the whole could be more than the sum of its parts. Ever since, philosophers, physicists, chemists, and biologists have periodically rediscovered the idea. But it was only in the computer age—with the ability to iterate simple rule sets millions of times over—that this hazy concept came into sharp focus.
HOW SWARMS EMERGE
Individuals in groups from neurons and cancer cells to birds and fish organize themselves into collectives, and those collectives move in predictable ways. But the ways those swarms, schools, flocks, and herds flip from chaos to order differ. Here’s a look at some of the behaviorial triggers. —Katie M. Palmer
GOLDEN SHINERS
Behavior: Seek darkness
Presumably for protection, shiners search out dark waters. But they can’t actually perceive changes in light levels that might guide their way. Instead, they follow one simple directive: When light disappears, slow down. As a result, the fish in a school pile up in dark pools and stay put.
ANTS
Behavior: Work in rhythm
When ants of a certain species get crowded enough to bump into each other, coordinated waves of activity pulse through every 20 minutes.
HUMANS
Behavior: Be a follower
Absent normal communication, humans can be as impressionable as a flock of sheep. If one member of a walking group is instructed to move toward a target, though other members may not know the target—or even that there is a target—the whole group will eventually be shepherded in its direction.
LOCUSTS
Behavior: Cannibalism
When enough locusts squeeze together, bites from behind send individuals fleeing to safety. Eventually they organize into conga-line-like clusters to avoid being eaten. They also emit pheromones to attract even more locusts, resulting in a swarm.
STARLINGS
Behavior: Do what the neighbors do
These birds coordinate their speed and direction with just a half dozen of their closest murmuration-mates, regardless of how packed the flock gets. Those interactions are enough to steer the entire group in the same direction.
HONEYBEES
Behavior: Head-butting
When honeybees return from searching for a new nest, they waggle in a dance that identifies the location. But if multiple sites exist, a bee can advocate for its choice by ramming its head into other waggling bees. A bee that gets butted enough times stops dancing, ultimately leaving the hive with one option.
For most of the 20th century, biologists and physicists pursued the concept along parallel but separate tracks. Biologists knew that living things exhibited collective behavior—it was hard to miss—but how they pulled it off was an open question. The problem was, before anyone could figure out how swarms formed, someone had to figure out how to do the observations. In a herd, all the wildebeests/bacteria/starlings/whatevers look pretty much alike. Plus, they’re moving fast through three-dimensional spaces. “It was just incredibly difficult to get the right data,” says Nigel Franks, a University of Bristol biologist and Couzin’s thesis adviser. “You were trying to look at all the parts and the complete parcel at the same time.”
Physicists, on the other hand, had a different problem. Typically biologists were working with collectives ranging in number from a few to a few thousand; physicists count groups of a few gazillion. The kinds of collectives that undergo phase transitions, like liquids, contain individual units counted in double-digit powers of 10. From a statistical perspective, physics and math basically pretend those collectives are infinitely large. So again, you can’t observe the individuals directly in any meaningful way. But you can model them.
A great leap forward came in 1970, when a mathematician named John Conway invented what he called the Game of Life. Conway imagined an Othello board, with game pieces flipping between black and white. The state of the markers—called cells—changed depending on the status of neighboring cells. A black cell with one or no black neighbors “died” of loneliness, turning white. Two black neighbors: no change. Three, and the cell “resurrected,” flipping from white to black. Four, and it died of overcrowding—back to white. The board turned into a constantly shifting mosaic.
Conway could play out these rules with an actual board, but when he and other programmers simulated the game digitally, Life got very complicated. At high speed, with larger game boards, they were able to coax an astonishing array of patterns to evolve across their screens. Depending on the starting conditions, they got trains of cells that trailed puffs of smoke, or guns that shot out small gliders. At a time when most software needed complex rules to produce even simple behaviors, the Game of Life did the opposite. Conway had built a model of emergence—the ability of his little black and white critters to self-organize into something new.
