martes, 31 de agosto de 2010

Why try to change me now





I'm sentimental, so I walk in the rain
I've got some habits even I can't explain
I go to the corner and I end up in Spain
 Why try to change me now?
I sit and daydream, I've got daydreams galore
Cigarette ashes, there they go on the floor
I'll go away weekends, leave my keys in the door
 Why try to change me now?
Why can't I be more conventional?
People talk and they stare, so I try
But that can´t be, 'cause I can't see
My strange little world just go passing me by
So, let people wonder, let 'em laugh, let 'em frown
You know I'll love you till the moon's upside down
Don't you remember I was always your clown?
Why try to change me
Why would you want to change me
Why try to change me now



La mar











"Decía siempre "la mar". Así es como le dicen en español cuando la quieren. A veces los que la quieren hablan mal de ella, pero lo hacen siempre como si fuera una mujer. Algunos de los pescadores más jóvenes, los que usaban boyas y flotadores para sus sedales y tenían botes de motor comprados cuando los hígados de tiburón se cotizaban altos, empleaban el artículo masculino, le llamaban el mar. Hablaban de el mar como de un contendiente o un lugar, o un enemigo. Pero el viejo lo concebía siempre como perteneciente al género femenino y como algo que concedía o negaba grandes favores, y si hacía cosas perversas y terribles era porque no podía remediarlo. La luna, pensaba, le afectaba lo mismo que a una mujer."


Ernest Hemingway, El viejo y el mar (Pdf).




Emotional landscape







"And then I remember a story my friend deborah the psychologist told me once. Back in the 1980s, she was asked by the city of Philadelphia if she could volunteer to offer psychological counseling to a group of Cambodian refugees -boat people- who had recently arrived in the city. Deborah is an exceptional psychologist, bit she was terribly daunted by this task. These Cambodians had suffered the worst of what humans can inflict on each other -genocide, rape, torture, starvation, the murder of their relatives before their eyes, then long years in refugee camps and dangerous boat trips to the West where people died and corpses were fed to sharks -what could Deborah offer these people in terms of help? How could she possibly relate to their suffering?
"But don´t you know," Deborah reported to me, "what all these people wanted to talk about, once they could see a counselor?"
It was all: I met this guy when I was living in the refugee camp, and we fell in love. I thought he really loved me, but then we were separated on different boats, and he took up with my cousin. Now he´s married to her, but he says he really loves me, and he keeps calling me, and I know I should tell him to go away, but U still love him and I can´t stop thinking about him. And I don´t know what to do...
This is what we are like. Collectively, as a species, this is our emotional landscape. I met an old lady once, almost one hundred years old, and she told me, "There are only two questions that human beings have ever fought over, all through history. How much do you love me? And Who´s in charge?" Everything else is somehow manageable."

Elizabeth Gilbert, Eat, Pray, Love.


lunes, 30 de agosto de 2010

Lo digo como lo dicen las palabras
















Todos los días descubro
la espantosa realidad de las cosas:
cada cosa es lo que es.
Qué difícil es decir esto y decir
cuanto me alegro y me basta.
Para ser completo existir es suficiente.
No se qué pensarán los otros al leer esto;
creo que es bueno porque lo pienso sin esfuerzo;
lo pienso sin pensar que otros me oyen pensar;
lo pienso sin pensamientos,
lo digo como lo dicen las  palabras.

Fernando Pessoa




domingo, 29 de agosto de 2010

The Secret Powers of Time








Un barco







"Explicar con palabras de este mundo que partió de mi un barco llevándome."

Alejandra Pizarnik, Árbol de Diana.




sábado, 28 de agosto de 2010

Tremendo y soberano













"He alcanzado a comprender que la vida en cualquier ciudad es siempre más de lo mismo. París, Nueva York, Hollywood, Londres. Ves a la misma gente y entras en la rutina. Los restaurantes, los despachos, las habitaciones de los hoteles..., esos cócteles en los que no se oyen más que tonterías y en los que se te va el tiempo en perseguir a alguna dama. Es un ciclo eterno en el que uno desea cosas, las consigue y desea siempre algo más..., hasta que acabas por descubrir que la vida mundana es un tremendo y soberano aburrimiento."

Peter Viertel, Cazador blanco, corazón negro.




Turn me on




Like a flower waiting to bloom
Like a lightbulb in a dark room
I'm just sitting here waiting for you
To come on home and turn me on

Like the desert waiting for the rain
Like a school kid waiting for the spring
I'm just sitting here waiting for you
To come on home and turn me on

My poor heart, it's been so dark
Since you've been gone
After all, you're the one who turns me off
You're the only one who can turn me back on

My hi-fi is waiting for a new tune
My glass is waiting for some fresh ice cubes
I'm just sitting here waiting for you
To come on home and turn me on, turn me on



Internet Memes



Online University



No harder work





"The prospect of a long day at the beach makes me panic. There is no harder work I can think of than taking myself off to somewhere pleasant, where I am forced to stay for hours and 'have fun'."

Phillip Lopate




Sylvia





"La película cuenta la historia de la relación en-tre los poetas Sylvia Plath (Gwyneth Paltrow) y Ted Hughes (Daniel Craig) y su influencia en los problemas psicológicos de Plath y la trans-formación de ésta en uno de los poetas más destacados del siglo veinte"




"A veces sueño con un árbol
y el árbol es mi vida
Una rama es el hombre con el que me casaré
y las hojas mis hijos
Otra rama es mi futuro como escritora
y cada hoja es un poema

Otra rama es una triunfante carrera académica

Pero mientras intento elegir
las hojas empiezan a secarse
y el viento las aleja
hasta que las ramas del árbol
se quedan sin hojas."





