Broken Symmetry

What's the New Exit for Patents?

I'm intrigued by the way crowdsourced funding is upsetting the balance of power in financing new films, and companies. The basic trend is toward less and less friction (or what Coase calls "transaction costs") in obtaining financing. At the limit, we have perfectly efficient financing in which ideas receive funding up to the opportunity cost of capital (i.e., up to the next best startup or film that could have been funded). Five years ago, things looked grim thanks to the costs of SOX and the resulting shift of new listings overseas. Technology to the rescue! Gives warm fuzzies to those who tend to be more pessimistic.

But is there less friction for inventors and patentees? Maybe a little, but not the dramatic shift we've seen in private equity financing. And why not? Here things get murky. Looking at equity financing, we can see that at least part of the reason crowdsourced financing has taken off is because of the new "exit" opportunities it offers. At least in relative terms, people prefer what they get from contributing on kickstarter to watching their money go down the tubes in a Facebook IPO. Are there new exit opportunities for inventors and patentees? Nope. It's the same exit opportunities that have always been there. So is there less friction? Maybe a little less. But without the new exit opportunities, it's unclear whether what appears to be reduced friction for inventors and patentees is, in fact, improved allocative efficiency, or whether instead it's an opportunistic redistribution of wealth. We shall see.

Regardless, it's worth asking: if we could dramatically reduce the friction for inventors and patentees, would the public at large view the patent system as more effective or less effective at its goal of promoting innovation? The answer, I think, depends not on whether friction is reduced, but how. It seems to me that there is simply no (lasting) benefit to any stakeholder in the patent system to reducing the friction in reallocations of ownership until the friction in exits can be reduced even further. Universities avoid the problem by limiting the intake to their pipeline, which is wiser than clogging it up with inventory that goes stale. What's the new exit? That's the billion dollar question to be answered by anybody with an interest in seeing the patent system evolve.

Inventory Turnover Ratios as a Metric for Innovation

Long time readers may recall posts discussing the potential use of inventory turnover ratios in measuring the value of an invention. See How do you measure innovation without reference to patents? and Reigniting the Engine of Growth with the Sparkplug of Invention. Here's a nice article summarizing how that plays out for real companies:

So a typical company in manufacturing might do 8 inventory turns. Samsung does 17. Dell, which practically invented hardcore electronics supply chain management, does 36. Apple is doing 74!

Click through to Gartner to get the full report.

Resolving the St. Petersburg Paradox and the Flaws in the Efficient Market Hypothesis Too

How much would you pay to play a game in which a coin was flipped, and each time it landed heads the payout was doubled from a starting value of $1? The expected value of this game is not finite, and yet most people are not willing to pay more than a few bucks for it, much less borrow to play.

This is the so-called St. Petersburg Paradox. The earliest solution was proposed by Bernoulli, who suggested that the value people attached to their wealth went as a logarithmic rather than linear function of increasing wealth. Given that assumption, he calculated the amount a person should be willing to wager as log(change in wealth due to payout from lottery) - log (wealth - wager).

But this calculation is not quite correct even though it resolves the paradox by explaining why most people won't wager much: People won't wager much because that logarithm runs off to infinity fast as the wager approaches even a fraction of wealth -- producing a zero in the denominator.

One way to see that this calculation is incorrect is because it treats the wager as a static cost, existing independent of how the payoff increases with increasing flips. Another way to say the same thing is that the wager has time-value in the form of interest. So the expected value and the wager should be treated together in the utility function in successive flips of the coin. What you should wager depends not just on how much, but when the payoff comes.

Ole Peters tells the full story of the St. Petersburg Paradox, including the errors in its analysis later introduced by economist Karl Menger, and propagated by Arrow and Samuelson, in glorious detail here.

I am grateful to Rick Bookstaber for pointing me to the paper.

"So what?" you might ask. Well, if one gets into the habit of treating economic variables as time-series rather than ensemble averages -- as the correction demands -- then one immediately is forced out into the realm of non-equilibrium statistical mechanics and dynamics.

Ole Peters and Alexander Adamou have already proposed a correction to EMH. According to them, it is not prices that are efficient and cannot be arbitraged, but rather the use of leverage to produce larger than logarithmic gains.

I'm completely drowning in patent litigation right now, but eventually I would like to offer some thoughts on what these papers mean for competition and innovation policy. What should be considered analogous to leverage in intellectual property markets?

The potential implications for investors are also interesting and need to be thought through. Do we now have a quantitative insight into how and why so many of the investors who have consistently beat the market over the past few decades have advised against leverage. Buffett, in particular, is famous from eschewing leverage and advising others to do the same.