Sixteen years later, a computer animator named Craig Reynolds set out to find a way to automate the animated movements of large groups—a more efficient algorithm would save processing time and money. Reynolds’ software, Boids, created virtual agents that mimicked a flock of birds. It included behaviors like obstacle avoidance and the physics of flight, but at the heart of Boids were three simple rules: Move toward the average position of your neighbors, keep some distance from them, and align with their average heading (alignment is a measure of how close an individual’s direction of movement is to that of other individuals). That’s it.
Boids and its ilk revolutionized Hollywood in the early ’90s. It animated the penguins and bats of Batman Returns. Its descendants include software like Massive, the program that choreographed the titanic battles in the Lord of the Rings trilogy. That would all be miraculous enough, but the flocks created by Boids also suggested that real-world animal swarms might arise the same way—not from top-down orders, mental templates of orderly flocks, or telepathic communication (as some biologists had seriously proposed). Complexity, as Aristotle suggested, could come from the bottom up.
The field was starting to take off. Vicsek, the Hungarian physicist, simulated his flock in 1995, and in the late 1990s a German physicist named Dirk Helbing programmed sims in which digital people spontaneously formed lanes on a crowded street and crushed themselves into fatal jams when fleeing from a threat like a fire—just as real humans do. Helbing did it with simple “social forces.” All he had to do was tell his virtual humans to walk at a preferred speed toward a destination, keep their distance from walls and one another, and align with the direction of their neighbors. Presto: instant mob.
By the early 2000s, the research in biology and physics was starting to intersect. Cameras and computer-vision technologies could show the action of individuals in animal swarms, and simulations were producing more and more lifelike results. Researchers were starting to be able to ask the key questions: Were living collectives following rules as simple as those in the Game of Life or Vicsek’s models? And if they were … how?
TAKING SHAPE
Changing simple parameters has profound effects on a swarm. By controlling only attraction, repulsion, and alignment (how similar a critter’s direction is to that of its neighbors), researcher Iain Couzin induced three different behaviors in a virtual collective, all akin to ones in nature.—Katie M. Palmer
DISORDER Alignment with only the closest neighbors produces … nothing but a disordered swarm.
TORUS Raise the alignment and the chaotic swarm swirls into a doughnut shape called a torus.
FLOCK Maximize alignment across the flock and the torus shifts; everyone travels in the same direction.
Before studying collectives, Couzin collected them. Growing up in Scotland, he wanted pets, but his brothers’ various allergies allowed only the most unorthodox ones. “I had snails at the back of my bed, aphids in my cupboard, and stick insects in my school locker,” he says. And anything that formed swarms fascinated him. “I remember seeing these fluidlike fish schools on TV, watching them again and again, and being mesmerized. I thought fish were boring, but these patterns—” Couzin pauses, and you can almost see the whorls of schooling fish looping behind his eyes; then he’s back. “I’ve always been interested in patterns,” he says simply.
When Couzin became a graduate student in Franks’ lab in 1996, he finally got his chance to work on them. Franks was trying to figure out how ant colonies organize themselves, and Couzin joined in. He would dab each bug with paint and watch them on video, replaying the recording over and over to follow different individuals. “It was very laborious,” he says. Worse, Couzin doubted it worked. He didn’t believe the naked eye could follow the multitude of parallel interactions in a colony. So he turned to artificial ones. He learned to program a computer to track the ants—and eventually to simulate entire animal groups. He was learning to study not the ants but the swarm.
For a biologist, the field was a lonely one. “I thought there must be whole labs focused on this,” Couzin says. “I was astonished to find that there weren’t.” What he found instead was Boids. In 2002 Couzin cracked open the software and focused on its essential trinity of attraction, repulsion, and alignment. Then he messed with it. With attraction and repulsion turned up and alignment turned off, his virtual swarm stayed loose and disordered. When Couzin upped the alignment, the swarm coalesced into a whirling doughnut, like a school of mackerel. When he increased the range over which alignment occurred even more, the doughnut disintegrated and all the elements pointed themselves in one direction and started moving together, like a flock of migrating birds. In other words, all these different shapes come from the same algorithms. “I began to view the simulations as an extension of my brain,” Couzin says. “By allowing the computer to help me think, I could develop my intuition of how these systems worked.”
