Broken Symmetry

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.

A Systems Theory of "Animal Spirits": an Application of the 1D Ising Model

According to Keynes, it is "animal spirits" that drive shifts in consumption and savings:

Even apart from the instability due to speculation, there is the instability due to the characteristic of human nature that a large proportion of our positive activities depend on spontaneous optimism rather than mathematical expectations, whether moral or hedonistic or economic. Most, probably, of our decisions to do something positive, the full consequences of which will be drawn out over many days to come, can only be taken as the result of animal spirits - a spontaneous urge to action rather than inaction, and not as the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities.

Condensed matter physicists, including Leo Kadanoff, developed a mathematical model that we might find useful in modeling what Keynes described as a "spontaneous urge to action." Although a toy model even for physicists, the 1D Ising model (described by Kadanoff here and by wikipedians here) provides a useful way of understanding the relationship between symmetries in microdynamics and the coarse-grained, macrodynamical variables that are used to categorize the state of a system.

In condensed matter physics, the system is a large number N (at least thousands) of magnetic dipoles arranged on a line, which are exposed both to a heat bath (that guarantees a certain average temperature for the overall system) and a magnetic field (that can be turned on and off to introduce more energy and order into the system). Each of these dipoles can point either up or down (i.e., parallel or antiparallel to the magnetic field).

For economists, the system is a large number N of individuals (or firms), each of which can exhibit either a preference for consumption or a preference for savings. We shall keep track of these preferences by assigning a preference for consumption to a dipole pointing down (i.e., by analogy, against the magnetic field) and a preference for savings with a dipole pointing up (i.e., by analogy, aligned with the magnetic field). Rather than a magnetic field, these individuals are subject to the influence of an interest rate announced by some regulatory authority. Rather than a heat bath, we're going to have a regulated money supply, which guarantees that there is a certain total amount of cash in the system.

Most importantly, the Ising model postulates a link or coupling to the two nearest neighbors. In the condensed matter case, this means that the overall energy of a local region of the system is lower (or higher depending on the type of magnetism) when the individuals in that local region are either all parallel with the magnetic field or all anti-parallel. In our case, this means that the average cashflow in a local region is lower (never higher as it might be in the condensed matter case) when the individuals in that local region are all either saving or consuming. What's the explanation for this coupling? "Animal spirits."

Before proceeding to the most surprising results, let's get a feeling for the microeconomics of our system to make sure they make sense. Given a fixed amount of cash in the system, if we increase the interest rate, we expect the number of individuals expressing a preference for consumption to decrease -- i.e., for their preferences to flip from consumption to savings. By contrast, given a fixed interest rate, we expect the absolute amount of consumption to increase as the amount of cash in the system is increased because the real cost of interest payments decreases with inflation in the money supply.

Now for the results. Without bothering to reproduce the detailed mathematical analysis (the most elegant solution involves use of renormalization group methods), here is what one finds from the exact solution of the 1D Ising model: (the graph is reproduced from the paper by Kadanoff):

The y-axis on the graph corresponds to the average rate of savings (positive values) or consumption (negative values). The x-axis corresponds to the reciprocal of money supply so that the origin of the x-axis corresponds to an infinite money supply. The x-axis is also scaled by a factor, Tc, which corresponds to a critical threshold for money supply to the system (further explained below). The different plots correspond to different values of the interest rate (note that these can go negative, a scenario that has obtained only in Sweden so far).

The amount of insight this diagram gives us into the system is extraordinary. Consider the interpretation of these various plots:

• High positive interest rate: (H=0.5) As money supply decreases from infinite to fractions of its critical value, the average amount of savings smoothly increases from about 75% of the population saving to the point at the top right where everybody in the system is saving.
• High negative interest rate: (H=-0.5) As money supply decreases from infinite to fractions of its critical value, the average amount of consumption smoothly increases from about 75% of the population consuming to the point at the bottom right where everybody in the system is consuming.
• Zero interest rate: (H=0) The average amount of consumption and savings is perfectly balanced, even with an infinite money supply. In other words, when the system is flooded with money and interest rates are held at zero, exactly half of the population consumes and exactly half of the population saves. When interest rates are zero and money is plentiful, individuals basically act as if independent of their nearest neighbors. But note well what happens when interest rates are zero and the amount of cash in the system is withdrawn. At a critical threshold (Tc in the diagram), individuals spontaneously start to align with their nearest neighbors by either saving or consuming -- either is possible -- even when interest rates are zero. This is a textbook example of spontaneous symmetry breaking, which is the phenomenon from which this blog takes its name. The broken symmetry is a consequence of the coupling among nearest neighbors -- i.e., "animal spirits."

