Broken Symmetry

Whom can you trust?

From Clay Shirky:

To describe the world before or after the spread of print was child’s play; those dates were safely distanced from upheaval. But what was happening in 1500? The hard question Eisenstein’s book asks is “How did we get from the world before the printing press to the world after it? What was the revolution itself like?”

Chaotic, as it turns out. The Bible was translated into local languages; was this an educational boon or the work of the devil? Erotic novels appeared, prompting the same set of questions. Copies of Aristotle and Galen circulated widely, but direct encounter with the relevant texts revealed that the two sources clashed, tarnishing faith in the Ancients. As novelty spread, old institutions seemed exhausted while new ones seemed untrustworthy; as a result, people almost literally didn’t know what to think. If you can’t trust Aristotle, who can you trust?

Our notion of credibility and authority is changing in fundamental ways because of the Internet.

Organic Economics

Since the Arrow-Debreu-McKenzie model was introduced to economics in the 1950s, changes over time have been given less consideration in economic models. See, for example, this statement about changes in market prices by Eugene Fama. The efficient-market hypothesis implies that at any given moment an equilibrium market price exists, and (here's where the flow of time drops out of the picture) that the current price at any given moment is most likely the equilibrium price. Unless you have some information that nobody else in the market has, then you're probably making a mistake by guessing that most people are either underestimating or overestimating the value of a particular good. There are exceptions, of course, but in general economists and policymakers (including judges and lawmakers) think about (or at least did think about prior to this recession) economic equilibrium as static —- a perfect allocation of people and things that the market maintains more or less constantly.

Is there a more realistic way to model an economy? How about as an hierarchical social network? The social network must be hierarchical because smaller markets (i.e., smaller subnetworks) are usually connected to larger markets (i.e., bigger networks) such that there is nesting and overlap among them through the mechanism of cross-elasticity in demand.

Already, our model of the economy is of networks within networks. But the hierarchy goes deeper still. Each individual within an economy has his or her own neural network, which orders decisionmaking and behavior. In principle at least, we could identify subnetworks within an economy by looking to see whether particular individuals share substantially similar neural networks. Similar brain structure and dynamics are probably a physical (and biological) manifestation of consensus in belief and perception about past, present, and future within a subnetwork. Market equilibrium could be identified as the state of the system in which neural networks are matched in structure and dynamics so that the flow of goods through the economy is stable over a period of measurement. Such stability would be manifest in a market price that fluctuates only within the range of transactions costs.

From time to time, however, an individual within a subnetwork might make a new connection in his or her neural network. The new connection would arise from a new structural or dynamical pattern within that individual's neural network. Such a new pattern might be induced directly by exposure to new sensory information or statistically random variations in the individual's neural network, or indirectly from exposure to a new pattern communicated by another outside the subnetwork. In any case, the result would be a modification to the preexisting structure and dynamics of that individual's neural network.

An individual with a new pattern in mind (literally) might then attempt to reproduce the new pattern in the subnetwork of individuals who share (now similar, but not the same) neural network structure and dynamics. Through communication, the new pattern is either recognized, thereby rerouting every neural network in the subnetwork (albeit, through feedback loops, probably with some refinements), not recognized at all because too great a variation from preexisting patterns to be communicated, or rejected as inconsistent with important preexisting accepted patterns. Although a ridiculously difficult problem to solve mathematically, whether a new pattern would be accepted or rejected by the subnetwork could in principle be characterized using control theory to analyze the stability of the system including the new pattern within its preexisting environment. In practice, it's probably necessary to carry out the experiment to know.

The iteration of such a process eventually results in an increasingly sophisticated neural network shared among the subnetwork, with more and more specialized ability to recognize and explain more and more complex information over time. Interconnections among subnetworks, although difficult to achieve, might be expected to produce large returns to the individuals who are capable of matching the patterns in otherwise disconnected subnetworks.

To understand the new insights that such a model would give us in understanding economics, consider how individuals within the network of an economy include both entrepreneurs and customers. A successful entrepreneur, in practice, is one who is able to anticipate what patterns are going to be accepted by the neural networks of customers. Within this organic model of economics, capitalism is a game in which entrepreneurs conduct experiments in order to discover what patterns of behavior (i.e., what neural networks of prospective customers) will adapt by accepting a new pattern offered as an alternative to a preexisting pattern of behavior used to live with time-evolving resource constraints. Correct predictions are rewarded by the mass of individuals that adopts a new pattern of behavior. Incorrect predictions -- or inappropriately timed predictions -- generally disappear without a trace.

