Broken Symmetry

Harnessing the Power of Feedback Loops

From Thomas Goetz at Wired:

The basic premise is simple. Provide people with information about their actions in real time (or something close to it), then give them an opportunity to change those actions, pushing them toward better behaviors. Action, information, reaction. It’s the operating principle behind a home thermostat, which fires the furnace to maintain a specific temperature, or the consumption display in a Toyota Prius, which tends to turn drivers into so-called hypermilers trying to wring every last mile from the gas tank. But the simplicity of feedback loops is deceptive.

Via Farnam Street.

I hadn't heard about Bandura's work in the 1960s, although I find that he was cited in Csikszentmihalyi's Flow in passing. It was unclear from the citation how much of an influence he was on C.'s own theory. Although C. does not emphasize flow as a feedback loop, from a systemic perspective that is pretty much what one observes when people are in a state of flow. The topologies get complicated since many experiences of flow involve other people. Think of a jazz quartet and its audience -- live and for its recordings. Or of Congress and the stock market.

I like how this dovetails neatly in with Dweck's mindset theory also. The fixed mindset interrupts any feedback loop, and hence disrupts learning. But too little self-criticism and introspection and there is no feedback.

One of C.'s observations in Creativity is that creative individuals often lose a parent early in life. It seems to me that one would have to develop a capacity for self-criticism earlier on in that instance. Perhaps such individuals have a better capacity for self-generating feedback than do the rest of us, who are looking to others -- especially family -- for queues as to whether we are doing well at this ambiguous assignment that is our existence.

Broken Symmetries and the Culture of the Ants

From Deborah M. Gordon:

Ants can show us how the rhythm of local interactions creates patterns in the behavior and development of large groups.

Via Teppo Felin at Orgtheory What Gordon suggests is that spatial and temporal periodicities could be used to coordinate (unconsciously) the behavior of groups of people. Isn't this what Thomas Schelling suggested decades ago? Why haven't economists (in particular) made more progress on these fronts? Here are my guesses:

1. The rational expectations model of group behavior is operationalized through revealed preferences, and tests of the model have been possible only through aggregate statistics like the CPI. (Maybe Google will have a hand in more fine-grained tests.)
2. Most economists never ask the why or how questions when it comes to preference formation. Take the Becker and Stigler classic as evidence.
3. (Most importantly) The data didn't exist at least until Csikszentmihalyi started handing out pagers for the ESM project in the 1980s.

One and two haven't changed.

Utility is Local and Ephemeral

From a commencement address by Ben Bernanke:

Your sense of how well off you are economically depends a great deal on your expectations and aspirations, which in turn are largely formed by the community in which you live.

Via Tyler Cowen. Not new to readers of B.S., but encouraging to see also is the reference to Csikszentmihalyi's theory of flow:

Another factor in happiness, perhaps less obvious, is based on the concept of "flow."12 When you are working, studying, or pursuing a hobby, do you sometimes become so engrossed in what you are doing that you totally lose track of time? That feeling is called flow. If you never have that feeling, you should find some new activities--whether work or hobbies.

Where to find the mind: Identifying the scale of cognitive dynamics

from Luke Conlin, Ayush Gupta, and David Hammer:

There are ongoing divisions in the learning sciences between perspectives that treat cognition as occurring within individual minds and those that treat it as irreducibly distributed or situated in material and social contexts. We contend that accounts of individual minds as complex systems are theoretically continuous with distributed and situated cognition. On this view, the difference is a matter of the scale of the dynamics of interest, and the choice of scale can be informed by data. In this paper, we propose heuristics for empirically determining the scale of the relevant cognitive dynamics. We illustrate these heuristics in two contrasting cases, one in which the evidence supports attributing cognition to a group of students and one in which the evidence supports attributing cognition to an individual.

Feedback Loops in Education

From the Frontal Cortex blog:

Kids learn material much faster when they screw-up first. In other words, getting the wrong answer helps us remember the right one.

Not only kids, I might add. The information content of negative feedback is greater than that of positive feedback. The trick is creating an environment in which it is both encouraged and welcomed.

The work of Carol Dweck has been covered in earlier posts here and here. When I heard her speak to an audience of parents in Palo Alto about a year ago (all there to figure out how to get their kids into Stanford, natch), I asked her whether anybody had looked to see whether the growth mindset was correlated with higher levels of flow experiences. She said that a study had been done of race car drivers, which found some correlation between the two. I haven't been able to find a link on the web.