I realize that most economists have never heard of the 1D Ising model. But don't you think this kind of model might be useful in understanding macroeconomics? In subsequent posts, I will try to discuss how the Ising model has been extended into higher dimensions -- an important generalization for economics. For the time being, it must suffice to say that this toy model has nonetheless been highly influential among physicists in understanding nonequilbrium dynamics.

Update: A reader comments:

One problem with this model is that as the money supply goes to infinity, you get hyperinflation. This makes interest rates rise sharply. But your model has rates go to zero.

The answer to this comment is that interest rates might still depend on money supply. Referring back to the diagram, we can see that interest rates and money supply are treated as independent variables -- i.e., both are independent of the dependent variable (average consumption/savings). So in the zero interest rate case (H=0), interest rates are held as money supply is increased and decreased to see what happens to savings and consumption. The diagram doesn't make obvious how interest rates might depend on money supply (or vice versa).

Nonethelesss, we can deduce a few things about such a relationship from the diagram. For example, from the diagram it is clear that if we were to set a very high or very low interest rate (i.e., an H near +1 or -1), then everybody would be either saving (H=+1) or consuming (H=-1) regardless of whether the money supply was infinite or zero. In that sense at least, the comment about infinite money supply and hyperinflation being correlated is still consistent with the model. The diagram simply doesn't show how interest rates might depend upon money supply (or vice versa).

But we can develop a more complete answer by peaking back at the condensed matter system that inspired the 1D Ising Model. How does the strength of the magnetic field affect temperature? It is important, in this context, to remember that "temperature" is actually a number assigned to a distribution of energy in the system that has a particular shape. Whether an increase or decrease in the strength of a magnetic field will affect the shape of the distribution depends on the details of the microscopic dynamics. With too much or too little energy available to the system, a change in the magnetic field isn't going to have a big influence on the microdynamics, and hence the temperature will be insensitive to the magnetic field at these extremes.

Translating back into economic terms, this means that when individual decisions about savings and consumption are either perfectly independent or perfectly correlated (conditions which correspond to very high and very low total energy in the system), then a change in interest rates isn't going to have a big influence on money supply.

And does that not seem to be the case in fact?

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.

Okay, Sornette. You have our attention. Now show us the Money!

The Physics arXiv blog today recaps the prediction of Didier Sornette and crew that the Shanghai Composite was a bubble that would pop between 17 July and 27 July. It didn't. It popped on 4 August instead:

Thanks to the Physics arXiv blog for putting together the graphic. I was bemused when I first saw Sornette's paper. See earlier post on the prediction here. The Shanghai Composite indeed looked a little overheated. But nobody can see into the future, and (more importantly) Sornette gave us almost no information about how and why he was predicted a crash within this window. Nevertheless I was impressed with his courage to make a prediction that is so easily falsifiable. Of course, this high-risk, high-reward strategy is also a favorite of cranks.

That being said, the general idea that super-exponential growth is unsustainable is one that I have explored myself on this very blog. See excerpt from this earlier post on e: The Fundamental Speed Limit to Startup Growth.:

the very fastest that you can grow anything is by continuously compounding -- by feeding whatever new revenue you generate back into generating more revenue instantly. If you plot revenue as a function of time, and look at the base of the logarithm, the largest base results when you instaneously begin compounding every new bit of revenue as soon as it comes in. That base is e. There is no way to grow revenue faster than e.

Although these statements are unequivocal, I will be the first to admit that it's very difficult to verify any of them empirically by looking at actual firm growth rates -- much less the Shanghai Composite.

So here we are, with a falsified prediction and no information about how it was produced -- only tantalizing hints. Okay, Sornette. For better or worse, you have our attention. Let's see how you came up with your prediction so that we can understand whether this was simply a lucky guess.

The Dark Side of Systems Theory?