Not coincidentially, this game of capitalism provides a social parallel to the flow experience described by Mihalyi Csikszentmiahlyi as optimal to individual psychology. The same basic rules of the game apply both at the level of neural networks and at the level of networks of individuals within an economy. Because of how neural networks constitute economic networks, it should not surprise us to see that similar mathematical structure and dynamics apply to both. Consumer internet marketing professionals probably already know more about how neural networks work than do many neurologists!

Neoclassical theory offers zero insight into any of these dynamics.

Google Wave is a Really Big Deal

This is going to change how people use the Internet in some fundamental ways.

Thanks to Venture Beat for the scoop.

As of a few minutes ago, the public market had not noticably reacted to this new product info. I find this interesting because the impact of this news on profitability is at least as relevant as news of new product releases are for Apple at MacWorld. The market is not instaneously efficient.

Transitions to Synchronization on a Lattice Obey the Same Power Law as Directed Percolation

From F. Ginelli, R. Livi, A. Politi, and A. Torcini's paper On the relationship between directed percolation and the synchronization transition in spatially extended systems:

In this paper we have expounded a partially rigorous argument to show why the synchronization transition in spatially extended systems may belong to the DP universality class....

The crucial difficulty to determine the universality class for the synchronization transition is that the order parameter (the difference field) can be arbitrarily small. This casts doubts about the very definition of the zero-difference field as a truly absorbing state. In fact, in a previous paper [15], it was speculated that the DP scaling behaviour might be restricted to a finite range. The analysis carried on in this paper clarifies that the synchronization transition genuinely belongs to DP universality class...

In other words, the structure of a network of synchronized oscillators and the structure of a percolating flow in a weak field are the same in their critical state.

It's just speculation, but could the reason that these systems share critical structure be because they also share dynamics? Perhaps there are vortices that form during directed percolation whose angular momentum corresponds to the frequency and phase of syncrhonized oscillators on a lattice?

Published Patents and Patent Applications Now Available via XML from Data.Gov

See here.

Regardless of how you may feel about Obama administration ideology, the implementation of transparency through data.gov is a huge win for us. I am really excited about the possibilities for more informed interaction with government.

Large-scale structure of time evolving citation networks

By E. A. Leicht, Gavin Clarkson, Kerby Shedden, and M. E. J. Newman.

As the plot shows, there is a marked trend for the average age to increase in step with the passage of time. This is precisely the behavior one would expect if the top authorities in the network are remaining the same as time goes by. Every once in a while, however, the plot shows a sudden and precipitous drop in the average age, indicating that a much younger set of vertices have, in a short space of time, taken over as the new leaders in the authority score rankings. Thus the plot indicates a repeated pattern in the evolution of the network in which a certain set of vertices—certain cases considered by the Supreme Court—remain the top authorities for substantial periods of time before being swiftly replaced by a different set. One example of such a turnover can be seen in Fig. 8 around 1900 and a smaller one around 1940, dates that, as we have seen, correspond roughly to the beginning and end of the Lochner era. Another very large dip in the curve occurs around 1970.

Available here. The authors don't say so in their analysis, but the associated graph suggests a mechanism of self-organized criticality.

Dynamic Centrality by Bar-Yam and Braha

Available here:

In summary, we have demonstrated that the static topology does not capture the dynamics of social networks. The prominence of nodes (as measured by degree) within the networks fluctuates widely from day to day, and a high degree in the aggregate network does not predict a high degree for individual days. Our conclusions are in sharp contrast to previous complex network research, which emphasizes the importance of aggregate nodal centrality in a static network topology [1– 4,6,10,11,14 –18].

Implications of a dynamic node centrality contrast with existing analyses that consider targeting nodes with the highest degrees to disrupt network communication or transport. Dynamic centrality implies that targeting nodes with the highest degrees at one time only weakly affects the nodes that are highly connected at another time. The approach of targeting high-degree nodes has been suggested, for example, to be an effective disease and computer virus prevention strategy; i.e., identification and “vaccination” of those nodes would inhibit the spread of infection or computer viruses [4,6,19 –22]. Our work implies that, at the very least, a more agile strategy of monitoring and vaccinating nodes based on centrality over time is necessary. Otherwise a treatment based on aggregate connectivity information will miss the impact of a node that otherwise has a low connectivity, becoming highly connected.

Traffic Flow Revisited: Why are we letting the Ants win?

