Broken Symmetry

Are Corporate R&D Labs Close Enough to Universities?

I have only one comment on the excellent New Yorker piece recently published about Stanford, whose title asks whether Stanford has become too influenced by commercial concerns.

As a culture, why shouldn't we be equally concerned with answering a related question -- i.e., have corporations, and especially their R&D groups, gotten close enough to Universities? I take it nobody would have any concern if corporate R&D groups were to become more open in publishing their results, disseminating their knowledge, and even teaching new engineers and scientists.

The poorly kept secret is that there aren't anymore corporate R&D labs like that. Bell Labs is gone. IBM labs are not what they used to be. So Stanford has stepped in to fill the gap. Should we be mourning the loss of its pure curiosity-driven culture -- a culture that probably never existed at Stanford anyway? No there are other universities that do that better. Note that Gerhard Casper, quoted in a few places in the article, hails from Chicago.

It's not wise to pass judgement on Stanford's culture without considering how it fits within the larger ecosystem.

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.)

What Your Computer Does While You Wait

Great piece of educational blogging by Gustavo Duarte:

Normally when the CPU needs to touch the contents of a memory region they must either be in the L1/L2 caches already or be brought in from the main system memory. Here we see our first major hit, a massive ~250 cycles of latency that often leads to a stall, when the CPU has no work to do while it waits. To put this into perspective, reading from L1 cache is like grabbing a piece of paper from your desk (3 seconds), L2 cache is picking up a book from a nearby shelf (14 seconds), and main system memory is taking a 4-minute walk down the hall to buy a Twix bar.

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Sadly the southbridge hosts some truly sluggish performers, for even main memory is blazing fast compared to hard drives. Keeping with the office analogy, waiting for a hard drive seek is like leaving the building to roam the earth for one year and three months. This is why so many workloads are dominated by disk I/O and why database performance can drive off a cliff once the in-memory buffers are exhausted. It is also why plentiful RAM (for buffering) and fast hard drives are so important for overall system performance.

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Finally, the diagram has various real-world throughputs for networking and other buses. Firewire is shown for reference but is not available natively in the Intel X48 chipset. It’s fun to think of the Internet as a computer bus. The latency to a fast website (say, google.com) is about 45ms, comparable to hard drive seek latency. In fact, while hard drives are 5 orders of magnitude removed from main memory, they’re in the same magnitude as the Internet. Residential bandwidth still lags behind that of sustained hard drive reads, but the ‘network is the computer’ in a pretty literal sense now. What happens when the Internet is faster than a hard drive?

Are Biological Systems Poised at Criticality?

From Thierry Mora and William Bialek:

The first question is why biological systems should be nearly critical. What benets does operation at this special point in parameters pace provide for these systems? For birds, we have seen that criticality confers high susceptibility to external perturbations, and this enhanced reactivity endows them with a better defense mechanim against predators. Similarly, in the auditory system, being close to a bifurcation point allows for arbitrarily high gains and accurate frequency selectivity in response weak sounds.

In neural populations, the naive idea underlying the theory of branching processes makes criticality seem almost inevitable, a middle point between death and epilepsy. However, the function of neural networks is not only to be reactive, but also to carry and process complex information in a collective manner through its patterns of activity. The observation and analysis of metastable states, both in retinal acitivity analyzed within the maximum entropy framework [20] and in the activity of cortical slices analyzed with the theory of branching processes [79], suggest that criticality may be coupled to the explosion of these states, allowing for a wider set of coding options.

Via the Physics arXiv blog.

Fiber to the Neuron?

arXiv blog provides a great summary of exciting new lines of research into how human neurons communicate with light:

In recent years, a growing body of evidence shows that photons play an important role in the basic functioning of cells. Most of this evidence comes from turning the lights off and counting the number of photons that cells produce. It turns out, much to many people's surprise, that many cells, perhaps even most, emit light as they work.

For me this ranks among the most exciting new discoveries in biology. We have incredibly sophisticated tools for manipulating and measuring these signals. What is science-fiction today (see, for example, Avatar), could be reality in our lifetime. In part because of the severe capital costs and regulatory hurdles to implementing any of these new technologies, however, I suspect that patents will play a key role in their commercialization.

