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The US Treasury OFR and CFPB : A Paradigm Shift to Complexity Economics

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With President Obama's recess appointment of Richard Cordray to head the Consumer Financial Protection Bureau ( CFPB ), it seems time to ask just how this organization is going to go about regulating the financial services industry.   One of their main goals is to lower the risk of large scale losses in financial markets.   How is this new bureau of the Treasury Department going to do that?
Another agency created by the Dodd-Frank Wall Street Reform and Consumer Protection Act, which is to aid the CFPB and congress with recommendations for their regulatory efforts, is the Office of Financial Research (OFR).  The OFR is tasked with developing the academic basis for a totally new type of market regulation to prevent systemic threats to the economy.  What does this mean, "systemic threats"?  The use of the term systemic seems to come from an application of what is called the General Systems Theory, an increasingly popular theoretical and research paradigm in interdisciplinary sciences.  
For this and other reasons, there has been some speculation that both the CFPB and OFR might attempt to apply a completely new paradigm of economic theory that has been slowly building evidence for about the past twenty years.   This new paradigm, termed Complexity Economics by Eric Beinhocker, Fortune magazine's "Business Leader of the Next Century", has not gotten much press, so far.   This is surprising.   While Democrat politicians have brought the Keynesian idea of large government stimulus back into their spotlight, and the Republican field continuously hammers home a Neo-liberal theory of "trickle down economics", this new third paradigm has mostly remained the focus of publicly obscure scientists, who tend to publish in Physics journals more than Economics journals.   Even though Eric Beinhocker himself wrote a well-selling book on the emergence of this new paradigm in Economics, neither the press nor the political establishment has appeared to have paid much attention.   But, now there is strong evidence that this new paradigm, relatively unknown outside of certain interdisciplinary circles of systems theorists and national security planners, may play the key roll in the operations of the CFPB and OFR.  
Complexity Economics is a fairly broad field, encompassing a wide range of economic models that originate mostly in the heterodox schools that have previously challenged the more mainstream Keynesian and Neo-classical theories.   This is a case in which a new scientific paradigm coming out of a new mathematical framework has come along to aid previously less mathematically tenable concepts.   And this scientific and mathematical paradigm is what defines the concept of Complexity Economics in relation to other methods of collecting data, modeling, and creating theory.   It is primarily based on the ability to do Complex Combinatorics, a form of non-linear mathematical operations, due mainly to advances in computer technology.   Simple rules which generate non-linear operations can be done and summed over millions, billions, or trillions of times, something that would have taken an eternity to calculate with pencil and paper.   This method is generally known as iterative computation, and is now widely used in fields ranging from evolutionary biology to international relations, from Complexity Science applied to living systems to Game Theory applied to the political theater.   To be clear, this is primarily a revolution in our ability to do a form of non-linear mathematics caused by the explosion of computing power, rather than some strange new science.   However, this mathematical ability makes new paradigms of research, and therefore new science, possible.
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While work on this form of mathematics goes back to the eighteen-eighties with Poincare' 's work on the Three Body Problem in Newtonian Mechanics, and computational efforts go back to Turing himself, the modern chapter of this story begins in 1984. This is when a group of Physicists from Los Alamos National Laboratory and others led primarily by 1969 Nobel Prize winner Murray Gell-Mann became tired of having their research into non-linear systems go unfunded by the major funding sources of the time.   That year, they founded the Santa Fe Institute, and interdisciplinary school for non-reductionist scientific research under the banner of the General Systems Theory, a theory that had emerged from Physics and work done in Chaos Theory on such highly unpredictable systems as the weather, for example the work of Lorenz circa 1961.   Lorenz discovered what came to be called Chaos Theory.   Many critics have brushed aside Chaos Theory ever since.   It describes systems that are unpredictable, and so its usefulness was obviously questioned.   But, the important advancement of Chaos Theory was not with regard to prediction of real phenomena; but rather it was a change in mathematical philosophy.   Classical statistics is based on the concept of randomness.   However, no physical experiment has ever demonstrated the existence of randomness in the physical world.   So, classical statistics is based upon this non-deterministic version of reality that isn't actually descriptive of the function of the physical world.   However, since classical statistics is wonderful at making predictions about many phenomena in the world, its assumption of random, and therefore literally unexplained and unexplainable, operations of things in the real world began to seem real to its practitioners.   Chaos Theory represents a different concept of unpredictable behavior.   Like randomness, chaos is an unpredictable behavior.   Unlike randomness, chaos is deterministic.   It assumes causality, and interaction.   The pseudo-randomness of classical statistics becomes a failure of computational power or measurement ability, rather than an indeterminism inherent to reality.   This is what gives the concept its mathematical power.   That mathematical power would eventually find real world statistical application and predictive validity.   Soon researchers found that there were other types of physical systems which were only partially chaotic, but also partially ordered, making these quite a bit more predictable than purely chaotic systems.