"A veces me siento como si nunca hubiese escrito nada. Como si nunca hubiese pensado nada"

viernes, 27 de agosto de 2010

Ese eterno vacío





La compra ha sido sencilla:

dos cajas de té con canela,
pasta de dientes
unos cepillos interdentales,
espárragos,
cerveza sin alcohol
y vitaminas para el pelo.

Lo complicado es comprender
porqué comienzo así un poema
cuando lo único que quería decir
es que echo de menos
tu cuerpo en la cama.


Javier Das, 




jueves, 26 de agosto de 2010

For anything else








Un cortejo de ideas triviales.






Es en verdad increíble cuan insignificante y desprovista de interés, viéndola desde afuera, y cuan sorda y obscura, sentida en los adentros, transcurre la vida de la mayor parte de los hombres. No es más que un conjunto de tormentos, de aspiraciones impotentes, la marcha vacilante de un hombre que sueña a través de las cuatro edades de la vida hasta la muerte, con un cortejo de ideas triviales.
Los hombres se parecen a esos relojes a los cuales se les ha dado cuerda y andan sin saber por qué. Cada vez que se engendra un hombre y se le hace venir al mundo, se da cuerda de nuevo al reloj de la vida humana, para que repita una vez más su rancio sonsonete gastado de eterna caja de música, frase por frase, tiempo por tiempo, con variaciones apenas perceptibles.
Cada individuo, cada faz humana, cada vida, no es sino un ensueño más, un efímero ensueño del espíritu infinito de la Naturaleza, de la voluntad de vivir persistente y obstinada. No es sino una imagen fugitiva más, que dibuja al desgaire en su infinita página del espacio y del tiempo, que deja subsistir algunos instantes de una brevedad vertiginosa, y borra en seguida para dejar sitio a otras. Sin embargo (y esto es el aspecto de la vida que más da que pensar y meditar), es preciso que la voluntad de vivir, violenta e impetuosa, pague cada una de esas imágenes fugaces, cada uno de esos vanos caprichos, al precio de profundos dolores sin cuento y de una amarga muerte, largo tiempo temida y que llega al fin. He aquí por qué nos deja de pronto graves el aspecto de un cadáver.

Arthur Schopenhauer, El amor las mujeres y la muerte (Pdf)



miércoles, 25 de agosto de 2010

The beauty of data visualization




"It feels like we're all suffering from information overload or data glut. And the good news is there might be an easy solution to that, and that's using our eyes more.So, visualizing information, so that we can see the patterns and connections that matter and then designing that information so it makes more sense, or it tells a story, or allows us to focus only on the information that's important. Failing that, visualized information can just look really cool.
So, let's see. 



This is the Billion Dollar Gram, and this image arose out of frustration I had with the reporting of billion-dollar amounts in the press. That is, they're meaningless without context. 500 billion for this pipeline. 20 billion for this war. It doesn't make any sense, so the only way to understand it is visually and relatively. So I scraped a load of reported figures from various news outlets and then scaled the boxes according to those amounts. And the colors here represent the motivation behind the money. So purple is fighting, and red is giving money away, and green is profiteering. And what you can see straight away is you start to have a different relationship to the numbers. You can literally see them. But more importantly, you start to see patterns and connections between numbers that would otherwise be scattered across multiple news reports.

Let me point out some that I really like. This is OPEC's revenue, this green box here -- 780 billion a year. And this little pixel in the corner -- three billion -- that's their climate change fund. Americans, incredibly generous people -- over 300 billion a year, donated to charity every year, compared with the amount of foreign aid given by the top 17 industrialized nations at 120 billion. And then of course, the Iraq War, predicted to cost just 60 billion back in 2003. And it mushroomed slightly. Afghanistan mushroomed now to 3,000 billion. So now it's great because now we have this texture, and we can add numbers to it as well. So we could say, well, a new figure comes out ... let's see African debt. How much of this diagram do you think might be taken up by the debt that Africa owes to the West? Let's take a look. So there it is. 227 billion is what Africa owes. And the recent financial crisis -- how much of this diagram might that figure take up? What has that cost the world? Let's take a look at that. Dooosh. I think is the appropriate sound effect for that much money. 11,900 billion. So, by visualizing this information, we turned it into a landscape that you can explore with your eyes, a kind of map really, a sort of information map. And when you're lost in information, and information map is kind of useful.

So I want to show you another landscape now. We need to imagine what a landscape of the world's fears might look like. Let's take a look.


 This is mountains out of mole hills, a timeline of global media panic. So, I'll label this for you in a second. But the height here, I want to point out, is the intensity of certain fears, as reported in the media. Let me point them out. So this, swine flu -- pink. Bird flu. SARS -- brownish here. Remember that one. The millennium bug -- terrible disaster. These little green peaks are asteroid collisions.  And in summer, here, killer wasps.

So these are what our fears look like over time in our media. But what I love -- and I'm a journalist -- and what I love is finding hidden patterns; I love being a data detective. And there's a very interesting and odd pattern hidden in this data that you can only see when you visualize it. Let me highlight it for you. See this line. (The red one) This is a landscape for violent video games. As you can see, there's a kind of odd, regular pattern in the data, twin peaks every year. If we look closer, we see those peaks occur at the same month every year. Why? Well, November, Christmas video games come out, and there may well be an upsurge in the concern about their content. But April isn't a particularly massive month for video games. Why April? Well, in April 1999 was the Columbine shooting, and since then, that fear has been remembered by the media and echoes through the group mind gradually through the year. You have retrospectives, anniversaries, court cases, even copy-cat shootings, all pushing that fear into the agenda. And there's another pattern here as well. Can you spot it? See that gap there? There's a gap, and it affects all the other stories. Why is there a gap there? You see where it starts? September 2001, when we had something very real to be scared about.