Improving on the Signal to Noise Ratio -- Stadium Edition

From Wired's Gadget Lab:

Squarehead’s new system is like bullet-time for sound. 325 microphones sit in a carbon-fiber disk above the stadium, and a wide-angle camera looks down on the scene from the center of this disk. All the operator has to do is pinpoint a spot on the court or field using the screen, and the Audioscope works out how far that spot is from each of the mics, corrects for delay and then synchronizes the audio from all 315 of them. The result is a microphone that can pick out the pop of a bubblegum bubble in the middle of a basketball game...

Feedback loops in your noodle

From the Oscillatory Thoughts blog:

So what’s really been twisting my noodle is that maybe this interpretation is wrong. Maybe across millions of years of divergent evolution, the axon of a giant squid has evolved to perform computations necessary for the survival of giant squids, and maybe mammalian neurons have evolved somewhat differently. Maybe individual action potentials are important in humans, but maybe they’re not the only things doing “computing” in the brain. Maybe these oscillations are also< playing an important computational role. Maybe they’re not just epiphenomena of action potentials. But maybe there’s a complex feedback system between action potentials and oscillations.

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.

The Internet as a Cricket Singing Burrows?

From a fascinating paper by Andy Clark:

Crickets sing to attract mates of their own species, and where these songs are concerned, louder is generally better. A louder song reaches more potential mates, and is typically deemed more attractive than any weaker-sounding competitor. The onboard mechanism responsible for producing the sound is a patch of flexible wing membrane, known as the harp, that is pulled taut and struck using a kind of tooth and peg arrangement. The overall process is termed ‘stridulation’.

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Amazingly, several species of mole cricket (especially Scapteriscus acletus, and Gryllotalpa vinae ) exploit physically perfect Klipsch horns as a means of efficient sound production. These crickets tweak their own burrow-building activities so that one tunnel takes the form of a large bulb, while another flares like an exponential horn. The cricket sits in a narrow constriction between these two animal-built structures and stridulates (sings). In this way the burrow acts as a ‘tuned impedance transformer’ carefully fitted to the specific carrier wave of the species. Sometimes, a single cricket will sit at the intersection of two such tunnel/bulb arrangements. With the ground itself acting as a very large (near-infinite) baffle, and the boost of a double exponential horn, these crickets obtain an almost unimaginable increase in acoustic efficiency. In the case of Gryllotalpa vinae a full 34% of the muscle power used for stridulation is now turned into sound . Compare this to a maximum of 5% for an unaided field cricket, though even this is admirable since high end audio loudspeakers, as Turner points out, manage only a meager conversion efficiency of about 2% (of power into sound). The souped-up mole cricket, however, produces one of the loudest sounds made by any animal. The sound can be heard at 600 metres while the ground itself vibrates for a 20 cm radius.

Turner argues that the mole cricket’s burrows, like several other animal-built structures that he considers in the same mongraph, are functionally equivalent to the possession of a near-perfect physiological sound-emitting organ. Turner’s larger goal, in fact, is to argue that for many biological purposes such animal-built structures should be counted as proper parts of the animal’s physiology and thus to breach what he describes as “the essentially arbitrary boundary between organisms and the environment.”

Even more interesting are the lessons about human cognition that Clark urges could be drawn from this and similar observations:

The Mole cricket scenario is simply a comparative springboard: a dramatic device to bring home the quite staggering transformative potential of our own ‘Cognitive Singing Burrows’. The dramatic device is helpful because, despite our familiarity with the notion of human technologies as ‘cognition amplifiers’ [18, 14], the ubiquitous presence of these amplifiers (pen, paper, models, words, numbers, blueprints, compasses) often blinds us to the depth and importance of their role in ‘distinctively human thought’. By the close of the present treatment we will also see many fundamental respects in which our own ‘Cognitive Singing Burrows’ far exceed (in the cognitive realm) the transformative impact of the simple acoustic Singing Burrow. We thus better appreciate the deeply hybrid (biological and artifactual) nature of the human mind.

I haven't seen any articles where Andy Clark directly addresses the question of human sociality, especially non-kin sociality. That would be particularly interesting.

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.

Eric Smith and Duncan Foley on the Parallels between Economics and Thermodynamics

Available here:

The most important point of this paper (more important than the establishment of formal parallels between thermodynamics and utility economics) is that economics, because it does not recognize an equation of state or define prices intrinsically in terms of equilibrium, lacks the close relation between measurement and theory physical thermodynamics enjoys.

There's plenty more where that came from at Eric Smith's page.