Gawande on Systems Theory

From a commencement address:

Medical performance tends to follow a bell curve, with a wide gap between the best and the worst results for a given condition, depending on where people go for care. The costs follow a bell curve, as well, varying for similar patients by thirty to fifty per cent. But the interesting thing is: the curves do not match. The places that get the best results are not the most expensive places. Indeed, many are among the least expensive. This means there is hope—for if the best results required the highest costs, then rationing care would be the only choice. Instead, however, we can look to the top performers—the positive deviants—to understand how to provide what society most needs: better care at lower cost. And the pattern seems to be that the places that function most like a system are most successful.

The observations apply to lawyers and law.

Symmetry and Entropy in Market Bubbles and Crashes

From Dion Harmon, Marcus A. M. de Aguiar, David D. Chinellato, Dan Braha, Irving R. Epstein, Yaneer Bar-Yam:

Predicting panic is of critical importance in many areas of human and animal behavior, notably in the context of economics. The recent financial crisis is a case in point. Panic may be due to a specific external threat, or self-generated nervousness. Here we show that the recent economic crisis and earlier large single-day panics were preceded by extended periods of high levels of market mimicry --- direct evidence of uncertainty and nervousness, and of the comparatively weak influence of external news. High levels of mimicry can be a quite general indicator of the potential for self-organized crises.

Via Physics arXiv blog.

What I like about this work is how at the conceptual level it fits with other theories of markets making phase transitions between high and low entropy states, with symmetries breaking as the transition begins and picks up steam.

There is a lot of talk about whether Quantitative Easing has done much. Folks like Scott Sumner seem to be arguing that it has not because market expectations were adjusted at the moment QE was announced (or quickly thereafer). I'm more skeptical about that claim because I imagine that it takes more than days or even weeks for organizations to propagate new information about prospective rates through their entire ranks, such that there remains an impedance mismatch between Fed signals and market reactions. But at an even more fundamental level, the market response to Fed signals does not appear to be linear within some regimes, including the one we live in now, which looks to me like a liquidity trap, albeit not homogeneously sticky for all industries and firms. (Consumer internet, in particular, seems to be attracting capital investment at an accelerated pace compared even with hardware and software solutions providers.)

Tyler Cowen on Methodological Individualism

Over at MR, Tyler offers some thoughts and a wealth of interesting links on the subject.

Foucault understood how actual historical explanation relies on the use of broad categories, classes, and exemplars, and in a manner which is not logically reducible to statements about individual beliefs and desires. The writer (theorist) has nothing close to a complete mental model of how the interacting categories reduce to component individual parts, and so some or most of the moving parts of the explanation retain their autonomy at a partially macro level. The Austrians will kick and scream on this one, but if you combine imperfect information and the sense/reference distinction, methodological individualism ends up as more of a slogan than anything else. There is a reflective equilibrium to the explanatory process, and micro relies on some macro foundations, not just vice versa, and individuals rely on the social for some of their cues. Atomistic reduction to the level of the individual in general will not succeed.

For earlier posts on the horribles that result from methodological individualist theory in antitrust law, see here. Also note that Kenneth Arrow has expressed at least strong skepticism about methodological individualism. The picture that develops is of a feedback loop between individuals, groups, and the set of formal rules and procedures for encoding and transmitting them. We can't understand the behavior of the system with a myopic focus on any single part.

Henry Manne on the Limitations of Behavioral Economics

Here:

The problem in traditional economics style (I think “style” a more appropriate word here than “methodology”) is a general failure to keep separate that part of economic assumptions which pertains to individual behavior and that which properly pertains to aggregate behavior. About the latter, BE has very little to say (here I am talking exclusively abut microeconomics, as I am really not sure how much relevance BE, aka psychology, has to macro areas), and Alchian simply carries the day.

Part of a Truth on the Market Symposium on the same.

The Coase Theorem Does Not Apply to Multilateral Agreements

In The Problem of Social Cost (available here), Ronald Coase presented the case for what became his eponymous Theorem. In short, Coase argued that in the absence of transactions costs, parties would contract around default legal rules to achieve an economically efficient allocation of resources. As a result, in the absence of transactions costs, lawmakers should only need to ensure a clear definition of property rights and the enforcement of contracts, without worrying too much about who starts off with the property rights. The Coase Theorem has been on my mind a bit lately thanks, in part, to the Supreme Court grant of certiorari in the Stanford v. Roche case. If transactions costs could be ignored, then (according to Coase) none of the stakeholders in the tech transfer system should worry too much about a reallocation of ownership rights by the Supreme Court. An economically efficient bargain would always be reachable under a new allocation of rights -- assuming the transactions costs can be ignored.

