Archivio per 20 novembre 2014

20
Nov
14

Computation: Information, adaptation, and evolution in silico — Foundations & Frontiers — Medium

In 1984 the nascent Santa Fe Institute sponsored two workshops on “Emerging Syntheses in Science,” at which the Institute’s founders brainstormed their plans for the future. At the time I was a beginning graduate student in computer science and had never heard of SFI, but reading the workshop proceedings a few years later, I was very excited by the Institute’s goal to “pursue research on a large number of highly complex and interactive systems which can be properly studied only in an interdisciplinary environment.”

The founders planned to define particular themes or programs that would benefit from the kind of intensive cross-disciplinary interaction offered by the new institute. SFI’s first official program, formed in 1987, was Economics. Before long, several influential players in the field took note of SFI’s novel interdisciplinary approach to economics, and the program grew quickly, in fact threatening to take over the fledgling organization.

Founder and first SFI President George Cowan wanted to make sure economics did not come to dominate. He wrote: “We had to start somewhere, but we also had to make sure from the beginning that economics didn’t become the one interest of the institute…I pushed hard to support at least one other program that would be equal in size to the economics program. We needed to broaden our academic agenda, and spread our bets.” Cowan’s push was to start a program in “adaptive computation.”

Source: medium.com

See on Scoop.itBounded Rationality and Beyond

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20
Nov
14

Computation: Information, adaptation, and evolution in silico — Foundations & Frontiers — Medium

See on Scoop.itBounded Rationality and Beyond

In 1984 the nascent Santa Fe Institute sponsored two workshops on “Emerging Syntheses in Science,” at which the Institute’s founders brainstormed their plans for the future. At the time I was a beginning graduate student in computer science and had never heard of SFI, but reading the workshop proceedings a few years later, I was very excited by the Institute’s goal to “pursue research on a large number of highly complex and interactive systems which can be properly studied only in an interdisciplinary environment.”

The founders planned to define particular themes or programs that would benefit from the kind of intensive cross-disciplinary interaction offered by the new institute. SFI’s first official program, formed in 1987, was Economics. Before long, several influential players in the field took note of SFI’s novel interdisciplinary approach to economics, and the program grew quickly, in fact threatening to take over the fledgling organization.

Founder and first SFI President George Cowan wanted to make sure economics did not come to dominate. He wrote: “We had to start somewhere, but we also had to make sure from the beginning that economics didn’t become the one interest of the institute…I pushed hard to support at least one other program that would be equal in size to the economics program. We needed to broaden our academic agenda, and spread our bets.” Cowan’s push was to start a program in “adaptive computation.”

See on medium.com

20
Nov
14

Do NYC cab drivers quit too early when it rains? – Decision Science News

Do cab drivers ignore opportunities to make more money when it rains?

ABSTRACT

In a seminal paper, Camerer, Babcock, Loewenstein, and Thaler (1997) find that the wage elasticity of daily hours of work New York City (NYC) taxi drivers is negative and conclude that their labor supply behavior is consistent with target earning (having reference dependent preferences). I replicate and extend the CBLT analysis using data from all trips taken in all taxi cabs in NYC for the five years from 2009-2013. The overall pattern in my data is clear: drivers tend to respond positively to unanticipated as well as anticipated increases in earnings opportunities. This is consistent with the neoclassical optimizing model of labor supply and does not support the reference dependent preferences model.

I explore heterogeneity across drivers in their labor supply elasticities and consider whether new drivers differ from more experienced drivers in their behavior. I find substantial heterogeneity across drivers in their elasticities, but the estimated elasticities are generally positive and only rarely substantially negative. I also find that new drivers with smaller elasticities are more likely to exit the industry while drivers who remain learn quickly to be better optimizers (have positive labor supply elasticities that grow with experience).

 

Source: www.decisionsciencenews.com

See on Scoop.itBounded Rationality and Beyond

20
Nov
14

Do NYC cab drivers quit too early when it rains? – Decision Science News

See on Scoop.itBounded Rationality and Beyond

Do cab drivers ignore opportunities to make more money when it rains?

ABSTRACT

In a seminal paper, Camerer, Babcock, Loewenstein, and Thaler (1997) find that the wage elasticity of daily hours of work New York City (NYC) taxi drivers is negative and conclude that their labor supply behavior is consistent with target earning (having reference dependent preferences). I replicate and extend the CBLT analysis using data from all trips taken in all taxi cabs in NYC for the five years from 2009-2013. The overall pattern in my data is clear: drivers tend to respond positively to unanticipated as well as anticipated increases in earnings opportunities. This is consistent with the neoclassical optimizing model of labor supply and does not support the reference dependent preferences model.

I explore heterogeneity across drivers in their labor supply elasticities and consider whether new drivers differ from more experienced drivers in their behavior. I find substantial heterogeneity across drivers in their elasticities, but the estimated elasticities are generally positive and only rarely substantially negative. I also find that new drivers with smaller elasticities are more likely to exit the industry while drivers who remain learn quickly to be better optimizers (have positive labor supply elasticities that grow with experience).

 
See on decisionsciencenews.com

20
Nov
14

Emergence: A unifying theme for 21st century science — Foundations & Frontiers — Medium

When electrons or atoms or individuals or societies interact with one another or their environment, the collective behavior of the whole is different from that of its parts. We call this resulting behavior emergent. Emergence thus refers to collective phenomena or behaviors in complex adaptive systems that are not present in their individual parts.

Examples of emergent behavior are everywhere around us, from birds flocking, fireflies synchronizing, ants colonizing, fish schooling, individuals self-organizing into neighborhoods in cities – all with no leaders or central control – to the Big Bang, the formation of galaxies and stars and planets, the evolution of life on earth from its origins until now, the folding of proteins, the assembly of cells, the crystallization of atoms in a liquid, the superconductivity of electrons in some metals, the changing global climate, or the development of consciousness in an infant.

Indeed, we live in an emergent universe in which it is difficult, if not impossible, to identify any existing interesting scientific problem or study any social or economic behavior that is not emergent.

Source: medium.com

See on Scoop.itBounded Rationality and Beyond

20
Nov
14

Emergence: A unifying theme for 21st century science — Foundations & Frontiers — Medium

See on Scoop.itBounded Rationality and Beyond

When electrons or atoms or individuals or societies interact with one another or their environment, the collective behavior of the whole is different from that of its parts. We call this resulting behavior emergent. Emergence thus refers to collective phenomena or behaviors in complex adaptive systems that are not present in their individual parts.

Examples of emergent behavior are everywhere around us, from birds flocking, fireflies synchronizing, ants colonizing, fish schooling, individuals self-organizing into neighborhoods in cities – all with no leaders or central control – to the Big Bang, the formation of galaxies and stars and planets, the evolution of life on earth from its origins until now, the folding of proteins, the assembly of cells, the crystallization of atoms in a liquid, the superconductivity of electrons in some metals, the changing global climate, or the development of consciousness in an infant.

Indeed, we live in an emergent universe in which it is difficult, if not impossible, to identify any existing interesting scientific problem or study any social or economic behavior that is not emergent.

See on medium.com




Time is real? I think not

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