Etoys Particles/Partículas Challenge/Desafío

English text follows the Spanish text

Acabamos de leer este p?rrafo en un art?culo de diario sobre por qu? algunos mensajes de Twitter se propagan mucho y otros no:

>> Resulta que la forma como se difunde la informaci?n [en los medios sociales] en l?nea es,  frecuentemente,  m?s complicada que transmisi?n viral.
Transmisi?n viral es el caso, por ejemplo,  en que una persona env?a un enlace a un video en YouTube directamente a otra persona.  Mientras que lo que sucede con temas pol?ticos,  la gente usualmente espera hasta que cierto n?mero de amigos o fuentes de confianza han promovido la idea,  antes de promulgarla ellos. <<

El art?culo completo:

http://www.nytimes.com/2011/02/06/business/06stream.html?emc=tnt&tntemail0=y

Sentimos podr?a ser un tema muy interesante para explicar con Etoys, usando el objeto "Part?culas".

"Part?culas" se encuentra en la p?gina "b?sico" del "Cat?logo de Objetos" en "Provisiones".

Carlos Rabassa
Voluntario
Red de Apoyo al Plan Ceibal
Montevideo, Uruguay


English text:

We just read this paragraph in a newspaper article on why some Twitter posts catch on and some don?t:

>> It turns out that the way information spreads online is often more complicated than viral transmission, in which one person passes a link to, say, a YouTube video directly to another person. As with political topics, people often wait until a number of friends or trusted sources have promoted an idea before promulgating it themselves. <<

The complete article:

http://www.nytimes.com/2011/02/06/business/06stream.html?emc=tnt&tntemail0=y

We feel it could be a very interesting subject to explain with Etoys, using the "Particles" object.

"Part?cles" is located in the "basic" page of the "Objects Catalog" in "Supplies".


Carlos Rabassa
Volunteer
Plan Ceibal Support Network
Montevideo, Uruguay
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The dynamics of this is pretty well modeled by using the "forest fire" particle
simulation. Basically about 80% of the human population needs to feel that
others around have already come to a conclusion before they will conclude. This
inserts huge delays on ideas (sometimes 30 to over 100 years).

The forest fire particle simulation (originally done in Starlogo) allows you to
choose the ratio of forest to clearings and start a fire to see if it
propagates. One of these models requires at least 66% connectivity before the
fire will spread.

Cheers,

Alan





________________________________
From: Carlos Rabassa <carnen at mac.com>
To: squeakland.org mailing list <squeakland at squeakland.org>;
america-latina at squeakland.org; Maho 2010 <maho at realness.org>; IAEP SugarLabs
<iaep at lists.sugarlabs.org>; voluntarios y administradores OLPC para usuarios
docentes <olpc-sur at lists.laptop.org>; olpc bolivia
<olpc-bolivia at lists.laptop.org>; OLPC Puno <olpcpuno at gmail.com>
Sent: Sun, February 6, 2011 12:42:28 AM
Subject: [IAEP] Etoys Particles/Part?culas Challenge/Desaf?o

English text follows the Spanish text

Acabamos de leer este p?rrafo en un art?culo de diario sobre por qu? algunos
mensajes de Twitter se propagan mucho y otros no:

>> Resulta que la forma como se difunde la informaci?n [en los medios sociales] en
>>l?nea es,  frecuentemente,  m?s complicada que transmisi?n viral.
>Transmisi?n viral es el caso, por ejemplo,  en que una persona env?a un enlace a
>un video en YouTube directamente a otra persona.  Mientras que lo que sucede con
>temas pol?ticos,  la gente usualmente espera hasta que cierto n?mero de amigos o
>fuentes de confianza han promovido la idea,  antes de promulgarla ellos. <<


El art?culo completo:

http://www.nytimes.com/2011/02/06/business/06stream.html?emc=tnt&tntemail0=y

Sentimos podr?a ser un tema muy interesante para explicar con Etoys, usando el
objeto "Part?culas".

"Part?culas" se encuentra en la p?gina "b?sico" del "Cat?logo de Objetos" en
"Provisiones".

Carlos Rabassa
Voluntario
Red de Apoyo al Plan Ceibal
Montevideo, Uruguay

English text:

We just read this paragraph in a newspaper article on why some Twitter posts
catch on and some don?t:

>> It turns out that the way information spreads online is often more complicated
>>than viral transmission, in which one person passes a link to, say, a YouTube
>>video directly to another person. As with political topics, people often wait
>>until a number of friends or trusted sources have promoted an idea before
>>promulgating it themselves. <<


The complete article:

http://www.nytimes.com/2011/02/06/business/06stream.html?emc=tnt&tntemail0=y

We feel it could be a very interesting subject to explain with Etoys, using the
"Particles" object.

"Part?cles" is located in the "basic" page of the "Objects Catalog" in
"Supplies".


Carlos Rabassa
Volunteer
Plan Ceibal Support Network
Montevideo, Uruguay


     
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Texto en Espa?ol sigue al Ingl?s

Alan and All,

this is a link to the Forest Fire project published by Yoshiki Ohshima in the Squeakland.org showcase.

Many of us in these lists met him during his visit to Porto Alegre, Brazil to Squeakfest 2009.

http://squeakland.org/showcase/project.jsp?id=7854


On Feb 6, 2011, at 1:04 PM, Alan Kay wrote:

> The dynamics of this is pretty well modeled by using the "forest fire" particle simulation. Basically about 80% of the human population needs to feel that others around have already come to a conclusion before they will conclude. This inserts huge delays on ideas (sometimes 30 to over 100 years).
>
> The forest fire particle simulation (originally done in Starlogo) allows you to choose the ratio of forest to clearings and start a fire to see if it propagates. One of these models requires at least 66% connectivity before the fire will spread.
>
> Cheers,
>
> Alan
>




Texto en Espa?ol:

Alan y Todos,

Este es un enlace al proyecto "Incendio Forestal" publicado por Yoshiki Ohshima en la Vitrina de Squeakland.org:

http://squeakland.org/showcase/project.jsp?id=7854

Muchos de nosotros conocemos personalmente a Yoshiki de su visita a Porto Alegre, Brasil,  cuando particip? en Squeakfest Brasil 2009.


On Feb 6, 2011, at 1:04 PM, Alan Kay wrote:

> La din?mica de esto est? bastante bien modelada usando la simulaci?n de part?culas en "incendio forestal".  B?sicamente,  alrededor del 80% de la poblaci?n humana necesita sentir que otros a su alrededor han llegado a una conclusi?n,  antes de que ellos lleguen a la misma conclusi?n. Esto inserta grandes demoras en [la aceptaci?n de] ideas (a veces desde 30 a m?s de 100 a?os).
>
> La simulaci?n con part?culas del incendio forestal (originalmente creada en Starlogo) te permite elegir la relaci?n entre bosque y claros y comenzar un incendio para ver si se propaga. Uno de estos modelos requiere una conectividad [entre ?rboles] de por lo menos 66% antes de que se propague el fuego.
>
> Saludos,
>
> Alan







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