But can they? Of course not. The uncertainty associated with tech transfer investments is often so great as to swamp the costs of negotiating these agreements. In this post, however, I want to abstract away from the specific economics of tech transfer, mortgage finance, corporate chartering, insurance, derivatives, or any other economically multilateral agreement in order to emphasize just how limited the Coase Theorem is in its general applicability to the analysis of large-scale social costs.

The Problem of Social Costs makes its case using a hypothetical example of a negotiation between a rancher and a farmer over who should have to pay for a fence and/or crop damage. That archetypical case comes in the first main section of the paper, titled "The Reciprocal Nature of the Problem." Coase's hypothetical is explicitly a bilateral agreement. Coase's emphasis of the "reciprocal" nature of the problem is implicitly bilateral in its symmetry.

So what? Consider the graph below, which compares how the transactions costs of bilateral contracts and the transactions costs of trilateral contracts add up as a population of parties increases. The calculation of both is based on the assumption that each party to an agreement (whether bilateral or trilateral) will pay the same fee for entering into an agreement. The calculation of both is made assuming every party in the population will enter into a single agreement with every unique combination of counter-parties. (In detail, these are total unique combinations of two and three picks from a population of n graphed as a function of n.) The graph shows how the relative difference in transactions costs diverges dramatically even for a population of 15 parties.

The difference is even more dramatic for a population of 100 parties.

What does this mean? At the very least it means that in policy debates about the allocation or reallocation of resources whose use involve three or more distinct economic interests (i.e., stakeholders), the Coase Theorem should not be invoked, or at the very least invoked only after everybody is satisfied that the transactions costs are completely overwhelmed by the economic benefits of a bargain.

But nearly every policy question involves more than two stakeholders. Even consumer transactions -- which seem like the quintessential bilateral agreement -- have indirect effects, compensated for through indemnifications and warranties. In other words, the Coase Theorem simply doesn't apply to any practical policy problem of interest.

At the same time, the Coase Theorem was very useful because it focusses our attention on transactions costs. In many cases, it may be easier to lower transactions costs than to decide how rights should be allocated. My point in this post is not to say that The Problem of Social Cost was fundamentally wrong, or even useless. Rather, I want to point out that we can usefully analyze transactions costs by breaking them out according to the number of parties necessary to an agreement.

This point gains strength as the number and variety of multilateral agreements increases. Nothing much at all seems to happen in our economy without getting buy-in from a host of stakeholders, either formally or informally. And the biggest economic issues of our day -- from Fed policy, to mortgage financing, to tech transfer models -- are all examples of vast, multilateral negotiations. Our world is no longer Coasean.

Positional goods present an especially interesting application of this analysis of transactions by number of parties. A positional good, by definition, is a good whose value depends both on the supply available and on the demand for the good by other consumers. Every transaction involving a positional good is at least a trilateral agreement, and arguably much more multilateral than that since the other consumer might not be based on the activity of a particular person, but rather on a general perception of the activity of an entire group of other consumers. The Coase Theorem simply doesn't apply to positional goods.

Valuing Patented Features of Complex Systems Using Frequency-Averaged Measures of Demand

[Submitted today in response to this call for papers. -MFM]

ABSTRACT

Thanks to the ubiquity and integration of digital computers, each consumer of a product or service now produces a digital stream of data. Bundled together, these streams of data record how particular products or services, including any patented features, are valued by actual consumers in different geographical regions and in different windows of time. Most relevant to patent royalty calculations are the histories of transactions for a particular product or service. These transactions eventually show up in financial statements, which permit top-down estimates of value, such as revenue or gross margins. But alternative metrics drawn from these streams, such as change in the frequency of transactions before and after the introduction of a patented feature, also permit bottom-up estimates of the value of patented features. Demand can also be estimated using metrics for customer engagement, such as the frequency and length of visits to a particular website or the conversion ratio of number of independent visits per completed transaction. In some cases, analyses of these data permit calculation of a more reliable lower bound for the marginal value of a patented feature to a complex product or service. More generally, time-frequency analyses of data on consumer behavior complement existing methods for calculating lost profits or reasonable royalties, and can help resolve questions about the marginal value a patented feature adds to a complex or highly integrated product or service.

The Damages Apportionment Problem for Complex Systems

Under 35 U.S.C. § 284, “[u]pon finding for the claimant the court shall award the claimant damages adequate to compensate for the infringement but in no event less than a reasonable royalty for the use made of the invention by the infringer, together with interest and costs as fixed by the court.” Famously, the Southern District of New York laid out a “comprehensive list of [fifteen] evidentiary facts  relevant, in general, to the determination of the amount of a reasonable royalty for a patent license” Georgia-Pacific Corp. v. U.S. Plywood Corp., 318 F.Supp. 1116, 1120 (S.D.N.Y.1970). The thirteenth in this list is “[t]he portion of the realizable profit that should be credited to the invention as distinguished from non-patented elements, the manufacturing process, business risks, or significant features or improvements added by the infringer.” Id.

