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Relevancy of Machine Learning to climatic models

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AbruptSLR:

--- Quote from: 6roucho on December 13, 2016, 03:57:58 AM ---Murray Gell-Mann famously called such systems "an accumulation of frozen accidents."

--- End quote ---

I provide the following reference, co-authored by Murray Gell-Mann, that could be used to better address climate change issues including:  risk, insurance, revenue neutral carbon pricing, and other topics.  This reference makes it very clear that most humans (even most experts) have a very weak intuitive understanding of their own ignorance (which results in a poor understanding of gambles/risk that we are all exposed to w.r.t. climate consequences).

Ole Peters and Murray Gell-Mann (Feb. 2, 2016), "Evaluating gambles using dynamics," Chaos, DOI: 10.1063/1.4940236

http://scitation.aip.org/content/aip/journal/chaos/26/2/10.1063/1.4940236

Abstract: "Gambles are random variables that model possible changes in wealth. Classic decision theory transforms money into utility through a utility function and defines the value of a gamble as the expectation value of utility changes. Utility functions aim to capture individual psychological characteristics, but their generality limits predictive power. Expectation value maximizers are defined as rational in economics, but expectation values are only meaningful in the presence of ensembles or in systems with ergodic properties, whereas decision-makers have no access to ensembles, and the variables representing wealth in the usual growth models do not have the relevant ergodic properties. Simultaneously addressing the shortcomings of utility and those of expectations, we propose to evaluate gambles by averaging wealth growth over time. No utility function is needed, but a dynamic must be specified to compute time averages. Linear and logarithmic “utility functions” appear as transformations that generate ergodic observables for purely additive and purely multiplicative dynamics, respectively. We highlight inconsistencies throughout the development of decision theory, whose correction clarifies that our perspective is legitimate. These invalidate a commonly cited argument for bounded utility functions."


Also see:
http://www.newswise.com/articles/exploring-gambles-reveals-foundational-difficulty-behind-economic-theory-and-a-solution

Extract: " In the wake of the financial crisis, many started questioning different aspects of the economic formalism.

This included Ole Peters, a Fellow at the London Mathematical Laboratory in the U.K., as well as an external professor at the Santa Fe Institute in New Mexico, and Murray Gell-Mann, a physicist who was awarded the 1969 Nobel Prize in physics for his contributions to the theory of elementary particles by introducing quarks, and is now a Distinguished Fellow at the Santa Fe Institute. They found it particularly curious that a field so central to how we live together as a society seems so unsure about so many of its key questions.

So they asked: Might there be a foundational difficulty underlying our current economic theory? Is there some hidden assumption, possibly hundreds of years old, behind not one but many of the current scientific problems in economic theory? Such a foundational problem could have far-reaching practical consequences because economic theory informs economic policy.

As they report in the journal Chaos, from AIP Publishing, the story that emerged is a fascinating example of scientific history, of how human understanding evolves, gets stuck, gets unstuck, branches, and so on.



The key concepts of time and randomness are at the heart of their work. "Questions of an economic nature stood at the beginning of formal thinking about randomness in the 17th century," he explained. "These are all relatively young concepts -- there's nothing in Euclid about probability theory." Think of it simply in terms of: Should I bet money in a game of dice? How much should I pay for an insurance contract? What would be a fair price for a life annuity?
"All of these questions have something to do with randomness, and the way to deal with them in the 17th century was to imagine parallel worlds representing everything that could happen," Gell-Mann said. "To assess the value of some uncertain venture, an average is taken across those parallel worlds."

This concept was only challenged in the mid-19th century when randomness was used formally in a different context -- physics. "Here, the following perspective arose: to assess some uncertain venture, ask yourself how it will affect you in one world only -- namely the one in which you live -- across time," Gell-Mann continued.

"The first perspective -- considering all parallel worlds -- is the one adopted by mainstream economics," explained Gell-Mann. "The second perspective -- what happens in our world across time -- is the one we explore and that hasn't been fully appreciated in economics so far."
The real impact of this second perspective comes from acknowledging the omission of the key concept of time from previous treatments. "We have some 350 years of economic theory involving randomness in one way only -- by considering parallel worlds," said Peters. "What happens when we switch perspectives is astonishing. Many of the open key problems in economic theory have an elegant solution within our framework."