From Ben Heineman at the Harvard Law corporations blog:

An important question raised by the story–and, of course, the broader, deeper trend of using mathematics and systems analysis to “understand” complex human behavior–is whether, before they have deleterious effects, emerging theories and products and ideas can be advanced with a strong measure of humility and put in the context of complex human society, where some key factors exist that cannot be quantified. Will the already potent but ever-emerging “numbers” class have the broad education and training to understand the “benefits” but also the “limits” of all this numbers crunching?

...

As is well known, McNamara was a part of a World War II “systems analysis” team at Defense, led by Tex Thornton, a “whiz kid” who rose to the top of Ford Motor and a civilian technocrat who brought a powerful systems orientation to the Pentagon in the early 60s. While this approach certainly had relevant application to an attempt to rationalize the Pentagon’s corpulent competition between the Army, Navy and Air Force, it became famous in the Vietnam War when numbers like body counts, targets hit, enemy forces captured, weapons seized, tons of bombs dropped, and hamlets protected were used to argue that the war was being prosecuted successfully. The origins of the war were not in systems theories (rather, to take one strand, a belief that monolithic Russian-Chinese Communism would overrun Southeast Asia). But those theories played an important part in convincing McNamara and President Johnson that the war could be won and, therefore, in deepening our involvement, resulting in tragedy both for the U.S. and for Vietnam.

Thirty years later, McNamara declared in his book (positions repeated later in the award winning documentary, “Fog of War“) that, although his aspirations were idealistic, “we were wrong. We were terribly wrong.” Among his “lessons” about the causes of the Vietnam mistake: “our profound ignorance of the history, culture and politics of the people…[the failure] to recognize the limitations of modern high technology military equipment.” After McNamara’s death, the historian Michael Beschloss wrote: “McNamara knew little about Southeast Asia, and made no conspicuous effort to learn more. His penchant for numbers left him ill-equipped to understand that some human motivations cannot be quantified or predicted. As a result, he drastically underestimated the Viet Cong’s determination.”

Go read the whole thing. Models are maps. Having accurate maps is always useful. But maps don't tell you where to go, why you should or shouldn't, or what to do when you get there.

Mark Cuban on Trading versus Investing

From his blog:

The government should raises taxes significantly on profits from short term capital gains on the sale of public stocks, indexes, commodities, futures held for 24 hours or less and extend the length of time required to qualify for Long Term capital gains and reduce the tax rate on Long Term gains. This will discourage flash trading, ETFs that move markets purely on cash inflows rather than fundamentals and also reduce the amount of speculation on commodities. It will also reward companies that act in the financial interests of long term holders and their employers. I think the impact on the economy would be far fewer layoffs as CEOs find themselves with more shareholders who think long term rather than short term. Believe it or not, there are shareholders who are fine with companies not beating their numbers if the company is making progress towards a clearly defined goal. I don’t care if the P/E of the stocks I own is 14 or 20. I want the companies investing in being a great company rather than trying to make traders of their stock happy. Most CEOs give great lipservice to this approach, but they are so focused on marking to market their own personal stock portfolios, they emphasize stock performance over doing the right thing for the company. Taxation can change the focus on public companies and stock trading. That would be a great thing for the economy.

Great ideas. As an alternative, one could increase the frequency of financial statement reporting -- for example, by requiring public companies to report a time-delayed real-time series of cash-flows -- so that higher frequency trading was based on more substantive information about the value of the security.

A Reynolds Number for Cash-Flow?

George Dyson explains:

In any hydrodynamic system, the non-dimensional Reynolds Number characterizes the ratio between inertial forces (the result of mass and velocity) to viscous forces (the result of the inherent stickiness of the fluid). When the Reynolds number reaches a certain critical value, the system changes from laminar to turbulent flow. There is an equivalent to the Reynolds Number for an economic system: the ratio between the speed (and amplitude) at which currency is flowing through the system to the viscosity of the financial medium. The Reynolds number of our electronically-mediated economy has recently gone way up, with destabilizing results. The latest problem is that automated programs — -the barnacles of the New Economy — -are now trading *within* the frequency spectrum of the turbulent boundary layer. If this happens to a ship, it will slow down, and if it happens to an airplane, it will go into a stall. Where’s the anti-fouling paint?