How Things Break

From Michael Marder and Jay Fineberg:

Galileo was seventy-two years old, his life nearly shattered by a trial for heresy before the Inquisition, when he retired in 1635 to Florence to construct the Dialogues Concerning Two New Sciences. His first science is the study of the forces that hold objects together, and the conditions that cause them to fall apart, the dialogue taking place in a shipyard, triggered by observations of craftsmen building the Venetian fleet. The second science concerns ``local motions:'' laws governing the movement of projectiles. The two subjects Galileo founded have fared differently. One is a respectable branch of mechanical engineering, while the other is a core subject that physicists learn at the beginning of their education. Although now, as in Galileo's time, ship-builders need good answers to questions about the strength of materials, the subject has never yielded easily to basic analysis. Galileo identified the main difficulty: ``one cannot reason from the small to the large, because many mechanical devices succeed on a small scale that cannot exist in great size.[1]'' Nearly three hundred years elapsed after Galileo wrote these lines before science reached the atomic scale and began to answer the questions he had posed on the origins of strength, and the relation between large and small.

...

An engineer and a physicist compete to find the best material to build a house. The engineer chooses brick because she knows it is what everyone else uses. The physicist decides to conduct some basic research. Turning to the periodic table, he finds the element with the highest bonding strength and melting point, and first proposes diamond. Trying to find something cheaper, he next proposes a vitreous mixture of silicon and oxygen, since the raw materials are abundant and safe and form strong bonds. All is well until someone throws the first stone. In fact, the relation between bonding energies and strength of materials is far from direct; physicists had best respect the practical experience of engineers until they can really explain why one should not build glass houses.

...

Take a slab of material, make a saw cut in it, and pull. In a brittle material, the tip of the saw cut spontaneously sharpens down to atomic dimensions, and like a knife blade one atom wide slices its way forward[3]. In a ductile material the tip of the saw cut blunts, broadens and flows, so that great effort is required to make it progress.

See earlier post: An Economy Made of Glass

Interesting Interview of Michael J. Mauboussin

At Simolean Sense:

Q: You were a top analyst for many years, can you pinpoint when you first became interested in a multidisciplinary approach to finance/investing?

A: I’d trace my thinking back to the moment I read Creating Shareholder Value by Al Rappaport, who I had the honor of working with on Expectations Investing. That book encouraged me to approach stock valuation in a way that was—and still is, to a large degree—different than the rest of the financial community. Specifically, he emphasized the importance of long-term cash flows, showed how competitive strategy analysis should be used, and made a persuasive case that expectations are the key to stock prices.

As I was implementing Rappaport’s ideas in the early 1990s, I started to read more widely in areas like evolutionary theory and psychology. Around the mid 1990s, Bill Miller, who’s now my boss, invited me to visit the Santa Fe Institute. SFI is dedicated to multidisciplinary research in the physical and social sciences. Immersion into the research at SFI really catalyzed my interest in applying multi-disciplinary approaches to the world of investing.

Via Reflections on Value Investing

Interesting to note Mauboussin's choice of words in a few places -- a perfect match to Charlie Munger's. Who is mimicking whom I cannot say for sure, but I'm guessing Munger has priority here.

What Mandelbrot and Fama Both Got Wrong: Buyers and Sellers Can Synchronize

I've been working through a paper by Eugene Fama, titled "Madelbrot and the Stable Paretian Hypothesis." The paper is from 1963. I found it in Modern Developments in Investment Management, edited by Lorie and Brealey and published in 1976. It was in the free books bin outside a bookstore in Noe Valley!

As a historical record, this is a wonderful article. In the article, Fama finds the data inconclusive as to whether the Pareto distribution applies better as a fit to the distribution of price changes during a given period of time. The punchline, of course, is that Fama's efficient market hypothesis (which he developed in the decade after this paper was published) gave the confidence at least to Black and Scholes and many others to base their models of price changes on Gaussian distributions. At least at one point, Fama was open to another possibility.