By 2003, Couzin had a grant to work with locusts at Oxford. Labs around the world were quietly putting other swarms through their paces. Bacterial colonies, slime molds, fish, birds … a broader literature was starting to emerge. Work from Couzin’s group, though, was among the first to show physicists and biologists how their disciplines could fuse together. Studying animal behavior “used to involve taking a notepad and writing, ‘The big gorilla hit the little gorilla,’ ” Vicsek says. “Now there’s a new era where you can collect data at millions of bits per second and then go to your computer and analyze it.”
A swarm of locusts.
Photo: Mitsuhiko Imamori/Minden
Today Couzin, 39, heads a lab at Princeton University. He has a broad face and cropped hair, and the gaze coming from behind his black-rimmed glasses is intense. The 19-person team he leads is ostensibly part of the Department of Ecology and Evolutionary Biology but includes physicists and mathematicians. They share an office with eight high-end workstations—all named Hyron, the Cretan word for beehive, and powered by videogame graphics cards.
Locusts are verboten in US research because of fears they’ll escape and destroy crops. So when Couzin came to Princeton in 2007, he knew he needed a new animal. He had done some work with fish, so he headed to a nearby lake with nets, waders, and a willing team. After hours of slapstick failure, and very few fish, he approached some fishermen on a nearby bridge. “I thought they’d know where the shoals would be, but then I went over and saw tiny minnow-sized fish in their buckets, schooling like crazy.” They were golden shiners—unremarkable 2- to 3-inch-long creatures that are “dumber than I could possibly have imagined,” Couzin says. They are also extremely cheap. To get started he bought 1,000 of them for 70 bucks.
When Couzin enters the room where the shiners are kept, they press up against the front of their tanks in their expectation of food, losing any semblance of a collective. But as soon as he nets them out and drops them into a wide nearby pool, they school together, racing around like cars on a track. His team has injected colored liquid and a jelling agent into their tiny backs; the two materials congeal into a piece of gaudy plastic, making them highly visible from above. As they navigate courses in the pool, lights illuminate the plastic and cameras film their movements. Couzin is using these stupid fish to move beyond just looking at how collectives form and begin to study what they can accomplish. What abilities do they gain?
For example, when Couzin flashes light over the shiners, they move, as one, to shadier patches, presumably because darkness equals relative safety for a fish whose main defensive weapon is “run away.” Behavior like this is typically explained with the “many wrongs principle,” first proposed in 1964. Each shiner, the theory goes, makes an imperfect estimate about where to go, and the school, by interacting and staying together, averages these many slightly wrong estimations to get the best direction. You might recognize this concept by the term journalist James Surowiecki popularized: “the wisdom of crowds.”
But in the case of shiners, Couzin’s observations in the lab have shown that the theory is wrong. The school could not be pooling imperfect estimates, because the individuals don’t make estimates of where things are darker at all. Instead they obey a simple rule: Swim slower in shade. When a disorganized group of shiners hits a dark patch, fish on the edge decelerate and the entire group swivels into darkness. Once out of the light, all of them slow down and cluster together, like cars jamming on a highway. “That’s purely an emergent property,” Couzin says. “The sensing ability really happens only at the level of the collective.” In other words, none of the shiners are purposefully swimming toward anything. The crowd has no wisdom to cobble together.
Other students of collectives have found similar feats of swarm intelligence, including some that happen in actual swarms. Every spring, honeybees leave their old colonies to build new nests. Scouts return to the hive to convey the locations of prime real estate by waggling their bottoms and dancing in figure eights. The intricate steps of the dances encode distance and direction, but more important, these dances excite other scouts.
Thomas Seeley, a behavioral biologist at Cornell, used colored paint to mark bees that visited different sites and found that those advocating one location ram their heads against colony-mates that waggle for another. If a dancer gets rammed often enough, it stops dancing. The head-butt is the bee version of a downvote. Once one party builds past a certain threshold of support, the entire colony flies off as one.
House-hunting bees turn out to be a literal hive mind, composed of bodies. This is no cheap metaphor. In the 1980s cognitive scientists began to posit that human cognition itself is an emergent process. In your brain, this thinking goes, different sets of neurons fire in favor of different options, exciting some neighbors into firing like the waggling bees, and inhibiting others into silence, like the head-butting ones. The competition builds until a decision emerges. The brain as a whole says, “Go right” or “Eat that cookie.”
