Re: The Economic Crisis and its Implications for The Science of Economics

[David Farber <dave () farber net>]

Begin forwarded message:

From: Dennis Allison <drallison () gmail com>
Date: April 25, 2009 11:12:05 PM EDT
To: Bob Frankston <Bob19-0501 () bobf frankston com>, Eric Weinstein <erw.phd () gmail com 
>
Cc: dave () farber net, ip <ip () v2 listbox com>
Subject: Re: [IP] The Economic Crisis and its Implications for The  
Science of  Economics

I  listed some pointers to reference materials in the abstract for  
Eric Weinstein's EE380 presentation.  You can find the abstract at http://ee380.stanford.edu 
 and the references page at http://ee380.stanford.edu/Abstracts/090429-references.html 
.  It is not intended to be comprehensive and can certainly be  
improved.  I think I would start with the EDGE 269 article and  
comments:  CAN SCIENCE HELP SOLVE THE ECONOMIC CRISIS?   I think you'd  
also find Stuart Kauffman's Reinventing the Sacred to be interesting  
reading, particularly Chapter 11 which deals with economics.

You are not alone in seeing the currently naive trust in a market  
economy as faith-based rather than rational.  Neo-classical economics  
seems to be an elegant solution to a problem quite removed from the  
real world.  Much of the work of the Wall Street "quants" has been  
directed at extracting money from the existing system and not at  
understanding how the financial system works and considering the long  
term impacts of particular strategies.  And, it is the latter which is  
badly needed.

I think there are different levels of modeling which make sense.  I  
don't think we have a descriptive model of the current financial  
system and certainly do not have one which is detailed and complex  
enough to deal with resource issues, global warming, and the like.  I  
think the first step in understanding the financial crisis would be to  
build a model of what's out there and what the dependencies are.

The complex systems folks argue that a bottom-up model using very low- 
level agent-based elements is likely to be a better approach than a  
top-down one.  This sort of system tends to evolve towards something  
similar to what is observed in the wild.  Beinhocker's The Origin of  
Wealth describes some experiments in this area, albeit with highly  
simplified systems and limited behavioral rule sets.

I think you dismiss the 70's classic Limits to Growth study a bit too  
quickly.  You might want to take a look at Graham Turner's 2008 paper.  
"A Comparison of 'Limits to Growth' with 30 Years of Reality" ( http://www.csiro.au/files/plje.pdf 
 )  On the one hand, the Limits to Growth results are based on a  
highly simplified dynamic model of global resources and the global  
economy and did not take into account (nor could it take into account)  
such factors as innovation.  On the other hand, Limits to Growth did  
attempt to look realistically at the problems of limited resources and  
their impact on the society.  The detractors resorted to comments like  
"a piece of irresponsible nonsense" (Henry Wallach, Newsweek) and hang  
their hats on arguments like "human beings have always found a way  
around problems and we will continue to do it" but did not present  
many credible arguments to refute the basic qualitative result.  I  
think we, as a society, learned a lot from Limits to Growth even  
though the model was only qualitative and not predictive.

Just how to build a reasonable model of the economy is an open  
question.  Given the emergent, self-organizing, and evolutionary  
nature of the system(s) in question, it is not even clear that a  
deterministic model can be built.  Many people think that we should  
look more to biology than to physics for inspiration in learning how  
to model complex systems.



On Sat, Apr 25, 2009 at 6:43 PM, Bob Frankston <Bob19-0501 () bobf frankston com 
> wrote:
Rather than studying ethical behavior in a moral contexts we should be  
less naïve about the role of people in these systems. Perhaps  
behavioral economics is more on-topic in that sense. I see native  
trust in markets as a form of intelligent design. I see prediction (as  
in portfolio evaluation) as more art than science – it’s like playing  
poker to see what price you can get.


I would appreciate any pointers to background material for these  
presentations. I’m particularly interested in any attempt to shift  
from predictive models in a Malthusian mold (claiming to know the  
future by projecting from the present) to a framework building on what  
I consider the essence of evolutionary processes – the ability to  
capture success and survive failure without having to be predictive  
beyond very modest contexts. (Too bad we use phrases like “survival of  
the fittest” which confuses a mechanism with the dynamic that drives  
evolution).