Mammalian Cells Can Feel their Physical Surroundings

From Berkeley:

Observations have indicated that mammalian cells are sensitive to the physical aspects of their environment, such as the texture or geometry of the surrounding tissue. However, evidence that physical forces impact freely-moving signaling molecules (as opposed to focal adhesion molecules) in the membranes of cells has been lacking because the cell membrane is an environment that has always been difficult to characterize and manipulate. Groves and his research group have found a way to overcome this obstacle with the development of unique synthetic membranes constructed out of lipids and assembled onto a substrate of solid silica that enables them to directly control cellular signaling activities.

The nominal goal for the research is fighting cancer. The potential therapeutic and other consequences are much broader. I can hardly grasp the picture of a web of chemical and mechanical feedback loops that constitute animal architecture.

Kleinberg and Easley on Networks, Crowds, Markets, and Intellectual Property

Kleinberg (as in the Kleinberg algorithm) and Easley have posted online an amazing book Networks, Crowds, and Markets: Reasoning About a Highly Connected World . The last chapter addresses the question of intellectual property, and gives the clearest, most concise summary of costs and benefits I've read in a while. The chapter concludes with the following observation:

[T]he investment in research and development needed to create many patentable commodities is very large relative to the investment needed to create most works of art. The pharmaceutical industry, for example, spends large sums on R&D and it seems likely that this would not occur without the ability to patent discoveries. So the case for restrictive and strongly enforced patent law is more compelling than that for copyright law.

Via Michael Nielsen.

Do animals cooperate with non-kin?

From Nature:

Since Robert Trivers' seminal paper on reciprocal altruism in 1971, models of reciprocity and the Prisoner's Dilemma resource-sharing game have provided the dominant theoretical framework for explaining cooperation between unrelated individuals. Yet there is little evidence that, under natural conditions, animals other than man commonly exchange either resources or services. In a Review, Tim Clutton-Brock examines the case for reciprocal altruism in natural populations of non-human animals. The evidence is thin, he concludes, and many examples of apparent cooperation between non-kin are probably cases of mutualism or manipulation.

Thanks to Tyler Cowen for pointing out both this and the last post. We seem to be tuned into a similar wavelength today!

Homophily and Mimetic Desire

From Rene Girard:

My work has often been presented as a discussion of archaic religion through comparative anthropology. Its goal is to shed light on the process of hominization: the fascinating passage from animality to humanity that occurred thousands of years ago.

In all of this, my hypothesis has concerned mimesis: Because humans imitate one another, they have had to find a means of dealing with contagious similarity, which could lead to the pure and simple disappearance of their society. The mechanism by which they have done that is sacrifice, which reintroduces difference into a situation in which everyone has come to resemble everyone else.

Seemed topical given the recent post on homophily and influence.

See also this earlier post relating mirror neuron's to Girard's theory of mimesis.

How Persuasive Are You?

When you recommend a new product to your friend and she tries it out, is it because you've persuaded her, or because she shares your preferences? If you're like me, you don't generally bother recommending things to people unless you know they happen to share your tastes along some dimension. Yet in principle there are some people out there whose opinions are "influential" -- coaxing otherwise disinterested people to try new things despite having different tastes. In principle, that's part of why professional athletes and celebrities are paid to endorse products. But teasing out exactly how much of the adoption of a new product is due to influence and how much is due to shared characteristics (i.e., "homophily") in the network can be difficult.

One cut at this question is forthcoming in PNAS in an article titled, "Distinguishing Influence Based Contagion from Homophily Driven Diffusion in Dynamic Networks" by Sinan Aral, Lev Muchnik,and Arun Sundararajan (as noted on the OrgTheory blog) (try emailing Aral for a copy):

In the context of product adoption, peer influence is associated with the presence of adopters in one‟s local network... However, identification of causal peer influence effects is complicated by the unobservability problem. Each user either has adopter friends or not, making it impossible to observe whether those with adopter friends ... would have adopted had they not had adopter friends. Homophily in this case creates a selection bias because treatments are not randomly assigned: adopters are more likely to be treated due to similarity with their neighbors. Thus, frequently used methods such as regression analysis, which can only establish correlation, are insufficient. Causal treatment effects can on the other hand be estimated via matched sampling, which controls for confounding factors and overcomes selection bias by comparing observations that have the same likelihood of treatment.