But what is more interesting is the evidence from this paper, which suggests that both Mandelbrot and Fama may have made the same mistake in analyzing time series of prices. In particular, it seems that both Mandelbrot and Fama wanted to maintain the assumption of independence in price changes in successive intervals. They seem to have had the same reason too. Both wanted whatever distribution that might apply in a shorter interval to apply also in longer or successive intervals -- the very condition that fails when the price changes are not independent. Here's how Fama puts it:

If the changes between transactions are independent, identically distributed, stable Paretian variables, daily, weekly, and monthly changes will follow stable Paretian distributions of exactly the same form, except for origin and scale.

Mandelbrot, recognizing that a Gaussian was not a good fit, picked the Pareto distribution as an alternative. Why did he choose the Pareto distribution? I'm not sure, but there is a good chance that he did so because while its infinite variance permitted wider swings in price than the Gaussian, the Pareto distribution is still stationary in the sense that it treats the price changes during particular intervals as wholly independent.

Both Mandelbrot and Fama seem to have let themselves be guided too strongly by their theoretical ambitions. If we observe what actually happens in crowded marketplaces (such as a crowded trading floor), it is easy to observe how buying and selling behavior is correlated. This kind of correlation (which Keynes attributed to "animal spirits") destroys the very independence that is necessary to the assumption of stationarity made by both Mandelbrot and Fama in their models of market prices!

What alternative models might we use to understand price changes? We have to start from models like the 1D Ising model described in the last post, models which show how correlations at the microeconomic scale can aggregate up into macroeconomic fluctuations. We must begin with the assumption that buying and selling decisions are at least weakly coupled, so that market price changes reflect the large swings that can result from synchronization. In future posts, I shall attempt an explanation for how the Kuramoto Model of synchronization could be used to model the non-stationary distribution(s) of market prices.

UPDATE: My sincere apologies to Mandelbrot. I find in this early work that he did not assume stationarity, and was aware of the non-stationarity in these distributions all along.

Ecological and Constructivist Rationality

Vernon Smith has articulated a distinction between "constructivist" and "ecological" rationality in economics:

We have become accustomed to the idea that a natural system like the human body or an ecosystem regulates itself. To explain the regulation, we look for feedback loops rather than a central planning and directing body. But somehow our intuitions about self-regulation do not carry over to the artificial systems of human society...

"[C]onstructivist rationality (or ‘constructivism’) ... stems particularly from Descartes (also Bacon and Hobbes), who believed and argued that all worthwhile social institutions were and should be created by conscious deductive processes of human reason...In summary, constructivism uses reason to deliberately create rules of action, and create human socioeconomic institutions that yield outcomes deemed preferable, given particular circumstances, to those produced by alternative arrangements...

Since our theories and thought processes about social systems involve the conscious and deliberate use of reason, it is necessary to constantly remind ourselves that human activity is diffused and dominated by unconscious, autonomic, neuropsychological systems that enable people to function effectively without always calling upon the brain’s scarcest resource– attentional and reasoning circuitry...

These considerations lead to the second concept of a rational order, as an undesigned ecological system that emerges out of cultural and biological evolutionary processes: home grown principles of action, norms, traditions, and ‘morality’.

An analogy may be helpful in understanding and remembering the difference between these two concepts of rationality. In a dance performance, the choreographer may exercise constructivist rationality by designing and explaining the moves to the individual performers. But it is ecological rationality that takes over as the individual performers together -- though practice and attention to their neighbors -- execute the performance:

Both forms of rationality are necessary to the pleasing result.

Here's an interesting question to consider: By what mechanism does constructivist order become embedded in ecological order? By reference to the analogy we can make this question more concrete: By what mechanism does the choreography influence the performance of the dancers?

In considering the dance analogy, the answers are not so difficult to see, although the details are surely interesting and important. The choreographer communcates her design with verbal or written instructions to the dancers, who then practice under the choreographer's direction until the design is reproduced. But when we drop the analogy and consider the question more broadly in the context of markets and market-based institutions, the answers seem fuzzier and more difficult to envision.

Perhaps one slice at the answer is provided in a paper by Barbara Drossel reviewed earlier here on Broken Symmetry:

These systems essentially have three states which are called quiescent, excited, and refractory. If there is an excitation, it spreads to the quiescent neighbors. After excitation, a region is refractory to further stimulation and needs some time to recover its quiescent state. The model discussed in this paper describes an excitable medium in the limit where excitation spreads fast on the scale of the refractory time, and where spontaneous excitation is rare. In cases where this limit can be realized experimentally, such a system should show either synchronization or SOC, depending on whether the transition from the refractory to the quiescent state and from the quiescent to the excited state are deterministic or stochastic.