The reason why I put so much emphasis on evolution is that we have a  
system that survives (and thrives) despite the essential unknowabilty  
of future interactions and the absence of a single goal or optima.  
Thus I consider efforts to develop sophisticated quantitative models  
and tools to be the wrong framing. The primary “given” should be that  
the future is unknowable – if we try too hard to predict the future  
we’ll find ourselves in the Malthusian trap that Forester’s System  
Dynamics did with the Club of Rome reports in the 70’s.


I bought a book, The Limits of Market Organization, hoping that it  
would move beyond uniformism and look at the characteristics of  
particular markets but instead I found the chapters on The Internet  
and Telecom hopeless mired in false premises in treating telecom as a  
railroad and the Internet as just another telecom service. It’s a  
classic example of trying too hard to predict the future while naively  
misinterpreting the present – a present that is inherently ambiguous.


It may be unfair to characterize economists as being too willing to  
accept premises so they could then write papers. This is why I’m  
interested in counter-examples.


I’ve been arguing that we need an economic/financial system that  
creates opportunities for many small efforts rather than large all-or- 
nothing projects. Or, to use evolutionary framing – we need many  
ecological niches as units of failure. A corporation can be such a  
unit if but we try too darn hard to keep them alive instead of  
treating failure as a learning experience.


The question is whether the concept of “economics” is the right  
framing and whether we should leave evolution to the biologists. We do  
have “disciplines” (an ironic term in this case) such financial  
management which does calculations within a framework and should treat  
valuations as tentative and context-dependent. Unfortunately the naïve  
(as well as willful) hubris leads the practitioners to act with  
unjustified certainty. (Robert Laughlin’s A Different Universe tells  
about the limits of piling models upon models).



From: David Farber [mailto:dave () farber net]
Sent: Saturday, April 25, 2009 18:59
To: ip
Subject: [IP] The Economic Crisis and its Implications for The Science  
of Economics


Prior to any Economic Manhattan project maybe a study of Ethical  
behavior in the financial area would be more profitable djf



Begin forwarded message:


From: Dennis Allison <drallison () gmail com>

Date: April 25, 2009 3:22:35 PM EDT

To: Dave Farber <dave () farber net>

Cc: allison () stanford edu

Subject: The Economic Crisis and its Implications for The Science of  
Economics


For IP if you think it worthy:

Dave,

I'd like to call the attention of IP readers to an interdisciplinary  
conference on The Economic Crisis and its Implications for The Science  
of Economics being held in Toronto May 1-4 at the Perimeter Institute  
and to a talk being given in the EE Computer Systems Colloquium at  
Stanford this Wednesday, April 29th.  Both are related to the current  
financial crisis and what we might do, as scientists and engineers, to  
help resolve the situation.

CONFERENCE:

For more information on the conference.

http://www.perimeterinstitute.ca/en/Events/The_Economic_Crisis_and_Implications_for_Science/The_Economic_Crisis_and_its_Implications_for_The_Science_of_Economics/

Registration is required (seating is limited) but there is no charge  
for the event.

From the Conference Abstract:

Concerns over the current financial situation are giving rise to a  
need to evaluate the very mathematics that underpins economics as a  
predictive and descriptive science. A growing desire to examine  
economics through the lens of diverse scientific methodologies -  
including physics and complex systems - is making way to a meeting of  
leading economists and theorists of finance together with physicists,  
mathematicians, biologists and computer scientists in an effort to  
evaluate current theories of markets and identify key issues that can  
motivate new directions for research. Perimeter Institute was  
suggested to be the gathering point and conference organizers plan to  
foster a very careful, dispassionate discussion, in an atmosphere  
governed by the modesty and open mindedness that characterizes the  
scientific community.

COLLOQUIUM:

One of the conference organizers, Eric Weinstein, will be speaking in  
the Stanford EE Computer Systems Colloquium (http:// 
ee380.stanford.edu) Wednesday 4:15-5:30PM Pacific.  He will argue for  
the need to have a broadly based, interdisciplinary "Economic  
Manhattan Project", a global effort to develop sophisticated  
quantitative models and tools that could be used to guide us out of  
the financial/economic current crisis.

Eric's lecture will be given before a live audience in the Gates  
Computer Science Building (room B1) on the Stanford Campus; this is a  
public lecture and anyone is welcome to attend.  The lecture will be  
available over the Internet by webcast in real time (questions via  
Twitter) and will be available for on-demand viewing from the  
Colloquium website about an hour or two following completion of the  
talk.  In time, Colloquium lectures are available on YouTube and  
iTunes and other distribution channels.  See the website for details.

Dennis Allison
Computer Systems Laboratory
Stanford University


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