So, I've been working as a data journalist for about a year, and I keep hearing a phrase all the time, which is this: "Data is the new oil." And data is the kind of ubiquitous resource that we can shape to provide new innovations and new insights, and it's all around us, and it can be mined very easily. And it's not a particularly great metaphor in these times, especially if you live around the Gulf of Mexico, but I would, perhaps, adapt this metaphor slightly, and I would say that data is the new soil. Because for me, it feels like a fertile, creative medium. You know, over the years, online, we've laid down a huge amount of information and data, and we irrigate it with networks and connectivity, and it's been worked and tilled by unpaid workers and governments. And, all right, I'm kind of milking the metaphor a little bit. But it's a really fertile medium, and it feels like visualizations, infographics, data visualizations, they feel like flowers blooming from this medium. But if you look at it directly, it's just a lot of numbers and disconnected facts. But if you start working with it and playing with it in a certain way, interesting things can appear and different patterns can be revealed.

Let me show you this.



Can you guess what this data says? What rises twice a year, once in Easter and then two weeks before Christmas, has a mini peak every Monday and then flattens out over the summer. I'll take answers. (Audience: Chocolate.) David McCandless: Chocolate. You might want to get some chocolate in. Any other guesses? (Audience: Shopping.) DM: Shopping. Yeah, retail therapy might help. (Audience: Sick leave.) DM: Sick leave. Yeah, you'll definitely want to take some time off. Shall we see?
So, the information here, Lee Byron and myself, we scraped 10,000 status Facebook updates for the phrase "break-up" and "broken-up" and this is the pattern we found -- people clearing out for spring break, (Laughter) coming out of very bad weekends on a Monday, being single over the summer. And then the lowest day of the year, of course: Christmas Day. Who would do that? So there's a titanic amount of data out there now, unprecedented. But if you ask the right kind of question, or you work it in the right kind of way, interesting things can emerge.

(...)  But I wanted to convey something to you. I started as a programmer, and then I worked as a writer for many years, about 20 years, in print, online and then in advertising, and only recently have I started designing. And I've never been to design school. I've never studied art or anything. I just kind of learned through doing. And when I started designing, I discovered an odd thing about myself. I already knew how to design, but it wasn't like I was amazingly brilliant at it, but more like I was sensitive to the ideas of grids and space and alignment and typography. It's almost like being exposed to all this media over the years had instilled a kind of dormant design literacy in me. And I don't feel like I'm unique.

I feel that everyday, all of us now are being blasted by information design. It's being poured into our eyes through the Web, and we're all visualizers now; we're all demanding a visual aspect to our information. And there's something almost quite magical about visual information. It's effortless; it literally pours in. And if you're navigating a dense information jungle, coming across a beautiful graphic or a lovely data visualization, it's a relief, it's like coming across a clearing in the jungle. And I was curious about this, so it led me to the work of a Danish physicist called Tor Norretranders, and he converted the bandwidth of the senses into computer terms.

So here we go. This is your senses, pouring into your senses every second.


Your sense of sight is the fastest. It has the same bandwidth as a computer network. Then you have touch, which is about the speed of a USB key. And then you have hearing and smell, which has the throughput of a hard disk. And then you have poor, old taste, which is like barely the throughput of a pocket calculator. And that little square in the corner, 0.7 percent, that's the amount we're actually aware of. So a lot of your vision -- the bulk of it is visual, and it's pouring in. It's unconscious. And the eye is exquisitely sensitive to patterns in variations in color, shape and pattern. It loves them, and it calls them beautiful. It's the language of the eye. And if you combine that language of the eye with the language of the mind, which is about words and numbers and concepts, you start speaking two languages simultaneously, each enhancing the other. So, you have the eye, and then you drop in the concepts. And that whole thing -- it's two languages both working at the same time.

So we can use this new kind of language, if you like, to alter our perspective or change our views. Let me ask you a simple question with a really simple answer. Who has the biggest military budget? It's got to be America, right?



Massive. 609 billion in 2008 -- 607, rather. So massive, in fact, that it can contain all the other military budgets in the world inside itself. 


Gobble, gobble, gobble, gobble, gobble. Now, you can see Africa's total debt there and the U.K. budget deficit for reference. So that might well chime with your view that America is a sort of warmongering, military machine, out to overpower the world with its huge, industrial-military complex. But is it true that America has the biggest military budget? Because it is an incredibly rich country. In fact, it's so massively rich that it can contain the four other top industrialized nations economies inside itself, it's so vastly rich.


 So its military budget is bound to be enormous. So, to be fair and to alter our perspective, we have to bring in another data set, and that data set is GDP, or the country's earnings. Who has the biggest budget as a proportion of GDP? Let's have a look.


 That changes the picture considerably. Other countries pop into view that you, perhaps, weren't considering, and American drops into eighth.
Now you can also do this with soldiers. Who has the most soldiers? It's got to be China. 


Of course, 2.1 million. Again, chiming with you view that China is a militarized regime ready to, you know, mobilize enormous its forces. But of course, China has an enormous population. So if we do the same, we see a radically different picture. 


China drops to 124th. It actually has a tiny army when you take other data into consideration. So, absolute figures, like the military budget, in a connected world, kind of don't give you the whole picture. They're not as true as they could be.

We need relative figures that are connected to other data so that we can see a fuller picture, and then that can lead to us changing our perspective. As Hans Rosling, the master, my master, said, "Let the dataset change your mindset." And if it can do that, maybe it can also change your behavior.

Take a look at this one. 



