In the broadest sense, there are three types of systems in the world.
The first are simple systems which are characterized by only a few variables or agents, and which can be described by perhaps a handful of equations (or even one).
The second are systems which are characterized by disorganized complexity. These may consist of huge numbers of agents or variables, and their interactions cannot be described by simple equations; yet the overall system is well-described statistically through averages and can be described as being stochastic. Such systems are typically characterized by a stable equilibrium, provided there are no external shocks to the system. They are incapable of generating internal shocks or surprises. For example, you might consider the distribution of air molecules in a room. You may not be able to predict the motion of any particular air molecule, but you can be reasonable certain that the global population won't do anything unexpected (like all move into one side of the room leaving a vacuum on the other side).
The third type of system is characterized by organized complexity. As the systems above, one may consist of many variables or agents, each of which is simple, but the system's behaviour does not lend itself to statistical description because instead of the activities of each component dissolving into a background equilibrium, large-scale (even global scale) structure "emerges" instead of seething chaos. Along with these "emergent properties", common features of such a system include multiple equilibria, adaptive behaviour, and feedbacks. There is no simple way to describe its behaviour, as much of the system's history is bound up in its behaviour (what economists call "long memory").
Complex systems, for all their unpredictability are remarkably resilient. The resilience arises from the way in which this type of system interacts with its environment--through the individual actions of its simple components, the system is able to gather information about its environment and modify its operations to adapt. Yet this adaptation and evolution all occur in the absence of central control.
The above descriptions--and characterizations of three types of systems--go back to 1948. Unfortunately it appears that Dr. Weaver was too optimistic when he recommended science develop an understanding of the third type of system "over the next 50 years". Here we are 65 years later and we have made only basic improvements in our understanding of such systems.
What has gone wrong? I think it is partly due to the limitations of the Newtonian paradigm on which science has rested over the past few hundred years.
Back to Weaver. He asks,
Sixty-five years ago, economics was known to be a complex, organized system. Yet today, the Fed continues to set policy as if the economy were a stochastic system that could be sledgehammered into whatever equilibrium state is deemed politically expedient. I would further argue that the Fed has not managed to succeed even in hammering the economy into a desirable equilibrium, but rather has mastered the ability to create artificial statistics to "justify" its actions.
The system is doomed to fail, because the resilience of natural complex systems requires freedom of action for its individual components. We do not observe resilient complex systems with central control. Yet central control is the dominant ideology of our present political and economic systems. Total control, with a vanishingly thin veneer of democracy, ephemeral as the morning dew.
The first are simple systems which are characterized by only a few variables or agents, and which can be described by perhaps a handful of equations (or even one).
The second are systems which are characterized by disorganized complexity. These may consist of huge numbers of agents or variables, and their interactions cannot be described by simple equations; yet the overall system is well-described statistically through averages and can be described as being stochastic. Such systems are typically characterized by a stable equilibrium, provided there are no external shocks to the system. They are incapable of generating internal shocks or surprises. For example, you might consider the distribution of air molecules in a room. You may not be able to predict the motion of any particular air molecule, but you can be reasonable certain that the global population won't do anything unexpected (like all move into one side of the room leaving a vacuum on the other side).
The third type of system is characterized by organized complexity. As the systems above, one may consist of many variables or agents, each of which is simple, but the system's behaviour does not lend itself to statistical description because instead of the activities of each component dissolving into a background equilibrium, large-scale (even global scale) structure "emerges" instead of seething chaos. Along with these "emergent properties", common features of such a system include multiple equilibria, adaptive behaviour, and feedbacks. There is no simple way to describe its behaviour, as much of the system's history is bound up in its behaviour (what economists call "long memory").
Complex systems, for all their unpredictability are remarkably resilient. The resilience arises from the way in which this type of system interacts with its environment--through the individual actions of its simple components, the system is able to gather information about its environment and modify its operations to adapt. Yet this adaptation and evolution all occur in the absence of central control.
The above descriptions--and characterizations of three types of systems--go back to 1948. Unfortunately it appears that Dr. Weaver was too optimistic when he recommended science develop an understanding of the third type of system "over the next 50 years". Here we are 65 years later and we have made only basic improvements in our understanding of such systems.
What has gone wrong? I think it is partly due to the limitations of the Newtonian paradigm on which science has rested over the past few hundred years.
Back to Weaver. He asks,
How can currency be wisely and effectively stabilized? To what extent is it safe to depend on the free interplay of such forces as supply and demand? To what extent must systems of economic control be employed to prevent the wide swings from prosperity to depression? These are also obviously complex problems, and they too involve analyzing systems which are organic wholes, with their parts in close interrelation.The Fed has answered.