When Georgia-Pacific was decided in 1970, Jimi Hendrix was still performing, the Internet was known only as ARPANET, and access to information about cutting edge research and development usually required national security clearance. Four decades later, instead of secrets about national defense leaking to the press through a former first lieutenant in the Marine Corps with high-level security clearance, an Australian hacker is leaking them to the Internet. Instead of a carefully choreographed ballet of contractors producing a 747, a globally distributed swarm of programmers continuously releases new features and combinations of features in an effort to attract a stable base of users. Consumers depend upon producers – producers who compete vigorously with each other to make the sales necessary for their own survival – to cooperate in the development and adoption of industry standards that are necessary for complex, integrated systems to exist at all. The Federal Circuit put it well in describing one of the most commercially successful productivity software products as “an enormously complex software program comprising hundreds, if not thousands or even more, features.” Lucent Technologies, Inc. v. Gateway, Inc., 580 F. 3d 1301, 1333 (Fed. Cir. 2009). The Federal Circuit found “it inconceivable to conclude, based on the present record, that the use of one small feature … constitutes a substantial portion of the value.” Id.

Most lawyers agree with the Federal Circuit that the base for calculating royalties should be discounted to adjust for the value that other features contribute to a product. Other features may include features themselves covered by other patents (not owned in common with a patent-in-suit), or features not easily protectable through patents (such as the durability of parts, precision of manufacturing tolerances, or the timely availability of the product or service at a requested order volume). Moreover, what feature or features were sufficient or necessary to the completion of a sale may vary from customer to customer, or even for the same customer over time. In most cases, it is indeed inconceivable that any single patented feature could be responsible for the entire revenue (much less profit) generated by a complex or highly integrated product or service.

On the other hand, the very fact that an infringer has chosen to implement a patented feature rather than a non-infringing alternative is strong evidence that the patented feature most likely contributes some finite value to the product or service. That value, in turn, is ultimately reflected in the revenue and profit generated by the sale of the complex product or service that includes the patented feature.

[Discussion of economic case for patents, focusing on the potential social benefit of granting property rights in easily appropriable innovations in markets with high barriers to entry. Exclusionary rights permit supracompetitive prices in proportion to inelasticity of demand for patented innovations. Thus, measuring inelasticity of demand is core problem for patent valuation in general, and for damages apportionment in particular.]

Measuring the Price Elasticity of Demand

The Federal Circuit has acknowledged that price elasticity is at the core of the problem of patent valuation. The opinion in Crystal Semiconductor v. Tritech Microelectronics, 246 F. 3d 1336 (2001) is particularly instructive, although it deals specifically with price erosion. In Crystal Semiconductor, the Federal Circuit explained the economic theory of price elasticity:

For example, if substitution of a product were impossible and the product were a necessity, elasticity of demand would be zero — meaning consumers would purchase the product at identical rates even when the price increases. This very rare type of market is called inelastic. On the other side of the spectrum, if any price increase would eradicate demand, elasticity of demand would be infinite — meaning consumers would decline to purchase another single product if the price increases by any amount. This very rare type of market is called perfectly elastic. Markets typically have an elasticity greater than zero and less than infinity.

Crystal Semiconductor, 246 F.3d at 1359 (italics added). In other words, the market value of a patented feature should be tied to its effect on price elasticity, and price elasticity can be estimated by reference to the relative rate of sales at different price levels. The Federal Circuit went on to rule that the patentee in Crystal Semiconductor had failed to show sufficient evidence of whether or how much the rate of sales would have declined as a result of a price increase. Id. at 1360-1. It is enough for my purposes, however, to note that the Crystal Semiconductor case provides a legal precedent for the use frequency-averaged measures of the value of patented innovations, such as relative rates of sales.

Top-Down Estimates from Time-Averaged Revenues and Profits

The approach to estimating price elasticity described in Crystal Semiconductor requires the use of revenue and profit figures, which would typically be derived from financial statements. Financial statements provide a highly compact summary of the entire history of transactions that occurred among a company and its many stakeholders, from the purchasing of capital equipment and parts from suppliers, to the payment of labor for design and assembly, to the actual payments of cash from customers for delivery of the final product. Thus, financial statements provide a good foundation for what I will call “top-down” estimates of the value of patented features.

[Discussion of the information architecture of financial statements: Income and cash-flow statement record time-integrated flow of revenue and expenses; balance sheet records snapshot in time of assets and liabilities. The information architecture of financial statements lends itself to optimization of time-averaged measures of efficiency in increasing sales, decreasing costs of sales. Trends in sales or costs of sales are visible only after two or more periods have elapsed.]