I was unimpressed with the Rushkoff essay to which Dyson is responding. The historical arguments are far too selective in their use of evidence. What about the invention of double-entry bookeeping or the perfection of the patent system in the 19th century? Markets are neither purely constructed nor purely ecological -- a key point made by Vernon Smith in his latest treatise.

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

Feedback Loops at Stanford

From Steve Blank's series on the Secret History of Silicon Valley:

The close tie between the two labs was a unique aspect of the Stanford Lab. Stanford had a Customer Development loop going on inside their own lab. The discoveries in tube and circuit research suggested new electronic intelligence and countermeasure techniques and systems; in turn the needs of the Applied Lab pushed tube and circuit development. With the Applied Electronics Lab Stanford was becoming something akin to a federal or corporate lab run under university contract. The university found government contracts profitable as the government reimbursed their overhead charges (their indirect costs.) This means they could fund other non-military academic programs from this overhead.

Leverage Causes Fat Tails and Clustered Volatility

From Stefan Thurner, J. Doyne Farmer, John Geanakoplos:

When funds use leverage, price fluctuations become heavy tailed and display clustered volatility, similar to what is observed in real markets. Previous explanations of fat tails and clustered volatility depended on 'irrational behavior', such as trend following. We show that the immediate cause of the increase in extreme risks in our model is the risk control policy of the banks: A prudent bank makes itself locally safer by putting a limit to leverage, so when a fund exceeds its leverage limit, it must partially repay its loan by selling the asset. Unfortunately this sometimes happens to all the funds simultaneously when the price is already falling. The resulting nonlinear feedback amplifies downward price movements. At the extreme this causes crashes, but the effect is seen at every time scale, producing a power law of price disturbances. A standard (supposedly more sophisticated) risk control policy by individual banks makes these extreme fluctuations even worse. Thus it is the very effort to control risk at the local level that creates excessive risk at the aggregate level, which shows up as fat tails and clustered volatility.

I Guess Southern Californians Really Are More Laid Back

From Twittermood.Org: See explanation here. Via Prasanna.

What do Ants and Neurons Have in Common?

From Seed Magazine:

According to Cornell University neurobiologist Thomas Seeley, the social insect metaphor can be scaled up even further, all the way to government. He is currently writing a book entitled Honeybee Democracy (Princeton University Press, 2010) about how we can improve our group decision making and government by imitating honeybees, which, like ants, are social insects. Honeybees have been making group decisions for tens of millions of years. And as Seeley says, “Democracy among humans traces back only 2,500 years or so to Athens, Greece, so it is perhaps not surprising that we are still fumbling around.”

Again via Omnivore

Self-Organized Criticality in Neural Networks

From the New Scientist:

In 2003, John Beggs of Indiana University in Bloomington began investigating spontaneous electrical activity in thin slices of rat brain tissue. He found that these neural avalanches are scale invariant and that their size obeys a power law. Importantly, the ratio of large to small avalanches fit the predictions of the computational models that had first suggested that the brain might be in a state of self-organised criticality (The Journal of Neuroscience, vol 23, p 11167).

To investigate further, Beggs's team measured how many other neurons a single cell in a slice of rat brain activates, on average, when it fires. They followed this line of enquiry because another property of self-organised criticality is that each event, on average, triggers only one other. In forest fires, for example, each burning tree sets alight one other tree on average - that's why fires keep going, but also why whole forests don't catch fire all at once.

Via Omnivore.

Judge Posner's Question about Hayek, the Austrian School, and Interventionism

From his response to comments:

Members of the Austrian school of economics such as Friedrich Hayek argued that depressions can arise from unsound monetary policy--a policy that forces down interest rates, creating an artificial boom. Obviously I am sympathetic to that argument. But my impression (no more than that) is that, at least in the 1930s and perhaps still, Austrian economists do not advice any interventions designed to stop a depression--they regard the depression as just punishment for the unsound policy that precipitated it. That seems to me to give too little weight to the immense costs that a depression can impose on a society. But I am no expert on the Austrian school, and invite correction.

I'm hoping Cafe Hayek or ThinkMarkets will weigh in here. Click through to see my own comment.

Music and the Brain

From an interview of Oliver Sacks:

Using loud music as torture draws on these qualities of music, as well as simple sensory overload. I personally find the assault of loud public music—in stores, restaurants, airports–a minor form of torture. One wants to listen to one’s own music, in one’s own way, not to have it force-fed, especially at great volume.