If a falcon attacks, all the starlings dodge almost instantly—even those on the far side of the flock that haven’t seen the threat.
The same dynamics can be seen in starlings: On clear winter evenings, murmurations of the tiny blackish birds gather in Rome’s sunset skies, wheeling about like rustling cloth. If a falcon attacks, all the starlings dodge almost instantaneously, even those on the far side of the flock that haven’t seen the threat. How can this be? Italian physicist Andrea Cavagna discovered their secret by filming thousands of starlings from a chilly museum rooftop with three cameras and using a computer to reconstruct the birds’ movements in three dimensions. In most systems where information gets transferred from individual to individual, the quality of that information degrades, gets corrupted—like in a game of telephone. But Cavagna found that the starlings’ movements are united in a “scale-free” way. If one turns, they all turn. If one speeds up, they all speed up. The rules are simple—do what your half-dozen closest neighbors do without hitting them, essentially. But because the quality of the information the birds perceive about one another decays far more slowly than expected, the perceptions of any individual starling extend to the edges of the murmuration and the entire flock moves.
All these similarities seem to point to a grand unified theory of the swarm—a fundamental ultra-calculus that unites the various strands of group behavior. In one paper, Vicsek and a colleague wondered whether there might be “some simple underlying laws of nature (such as, e.g., the principles of thermodynamics) that produce the whole variety of the observed phenomena.”
Couzin has considered the same thing. “Why are we seeing this again and again?” he says. “There’s got to be something deeper and more fundamental.” Biologists are used to convergent evolution, like the streamlining of dolphins and sharks or echolocation in bats and whales—animals from separate lineages have similar adaptations. But convergent evolution of algorithms? Either all these collectives came up with different behaviors that produce the same outcomes—head-butting bees, neighbor-watching starlings, light-dodging golden shiners—or some basic rules underlie everything and the behaviors are the bridge from the rules to the collective.
Stephen Wolfram would probably say it’s the underlying rules. The British mathematician and inventor of the indispensable software Mathematica published a backbreaking 1,200-page book in 2002, A New Kind of Science, positing that emergent properties embodied by collectives came from simple programs that drove the complexity of snowflakes, shells, the brain, even the universe itself. Wolfram promised that his book would lead the way to uncovering those algorithms, but he never quite got there.
Couzin, on the other hand, is wary of claims that his field has hit upon the secret to life, the universe, and everything. “I’m very cautious about suggesting that there’ll be an underlying theory that’ll explain the stock market and neural systems and fish schools,” he says. “That’s relatively naive. There’s a danger in thinking that one equation fits all.” Physics predicts the interactions of his locusts, but the mechanism manifests through cannibalism. Math didn’t produce the biology; biology generated the math.
Still, just about any system of individual units pumped with energy—kinetic, thermal, whatever—produces patterns. Metal rods organize into vortices when bounced around on a vibrating platform. In a petri dish, muscle proteins migrate unidirectionally when pushed by molecular motors. Tumors spawn populations of rogue, mobile cells that align with and migrate into surrounding tissues, following a subset of trailblazing leader cells. That looks like a migrating swarm; figure out its algorithms and maybe you could divert it from vital organs or stop its progress.
The same kind of rules apply when you step up the complexity. The retina, that sheet of light-sensing tissue at the back of the eye, connects to the optic nerve and brain. Michael Berry, a Princeton neuroscientist, mounts patches of retinas on electrodes and shows them videos, watching their electrophysiological responses. In this context, the videos are like the moving spotlights Couzin uses with his shiners—and just as with the fish, Berry finds emergent behaviors with the addition of more neurons. “Whether the variable is direction, heading, or how you vote, you can map the mathematics from system to system,” Couzin says.
A crowd of humans.