Last year I threw out an idea for how we could improve traffic flow with incentives: Install a device in everybody's car that permits the driver to offer a tip to a car nearby. My hypothesis is that nothing more articulate than that would be necessary for two drivers to cooperate in many situations that now lead to congestion. Think about the case of trying to pass somebody in the fast lane who doesn't see you or doesn't want to pull over. By implementing such a system, polite driving would be encourage and congestion diminished as those who really needed the road or were willing to pay for it would be able to spend money to get more of it.

I still think it's a good idea, and if there are any engineers out there who agree with me, I'd be interested in talking.

I was thinking about this again today as I sat in traffic on the freeway. It occurred to me after I realized that this traffic was just due to congestion, not an accident, that we sophisticated apes are being outdone by the ants that I observe from time to time in my California living room. These ants are very coordinated in their flow. No traffic jams. Why are we letting them show us up this way?

One commenter last year noted that such a system might lead to more accidents because of the distraction. That's a problem. But then why not make it even simpler. Why not setup something like bluetooth that connected by audio all drivers within a certain bubble around your car onto the same channel for communication? In other words, why not setup a public communications channel that was available at the push of a button so that people in cars nearby could start communicating with one another? Why are we letting the ants win?

Csikszentmihalyi on Flow

See here for an introduction to flow psychology written by Csikszentmihalyi himself:

A successful interaction involves finding some compatibility between our goals and those of the other person or persons, and becoming willing to invest attention in the other person's goals. When these conditions are met, it is possible to experience the flow that comes from optimal interaction. For example, to experience the simple pleasures of parenting, one has to pay attention, to know what the child is "proud of" or "into"; then to share those activities with her. The same holds true for any other type of interaction. The secret of starting a good conversation is to find out what the other person's goals are: What is he interested in at the moment? What is she involved in? What has he or she accomplished, or is trying to accomplish? If any of this sounds worth pursuing, the next step is to utilize one's own experience or expertise on the topics raised by the other person--without trying to take over the conversation, but developing it jointly. A good conversation is like a jam session in jazz, where one starts with conventional elements and then introduces spontaneous variations that create an exciting new composition.

For a particularly inspiring historical example of how flow can be achieved in even the humblest of lifestyles, see here.

What Could Kin Selection Theory and Negligence Law Possibly Have in Common?

The same equation has been applied in both. See David Sloan Wilson's description of W.D. Hamilton's discovery of kin selection theory:

Hamilton became world famous for explaining how altruism can evolve according to the rule br - c >0, where b is the benefit that the altruist gives a recipient, c is the cost to the altruist, and r is the chance that the recipient shares the same altruistic gene through a common ancestor.

And compare with Judge Learned Hand's definition of negligence in United States v. Carroll Towing:

Since there are occasions when every vessel will break from her moorings, and since, if she does, she becomes a menace to those about her; the owner’s duty, as in other similar situations, to provide against resulting injuries is a function of three variables: (1) The probability that she will break away; (2) the gravity of the resulting injury, if she does; (3) the burden of adequate precautions. Possibly it serves to bring this notion into relief to state it in algebraic terms: if the probability be called P; the injury, L; and the burden, B; liability depends upon whether B is less than L multiplied by P: i.e., whether B < PL.

Make sure to read all of Wilson's blog post. Hamilton later expanded his theory. Note that Hand's formula has been indicated as a foundation of law and economics by Judge Posner.

Time and Turbulence

Although nobody has done it yet for economics, the connection between microscopic and macroscopic dynamics is old news in physics. Over fifty years ago, and with antecedents even earlier, mathematicians and physicists discovered that by coarse-graining certain variables that describe how units of a system interact, the macroscopic dynamics of a large system could be derived from microscopic variables. The technique is called renormalization, and the wikipedia entry on renormalization group is decent enough.

To visualize how this works, it helps to picture the microscopic system as a network of nodes distributed spatially, with one or more vectors associated with each node denoting how some observable quantity flows from one node to another. In general, the magnitude and direction of these vectors may vary, but in the simplest models (such as the Ising model), there is only one vector and it has the same magnitude at each node although it varies in direction.

With nodes distributed along only one-dimension, feedback doesn't usually develop within the network. Interactions, by construction, are limited to nearest neighbors in such a network. (Doesn't this explain why long supply chains can be unstable?) The exception is for when the end of a one-dimensional chain is linked to the beginning, creating a ring. With that geometry, even a one-dimensional network can display non-transient periodicities.