Within Drossel's terminology, sync is acheived through excitation from a quiescent to a refractory state. When in a refractory state, any additional excitation will be passed onto neighboring states. In this regard, Drossel's model seems to have fundamental links to models of population inversions, which produce the coherent radiation of masers and lasers.

Given the right circumstances, even a weak coupling between the neural networks of individuals might be enough to induce a group of people into a state of sync or self-organized criticality.

R.I.P. David Foster Wallace

The really important kind of freedom involves attention, and awareness, and discipline, and effort, and being able truly to care about other people and to sacrifice for them, over and over, in myriad petty little unsexy ways, every day.

From the Guardian via nicolelv

Utility is Not a Monotonic Function

From Gretchen Rubin's Happiness blog, interviewee Karen Leland offers the following thoughts about happiness:

[I]t's not a constant condition, and it's not supposed to be. The amount of happiness I experience ebbs and flows and that's ok. Sometimes I'm super-joyful, sometimes content and sometimes down. It's a cycle, and it all changes.

Sorry economic theorists, homotheticity is an artifact of large averages.

Why I love Paul Graham

Check out his latest essay here.

But there's another way of using time that's common among people who make things, like programmers and writers. They generally prefer to use time in units of half a day at least. You can't write or program well in units of an hour. That's barely enough time to get started.

When you're operating on the maker's schedule, meetings are a disaster. A single meeting can blow a whole afternoon, by breaking it into two pieces each too small to do anything hard in. Plus you have to remember to go to the meeting.

The humor and sincerity of his essays is rare.

What do Music and Quantum Mechanics Have in Common?

Time and frequency are a Fourier pair:

The key to using music to teach modern physics, in my mind, is harmonic analysis. Harmonic analysis is nothing other than Fourier analysis, a tool which is highly valued in physics and engineering. It is not widely known by the non-scientist, yet it is easy to explain in the musical context—when someone looks at a graphic equalizer on their stereo they are looking at a real-time Fourier amplitude analysis of the music that is being played. One can explain how the synthesis of harmonics affects the timbre of musical sound—how the coefficients in a Fourier series can describe different real functions. A very simple example is the comparison between the organ pipe and the piano string. The ‘stopped’ organ pipe has only odd-numbered harmonics and the piano has all harmonics. (Because the ‘stopped’ organ pipe has a pressure node at one end and a pressure antinode at the other, whereas a string has nodes at both ends.) The different symmetry of the boundary conditions accounts for a major aspect of the difference in the timbre of the organ and the piano.

That's from The birth of the blues: how physics underlies music by J.M. Gibson. Via Physics and Physicists

If you're a regular Broken Symmetry reader, then you should know already that it is not only music that can be understood better with Fourier Analysis, but any time-series that can be decomposed into ocillations of different frequency, including cash-flows. The balance sheet is sort of like the graphic equalizer, except that the frequency of the accounts is not rigorously ordered in terms of liquidity. If it were, then the analogy would be complete.

Goldratt, Lean, Flow, and Financial Statements

Over at Evolving Excellence, Bill Waddell writes about how Lean Theory and Eli Goldratt's Theory of Constraints are part and parcel:

That focus on flow is at the very heart of excellent manufacturing - not merely finding tidbits of waste and eliminating it. The final step in the logical thought progression is that the most fertile ground in the effort to accelerate the flow of products across the fixed costs within the value stream is to know what is limitingthe flow - and that is where Eli Goldratt comes in. He said that an hour saved at the bottleneck (constraint) is an hour saved for the system (in lean terms, an hour saved for everything within the value stream). An hour saved anywhere else is a mirage.

Both theories focus on the frequency spectrum of activities within the firm rather than a time-averaged picture of costs. That's why lean is such a pain for cost accounting, which is based on time-averaged measurement of costs.

As mentioned in an earlier post here, what Goldratt calls "throughput" could also be called flow. Both are quantities per unit time.

If balance sheets included frequency-averages (i.e., turnover rates) for each account, would accountants be able to move assets or liabilities on and off the accounts without there being a trace on the financial statements? Right now, an asset moved on and off the balance sheet betwen the period of financial statements may not show up in the income statement or cashflow. These are time-average pictures of income and cash, and by definition an asset moved on and off within the period of time produces no net effect on the time-average.