I'm a bit of a health nut. I love kind of like taking supplements and being fit, but I can never understand what's going on in terms of evidence. There's always conflicting evidence. Should I take vitamin C? Should I be taking wheatgrass? This is a visualization of all the evidence for nutritional supplements. This kind of diagram is called a balloon race. So the higher up the image, the more evidence there is for each supplement. And the bubbles correspond to popularity as regards to Google hits. So you can kind of immediately apprehend the relationship between efficacy and popularity, but you can also, if you grade the evidence, sort of do a "worth it" line. And so supplements above above this line are worth investigating, but only for the conditions listed below. And then the supplements below the line are, perhaps, not worth investigating.

Now this image constitutes a huge amount of work. We scraped like 1,000 studies from PubMed, the biomedical database, and we compiled them and graded them all. And it was incredibly frustrating for me because I had a book of 250 visualizations to do for my book, and I spent a month doing this, and I only filled two pages. But what it points to is that visualizing information like this is a form of knowledge compression. It's a way of squeezing an enormous amount of information and understanding into a small space. And once you've curated that data, and once you've cleaned that data, and once it's there, you can do cool stuff like this.

So I converted this into an interactive app, so I can now generate this application online -- this is the visualization online -- and I can say, "Yeah, brilliant." So it spawns itself. And then I can say, "Well, just show me the stuff that affects heart health." So let's filter that out. So heart is filtered out, so I if I'm curious about that. I think, "No, no. I don't want to take any synthetics. I just want to see plants and -- just show me herbs and plants. I've got all the natural ingredients." Now this app is spawning itself from the data. The data is all stored in a Google doc, and it's literally generating itself from that data. So the data is now alive; this is a living image, and I can update it in a second. New evidence comes out -- I just change a row on a spreadsheet. Doosh! Again, the image recreates itself. So it's cool. It's kind of living.

But it kind of can go beyond data, and it can go beyond numbers. And I like to apply information visualization to ideas and concepts. 


This is a visualization of the political spectrum, in an attempt for me to try and understand how it works and how the ideas percolate down from government into society and culture, into families, into individuals, into their beliefs and back around again in a cycle. What I love about this image is it's made up of concepts, it explores our worldviews and it helps us -- it helps me anyway -- to see what others think, to see where they're coming from. And it feels just incredibly cool to do that.

And what was most exciting for me designing this, was that, when I was designing this image, I desperately wanted this side, the left side, to be better than the right side -- being a kind of journalist, a left-leaning person -- but I couldn't, because I would have created a lopsided, biased diagram. So, in order to really create a full image, I had to honor the perspectives on the right-hand side and at the same time, kind of uncomfortably recognize how many of those qualities were actually in me, which was very, very annoying and uncomfortable. (Laughter) But not too uncomfortable, because there's something unthreatening about seeing a political perspective, versus being told or forced to listen to one. It's actually -- you're capable of holding conflicting viewpoints joyously, when you can see them. It's even fun to engage with them because it's visual. So that's what's exciting to me, seeing how data can change my perspective and change my mind midstream -- beautiful, lovely data.

So, just to wrap-up, I wanted to say that it feels to me that design is about solving problems and providing elegant solutions. And information design is about solving information problems. And it feels like we have a lot of information problems in our society at the moment, from the overload and saturation to the breakdown of trust and reliability and runaway skepticism and lack of transparency, or even just interestingness. I mean, I find information just too interesting. It has a magnetic quality that draws me in.

So, visualizing information can give us a very quick solution to those kinds of problems. And even when the information is terrible, the visual can be quite beautiful. And often we can get clarity or the answer to a simple question very quickly, like this one, the recent Icelandic volcano. Which was emitting the most CO2? Was it the planes or the volcano, the grounded planes or the volcano? So we can have a look. 

We look at the data and we see, yep, the volcano emitted 150,000 tons; the grounded planes would have emitted 345,000 if they were in the sky. So essentially, we had our first carbon-neutral volcano.



martes, 24 de agosto de 2010

Preocupaciones de un padre de familia








Algunos opinan que la palabra Odradek es de origen eslovaco y tratan de explicar su etimología de acuerdo con esta suposición. Otros, en cambio, creen que es de origen alemán con apenas alguna influencia eslovaca. La imprecisión de ambas interpretaciones permite suponer que ambas son erróneas, sobre todo porque ninguna de las dos nos brinda significado alguno para la palabra en cuestión.
Naturalmente, nadie se ocuparía de esto si de hecho no existiera un ser que se llama Odradek. A primera vista se parece a un carrete de hilo, chato y en forma de estrella, con hilos arrollados; por supuesto, sólo con trozos de hilo viejos y rotos, de diversos tipos y colores, enredados y llenos de nudos. Pero no es solamente un carrete, porque del centro de la estrella sobresale un pequeño travesaño, y sobre éste, en ángulo recto, se inserta otro. Con ayuda de este último, de un lado, y de una de las puntas de la estrella, del otro, el conjunto puede sostenerse como sobre dos patas.
Cabría pensar que este ser tuvo en otro tiempo alguna forma identificable y ahora está roto. Pero no parece probable; por lo menos, no hay nada que lo indique; no se ve ningún muñón o superficie de rotura que corrobore esta hipótesis; es un conjunto bastante insensato, pero a su manera bien definido. En cualquier caso , no se puede llevar a cabo un estudio detallado, porque Odradek es extraordinariamente ágil y no se le puede apresar.
Se esconde alternativamente en la buhardilla, debajo de la escalera, en los pasillos, en el vestíbulo. A veces no se le ve durante meses; seguramente se ha ido a otra casa; pero siempre regresa, fielmente, a la nuestra. A veces, al salir y encontrarlo en la escalera, uno siente deseos de hablarle. Naturalmente, sin hacerle preguntas difíciles, más bien tratándolo -su tamaño diminuto en tal vez un motivo- como a un niño.
- ¿Cómo te llamas?
- Odradek -contesta.
-¿Dónde vives?
-Domicilio desconocido -dice, y se ríe, con la risa de alguien que no tiene pulmones. Recuerda el susurro de las hojas caídas.
Y así termina generalmente la conversación. Por otra parte no siempre contesta: con frecuencia de queda mucho tiempo callado, como la madera de que parece estar hecho.
Me pregunto qué será de él. ¿Puede morir? Todo lo que muere tiene que haber tenido alguna clase de actividad que lo haya gastado; pero no puede decirse tal cosa de Odradek. ¿Seguirá, pues, rodando por las escaleras y arrastrando pedazos de hilo ante los pies de mis hijos y de los hijos de mis hijos? Desde luego, no hace daño a nadie; pero la idea de que pueda sobrevivirme me resulta casi dolorosa.