Toward this end we adapt matched sample estimation for use in dynamic networked settings. Conditioning matches on a vector of observable characteristics, behaviors and attributes yields influence estimates that account for the homophily that may make product adoption decisions cluster in the network even if no influence exists. This procedure establishes upper bounds on the degree to which influence (rather than homophily) explains assortative mixing and temporal clustering in networks. Since influence can vary over time, our framework provides estimates of its evolution. We can also assess the marginal influence of having an additional adopter friend for any arbitrary number of friends.

The upshot of this work is that shared characteristics among individuals accounts for almost all of the "viral" spread of new product adoption -- i.e., that preferences are largely fixed within the time-scale of weeks or months after a new product is introduced. There exists, nonetheless, a small group of people who are "influenced" into adopting a new product -- i.e., whom adopt the product through social interactions with other adopters with whom few characteristics are shared.

At least on its face, the work seems to affirm the use of fixed preferences as an approximation in this context. This validates the use of this approximation by economists. It also demonstrates, however, that the approximation is not without exception. Also of interest to economists.

One question I had on considering the work is whether it would be possible to single out the "influenced" and "influencers" in this survey, and determine whether or not such people occupy a position as bridges or "weak-tie" links within the social networks. Along the lines of Granovetter, the idea is that influence here is largely a function of whether or how often individuals come into contact with others with characteristics far different from their own. It would be interesting to know whether these individuals could be identified using this approach, which isolates "influence" from homophily.

Reflexivity Goes Deeper than Soros Himself Seems to Realize

I was piqued by this bit at the end of Soros's recent lecture on his "general theory of reflexivity":

I am somewhat troubled, however, about drawing too sharp a distinction between natural and social science. Such dichotomies are usually not found in reality; they are introduced by us, in our efforts to make some sense out of an otherwise confusing reality. Indeed while a sharp distinction between physics and social sciences seems justified, there are other sciences, such as biology and the study of animal societies that occupy intermediate positions.

But I had to abandon my reservations and recognize a dichotomy between the natural and social sciences because the social sciences encounter a second difficulty from which the natural sciences are exempt.

And that is that social theories are reflexive. Heisenberg’s discovery of the uncertainty principle did not alter the behavior of quantum particles one iota, but social theories, whether Marxism, market fundamentalism or the theory of reflexivity, can affect the subject matter to which it refers. Scientific method is supposed to be devoted to the pursuit of truth. Heisenberg’s uncertainty principle does not interfere with that postulate but the reflexivity of social theories does. Why should social science confine itself to passively studying social phenomena when it can be used to actively change the state of affairs? As I remarked in The Alchemy of Finance, the alchemists made a mistake in trying to change the nature of base metals by incantation. Instead, they should have focused their attention on the financial markets where they could have succeeded.

Why piqued? Because Soros seems oblivious to the fact that Heisenberg's uncertainty principle implies exactly what it is that makes social science distinguishable from natural science! Part of what Heisenberg's principle tells us (in mathematically precise terms) is that what we decide to measure will have an infuence on the system we choose to measure. We cannot treat our probe as independent from the system it measures except as an approximation. In many cases, the interaction of the system and probe through a measurement will irreversibly alter the system, making additional measurements impossible to reproduce without reproducing a copy of the system. That is all part of quantum mechanics.

To his credit, Soros acknowledges that there may not be a clean cut between the two -- he points to biology and animal sociology as intermediate cases. But his mistake is clear. His assumption that nature has not changed as a result of new theories and measurements is false. Sure enough, the changes are not as immediate or obvious. Yet there can be no doubt that the world is a different place because of quantum mechanics. What world would we live in had there been no Manhattan Project? Poverty of theory in Germany and Japan in WWII decided the fate of the world. The history of science is replete with similar examples of successful and failed experiments with nature, which have rippled out in their consequences for the rest of mankind.

But far from being fatal to Soros's "general theory of reflexivity," this observation of his mistake actually solidifies its claim to both validity and relevance. Instead of a dichotomy between natural and social science, What should Soros have called for? A better understanding of the system in which both natural and social scientists work. Our knowledge is a physical manifestation of the activities of scientists and non-scientists. What we measure is merely a function of the various activities of the system.

Many philosophers have made similar observations. In the legal context I think of Wittgenstein and H.L.A. Hart who wrote about how a rule does not include within it a rule for deciding how and when it should be enforced. But W. Edwards Deming had a simpler way of summarizing the point in a book of his that I've been reading with interest:

There is no true value of any characteristic, state, or condition that is defined in terms of measurement or observation. Change of procedure for measurement (change in operational definition) or observation produces a new number... There is no such thing as a fact concerning an empirical observation. Any two people may have different ideas about what is important to know about any event. Get the facts! Is there any meaning to this exhortation?