Photo: Amanda Mustard/Corbis
In a lab that looks like an aircraft hangar, several miles from Princeton’s main campus, an assortment of submersibles are suspended from the ceiling. The cool air has a tang of chlorine, thanks to a 20,000-gallon water tank, 20 feet across and 8 feet deep, home to four sleek, cat-sized robots with dorsal and rear propellers that let them swim in three dimensions.
The robots are called Belugas, and they’re designed to test models of collective behavior. “We’re learning about mechanisms in nature that I wouldn’t have dreamed of designing,” says engineer Naomi Leonard. She plans to release pods of underwater robots to collect data on temperature, currents, pollution, and more. Her robots can also track moving gradients, avoid each other, and keep far enough apart to avoid collecting redundant data—just enough programming to unlock more complex abilities. Theoretically.
Today it’s not working. Three Belugas are out of the tank so Leonard’s team can tinker. The one in the water is on manual, driven by a thick gaming joystick. The controls are responsive, if leisurely, and daredevil maneuvers are out of the question.
Leonard has a video of the robots working together, though, and it’s much more convincing. The bots carry out missions with a feedback-controlled algorithm programmed into them, like finding the highest concentrations of oil in a simulated spill or collecting “targets” separately and then reuniting.
Building a successful robot swarm would show that the researchers have figured out something basic. Robot groups already exist, but most have sophisticated artificial intelligence or rely on orders from human operators or central computers. To Tamás Vicsek—the physicist who created those early flock simulations—that’s cheating. He’s trying to build quadcopters that flock like real birds, relying only on knowledge of their neighbors’ position, direction, and speed. Vicsek wants his quadcopters to chase down another drone, but so far he’s had little success. “If we just apply the simple rules developed by us and Iain, it doesn’t work,” Vicsek says. “They tend to overshoot their mark, because they do not slow down enough.”
Another group of researchers is trying to pilot a flock of unmanned aerial vehicles using fancy network theories—the same kind of rules that govern relationships on Facebook—to communicate, while governing the flocking behavior of the drones with a modified version of Boids, the computer animation software that helped spark the field in the first place. Yet another team is working on applying flocking behaviors to autonomous cars—one of the fundamental emergent properties of a flock is collision avoidance, and one of the most important things self-driving cars will have to be able to do is not run into people or one another.
So far, the Belugas’ biggest obstacle has been engineering. The robots’ responses to commands are delayed. Small asymmetries in their hulls change the way each one moves. Ultimately, dealing with that messiness might be the key to taking the study of collectives to the next level. Ever since the days of Boids, scientists have made big assumptions about how animals interact. But animals are more than models. They sense the world. They communicate. They make decisions. These are the abilities that Couzin wants to channel. “I started off with these simple units interacting to form complex patterns, and that’s fine, but real animals aren’t that simple,” Couzin says. He picks up a plastic model of a crow from his bookshelf. “Here we have a pretty complex creature. It’s getting to the point where we’ll be able to analyze the behavior of these animals in natural, three-dimensional environments.” Step one might be to put a cheap Microsoft Kinect game system into an aviary, bathing the room in infrared and mapping the space.
Step two would be to take the same measurements in the real world. Every crow in a murder would carry miniature sensors that record its movements, along with the chemicals in its body, the activity in its brain, and the images on its retina. Couzin could marry the behavior of the cells and neurons inside each bird with the movements of the flock. It’s a souped-up version of the locust accelerator—combine real-world models with tech to get an unprecedented look at creatures that have been studied intensively as individuals but ignored as groups. “We could then really understand how these animals gain information from each other, communicate, and make decisions,” Couzin says. He doesn’t know what he’ll find, but that’s the beauty of being part of the swarm: Even if you don’t know where you’re going, you still get there.
Ed Yong (edyong209@gmail.com) writes the blog Not Exactly Rocket Science for National Geographic.
Every day, millions of people check in on Foursquare. We took a year's worth of check-ins in New York City and Tokyo and plotted them on a map. Each dot represents a single check-in, while the straight lines link sequential check-ins.
What you can see here represents the power of check-in data -- on Foursquare, every city around the world pulses with activity around places every hour of every day.
Related: Also see our data visualization of four days worth of Foursquare check-ins in New York CIty during Hurricane Sandy (and the subsequent power outage) during October 2012:
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