Franz Kafka.


lunes, 23 de agosto de 2010

Stuff



"That´s the whole meaning of life, isn´t it? trying to find a place for YOUR STUFF."


"Have you noticed that their stuff is shit, 
and your shit is stuff?" 


A.G






Tengo trabajo







"HAMM.- La naturaleza nos ha olvidado
CLOV.- La naturaleza ya no existe
HAMM.- ¡No existe la naturaleza! ¡Qué exageración!
CLOV.- En los alrededores.
HAMM.- ¡Pero nosotros respiramos, cambiamos! ¡Se nos cae el pelo, los dientes! ¡Nuestra lozanía, nuestros ideales!
CLOV.- Entonces, no nos ha olvidado.
HAMM.- Pero dices que ya no existe.
CLOV (con tristeza).- Nunca nadie en el mundo ha pensado de modo tan retorcido como nosotros
HAMM.- Hacemos lo que podemos
CLOV.- Nos equivocamos

Pausa

HAMM.- ¿Te crees alguien?
CLOV.- Sí, desde luego.

Pausa

HAMM.- Esto va despacio. (Pausa.) ¿No es la hora de tomarme el calmante?
CLOV.- No. (Pausa.) Te dejo, tengo trabajo.
HAMM.- ¿En la cocina?.
CLOV.- Sí.
HAMM.- Qué trabajo, me pregunto.
CLOV.- Miro la pared.
HAMM.- ¡La pared! ¿Y qué ves en la pared? ?Mané, mané? ¿cuerpos desnudos?
CLOV.- Veo mi luz que se extingue.
HAMM.- Tu luz que... ¡Lo que hay que oír! Bien, tu luz también puede extinguirse aquí. Mírame y me informarás acerca de tu luz.

Pausa.

CLOV.- Te equivocas hablándome de este modo.

Pausa.

HAMM (fríamente).- Perdón. (Pausa. Elevando la voz.) He dicho perdón.
CLOV.- Ya te he oído.

Samuel Beckett, Fin de partida (Pdf).


domingo, 22 de agosto de 2010

Lítost






"Lítost es una palabra checa intraducible a otros idiomas. Representa un sentimiento tan inmenso como un acordeón extendido, un sentimiento que es una síntesis de muchos otros sentimientos: la tristeza, la compasión, los reproches y la nostalgia. La primera sílaba de esta palabra, si se pronuncia alargada por el acento, suena como la queja de un perro abandonado
Pero en ciertas ocasiones lítost tiene por el contrario un significado muy estrecho, particular, estricto y preciso como el filo de un cuchillo. Busco para él, también en vano, un símil en otras lenguas, aunque no soy capaz de imaginarme cómo puede alguien sin él comprender el alma humana (...).
¿Qué es entonces la lítost?
La lítost es un estado de padecimiento producido por la visión de la propia miseria puesta repentinamente en evidencia."

Milan Kundera, El libro de la risa y el olvido.



I think







martes, 17 de agosto de 2010

In that sweet mood








I HEARD a thousand blended notes,
While in a grove I sate reclined,
In that sweet mood when pleasant thoughts
Bring sad thoughts to the mind.
To her fair works did Nature link
The human soul that through me ran;
And much it grieved my heart to think
What man has made of man.
Through primrose tufts, in that green bower,
The periwinkle trailed its wreaths;                         10
And 'tis my faith that every flower
Enjoys the air it breathes.
The birds around me hopped and played,
Their thoughts I cannot measure:--
But the least motion which they made
It seemed a thrill of pleasure.
The budding twigs spread out their fan,
To catch the breezy air;
And I must think, do all I can,
That there was pleasure there.                                20
If this belief from heaven be sent,
If such be Nature's holy plan,
Have I not reason to lament
 What man has made of man?    

  
William Wordsworth, 1798




Monkey cooperation and fairness



A monkey economy as irrational as ours






I want to start my talk today with two observations about the human species. The first observation is something that you might think is quite obvious, and that's that our species, Homo sapiens, is actually really, really smart -- like, ridiculously smart -- like you're all doing things that no other species on the planet does right now. And this is, of course, not the first time you've probably recognized this. Of course, in addition to being smart, we're also an extremely vain species. So we like pointing out the fact that we're smart. You know, so I could turn to pretty much any sage from Shakespeare to Stephen Colbert to point out things like the fact that we're noble in reason and infinite in faculties and just kind of awesome-er than anything else on the planet when it comes to all things cerebral.

But of course, there's a second observation about the human species that I want to focus on a little bit more, and that's the fact that even though we're actually really smart, sometimes uniquely smart, we can also be incredibly, incredibly dumb when it comes to some aspects of our decision making. Now I'm seeing lots of smirks out there. Don't worry, I'm not going to call anyone in particular out on any aspects of your own mistakes. But of course, just in the last two years we see these unprecedented examples of human ineptitude. And we've watched as the tools we uniquely make to pull the resources out of our environment kind of just blow up in our face. We've watched the financial markets that we uniquely create -- these markets that were supposed to be foolproof -- we've watched them kind of collapse before our eyes.