Communication and negotiation (as between customer and supplier, between management and union, between countries) require for optimization of operational definitions. An operational definition is a procedure agreed upon for translation of a concept into measurement of some kind.

Our perception of reality itself is more socially constituted than most of us appreciate! Consider how physicist Roger Penrose chose to depict reality as a cycle:

The cycle is manifest in the activities of people. The mathematical world is revealed, step by step, through consensus among living and dead mathematicians. The mental world represents the model everybody has, including mathematicians, of what exists. Both of these are embedded in a physical world along with a Noah's ark of other animals and a Garden of Eden of other living things.

We exercise control over our existence by formulating theories about what exists. There are plenty of things that exist that no person imagined to exist until a theory was developed that permitted for experiments, which in turn were consistent with other experiments, and so on. Nobody doubts anymore that we are made of atoms, quarks, and leptons. Yet none of us has seen any of these things. And if we were to stop looking for them, there is no doubt in my mind that we would eventually forget about them -- leaving their existence as ghostly as it was a hundred years ago.

There is no dichotomy between social and natural science. Rather, social science should embrace these constraints that have been on all science for as long as we've been doing experiments.

Mandelbrot and the Glassy State of the Economy

From Fractals and Scaling in Finance, page 367:

Everyone agrees that the distribution of firm sizes is far from Gaussian, hence the proper metaphor is not a "soft," but a "hard" object. This leaves open the question of whether it is a real solid or a glass. Physics describes glasses as being extremely viscous liquids that mimic solids and happen to be particularly difficult to study. In the same way, the lognormal distribution averages mildly in the very long run, but in the short run it mimics the wild randomness of the scaling in a very treacherous fashion. One can call it a sheep in wolf's clothing.

Why is Mandelbrot skeptical of lognnormal distributions of firm size? Because market concentration appears to be a scale invariant quantity -- i.e., the amount of concentration appears not to vary as much with the number of firms in the market as one would expect from distributions with finite variance.

More on the Long Tail

As Michael Nielsen explains, the value of the exponent in your power law matters a great deal:

If you’re developing a long tail business or collaboration, you need to make sure the exponent in the power law is less than one. The smaller the exponent, the better off you’ll be.

How can you make the exponent as small as possible? In particular, how can you make sure it’s smaller than the magic value of one? To understand the answer to this question, we need to understand what actually determines the value of the exponent. There’s some nice simple mathematical models explaining how power laws emerge, and in particular how the power law exponent emerges. At some point in the future I’d like to come back and discuss those in detail, and what implications they have for site architecture. This post is already long enough, though, so let me make just make three simple comments.

First, focus on developing recommendation and search systems which spread attention out, rather than concentrating it in the short head of what’s already popular. This is difficult to do without sacrificing quality, but there’s some interesting academic work now being done on such recommendation systems – see, for example, some of the work described in this recent blog post by Daniel Lemire.

Second, in collaborative projects, ensure a low barrier to entry for newcomers. One problem Wikipedia faces is a small minority of established Wikipedians who are hostile to new editors. It’s not common, but it is there. This drives newcomers away, and so concentrates edits within the group of established editors, effectively increasing the exponent in the power law.

Third, the essence of these and other similar recommendations is that they are systematic efforts to spread attention and contribution out, not one-off efforts toward developing a long tail of sales or contributions. The problem with one-off efforts is that they do nothing to change the systematic architectural factors which actually determine the exponent in the power law, and it is that exponent which is the critical factor.

If the power law is a description of network clustering, then you could say that the power law is likely to shift from less than 1 to greater than 1 as the strength of preferential attachment increases.

Renormalization Group in Macroeconomics

Should I be relieved that this was published in 1996?

A friend clued me in to the fact that macroeconomic theorists were applying the Ising Model a long time ago.

But how far have Prof. Aoki and his students and colleagues gotten with this?

Carol Mimura on the Nuances of Managing IP Rights

Since I recently complained about the culture in technology transfer offices, I thought it only fair to point out that there are indeed a few universities that have developed or are developing a healthier approach to university-industry relations. Carol Mimura at UC Berkeley recently posted a draft book chapter on SSRN:

[Tech transfer] is an ongoing relationship continuum, not just a single transaction. A relationship continuum spans many years. It results from many points of contact, and many engagement types. It is akin to building a pyramid or climbing a staircase, or contributing different sectors of a pie to create a whole. Our networks are essential. We cannot accelerate innovation on our own; rather, our public-private partnerships and product development partnerships demonstrate that we can expedite translational research and bridge funding gaps through creative partnering and flexible contracting.