One of the first examples of these partially chaotic - partially ordered systems to be discovered is called the Bak, Tang, Wiesenfeld Sandpile.   The concept is straight forward: Put a disk flat up on a pole; Have a machine that drops a single grain of sand at a time from above, directly on the center of the disk; A conical pile will form on the flat level disk; Sand will begin to fall off the edge of the disk.   Then it is possible to ask questions like, "How many grains of sand will fall off the edge of the disk after the next grain falls onto the top point of the pile, this time, and next time, and the next etc?"   This kind of system is called a Complex Adaptive System.   It is impossible to predict how many grains will fall off the edge of the disk the next time that a grain drops onto the top of the pile.   However, a plot of the size of such avalanches over time, in the infinite limit, shows a perfect power law distribution of the probability versus size of an avalanche.   Researchers noticed that this is only so once the pile has reached what they termed Self-Organized Criticality, when the pile is a cone that extends all the way to the edge of the disk.   Now this is one of the simplest versions of a Complex Adaptive System and is excellent for thinking about the basic rules that govern this type of physical system.   Then these scientists began to discover that Complex Adaptive Systems were actually everywhere, all around us.   In fact, the human body itself is one.   From earthquakes and sunquakes, to the human brain, evolving genomes, ecosystems, and even the economy all obeyed the same set of mathematical laws, just with different variables and different parameters between systems.   This was the beginning of an explosion of research into these kinds of systems, the explosion of Complexity Science. It absolutely revolutionized Biology, for example.   Scientists learned that Darwin's was only half the story!   But, with our economic problems today, one of the most important applications may be in Economics.  
Major government research into Complex Adaptive Systems Theory and other aspects of the General Systems Theory has been ongoing for many years.   Since many of the founders of the Santa Fe Institute were physicists at Los Alamos National Laboratory, the influence of that school was very much felt in the military and national security realm first.   Among many such research programs to advocate for the use of Complexity Economics to manage systemic risk in the markets is the Sandia National Laboratory's Complex Adaptive Systems of Systems Engineering Initiative ( CASoS ).   The United States Army and LSCITS in the United Kingdom has also been funding research into such policy applications for financial stabilization (   Other research institutions that will be working closely with CFPB and OFR are both promoting the use of the paradigm and calling for papers that utilize it for their research (   And economists such as Nassim Taleb have testified against the paradigm as a useful risk management strategy before congress (   I, however, would argue that his testimony conflated Complexity Theory with the less predictive Chaos Theory and that his belief in real-world randomness actually goes against all known empirical evidence.   Dr. Taleb seems to be supporting the establishment neo -liberal policies which most empirical studies seem to say got us into this mess in the first place.  
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The OFR itself has publicly released a working paper ( tAQHyNTv 1 AQHS 7 OMNXtb 4-b7 gkaxgUOCcU 9 BgCGtj 3 UE - UA ) that describes current planning at that agency.   Among many other things, this working paper clearly rejects Dr. Taleb 's random unpredictability view of the economy and, rather, embraces the view of Dr. J. Doyne Farmer of the Santa Fe Institute, that extensive modeling using agent-based, network, and other complex combinatoric models with real-world data as their inputs will provide important information regarding the statistical implications of specific sets of regulations upon systemic risk.   A very important part of this concept is that individual regulations cannot stand alone.   All regulations interact with each-other and with the systems which they are meant to regulate.   This, in itself, is a clear denunciation of the standard political model of the Left, which has, up to now, tended to view society and economy very mechanistically, assuming that one may create a specific regulation to address a specific problem.   Complexity Theory would tend to suggest that this linear model of regulation, whereby a specialized and specific regulatory effort targets a specific problem or social ill, is foolish and apt to backfire on the regulator - an argument made by conservatives for a long time.   This shift to a Complexity Theory based regulatory effort is a fundamental change to the concept of regulation itself as it has been approached from both the Left and the Right.   Another important aspect of Dr. Farmer's, and many others', concept is the need for rapid adaptation.   Highly detailed data on markets would have to be as real-time as possible and mechanisms for rapid response to emerging systemic risks would have to be in place, but would also have to be highly adaptable.   This is a reflection of the complexity and rapidity of interaction in modern globalized financial markets, but also a reflection of the uncertainty in planning faced by regulators of such risk.   It is this admission of uncertainty at certain levels of measurement resolution and the acknowledgment of the need for rapid adaptation that seem to be the real strengths of this paradigm, and of Dr. Farmer's argument for its use to regulate markets.  