One of the things I found remarkable about Beijing when I visited in 1998 was the ubiquity of public address speakers, which were usually playing some kind of music in the background in between and during public announcements. One had the sense that people there were uncomfortable without these sounds, since one could have observed these speakers installed even in improbable locations. Here's another quote from the interview:

Rhythm, actual or imagined, activates areas of the motor cortex, crucial in synchronizing and energizing movemen... There is a great deal of debate about the relationship between music and language, and speculation about which capacity evolved first. It has often been suggested that music emerged as a by-product of linguistic capacities. But musical rhythm, with its regular pulse, is very unlike the irregular stressed syllables of speech.

Musicophilia may be worth a look.

Do Good Fences Make Good Neighbors?

Why, yes, they do.

There's a debate among academics in patent law now over whether patents create an anticommons (or "gridlock") problem. The debate tends to assume without proving that there would not be a worse commons problem were patent rights peeled back to disable the system of exclusive rights to technology that developed in the 19th century in the United States.

But patent rights weren't the only property rights that were secured for the first time in the 19th century United States. In fact, the invention of barbed wire (itself patented) contributed to a dramatic decrease in the costs of securing real property in the 19th century. And to what effect? Richard Hornbeck reports:

This paper uses the introduction of barbed wire fencing to the American Plains in the late 19th century to estimate the effects of property rights on farmers' production decisions. Farmers were both formally and informally required to build fences to secure their land. From 1880 to 1900, the introduction and universal adoption of barbed wire reduced the cost of fencing, relative to wooden fences, most in counties with the least woodland. Over that period, counties with the least woodland experienced substantial relative increases in the improvement intensity of farmland, the value of farmland, and the productivity and production share of crops most in need of protection. This substantial increase in agricultural development appears to reject increased security of property rights due to barbed wire fencing. Some states' previous efforts to reduce the need for fencing are not found to have had similar effects, however, suggesting a difficulty protecting property rights in sparsely settled areas through legislation and the court system.

Why would state-enforced rules not work as well? See Robert C. Ellickson and Coase. (Still not transparent? See Calabresi and Melamed.) And note that patents are more like barbed wire than like state legislative efforts to shift the costs of fencing to herd owners (described in the paper).

If this seems like ancient history to you, then consider the fact that our government is going to have to learn how to be 100x more efficient with its tax revenues over the next few decades if we are to avoid insolvency.

Here's another interesting point: Ellickson found that even barbed wire was too expensive to be worthwhile in close-knit communities of ranchers and farmers like Shasta County, California.

Welcome Back to the IP Asset Maximizer Blog

Just a note to readers that Jackie Hutter's IP Asset Maximizer Blog is back and running after a delightful facelift, putting artistically challenged bloggers such as myself to shame. I especially enjoy the sunflower theme of her new blog. Here's a fun sunflower fact from Wikipedia:

What is usually called the flower is actually a head (formally composite flower) of numerous florets (small flowers) crowded together.

Welcome back, Jackie! We look forward to reading more excellent posts on IP strategy.

Why Inventory Turnover Rates Matter to Managers, Owners, and Investors

From the always illuminating Joe Ponzio:

Everything else being equal, the 10% margin business with one inventory turn is no better or worse than the 2% margin business with five turns a year.

In fact, as Joe explains in the rest of his post, the company doing five turns a year is often in better shape. If you want an analogy, it's like the difference between two cars, both of which can carry you 100 miles on 9 gallons of gasoline, but one of which does so at 5000 rpm the whole way while the other does so at 1000 rpm. Same fuel efficiency, but you'll pick the car with more torque every time. You can accelerate or slow down much quicker with the 5000 rpm machine.

Sometimes inventory turnover rates are reported as part of financial statements, usually in the notes. An inventory turnover rate is a frequency -- i.e., given a unit of inventory the turnover rate is in units # / time. Repeatedly on this blog I have suggested that financial statements should report frequencies for each balance sheet account. Such frequencies are called "turnover rates" when the accounts in question are inventory accounts.

Note that accountants usually order balance sheet accounts from short-term to longer-term liquidity. That's a convention, not a measurement. Wouldn't it be nice to have actual numbers for each account?

The Flipside to the Commensurability Problem?