Sixty-five years ago, economics was known to be a complex, organized system. Yet today, the Fed continues to set policy as if the economy were a stochastic system that could be sledgehammered into whatever equilibrium state is deemed politically expedient. I would further argue that the Fed has not managed to succeed even in hammering the economy into a desirable equilibrium, but rather has mastered the ability to create artificial statistics to "justify" its actions.
The system is doomed to fail, because the resilience of natural complex systems requires freedom of action for its individual components. We do not observe resilient complex systems with central control. Yet central control is the dominant ideology of our present political and economic systems. Total control, with a vanishingly thin veneer of democracy, ephemeral as the morning dew.
when SHTF, you better not be left holding the fiat.
ReplyDeleteYup! Buy gold!
ReplyDeleteOh, also - beware the aliens.
ReplyDeleteDid IWNATTOS send you?
DeleteI'm reminded of what Donella Meadows states about complex systems in her book, Thinking in Systems: A Primer: "Delays in feedback loops are critical determinants of systems behaviour. They are common causes of oscillations...overlong delays in a system with a threshold, a danger point, a range past which irreversible damage can occur, cause overshoot and collapse."
ReplyDeleteThat's a good comment.
DeleteI enjoyed your comment and agree whole heartedly on your assessment. We are at the end of what seems to be a multi decadal lull in thought.
ReplyDeleteI am working on a complexity analysis framework as well. My approach is based on Bayesian methods in information theory. So far all empirical work when examining macro phenomena (US data) has fallen into simple closed form differentials. I have not relied upon Markovian assumptions, other than where indicated by the data. My blog is http://statisticaleconomics.org
I've seen your blog before. It's very good.
DeleteThis is a comment I wrote last week for a similar blog topic. Not quite to the point of this one, but I don't have time to customize it.
ReplyDeleteThis essay certainly understands part of the problem, needs to go much
further in relating the fundamentals of the data and its limitations to
economics as a discipline.
Economists should read David Hackett Fisher's "Historians' Fallacies"
because economics data is a subset of historical data and historians are
far more sophisticated than economists in their interpretations of their
data and have major advantages in doing so.
Correlation is all that can be extracted from historical data, models
and sophisticated math not withstanding. In any complex system, there
are many causes for each effect and many measurements possible in many
different dimensions. In large complex systems, there is potentially
a large set of values for each measurement. In open complex systems,
neither new causes nor new effects may be obvious. In evolving
complex systems, the causes of effects and relative weightings of causes
will change with time due to feedback.
As there are an effectively-infinite number of correlations to be had
from a large, open, evolving, complex system, the problem of interpretation is
an infinite block for economics. Thus, extracting the 'cause and effect'
signal from an open, evolving, complex system such as the economy is
impossible.
Science has only progressed by using experiments that isolate one independent
variable at a time and produce causal relationships.
I think economics is in a very primitive state, e.g. why are prices the
major dependent variable? They supposedly reflect the flow of
information and decisions made, thus more fundamental, with prices a proxy
with a complex and changing relationship to them. There may well be
better proxies for any of them than have been detected.
It is a puzzle to me why economics is so highly valued by policy makers
as compared to history. Historians are greatly superior in their ability to
extract meaning from historical data, having the advantages of so many
very different sources with which to do their cross-correlations and all
actions done by humans individually and in their institutions. Those
are well-studied from many points of view.
In contrast, all concepts in economics must be derived from the
correlational data and are therefore human interpretations, hypotheses
about how to best interpret the data. There are few related areas of
science for economics research to use to become more than a search for
interpretations that bolster and extend these human-concensus hypotheses.
This process is quite different from non-experimental sciences such as
astronomy or geology, whose fundamental concepts are grounded in
the deepest experimental science and confirmed from many arenas of
experimental science.
Economic's primitive state is reflected in the fact that many reputable
economists persist in delusion, as one can select sets, often overlapping
at even a single point in time, of economists to support and oppose nearly
any real-world action. Based entirely on their profound economic
understandings, of course.
Human understandings are indeed models and all models are limited by the
quality, epistemological and practical, of their data. It should not
suprise us that some models are more useful than others, nor that
economics models aren't generally very useful.
To your question about why economists are valued over historians for dealing with economic questions--I think that society tends to value specialization in knowledge over breadth, despite the fact that there are times when breadth gives the better result. I have had this happen to me in my field of endeavours.
DeleteThere is at least a partial answer to your other criticisms, which would require much more length to answer than I wish to include here. You could consider some of the ideas of J. Crutchfield's research group on epsilon machine construction.
I use a bastardized method for geological time series summarized here. http://worldcomplex.blogspot.ca/2011/04/framework-for-applying-computation.html