We have blinded ourselves by excluding information about flow from our financial statements.

Jamming transition in air transportation networks

From Lucas Lacasa, Miguel Cea, Massimiliano Zanin:

In this work we present a model of an air transportation traffic system from the complex network modelling viewpoint. In the network, every node corresponds to a given airport, and two nodes are connected by means of flight routes. Each node is weighted according to its load capacity, and links are weighted according to the Euclidean distance that separates each pair of nodes. Local rules describing the behavior of individual nodes in terms of the surrounding flow have been also modelled, and a random network topology has been chosen in a baseline approach. Numerical simulations describing the diffusion of a given number of agents (aircraft) in this network show the onset of a jamming transition that distinguishes an efficient regime with null amount of airport queues and high diffusivity (free phase) and a regime where bottlenecks suddenly take place, leading to a poor aircraft diffusion (congested phase). Fluctuations are maximal around the congestion threshold, suggesting that the transition is critical. We then proceed by exploring the robustness of our results in neutral random topologies by embedding the model in heterogeneous networks. Specifically, we make use of the European air transportation network formed by 858 airports and 11170 flight routes connecting them, which we show to be scale-free. The jamming transition is also observed in this case. These results and methodologies may introduce relevant decision making procedures in order to optimize the air transportation traffic.

The frequency spectrum of the "brownian" states in the middle obey a power law that has been measured in other systems exhibiting flow congestion. Consider the possibility that other behavior modeled as "brownian" is actually the result of self-organized criticality in flow diffusion. Although it's difficult to visualize, it's not entirely unreasonable to consider "brownian" market price signals as flow-limiting nodes in a network of consumption and production. In other words, market price signals are the rate-limiting step in synchronizing flows of cash and goods within an economy.

Very nice, clearly written paper.

Via physics arXiv blog

What Network Structure Provides the Smoothest Flow?

From a paper by Zoltan Toroczkai, Balazs Kozma, Kevin E. Bassler, N.W. Hengartner, and G. Korniss:

If the flow received by a node has to be processed, it will happen at a finite rate. For example, a node receiving a packet, has to read off its destination and find out to which neighbor to send it. This is a physical process and takes a non-zero amount of time. Thus, if a node receives too many packets per unit time, they will form a queue, and long queues will generally cause delays in information transmission and thus leads to congestion, or jamming. An important question then arises: Can the topology of the underlying substrate graph influence the level of congestion in the network?

The answer is yes...

See also the wiki summary.

What is the Systems Theory of Economics?

Systems theory is an attempt to put Hayek, Coase, Williamson, North, and Ellickson on more quantitatively rigorous foundations. Systems theory is an umbrella term for physics models of networks and dynamical systems that are used to understand how interconnected flows can end up in stable or unstable local equilibria, spontaneously synchronize, and generally evolve over time in feedback loops.

Here are some of the basic hypotheses about human behavior that might be associated with systems theory:

• Individuals have preferences for goods that can be measured in units of flow (quantity consumed/produced per unit time).
• Individuals seek to optimize the fit between their flow preferences and available flow using available information about past and present available flow.

These together suggest that individuals can be modeled as a nexus of flows of cash and other goods. In turn, the interconnected streams of these flows lead to additional hypotheses about the behavior of the integrated system of flows.

• Feedback loops in flow among individuals can result in more spatially and temporally stable preferences by synchronization of flow.
• A nexus of synchronized flows may be a firm or a market depending on the structure of interconnections. (I.e., the ability to synchronize flow over time determines what structures will persist.)
• Firms that synchronize with higher-bandwidth can operate at lower cost, sell at higher margin, or both.
• Firms with scale-free architecture are the most cost-efficient, but also the most susceptible to catastrophic failure.
• (Perhaps most surprisingly) Under certain circumstances, individual preferences will spontaneously synchronize through market price signals.

There is an evolutionary mechanism at work here in that the nexus of flows that constitutes a firm is in competition with other nexuses of flows for additional flow. Over time, some network structures will survive better. And the success or failure of a particular network will, of course, have an impact on how the preferences of individuals within it will evolve over time. There are deep connections between systems theory and the evolutionary theory of psychologists like Mihaly Csikszentmihalyi and sociologists like Donald T. Campbell. But the kind of data and scale of the models used by systems theory is more traditionally associated with economics.