But both of these two embarrassing examples, I think, don't highlight what I think is most embarrassing about the mistakes that humans make, which is that we'd like to think that the mistakes we make are really just the result of a couple bad apples or a couple really sort of FAIL Blog-worthy decisions. But it turns out, what social scientists are actually learning is that most of us, when put in certain contexts, will actually make very specific mistakes. The errors we make are actually predictable. We make them again and again. And they're actually immune to lots of evidence. When we get negative feedback, we still, the next time we're face with a certain context, tend to make the same errors. And so this has been a real puzzle to me as a sort of scholar of human nature. What I'm most curious about is, how is a species that's as smart as we are capable of such bad and such consistent errors all the time?

You know, we're the smartest thing out there, why can't we figure this out? In some sense, where do our mistakes really come from? And having thought about this a little bit, I see a couple different possibilities. One possibility is, in some sense, it's not really our fault. Because we're a smart species, we can actually create all kinds of environments that are super, super complicated, sometimes too complicated for us to even actually understand, even though we've actually created them. We create financial markets that are super complex. We create mortgage terms that we can't actually deal with. And of course, if we are put in environments where we can't deal with it, in some sense makes sense that we actually might mess certain things up. If this was the case, we'd have a really easy solution to the problem of human error. We'd actually just say, okay, let's figure out the kinds of technologies we can't deal with, the kinds of environments that are bad -- get rid of those, design things better, and we should be the noble species that we expect ourselves to be.

But there's another possibility that I find a little bit more worrying, which is, maybe it's not our environments that are messed up. Maybe it's actually us that's designed badly. This is a hint that I've gotten from watching the ways that social scientists have learned about human errors. And what we see is that people tend to keep making errors exactly the same way, over and over again. It feels like we might almost just be built to make errors in certain ways. This is a possibility that I worry a little bit more about, because, if it's us that's messed up, it's not actually clear how we go about dealing with it. We might just have to accept the fact that we're error prone and try to design things around it.

So this is the question my students and I wanted to get at. How can we tell the difference between possibility one and possibility two? What we need is a population that's basically smart, can make lots of decisions, but doesn't have access to any of the systems we have, any of the things that might mess us up -- no human technology, human culture, maybe even not human language. And so this is why we turned to these guys here.


These are one of the guys I work with. This is a brown capuchin monkey. These guys are New World primates, which means they broke off from the human branch about 35 million years ago. This means that your great, great, great great, great, great -- with about five million "greats" in there -- grandmother was probably the same great, great, great, great grandmother with five million "greats" in there as Holly up here. You know, so you can take comfort in the fact that this guy up here is a really really distant, but albeit evolutionary, relative. The good news about Holly though is that she doesn't actually have the same kinds of technologies we do. You know, she's a smart, very cut creature, a primate as well, but she lacks all the stuff we think might be messing us up. So she's the perfect test case.

What if we put Holly into the same context as humans? Does she make the same mistakes as us? Does she not learn from them? And so on. And so this is the kind of thing we decided to do. My students and I got very excited about this a few years ago. We said, all right, let's, you know, throw so problems at Holly, see if she messes these things up. First problem is just, well, where should we start? Because, you know, it's great for us, but bad for humans. We make a lot of mistakes in a lot of different contexts. You know, where are we actually going to start with this? And because we started this work around the time of the financial collapse, around the time when foreclosures were hitting the news, we said, hhmm, maybe we should actually start in the financial domain. Maybe we should look at monkey's economic decisions and try to see if they do the same kinds of dumb things that we do.

Of course, that's when we hit a sort second problem -- a little bit more methodological -- which is that, maybe you guys don't know, but monkeys don't actually use money. I know, you haven't met them. But this is why, you know, they're not in the queue behind you at the grocery store or the ATM -- you know, they don't do this stuff. So now we faced, you know, a little bit of a problem here. How are we actually going to ask monkeys about money if they don't actually use it? So we said, well, maybe we should just, actually just suck it up and teach monkeys how to use money. So that's just what we did. What you're looking at over here is actually the first unit that I know of non-human currency. We weren't very creative at the time we started these studies, so we just called it a token. But this is the unit of currency that we've taught our monkeys at Yale to actually use with humans, to actually buy different pieces of food. It doesn't look like much -- in fact, it isn't like much.

Like most of our money, it's just a piece of metal. As those of you who've taken currencies home from your trip know, once you get home, it's actually pretty useless. It was useless to the monkeys at first before they realized what they could do with it. When we first gave it to them in their enclosures, they actually kind of picked them up, looked at them. They were these kind of weird things. But very quickly, the monkeys realized that they could actually hand these tokens over to different humans in the lab for some food. And so you see one of our monkeys, Mayday, up here doing this. This is A and B are kind of the points where she's sort of a little bit curious about these things -- doesn't know. There's this waiting hand from a human experimenter, and Mayday quickly figures out, apparently the human wants this. Hands it over, and then gets some food. It turns out not just Mayday, all of our monkeys get good at trading tokens with human salesman. So here's just a quick video of what this looks like. Here's Mayday. She's going to be trading a token for some food and waiting happily and getting her food. Here's Felix, I think. He's our alpha male; he's a kind of big guy. But he too waits patiently, gets his food and goes on.