In terms of applications for their work, its key concept can be used "to derive an entire economic formalism," said Peters. In their article, Peters and Gell-Mann explore the evaluation of a gamble. For example, is this gamble better than that gamble? This is the fundamental problem in economics. And from a conceptually different solution there follows a complete new formalism.
They put it to the test after their friend Ken Arrow -- an economist who was the joint winner of the Nobel Memorial Prize in Economic Sciences with John Hicks in 1972 -- suggested applying the technique to insurance contracts. "Does our perspective predict or explain the existence of a large insurance market? It does -- unlike general competitive equilibrium theory, which is the current dominant formalism," Peters said.

And so a different meaning of risk emerges -- taking too much risk is not only psychologically uncomfortable but also leads to real dollar losses. "Good risk management really drives performance over time," Peters added. "This is important in the current rethinking of risk controls and financial market infrastructure."

This concept reaches far beyond this realm and into all major branches of economics. "It turns out that the difference between how individual wealth behaves across parallel worlds and how it behaves over time quantifies how wealth inequality changes," explained Peters. "It also enables refining the notion of efficient markets and solving the equity premium puzzle."

One historically important application is the solution of the 303-year-old St. Petersburg paradox, which involves a gamble played by flipping a coin until it comes up tails and the total number of flips, n, determines the prize, which equals $2 to the nth power. "The expected prize diverges -- it doesn't exist," Peters elaborated. "This gamble, suggested by Nicholas Bernoulli, can be viewed as the first rebellion against the dominance of the expectation value -- that average across parallel worlds -- that was established in the second half of the 17th century."

What's the next step for their work? "We're very keen to develop fully the implications for welfare economics and questions of economic inequality. This is a sensitive subject that needs to be dealt with carefully, including empirical work," noted Peters. "Much is being done behind the scenes -- since this is a conceptually different way of doing things, communication is a challenge, and our work has been difficult to publish in mainstream economics journals."

Their results described in Chaos are easily generalized, which is necessary to reinterpret the full formalism. But it "may not add very much in practical terms, and it gets a little technical." So that's a future "to-do item" for Peters and Gell-Mann.

"Our Chaos paper is a recipe for approaching a wide range of problems," said Peters. "So we're now going through the entire formalism with our collaborators to see where else our perspective is useful.""

See also the following linked article entitled "Exploring gambles reveals foundational difficulty behind economic theory (and a solution)":


http://phys.org/news/2016-02-exploring-gambles-reveals-foundational-difficulty.html

Extract: ""Our Chaos paper is a recipe for approaching a wide range of problems," said Peters. "So we're now going through the entire formalism with our collaborators to see where else our perspective is useful.""

Bernard:
@AbruptSLR

Thanks for all the background reading material you bring in, but none of them unless I miss something seems to address directly the question set in this thread. Is machine learning bringing something new regarding climate/weather predictions?

It seems to be applied successfully for example in prediction of stock markets, see
http://fr.slideshare.net/iknowfirst/machine-learning-stock-market-and-chaos-56626648

AbruptSLR:

--- Quote from: Bernard on December 13, 2016, 07:11:35 PM ---@AbruptSLR

Thanks for all the background reading material you bring in, but none of them unless I miss something seems to address directly the question set in this thread. Is machine learning bringing something new regarding climate/weather predictions?

It seems to be applied successfully for example in prediction of stock markets, see
http://fr.slideshare.net/iknowfirst/machine-learning-stock-market-and-chaos-56626648

--- End quote ---

I don't think that machine learning will change the physics being modeled but it could help with selecting both the correct input and the correct interpretation of risk for the output.

AbruptSLR:
The linked article is entitled: "AI to Use Satellite Imaging Tech to Predict Food Crises Before They Happen", and it illustrates how machine learning can be used to better manage climate change risk:

http://www.natureworldnews.com/articles/33834/20161212/earth-food-food-shortages-satellite-geospatial-data-usda-descartes-labs.htm

Bernard:
AbruptSLR your latest post is spot on :)

The company behind the quoted article "AI to Use Satellite Imaging Tech to Predict Food Crises Before They Happen" is called Descartes Labs http://www.descarteslabs.com/

"The Descartes Labs platform combines massive data sources—whether public, private or proprietary—onto a single system. The data is then transformed into action by applying machine learning at scale to unlock the value in those datasets. We transform petabytes of data into action for your business.

Our platform enables a new way of doing science. We are asking new kinds of questions and solving the most challenging forecasting problems facing organizations today."

Of course this is a commercial pitch, there is a huge competition is this market where growth is impressive. "According to the new market research report on artificial intelligence, this market is expected to be worth USD 16.06 billion by 2022, growing at a CAGR of 62.9% from 2016 to 2022." http://www.researchandmarkets.com/reports/3979203/artificial-intelligence-market-by-technology

What I try to figure here is if there is a real opportunity for climate science here (beyond the marketing hype)

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