Much of systems theory has been anticipated by New Institutional Economics, and in some cases sociology and psychology. The work from Haidt and Ehrenreich on hive psychology and collective joy, for example, is consistent within broad contours of the physics models of self-organized synchronization. Robert H. Frank's work on positional goods can be understood in terms of the sustainability of certain integrated flow structures over long times. In general, Thomas Schelling seems to have had these sorts of models in mind in his writing, although he tended to focus on the potentially destructive outcomes after long periods of repeated interactions.

I'm going to try to turn each of these bullet points into a separate post over the next few weeks or months.

More Systems Theory from Judge Posner

My remaining proposals are of measures to improve regulatory performance, as distinct from the organization of financial regulation. First, we need a program that will rotate financial regulatory staff among the different financial regulatory agencies, to broaden the perspectives of regulators, reduce the "stovepiping" of information that may relate to a wide range of companies and financial markets, expose regulators to new ideas, reduce turf warfare based on misunderstandings, and make a career in financial regulation more interesting and challenging.

At his Atlantic Correspondents blog. This is a prescription that could be drawn directly from Ciskszentmihalyi's systems theory as applied to cultural economics.

How to Reduce Systemic Risk: Look at the Rates of Growth and Cross-Correlations

The White Paper last week was a disappointment. It was long on generalities and short on specifics of how and why we ended up in this mess. Lots of people are throwing their hands up in the air. A few are saying that the only way to avoid this problem is to go back -- i.e., to keep banks from ever growing so big again. Both of these are counterproductive. We have learned a few things from watching this crisis enfold that may help us avoid similar crises in the future.

One of those lessons is directly relevant to the argument that banks should simply be limited to a particular size. If there is any lesson that we have learned from this crisis, it is that size doesn't matter. Banks big and small all over the world have been brought to their knees by this crisis.

Systemic Risk = Large-Scale Correlations in Risk

What brought the financial industry all over the world to its knees can be attributed to a single mistake in reasoning, which was made by managers at every bank that ended up insolvent. Their mistake was in assuming that their risk of loss would range only within the boundaries set by local and recent events. The Royal Bank of Scotland, Kaupthing Bank in Iceland, Lehman Brothers in the United States, and your local Savings & Loan -- all made the same basic mistake of assuming that even if things got ugly for them, then there would be plenty of other banks around to pick up the slack. So long as I can depend on my counter-parties to behave exactly as we expect by contract, we will survive.

One thing that's worth noting is that this mistake wasn't fatal to the financial industry before the subprime mortgage bubble. The financial industry survived several bubbles before that, including the dot com bubble only ten years before. What changed?

The answer is cross-correlation. Over the past ten years, the financial industry became more interconnected through the financial engineering of derivatives and increased leverage than it has been ever before in history.

When any system becomes cross-linked beyond a certain low threshold, correlations in the dynamics of the system can spread from local to global. And this is exactly what we are living through. Although the system became exponentially more interconnected over the last ten years, the individual decisionmakers within the system (for the most part) kept their horizons relatively local and short-term. Nobody was looking at the large-scale, long-term trends and worrying about how those could ripple out in the event of a single default. Nobody was looking because no such cascade had ever occurred before.

The first role of any systemic risk regulator should thus be to quantify and limit the amount of cross-correlation in risk within the financial industry. We just can't afford to have every financial institution making the same kinds of bets. If a few are taking their cut from poker players, then the others need to take their cut from craps-players or roulette-players.

Regulating at Scale: Measuring Fundamental Limits to Growth

Systemic risk regulators have to be especially careful about the problem of scale. As numerous commentators have pointed out, there were a few people within the SEC that noticed the problems that might be caused by the unregulated sale of over-the-counter derivatives and the explosion in growth of the subprime mortgage market. How is putting a new agency in charge -- even if it has access to more information -- going to avoid such cascades in the future? In general, I don't think that any such agency will be able to prevent such cascades without having unintended negative consequences. And the scope of the authority needed to effect system-wide regulations is worrisome too from the point of view of suceptibility to political influence. A thumb on the scale at that scale is enough to crush entire markets.

To deal with systemic risk, any actual regulation will have to gloss over many of the fine details of what particular parties and counter-parties are doing. Getting too involved in these details simply invites corruption anyway. To deal with systemic risk, regulators need to change focus from the short-term and local, to the long-term and global.