So the monkeys get really good at this. They're surprisingly good at this with very little training. We just allowed them to pick this up on their own. The question is: is this anything like human money? Is this a market at all, or did we just do a weird psychologist's trick by getting monkeys to do something, looking smart, but not really being smart. And so we said, well, what would the monkeys spontaneously do if this was really their currency, if they were really using it like money? Well, you might actually imagine them to do all the kinds of smart things that humans do when they start exchanging money with each other. You might have them start paying attention to price, paying attention to how much they buy -- sort of keeping track of their monkey token, as it were. Do the monkeys do anything like this?

And so our monkey marketplace was born. The way this works is that our monkeys normally live in a kind of big zoo social enclosure. When they get a hankering for some treats, we actually allowed them a way out into a little smaller enclosure where they could enter the market. Upon entering the market -- it was actually a much more fun market for the monkeys than most human markets because, as the monkeys entered the door of the market, a human would give them a big wallet full of tokens so they could actually trade the tokens with one of these two guys here -- two different possible human salesmen that they could actually buy stuff from. The salesmen were students from my lab. They dressed differently; they were different people. And over time, they did basically the same thing so the monkeys could learn, you know, who sold what at what price -- you know, who was reliable, who wasn't, and so on. And you can see that each of the experimenters is actually holding up a little, yellow food dish. and that's what the monkey can for a single token. So everything costs one token, but as you can see, sometimes tokens buy more than others, sometimes more grapes than others.

So I'll show you a quick video of what this marketplace actually looks like. Here's a monkey-eye-view. Monkeys are shorter, so it's a little short. But here's Honey. She's waiting for the market to open a little impatiently. All of a sudden the market opens. Here's her choice: one grapes or two grapes. You can see Honey, very good market economist, goes with the guy who gives more. She could teach our financial advisers a few things or two. So not just Honey, most of the monkeys went with guys who had more. Most of the monkeys went with guys who had better food. When we introduced sales, we saw the monkeys paid attention to that. They really cared about their monkey token dollar. The more surprising thing was that when we collaborated with economists to actually look at the monkeys' data using economic tools, they basically matched, not just qualitatively, but quantitatively with what we saw humans doing in a real market. So much so that, if you saw the monkeys' numbers, you couldn't tell whether they came from a monkey or a human in the same market.

And what we'd really thought we'd done is like we'd actually introduced something that, at least for the monkeys and us, works like a real financial currency. Question is: do the monkeys start messing up in the same ways we do? Well, we already saw anecdotally a couple of signs that they might. One thing we never saw in the monkey marketplace was any evidence of saving -- you know, just like our own species. The monkeys entered the market, spent their entire budget and then went back to everyone else. The other thing we also spontaneously saw, embarrassingly enough, is spontaneous evidence of larceny. The monkeys would rip-off the tokens at every available opportunity -- from each other, often from us -- you know, things we didn't necessarily think we were introducing, but things we spontaneously saw.

So we said, this looks bad. Can we actually see if the monkeys are doing exactly the same dumb things as humans do? One possibility is just kind of let the monkey financial system play out, you know, see if they start calling us for bailouts in a few years. We were a little impatient so we wanted to sort of speed things up a bit. So we said, let's actually give the monkeys the same kinds of problems that humans tend to get wrong in certain kinds of economic challenges, or certain kinds of economic experiments. And so, since the best way to see how people go wrong is to actually do it yourself, I'm going to give you guys a quick experiment to sort of watch your own financial intuitions in action.

So imagine that right now I handed each and every one of you a thousand U.S. dollars -- so 10 crisp hundred dollar bills. Take these, put it in your wallet and spend a second thinking about what you're going to do with it. Because it's yours now; you can buy whatever you want. Donate it, take it, and so on. Sounds great, but you get one more choice to earn a little bit more money. And here's your choice: you can either be risky, in which case I'm going to flip one of these monkey tokens. If it comes up heads, you're going to get a thousand dollars more. If it comes up tails, you get nothing. So it's a chance to get more, but it's pretty risky. Your other option is a bit safe. Your just going to get some money for sure. I'm just going to give you 500 bucks. You can stick it in your wallet and use it immediately. So see what your intuition is here. Most people actually go with the play-it-safe option. Most people say, why should I be risky when I can get 1,500 dollars for sure? This seems like a good bet. I'm going to go with that. You might say, eh, that's not really irrational. People are a little risk-averse. So what?

Well, the "so what?" comes when start thinking about the same problem set up just a little bit differently. So now imagine that I give each and every one of you 2,000 dollars -- 20 crisp hundred dollar bills. Now you can buy double to stuff you were going to get before. Think about how you'd feel sticking it in your wallet. And now imagine that I have you make another choice But this time, it's a little bit worse. Now, you're going to be deciding how you're going to lose money, but you're going to get the same choice. You can either take a risky loss -- so I'll flip a coin. If it comes up heads, you're going to actually lose a lot. If it comes up tails, you lose nothing, you're fine, get to keep the whole thing -- or you could play it safe, which means you have to reach back into your wallet and give me five of those $100 bills, for certain.



And I'm seeing a lot of furrowed brows out there. So maybe you're having the same intuitions as the subjects that were actually tested in this, which is when presented with these options, people don't choose to play it safe. They actually tend to go a little risky. The reason this is irrational is that we've given people in both situations the same choice. It's a 50/50 shot of a thousand or 2,000, or just 1,500 dollars with certainty. But people's intuitions about how much risk to take varies depending on where they started with.

So what's going on? Well, it turns out that this seems to be the result of at least two biases that we have at the psychological level. One is that we have a really hard time thinking in absolute terms. You really have to do work to figure out, well, one option's a thousand, 2,000; one is 1,500. Instead, we find it very easy to think in very relative terms as options change from one time to another. So we think of things as, "Oh, I'm going to get more," or "Oh, I'm going to get less." This is all well and good, except that changes in different directions actually effect whether or not we think options are good or not. And this leads to the second bias, which economists have called loss aversion.