Success under the relationship model consists of rights transfer and knowledge transfer in both directions (into and out of the university) to enable innovation acceleration, deployment, uptake, and translation. By many measures, the relationship model has borne fruit. By valuing corporate relationships more than any single transaction, utilizing our networks to advantage, and valuing research support as highly (if not more highly) than license revenue, the overall industry dynamic has changed. Cultural and negotiation biases have been reduced, industry and foundation funding have risen dramatically, collaboration types and numbers have increased, barriers to gifting into the university have been lowered, and greater numbers and types of contracts and strategic alliances have been formed. We have also enjoyed reputational gains as a campus that has improved its corporate relations and contracting units.

The high-bandwidth feedback loops established through what Mimura calls a "relationship-based" approach to tech transfer are an example of the integrated complex systems that are a theme for this blog. Like the neural networks of its individual members, the social networks established can learn over time, gradually building up tacit knowledge of the science and engineering problems that pose challenges both to public and private organizations.

A key cultural difference between UC Berkeley's IPIRA and other university tech transfer offices is that Mimura and her team first took the time to learn about the needs of every stakeholder involved in these complex, multilateral agreements. As a result, the deals she has closed -- including the pathbreaking BP-Berkeley deal and the Socially Responsible Licensing Program (SRLP) -- are nothing less than the future of the IP market. Although it might be inconvenient for some market participants to acknowledge, ultimately the highest value of IP rights is realized through the increased collaboration (and hence learning) that they promote.

Acoustically driven programmable liquid motion using resonance cavities

From Sean M. Langelier, Dustin S. Chang, Ramsey I. Zeitoun and Mark A. Burns:

Performance and utility of microfluidic systems are often overshadowed by the difficulties and costs associated with operation and control. As a step toward the development of a more efficient platform for microfluidic control, we present a distributed pressure generation scheme whereby independently tunable pressure sources can be simultaneously controlled by using a single acoustic source. We demonstrate how this scheme can be used to perform precise droplet positioning as well as merging, splitting, and sorting within open microfluidic networks.

Via Technology Transfer Tactics

Quick Illustration of Rotational Symmetry

From the Built on Facts blog:

Why do mirrors reflect right and left, but not up and down?

After a conversation with a friend over the weekend about this, it occurred to me that this is actually a pretty good way to illustrate how broken symmetries may be hard to spot. You can click through to read the answer in the comments at Built on Facts.

As my friend pointed out, there is also a neurological component to this explanation. We know that our brains are pretty good at inversion -- the lens in our eyes is an inversion center, but we don't see everything upside down and backward. So why don't our brains simply undo the effects of a reflection in a mirror?

The answer is that they can. If you spend enough time studying organic chemistry, for example, you get pretty darn good at distinguishing right from left-handed symmetry. And when you look in a mirror, what you're seeing is actually your enantiomer. The trick is that most of us aren't a close enough study of our appearance to immediately notice the change in handedness. Most of the time, we assume we are looking at a 180 degree rotation of ourselves.

But when we see the reflection of an object without rotational symmetry -- such as English text -- our brain's rotation strategy runs into a snag. We read text from left to right and from top to bottom -- i.e., with z axis pointing out of the page. In a mirror, the same text reads from left to right and from top to bottom, but with z-axis pointing into the page. There's no rotation that will get you into that space.

Anachronism of the Day

In any event, positive-feedback trading is likely to increase volatility substantially. If one wants to design regulatory interventions that will decrease volatility, one must think about measures that will discourage positive-feedback trading rather than negative-feedback trading. Positive-feedback trading is substantially discouraged when traders using that strategy suffer massive losses, which is what one observed after the crash. Everyone who had been pursuing positive-feedback strategies bought more and more as the market went higher and higher, thinking that their portfolio insurance would enable them to get out. They were wrong. It's clear that the crash reduced volatility by reducing the attractiveness of positive-feedback trading.

That's Larry Summers writing in... 1988. Seems to me like he was ahead of the game while his physics training was still fresh in his head. Found via abnormal returns.

What do Music and Quantum Mechanics Have in Common?

Time and frequency are a Fourier pair:

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

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

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