For example, has anybody in government ever tried to figure out the highest rate of growth that has ever been achieved by a legitimate (i.e., non-fraudulent, sustainable) large corporation? If a company is growing faster than a certain rate then risk is being spread through the system. Such a company is either a Ponzi scheme or a gamble that some market exists that has not been demonstrated to exist.

A perfectly legitimate way for a systemic risk regulator to operate would be to set a threshold internal rate of return on cashflow -- say 30% annualized -- and then to demand that anybody with a higher IRR has to report to the systemic risk regulator on from whom and to whom that cash is flowing. Such a policy would have flagged the problem with subprime mortgages immediately. The size of the subprime mortgage market did not suddenly go from a few hundred million to billions in a year or two. At the very least, the government should have been carefully scrutinizing it after the cashflow increased by that number of orders of magnitude.

The Big Picture: A Network of Cashflows

At the end of the day, an economy is a network of cashflows. What makes our economy unique in the history of the world is its degree of interconnectedness. Those interconnections are both a blessing and a curse. They are a blessing in that they lower the costs of lending so that more and more people can get access to financing. (Think of Kiva, which is part of that interconnectedness.) The curse is that they permit for system-wide fluctuations to develop even from relatively small losses when decisionmaking is too tightly correlated.

Our goal should not be to go back to the smaller, less interconnected world. It should be to forge ahead into a fully networked economy, but with awareness of and capabilities to manage these global and long-term patterns of growth and decay in the network.

We need to think bigger.

The Glassy State of the Financial Services Industry

How might financial regulators have identified a problem with our market? By looking at cross-correlations like this one:

Again, this is from the paper on synchronized clusters reviewed here.

More on the Glassy Economy

I had to revisit the paper by Yik Wen Goo, Tong Wei Lian, Wei Guang Ong, Wen Ting Choi, and Siew-Ann Cheong, which I reviewed earlier here. As explained in the earlier review, these researchers found correlations in public security prices by looking for cross-correlation at successively longer and longer time scales. Lo and hehold, they were able to identify synchronized clusters that way:

We call these small clusters of very strongly correlated stocks financial atoms. The correlation levels within financial atoms rise significantly above the background level c0, because the dynamics of atomic stocks are coherent over time scales much longer than the average trading interval, which plays the role of the ‘nuclear/electronic’ time scale. The existence of such financial atoms is evident from earlier studies [citations omitted], where they are referred to as synchronized clusters. However, the separation of ‘nuclear/electronic’ and atomic time scales was not recognized. The potential of using financial atoms as effective variables to describe real financial markets is also not widely appreciated.

So not only is it possible that the economy is in a glassy state, there is some numerical data demonstrating that it actually is.

Their use of terminology is unfortunate, because it would be better to think of individuals as the "atoms" of an economy on my view. In that case, corporations would be molecules, and what they call "financial atoms" would be analogous to the chains and sheets of molecules described in the post yesterday on an economy made of glass.

Before I saw the White Paper from the Obama administration, I was hoping they had people that were already doing this kind of analysis. I'm no longer so optimistic. Can I ask for help from anybody and everybody who understands this type of analysis in educating the public and government in its implications? We'll all be better off that way in the end.

More Evidence for Synchronized Flow

Ironman provides an excellent review of some posts on Broken Symmetry, and (even better) provides a beautiful review of how the frequency spectrum of price fluctuations alternates between Gaussian and Levy statistics at Political Calculations:

We thought it would be appropriate to close out this post by reviewing the modern era of the stock market (or rather, most of it!) with our charts that identify the periods where Gaussian rules would apply, and the periods where Lévy flights rule. While we consider the modern era of the U.S. stock market to have begun in January 1952, we'll pick up the action in June 1954, extending to when the most recent period of disorder began in January 2008:

The bottom line here is that you can't understand build up a realistic model of an economy starting from the assumption that preferences are time-independent. The better way to start is with the assumption that preferences are oscillatory. I call this "the fundamental hypothesis of periodicity," and it produces the hypothesis of rationality at the limit of ergodicity -- i.e., when preferences behave the same way from one time-window to the next.

Do Email Traffic Statistics Really Cast Doubt on Small World Theory?