Top-down estimates of the value of patented innovations are most appropriate for valuation of products or services that are protected by a single patent. Top-down estimates provide only a ceiling on valuation when multiple patents cover each product or service sold in the transactions that ultimately result in the financial statements from which the top-down estimates are drawn.

Frequency-Averaged Measures of Productivity

Every time-averaged measure of profitability has a frequency-averaged counterpart. In practice, experienced investors and managers use both to develop a picture of the health of an organization. For example, in addition to gross margin – a time-averaged ratio of profit to revenue – experienced investors and managers also track inventory turnover – a frequency-averaged ratio of costs of goods sold to average inventory. A company with high gross margins but dropping inventory turnover is unlikely to maintain its margins going forward. Conversely, low margins with increasing inventory turnover signals that higher margins might be available in the future. [Discuss analogy to driving without a tachometer.]

Inside large corporations, managers don’t wait until the end of the quarter to spot trends in supply or demand for a particular good or service. Rather, managers monitor daily reports about order and inventory volume to ensure that the supply of products and services stays synchronized with customer demand.

Bottom-up Estimates from Frequency-Averaged Measures

The stream of data that management uses for frequency-averaged measures of productivity provides the basis for alternative, bottom-up estimates of demand for patented innovations. Bottom-up estimates derived from frequency-averaged measures are useful in valuing patents on innovations introduced as features of an existing product or service, or in valuing patents on products or services that are sold as a bundle with many other products or services. Bottom-up estimates may also be helpful in putting a more reliable floor on the minimum value of a patented innovation. A variety of techniques may be used to produce bottom-up estimates using frequency-averaged measures. I give two as examples: Fourier analysis of a time-series of transactions and A|B testing of a web service.

Sales of a particular product are recorded as a time-series of transactions, each of which is associated with a particular price. Within a particular window of time, one can count up the number of transactions and the total sales. The former is a frequency-integrated measure of sales, the latter the time-integrated measure of revenue. Focussing on the former, we can see how the frequency of sales changes within different same-width and different-width windows of time. Most relevant to patent valuation, we can look at how the frequency of sales changes before and after the introduction of a new patented feature. If frequency increases even as price is held constant or decreased, the patented feature probably increased the demand for the product or service.

Because frequencies change seasonally, many web services rely upon a more direct method for testing demand for a feature, called A|B testing. Using A|B testing, managers can watch how actual consumers interact in real-time with a web service that includes slightly different features. It is conceivable that A|B testing could be used in some cases to demonstrate that a patented feature is less desirable than an existing version of web service, effectively resolving some disputes that cannot be resolved using only top-down estimates of value. [Discussion of A|B testing, Google Analytics, Optimizely, &c.]

Van den Steen on Knightian Uncertainty and the Firm

From AER: (via MR)

According to Knight, then, people differ (with respect to uncertainty) in the confidence in their judgment and “the most fundamental change of all in the form of organization” is the “system under which the confident … ‘insure’ the doubtful … by guaranteeing to the latter a specified income in return for an assignment of the actual results” ( p. 269). While this is often interpreted as risk aversion, Knight’s “confidence” actually refers to the belief that your judgement is correct, i.e., ν. This description does fit the mechanism in this paper: the principal tells the agent what to do and gives the agent a fixed wage in exchange, i.e., he insures the agent against the principal’s mistakes; and it is the more confident player who becomes the boss.

From this interpretation of Knight, the essence of a firm is that it allows the confident entrepreneur to make all decisions under uncertainty and then bear all the financial consequences of these decisions, i.e., absorb the uncertainty. The driving force in the existence of a firm is, according to Knight, the difference in confidence, which was also one of the driving forces in this model. This is quite different from the Coasian view, where it is the cost of transactions, i.e., the cost of contracting on decisions, that drives firm boundaries...

Although I'm not convinced of the generality of Van den Steen's theory (which strikes me, because of his emphasis on differences in priors, as a better description of insolvent organizational dynamics than it does of technology startups, for example), I do find this interpretation of Knight pretty interesting.

Coming at this with my priors from having studied probability theory in the context of quantum mechanics, I tend to imagine Knightian subjective uncertainty as the Hilbert space for a particle, with objective uncertainty then being the expectation value for a particular observable. Then Van den Steen's theory suggests that the Knightian entrepreneur effects the collapse of a wave function into a particular state. But the actions of the Knightian entrepreneur may or may not be consistent with how a rational actor would behave in view of the expecation values! Rather, as Van den Steen suggests, it is the confidence of the entrepreneur (or at least the confidence projected), which results in a measurement and collapse into a particular state. That's very interesting because decisionmaking by an organization does seem to be driven at least partially by signals of confidence.