The working paper is very clearly Systems Theoretic in tone.   References are even made to the agent "vision" of the regulators themselves.   Much of the studies cited in the paper are high-dimensional complex combinatoric computer models utilizing Network Theory, agent-based modeling, and Complex Adaptive Systems Theory.   The approach to regulation demonstrated by the authors also clearly shows a keen understanding of Complexity and its implications for the regulators themselves, as well as regarding the adaptive behaviors likely to occur by market participants such as financial firms and traders.   They outline an open and transparent research initiative at OFR that foresees the inclusion of distributed computing, Open-source software development, public participation, dialog with institutions, social networking, and what sounds like an attempt to focus on voluntary participation to the extent possible.   The strategy outlined in this paper incorporates many of the effective new modes of research to be developed in academia and the private sector in the last twenty years.   It would be hard to argue, at least, that this is not truly a twenty-first century plan of research.  
Certainly, there is both planning and debate about utilizing this newer paradigm of Complexity Economics as a major factor in the risk management strategy.   The recent Davos summit of the World Economic Forum was full of presentations within this paradigm ( sAQHwCzCiAQHqmA 2 ydSTCbEoF 493 CEeAZymScLkS - UO 4 vuQ ).   The managing director in charge of Global Risks 2012 at the World Economic Forum, Lee Howell, recently wrote an Op-Ed in the New York Times calling for the use of Complexity Economics and regulation based upon Complexity Theory to manage systemic risk in the markets as well ( ).   If the Complexity Paradigm is to play a significant role in the management of our financial future, I think that it is time for the public to learn about it with all due diligence and debate its uses as a tool for regulation development with all intellectual honesty.  
So why does it seem that the media, the politicians, and the public have all turned a blind eye to this research and its implications for the Economy, so far?   The research isn't secret; and the other older models appear to be failing us.   Perhaps it is that this new paradigm is too novel.   Perhaps the establishment and those who have prospered on the basis of established paradigms are reluctant to give up those meal tickets.   Established powers will likely have trouble learning and adapting to a new paradigm.   But now there is strong evidence that the OFR and CFPB are about to implement this paradigm as a method of creating regulation for stabilizing the financial system, touching all sectors of the economy.   This represents a fundamental and historic shift in Federal economic policy.   As it was in the nineteen-thirties, when Keynesian policies began to be adopted, in the nineteen-seventies with the Neo-liberal Revolution of the Chicago School , so it is today.   This is a great change in our economic landscape.   Sociologists have long known of the centrality of economics to culture and politics.   This is likely to be the beginning of a great political reconfiguration, as might accompany such a paradigm shift in economic practice.  
It should be clear that this is not a change toward socialism.   It might even be likely that this shift in paradigm for generating regulatory policy will reduce the amount and complexity of regulations.   This likelihood is partially due to the non-mechanistic view of the regulation of Complex Systems that runs against the classical Left concept of a specific regulatory effort for each specific problem.   Another reason for this likelihood is that a major principle inherent to the Complexity Paradigm is that lower complexity tends to reduce systemic risk.   This is but a rule of thumb, since there is a point at which reducing the complexity of a system impinges upon the functionality of that system; but with the current high complexity of market regulations, it still seems likely that this principle would have a reducing effect on the amount and complexity of market regulations.   In many practical respects, adoption of the Complexity Paradigm for Economics by these regulatory bodies can be viewed as a win/win for both the Libertarian Right and the Regulatory Left.   Anarcho-capitalist factions are likely to be the only ones to reasonably see this as a political loss.
There is an increase in the amount of data that will need to be collected from financial institutions under this framework, but as the OFR working paper suggests, this can likely be done while maintaining the privacy of secret and proprietary information via encryption and data sampling strategies.   And, the OFR working paper also suggests the use of Open-source software for the computer modeling initiatives.   This means that everyone can participate in their research efforts in a competitive system in which the competition is not for dollars, but rather for predictive validity.   The regulatory project itself has taken the capitalist theory of innovation to heart toward better prediction and more realistically sound policy implementation.   It could even be said that the methods of research and policy implementation proposed by OFR represent a second paradigm shift in regulatory policy concurrent with the theoretical change toward Complexity Science.   Even in research method, this agency seems to have adopted the lessons of complex adaptive systems by creating a more heterarchical structure of collaboration with both the public and private intelligentsia.   It would appear to be an embrace of the capitalist form and method more closely resembling its' root philosophy in Adam Smith's "The Wealth of Nations", but one that utilizes our modern mathematical and computational power.   Adam Smith famously stated in "The Wealth of Nations" that his theory of capitalism was good to the extent that his "invisible hand" could promote the general welfare of the People.   The "invisible hand" concept itself is widely recognized as one of the first discoveries of what are now called emergent phenomena in Complexity Theory.   We have since learned that this "invisible hand" is only one of many emergent phenomena to be produced by capitalist market systems.   Another is what economists are now calling contagion, a form of Complexity Catastrophe that is clearly very harmful to the general welfare of the People, and which the OFR and CFPB were created to measure and combat.


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The US Treasury OFR and CFPB : A Paradigm Shift to Complexity Economics