From Roman Werpachowski, interviewed by Chad Orzel:

The fact that people in finance are mostly money-driven turns out to be a gift, not a curse -- it's easier to coexist peacefully by dividing cash than by dividing prestige and political influence.

See also the earlier post on Capitalism and Culture.

What do referees or umpires do?

Consider the analogy drawn by Hayek to illustrate the "pretence of knowledge" in the social sciences:

Consider some ball game played by a few people of approximately equal skill. If we knew a few particular facts in addition to our general knowledge of the ability of the individual players, such as their state of attention, their perceptions and the state of their hearts, lungs, muscles etc. at each moment of the game, we could probably predict the outcome. Indeed, if we were familiar both with the game and the teams we should probably have a fairly shrewd idea on what the outcome will depend. But we shall of course not be able to ascertain those facts and in consequence the result of the game will be outside the range of the scientifically predictable, however well we may know what effects particular events would have on the result of the game. This does not mean that we can make no predictions at all about the course of such a game. If we know the rules of the different games we shall, in watching one, very soon know which game is being played and what kinds of actions we can expect and what kind not. But our capacity to predict will be confined to such general characteristics of the events to be expected and not include the capacity of predicting particular individual events.

Yet we generally agree on the need for referees and umpires, however much we complain about their judgment after "a bad call." What makes for a good umpire or referee?

Having played water polo for a coach, Bruce Watson, who was widely regarded as one of the best refs at the international level in his time, I remember a few principles. One was that a good ref tends to call the game towards a tie. Why? Because it's not as much fun to watch a team get slaughtered. And it's not a good test of the skills of either team.

Bruce was rumored to have made a personal fortune in the market before "retiring" to coach water polo full-time.

I'm indebted to Mario Rizzo for the pointer to Hayek here and in the last post. Hayek is often a tough read -- probably something to do with his native language being Austrian -- but with wonderful gems that bear revisiting.

Why Hal Varian is Wrong about Statistics being the Future

The NYT yesterday picks up the story on how statistics training is the wave of the future. They pick up a quote from Hal Varian, who has been saying this for a while. Why might they be wrong? Consider what Hayek had to say about statistics in The Theory of Complex Phenomena:

[Statistics] avoids the problem of complexity by substituting for the information on the individual elements information on the frequency with which their different properties occur in classes of such elements, and it deliberately disregards the fact that the relative position of the elements in a structure may matter.

That, in a nutshell, is why I believe physicists rather than statisticians are better suited to solving economic problems. It's one thing to be able to do a Fourier decomposition. It's quite another to be able to understand how that spectra corresponds to underlying structure.

Mario Rizzo's Reply to Robert Lucas

From a new favorite, the ThinkMarkets blog:

what Hayek makes clear is that it would not be too much to ask of a theory that it at least point in the general direction of a problem. Lucas, and others, continue to think of recent events as purely a bubble phenomenon — and so there is a degree of randomness involved in the phenonemon, in their view. However, Hayek’s cycle theory pointed to an interest rate effects on higher-order investments that generally or systematically produce over- and mal-investment. But it says little about timing. Lucas seems oblivious to both.

Hayek's vision of the macroeconomy is at least consistent with an explanation of bubbles as a self-organized critical (SOC) phenomenon. For an introduction for those with background in physical sciences or mathematics, see the short treatise by Jensen. What these phenomena have in common is a large system of N components (such as atoms, molecules, sand grains, trees in a forest) that are driven into more-ordered states by a process that acts slowly over time (such as a low-frequency magnetic force, temperature gradient, or growth of a forest) and into less-ordered states by a process that happens rapidly through a mechanism of dense interconnections between components (such as electron-electron interactions, or the spreading of a forest fire). Not surprisingly since we now know that no system is deterministic, SOC does not let you predict when the system will relax. What it does let you do is predict what statistics will apply to the dynamical processes, and how those statistics are affected by the microdynamical variables.

In the case of a macroeconomy, SOC theory would suggest that an increase in money supply that is slow in comparison to the rate of exchange among market actors would generally lead to an overall increase in wealth over some period of time followed by rapid relaxations (i.e., crashes) once the patterns of activity encouraged by cooperation and exchange within a period exhaust their social benefits.