Incompatible observables permit another thought experiment. There are plenty of classical equivalents, and I see no reason why these should not constrain organizational economics as much as they do anything else. The most relevant pair of observables for financial statements are time and frequency. So one can imagine an entrepreneur who forces the system into a non-Gaussian state that maximizes or minimizes time-averaged measures of productivity, making it difficult, if not impossible for anybody who takes over to move the system back into a healtier state -- there simply isn't enough of what Van den Steen calls "confidence" available to make the measurement! But this too suggests that Van den Steen's might be a better theory of the insolvent firm than of a growing firm.

RIP Mandelbrot

A man after my own heart. Here's a short autobiographical summary of his intellectual odyssey. And here's a recent paper (via Marginal Revolution) applying some of his insights. I particularly liked this bit:

Economic systems tend to be around the critical point in general. Open markets are typical examples. When there are more buyers than sellers, the market price goes up causing decrease in the number of buyers and increase in sellers. Such well-known pulling back mechanism obviously keeps the system around the critical point. The important fact is that this criticality does not stabilize the absolute value of the market price but only stabilizes the statistics of market price fluctuations to follow the critical fluctuations. As the dealers in open markets tend to care about only the relative market price, whether it goes up or down, so the absolute value of the market price is nearly meaningless for the determination of the market price. Therefore, the market prices generally wander almost randomly having statistics characterized by the critical point.

What I picture is market price signals synchronizing the flux of supply with the flux of demand. See also the summary of the paper by Fretter, Krumov, Weihe, Müller-Hannemann, and Hütt.

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.

Historical Notes on Resale Price Maintenance and Financial Regulatory Reform

The various persons who are concerned in the process of marketing from producer to consumer are not really successive purchasers, but are bound to each other just as successive feoffees of manioral lands were bound by subinfeudation before the statute of Quia Emptores.

-Zecharia Chaffee, Jr.

And since we're on the topic of historical anachronisms...

The demise of the Bank of the United States as a national institution left the field to the banks chartered by the States. These at once made the most of their opportunity; and helped, as they were, by receiving on deposit large sums of the distributed public moneys, they increased rapidly, and 1837 saw 634 of them in the country, having an aggregate capital of $291,000,000. With the great prosperity which, in the shape of State bank-notes, came over the country with these financial changes, arose also a spirit of the wildest speculation. Public lands were the chosen field of the operators, and the dealing ran into millions. It was all based, though, on the current notes, many of these being issued by " wildcat" banks, and worthless. Trouble seemed certain, and President Jackson, in trying to establish our finances on a sound basis, issued his famous Specie Circular, ordering all agents to accept nothing but specie in payment for the public lands. This precipitated the crash. The banks were called upon at once to redeem all their circulation in specie, and after vainly attempting to do so, they suspended payment on May 9, 1837. Six months later, no relief having come, a meeting of 136 delegates from banks all over the country was held in New York to consider whether means could be devised for resumption, but no relief at that time was found possible.

Let's hope that promoters of financial regulatory reform have been students of financial history.

Martin Wolf on the Perils of the Land Cycle

Martin Wolf in the Financial Times (free registration required):

I am a land speculator – a mini-aristocrat in a land where private appropriation of the fruits of others’ efforts has long been a prime route to wealth. This appropriation of the rise in the value of land is not just unfair: what have I done to deserve this increase in my wealth? It has obviously dire consequences.

The problem is compounded in California by the redistributive transfer from individuals to corporations that is Proposition 13. The other day, a puzzling question came up: Why haven't voters in California managed at least to repeal Prop. 13 to the extent it benefits for-profit corporations?

Andrew Lo on Physics Envy and the Financial Crisis

Available here:

Faith in any person or organization claiming to have a deep and intuitive grasp of market opportunities and risks is no better or worse than putting the same faith and money behind a mysterious black-box strategy. What matters in each case is the transparency of the process, an opportunity to assess the plausibility and limitations of the ideas on which a strategy is based, clarity about expectations for risks as well as returns, an alignment of incentives between the investment manager and the investor, and proper accountability for successes and failures. Part of the recent distrust of quantitative models is that they are inanimate, and therefore inherently difficult to trust. Even when we manage to develop a certain level of comfort with a quantitative strategy, ongoing due diligence is needed to assess the consistency, integrity, and incentives of the human beings responsible for implementing the strategy. It is important to distinguish our emotional needs from the formal process of assessing the models themselves, for which a degree of quantitative literacy will always be required.

Lo improves a number of arguments that long-time readers of B.S. may find familiar. I couldn't agree more with his conclusions.

For an extended discussion of the history of the crisis, I recommend Gillian Tett's Fool's Gold.