The idea is that we really hate it when things go into the red. We really hate it when we have to lose out on some money. And this means that sometimes we'll actually switch our preferences to avoid this. What you saw in that last scenario is that subjects get risky because they want the small shot that there won't be any loss. That means when we're in a risk mindset -- excuse me, when we're in a loss mindset, we actually become more risky, which can actually be really worrying. These kinds of things play out in lots of bad ways in humans. They're why stock investors hold onto losing stocks longer -- because they're evaluating them in relative terms. They're why people in the housing market refused to sell their house -- because they don't want to sell at a loss.

The question we were interested in is whether the monkeys show the same biases. If we set up those same scenarios in our little monkey market, would they do the same thing as people? And so this is what we did, we gave the monkeys choices between guys who were safe -- they did the same thing every time -- or guys who were risky -- they did things differently half the time. And then we gave them options that were bonuses -- like you guys did in the first scenario -- so they actually have a chance more, or pieces where they were experiencing losses -- they actually thought they were going to get more than they really got.

And so this is what this looks like. We introduced the monkeys to two new monkey salesmen. The guy on the left and right both start with one piece of grape, so it looks pretty good. But they're going to give the monkeys bonuses. The guy on the left is a safe bonus. All the time, he adds one, to give the monkeys two. The guy on the right is actually a risky bonus. Sometimes the monkeys get no bonus -- so this is a bonus of zero. Sometimes the monkeys get two extra. For a big bonus, now they get three. But this is the same choice you guys just faced. Do the monkeys actually want to play it safe and then go with the guy who's going to do the same thing on every trial, or do they want to be risky and try to get a risky, but big, bonus, but risk the possibility of getting no bonus. People here played it safe. Turns out, the monkeys play it safe too. Qualitatively and quantitatively, they choose exactly the same way as people, when tested in the same thing.

You might say, well, maybe the monkeys just don't like risk. Maybe we should see how they do with losses. And so we ran a second version of this. Now, the monkeys meet two guys who aren't giving them bonuses; they're actually giving them less than they expect. So they look like they're starting out with a big amount. These are three grapes; the monkey's really psyched for this. But now they learn these guys are going to give them less than they expect. They guy on the left is a safe loss. Every single time, he's going to take one of these away and give the monkeys just two. the guy on the right is the risky loss. Sometimes he gives no loss, so the monkeys are really psyched, but sometimes he actually gives a big loss, taking away two to give the monkeys only one.

And so what do the monkeys do? Again, same choice; they can play it safe for always getting two grapes every single time, or they can take a risky bet and choose between one and three. The remarkable thing to us is that, when you give monkeys this choice, they do the same irrational thing that people do. They actually become more risky depending on how the experimenters started. This is crazy because it suggests that the monkeys too are evaluating things in relative terms and actually treating losses differently than they treat gains.

So what does all of this mean? Well, what we've shown is that, first of all, we can actually give the monkeys a financial currency, and they do very similar things with it. They do some of the smart things we do, some of the kind of not so nice things we do, like steal it and so on. But they also do some of the irrational things we do. They systematically get things wrong and in the same ways that we do. This is the first take-home message of the Talk, which is that if you saw the beginning of this and you thought, oh, I'm totally going to go home and hire a capuchin monkey financial adivser. They're way cuter than the one at ... you know -- Don't do that; they're probably going to be just as dumb as the human one you already have. So, you know, a little bad -- Sorry, sorry, sorry. A little bad for monkey investors.

But of course, you know, the reason you're laughing is bad for humans too. Because we've answered the question we started out with. We wanted to know where these kinds of errors came from. And we started with the hope that maybe we can sort of tweak our financial institutions, tweak our technologies to make ourselves better. But what we've learn is that these biases might be a deeper part of us than that. In fact, they might be due to the very nature of our evolutionary history. You know, maybe it's not just humans at the right side of this chain that's duncey. Maybe it's sort of duncey all the way back. And this, if we believe the capuchin monkey results, means that these duncey strategies might be 35 million years old. That's a long time for a strategy to potentially get changed around -- really, really old.

What do we know about other old strategies like this? Well, one thing we know is that they tend to be really hard to overcome. You know, think of our evolutionary predilection for eating sweet things, fatty things like cheesecake. You can't just shut that off. You can't just look at the dessert cart as say, "No, no, no. That looks disgusting to me." We're just built differently. We're going to perceive it as a good thing to go after. My guess is that the same thing is going to be true when humans are perceiving different financial decisions. When you're watching your stocks plummet into the red, when you're watching your house price go down, you're not going to be able to see that in anything but old evolutionary terms. This means that the biases that lead investors to do badly, that lead to the foreclosure crisis are going to be really hard to overcome.

So that's the bad news. The question is: is there any good news? I'm supposed to be up here telling you the good news. Well, the good news, I think, is what I started with at the beginning of the Talk, which is that humans are not only smart; we're really inspirationally smart to the rest of the animals in the biological kingdom. We're so good at overcoming our biological limitations -- you know, I flew over here in an airplane. I didn't have to try to flap my wings. I'm wearing contact lenses now so that I can see all of you. I don't have to rely on my own near-sightedness. We actually have all of these cases where we overcome our biological limitations through technology and other means, seemingly pretty easily. But we have to recognize that we have those limitations.

And here's the rub. It was Camus who once said that, "Man is the only species who refuses to be what he really is." But the irony is that it might only be in recognizing our limitations that we can really actually overcome them. The hope is that you all will think about your limitations, not necessarily as unovercomable, but to recognize them, accept them and then use the world of design to actually figure them out. That might be the only way that we will really be able to achieve our own human potential and really be the noble species we hope to all be.