Spectral Analysis of Different Economic Groups

The NYT yesterday ran a very cool infographic: How Different Groups Spend Their Day.

Be sure to click on the various groups in the top right and watch how the chart shifts. These shifts vividly show that different socioeconomic groups have different frequency spectra for a given activity of consumption or production. From the chart, all we can infer is the difference in mean among the distributions for various groups. But from the data that made up the chart, we might also be able to see the shape of the distributions -- i.e., how much variance there is within the group in time spent on a given activity each day.

Can you see how tracking the frequency of consumption and production for a given individual or group would be a quantitative way to classify different groups, or even organizations? Notice how people with college and advanced degrees spend many more hours working than people with high school degrees. Notice how people over 65 years spend more than twice as much time watching television as adolescents and young adults.

Oh, and guess what most people without children do with the time that people with children spend on family care? T.V. What a waste.

Netflix Culture

From MG Siegler at Techcruch: (why did he leave Venturebeat for what is turning into a rag?)

The real company values, as opposed to the nice-sounding values, are shown by who gets rewarded, promoted, or let go. Real company values are the behaviors and skills that we particularly value in fellow employees.

PowerPoint here.

The Netflix definition of values is the more coherent, and reminds me of Teece's capabilities models and Nelson and Winter's routines. So how is it that when we think of "company values" we think more of the aspirational statements that seem to have so little impact on how companies operate? Sometimes it seems that the more publicly committed an organization on a particular issue, the less trustworthy its commmitment. Like individuals, organizations that do things don't need to proclaim their ability to do said things.

How Economics Can Become Scientific

The MIT Technology Review blog today reviews a "paper describ[ing] a survey taken by graduate students at seven top-ranking economics programs" on answers to questions, including the following:

• Is neoclassical economics relevant?
• Do economists agree on fundamental issues?
• Can fiscal policy be an effective stabilizer?
• Should the Fed maintain a constant growth rate of the money supply?

The results are mixed. Not a surprise since even among economists the answers to these questions are mixed! But the title of the post raises a large question, which I'd like to answer using systems theory -- i.e., the theory of how highly integrated social networks self-organize. Systems theory has been a central theme for this blog.

If we understand the question in terms of systems, then we can answer in layers the question, "Is economics a science?" These categories are linked to the three generalized components that make "the system of economics":

1. The domain of rules and procedures that make up economic theory;
2. the field of recognized experts who act as gatekeepers to the domain; and
3. the stream of data in the form of observations or measurements used to falsify the domain.

The high-level answer is that economics is a science if it has active components of domain, field, and data. Economics is not only dismal, but dead if none of these is changing. But so long as each of these three have some time-dependence, the difference between economics and other sciences is a difference in degree, not kind.

But on closer inspection, there are indeed apparent problems with these components for economics that do not exist for other sciences. The biggest problem seems to be that at least until about ten or twenty years ago, there was no stream of data on individual or group behavior that could be used to falsify the domain of neoclassical theory that developed around WWII. In other words, there have been few new experiments or new kinds of data sets since neoclassical theory was developed. The field has become somewhat retrenched as it has labored at refining its theories using the same sets of data over and over again.

There are exceptions, of course. I'm in the middle of reading Vernon Smith's Rationality in Economics, in which he describes the interesting ways that actual behavior deviates from neoclassical predictions in actual experiments and field studies. And Smith is not laboring in obscurity, of course. He won a Nobel for his efforts in this regard. But in general, the "science" of economics seems to have been limited by a lack of data. There simply has not been an experimental or observational mechanism for the field to reach consensus on what hypotheses have been falsified.

In perhaps not-so-humble fashion for a non-economist, I would like to propose that experiments and observations of the average time period between individual acts of consumption or production is exactly the kind of data that economists could use to falsify models of individual or group behavior. The fact that such observations have not been made in the past is no accident, for they were generally too expensive to be collected before the Internet became widely available. (Csikszentmihalyi's Experience Sampling Method is a notable exception.)

Journal entries themselves encode the information that is necessary for managers, investors, and owners of corporations to see the frequency spectrum of activity within a firm that provides a unique signature of the firm's activities. Time-averaged measures of costs are not the only variables important to the health of a corporation. Profit is a function of sales and costs of sales over time.

Economics could become more scientific by statistical observations of individual and group behavior over time.