Andy Grove on the Human Capital Needed for Scaling

This is easily the most insightful analysis of the most important issue the United States now faces that I have read this month:

Since the early days of Silicon Valley, the money invested in companies has increased dramatically, only to produce fewer jobs. Simply put, the U.S. has become wildly inefficient at creating American tech jobs. We may be less aware of this growing inefficiency, however, because our history of creating jobs over the past few decades has been spectacular -- masking our greater and greater spending to create each position.

My hope is that the loosening of the currency peg will help reverse some of these trends over the coming decades.

Rick Bookstaber on Derivatives in the Financial Reform Bill

Available on his blog:

The bill divides the regulation of OTC derivatives between the SEC and the CFTC, assigning the SEC regulatory authority over some – but not all – securities-related derivatives and the CFTC authority for others, such as indexes of those securities. In a world where financial engineering can create an asset in any number of ways, this is an approach that is just asking to be gamed.

Let me illustrate this with a simple example. When I ran a long-short equity hedge fund a few years ago, I traded in the U.K. equity market. However, I never bought or sold a U.K. stock. I only traded total return swaps on U.K. equities. The reason I took this circuitous route is that by using a total return swap, I avoided the tax that the U.K. puts on stock transactions. My broker bought the stock I wanted and kept it on its books (apparently the transaction tax did not apply to the broker) and then the broker executed a swap with me. The swap gave me a payment equal to the return from the stock in exchange for a payment from me to the broker. Of course, this payment to my broker was identical to the cost of funding the stock.

Zoho Founder on Friedman vs. Hayek and Venture Capital

From Sridhar Vembu:

Now that brings up a philosophical point: what is the purpose of a business? Milton Friedman is unambiguous: The Social Responsibility of Business is to Increase its Profits . From the perspective of a financial investor, by which term I mean anyone investing other people's money, Professor Friedman would be absolutely right; profit maximization is the dharma of the financial investor. Indeed, Professor Friedman was mainly talking about publicly traded companies with broadly diversified ownership. But from a broader capitalistic, libertarian perspective, it is not for Professor Friedman or for that matter, the government, to suggest what the motivations and goals of a capitalist - I am using this word in the sense of a person risking his own money - ought to be.

Maximizing profit can be one goal, but who is to say that ought to be the goal of every capitalist? True libertarians like Mises and Hayek acknowledge that the ultimate ends of each person, certainly including men and women who create companies, are no one's business but their own.

Via peHUB

Popping Bubbles With Sornette

Here's the arXiv blog's summary of his latest gambit:

Sornette says there are two parts to his forecasting method. First, he says bubbles are markets experiencing greater-then-exponential growth. That makes them straightforward to spot, something that surprisingly hasn't been possible before.

Second, he says these bubble markets display the tell signs of the human behaviour that drives them. In particular, people tend to follow each other and this result in a kind of herding behaviour that causes prices to fluctuate in a periodic fashion.

I find the particular mathematical model (of log periodic oscillations) less interesting than what it's trying to capture in the underlying market dynamics. Canonical neoclassical models of markets give no insight into correlation and synchronization of market actors. These dynamics are the most obvious and important new facts about markets we (as global market participants) have learned as a result of the ongoing crisis. It would be nice to see more of these ideas percolate out into economic analysis of law, for example.

See here for an earlier post on Sornette. And here for my favorite model of synchronization -- introduced in a paper by Zanette and Kuperman.

What do B.S. readers think of Sornette's work?

Posner on Rational Bubbles and Adaptive Expectations

From the Becker-Posner blog:

Thus I don’t think bubble behavior is necessarily or even characteristically irrational. Often, including in the case of the housing and housing-credit bubbles, it is a rational adaptation to uncertainty. It is not efficient behavior in an overall social sense, and so efforts at detection and prevention of bubbles are probably worthwhile. Given the abundant warning signs and explicit warnings of a housing bubble and a housing-credit bubble, the failure of the Federal Reserve under Greenspan and Bernanke, the federal housing authorities, other economic organs of government, and almost the entire economics profession to detect these bubbles cries out for an explanation.

Becker's post is also worth reading.

Gene Fama could have a brilliant second act in his career by tackling non-equilibrium models of bubbles head on. Nobody could think more clearly about non-equilibrum models than he, were he to commit himself to that goal.