The answer is no. MIT Technology Review reports on work published by researchers at the Hebrew University in Jerusalem:

Goldenberg and Levy say that while it is just as easy to email somebody who lives on the same street as somebody on other other side of the world, it turns out we have a huge preference for sending messages over shorter distances... Their conclusion is that far from reducing the importance of geographical location, electronic communication appears to have increased it, probably because people swap more messages with those they have personal interaction with. If that's true, why have we gone so wrong in thinking the world is getting smaller? One source of confusion, argue Goldenberg and Levy, is the famous six-degrees-of-separation experiments peformed originally by Stanley Milgram with letters and later by Steve Strogatz and Duncan Watts using email. These seem to indicate that a "small world" effect is at work in social networks.

The major problem with this criticism is that it misunderstands the point of the Milgram and Watts-Strogatz work. Milgram, Watts, and Strogatz had little data on the dynamics of the network. They were studying the network structure. This looks like sensationalism to me.

If you asked any of them whether they would be surprised to hear that the frequency-spectrum of communication is spatially clustered, I'm sure they would say no.

An Economy Made of Glass

Marvel, Strogatz, and Kleinberg describe "jammed states" and note how they bear a distant relation to spin glasses. Although I hadn't thought much about it before, there are some relevant similarities between glasses and the transitional states described in Kuperman and Zanette.

There are a few properties that make glassy solids interestingly different from crystalline solids. These properties develop from the differences in microscopic structure and dynamics between crystals and glasses.

For example, glasses are viscoelastic -- they're a little bit viscous like honey, and a little bit elastic like rubber, but not quite either. Silly putty is a viscoelastic material that many people will recognize. When you hit a viscoelastic material with a hammer, it will shatter like a crystal (albeit not in the same neat pieces that a crystal shatters). But unlike crystal, if you pull at it or apply even strong forces to it at slower frequency, it will stretch.

At the molecular level, that's because of the cross-linking between the chains and sheets of molecules that make up the material. In a perfect crystal, the molecules are perfectly lined up with soldiers marching in formation. In a glass, there are still orderly groups, but they're not all arranged in nice rows and columns. Instead they're cross-linked to other groups, and the cross-links form and break as the groups slide around past each other when low-frequency forces are applied to the glass.

Another interesting property of glasses related to this cross-linking at the molecular level is the time-dependence of glass transitions. Glasses tend to form when liquids are cooled too fast for them to crystallize. If cooling happens slowly enough, then one might imagine that most fluids would crystallize rather than form glasses. One might imagine that, but it does not appear to be so. As some of the papers I've blogged about here suggest, there seem to be glassy states even at very low energies. A vivid name for this effect in condensed matter physics is "frustration."

By analogy, if an economy is modeled as cross-linked clusters of synchronized oscillations, then we might expect to see viscoelasticity and time-dependent transitions in its equilibriums also. Should bubbles and crashes be modeled as viscoelastic creep?

Such an analogy might offer practical value to financial regulators. Generally speaking, the fragility of a glass will depend on the size and shape of the molecular clusters and the number of cross-links among them. The bigger the clusters and the more the cross-links, the more fragile the glass to external shocks. Smaller clusters and fewer cross-links mean less viscosity and elasticity.

It's interesting too to note that there is always some tradeoff between frictional forces caused by internal viscosity and fragility. You can't make a glass less fragile without increasing the cross-links and thereby increasing viscosity. Of course crystals are less fragile to most perturbations than glasses. But if you happen to pick just the right frequency perturbation, then crystals will explode even more spectacularly than glass -- they have no viscosity because they're perfectly rigid until they break.

What kind of economy do we want? A glass like silly putty or a perfect crystal like ice?

The Energy Landscape of Social Balance

From Seth A. Marvel, Steven H. Strogatz, and Jon M. Kleinberg:

Taken together, the above results give us a first look at the energy spectrum of jammed states in completely connected social networks in which opportunities for greater relational consistency and cooperation are the driving forces for change. Since balanced states—the global energy minima—correspond to two mutually antagonistic cliques, they often represent socially undesirable outcomes such as intractable conflict. Viewed in this light, the presence of jammed states at higher energies suggests the possible beginnings of a companion theory of reconciliation and flexibility in the setting of social balance: since these jammed states show less large-scale antagonism, they may provide pathways to steer conflicts into structures where reconciliation can more easily occur.

Available here. For a variety of reasons, I believe these "jammed states" are related to the "twisted states" described in the paper by Kuperman and Zatteo reviewed earlier.

Found via Physics ArXiv blog.