Steve Randy Waldman's Pithy Wisdom on Investing

In responding to commenters at Interfluidity:

As an investor, one always should ask oneself the question, “why would taking this position be more beneficial to me than to a counterparty willing to escape or oppose it?” Here are three answers: i) Personal situation: the asset is more valuable to me than to others (I need wheat in two months!); ii) speculative: I simply know better than my counterparty. iii) hedging: I suspect the average counterparty is overweight this exposure, and is willing to offer what would be a decent value to someone whose portfolio is uncorrelated with the exposure. Unless one assumes both efficient markets and homogenous investors, all three of these things are reasonable to think about, and I think successful investors do think about them routinely. If an investor does not believe herself to have special information about the “fundamental” value of a contract or commodity, she still may believe that hedging demand for a product is one-sided, and that would tilt in favor of taking the other side of that trade at the margin. Obviously, suspecting that wheat farmers need to hedge wouldn’t be sufficient motivation for buying August wheat contracts. But when choosing between a menu of imperfect investments, the existence of imbalanced hedging interest can be important information. With standardized products, that is something one can try to learn and understand.

Read his series of posts on Goldman. Best commentary I've read so far.

The Social Benefit of Banking

From the profile of Joe Evans in the WSJ:

"Real wealth accumulation in banking is the law of compound interest for long periods of time," he says, criticizing bank CEOs and directors who became obsessed with their stock price.

By extension, wealth accumulation that occurs faster than interest can compound on principal must be derived from pecuniary transfers -- or at least the transactions costs charged for those transfers.

One of the simplest ways to check whether growth of a process is sustainable is to look at whether a stock price is going up faster than the company's free cash-flow can compound interest, even at an exponential rate. If so, then the market is guessing. Maybe it's guessing right. But it's still just guessing.

Markov Tree Options Pricing?

From friend of Broken Symmetry Harish Bhat and his student Nitesh Kumar:

This paper questions one of the fundamental assumptions made in options pricing: that the daily returns of a stock are independent and identically distributed (IID). We apply an estimation procedure to years of daily return data for all stocks in the French CAC-40 index. We find six stocks whose log returns are best modeled by a first-orderMarkov chain, not an IID sequence. We further propose the Markov tree (MT) model, a modification of the standard binomial options pricing model, that takes into account this first-orderMarkov behavior. Empirical tests reveal that, for the six stocks found earlier, the MT model’s option prices agree very closelywith market prices.

With a wink to commenters from yesterday. All models are wrong; but some are less wrong than others. See also earlier ruminations on assumptions of independence in price models.

Do Market Prices Synchronize Input and Output?

A couple of years ago I was reading Schelling's Micromotives and Macrobehavior, and was struck by his description of the phase mismatch in the marriage market. I wondered (out loud here, using sloppy terminology) whether market price signals couldn't be understood as a mechanism for ameliorating phase mismatch (or "latency"). Williamson identifies "frequency" as a category of transactions costs. But in fact it isn't frequency so much as differences in frequency (or differences in phase when buyers and sellers operate at the same frequency) that raises transaction costs.

If we think about markets as networks, then we can identify market makers as important nodes -- like train junctions or airports in transportation networks. Market makers and market prices haven't been studied much this way. But transportation networks have.

A paper by Lacasa, Cea, and Zanin is particularly suggestive in this regard. Lacasa et al. show that as the density of flow within an air transportation network increases, the network undergoes a phase transition above a certain threshold, whereby nodes of intermediate traffic density display "Brownian motion" in delay times. This "Brownian motion" ends when the traffic density increases still further, resulting in bottlenecks that, like traffic lights, effectively limit the rate of flow through the network.

Today the Physics arXiv blog calls my attention to a new paper by Fretter, Krumov, Weihe, Müller-Hannemann, and Hütt, which arrives makes similar observations about the traffic through rail and gene networks. But the newer paper takes a very interesting step forward by applying a phase synchronization analysis (related to the Kuramoto Model) to its data. Here's one of their conclusions:

We believe that a quantitative view on synchronization of arrival/departure events in the network of long-distance train connections, as presented here, can similarly serve as a starting point for a theoretical understanding, and subsequently systemic optimization, of the balance between eciency and robustness for such timetables underlying public transportation.

This work has direct relevance to scalable plans for regulation of systemic risk within our global economy.

What is organizational economics?

Teppo Felin at orgtheory points out a new issue of the Journal of Institutional Economics:

The most recent issue of the Journal of Institutional Economics has an excellent exchange of ideas on organizational economics. The issue begins with an essay by Richard Posner: “From the new institutional economics to organization economics: with applications to corporate governance, goverment agencies, and legal institutions.”

...

Even cooler than the essay itself: more than a dozen scholars were asked to write essays in response to the above, and they also raise a host of new issues: Who are actors and entities, what are markets? What is the role of intrinsic versus extrinsic motivation? Do theories readily apply across various contexts — e.g., across different types of organizations? Where is mainstream economics versus more heterodox approaches? Etc. The responders include Elinor Ostrom, Bruno Frey, John Roberts, etc. And, Posner then in turn responds to these comments.

Worth checking out.