But in many real networks, such as our economy, the linkages between nodes are multi-dimensional. Each link in the supply chain for a particular good is also part of multiple other supply chains for its inputs and outputs. This multi-dimensional network permits all sorts of stable and unstable feedback loops to form.

Renormalization entails combining smaller units of a system with a defined symmetry into larger units with the same symmetry. Iteration of this process eventually results in either a cancelling out or a convergence of microscopic variables. Usually, there is an "order parameter" associated with one of the microscopic variables that will end up in one of three states as the result of these iterations when it is "coarse-grained" to macroscopic scale -- perfect order, perfect randomness, or fractal (i.e., self-similar) structure at a very specific point in between. What is less well-understood is what drives the transitions from order to disorder, and especially the fractal state in between.

The dynamics of flow -- i.e., fluid dynamics -- provides some interesting clues. In fluid dynamics, physicists have used the momentum of vortices to model turbulence. Unless you already know a little physics, this may be hard to appreciate, but for every dynamic loop in space, there is an associated angular momentum, perpendicular to that loop. The sums of the angular momenta of vortices have been used in what is to date perhaps still the most successful model of turbulence in a fluid.

The formation of vortices (and so, by analogy, feedback loops) in fluids leads to all sorts of interesting dynamical patterns. In the past, it's been very difficult to study fluids such as water experimentally, so other fluids such as electron plasmas, have been studied instead. Among other interesting phenomena, one may observe "vortex crystals" form within a fluid as a turbulent flow steadies over time. These may be an example of what Ilya Prigogine calls dissipative structures. Feedback loops seems to be a key prerequisite to their formation.

At this point, we're already in deep waters, but if you've made it this far, then bear with me just a little further. From Kolmogoroff's theory, it appears that the vortex structure of turbulent flow bears similarity to the synchronized or self-organized critical structure observed in the brain during gamma wave resonance. Both the spatial and temporal structure of the vortex vectors associated with turbulent flow and the signal transduction vectors associated with gamma wave resonance obey power laws to a reasonable approximation.

Aside from the fact that such different phenomena can be approximated using similar mathematical models (not so remarkable to many physicists, really), this connection is remarkable for what it suggests about our perception of time.

Gamma waves are associated with a particular state of consciousness. A person can train to enhance the onset of gamma wave resonance. In fact, there are zen monks who have been experimentally verified to have this ability. A fact about zen meditation that might be familiar only to those who have practiced it is that it changes, at least temporarily, one's perception of time. As Csikszentmihalyi describes with respect to flow, the perception of time while in meditation may dilate or contract, but it does not remain steady.

The implication is that our perception of time is influenced by the structure of feedback loops within our brain. In particular, we lose our sense of the flow of time when these feedback loops are organized into a particular, self-similar structure that can be approximated (like turbulence) with power laws.

That's enough speculation for today. But as an aside I can't help wondering whether the measurement of time more generally cannot be said to depend on the underlying structure of feedback loops within the dynamical system studied. Doesn't the flow of time itself depend on the way in which the ticking clocks used to measure it out are linked to one other spatially? Does time flow because of an increase in turbulence?

Gravity causes convective flow. Convective flow can lead to turbulence. Thus, gravity causes time to flow?

Note that Roger Penrose has also speculated on how gravity plays a role in constituting our consciousness. Commenters more versed in the Second Law than I are welcome to rebut these speculations below!

Sync and Self-Organized Criticality

From an extraordinary article by Barbra Drossel:

The results of this paper might have implications for excitable media (e.g. spreading of diseases, autocatalytic chemical reactions, propagation of electrical activity in neurons or heart muscles). 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.

In experiments, higher dimensional systems have given rise to spiral waves. One can't help wondering about the connections between Kolmogoroff's theory of turbulence and the critical structure of networks. Maybe the "angular momentum" of feedback loops within networks is analogous to vortices in a turbulent flow.

Unethical Competition

From the NYT:

I am in full favor of competition. My message is: Compete ethically. Sadly, there is a lot of unethical competition that goes on in science. This year alone, I have heard of cases that are the scientific equivalent of insider trading, where reviewers of important papers exploit their access to privileged data to gain unfair advantages in the “race” to the next big discovery. I have heard of researchers being ignored when they request published materials from scientists.

Any institutional design that lets unethical behavior go unpunished invites unethical norms to be established, and a general decline in morals over time.

Synchronized Flow in Your Brain

From the NYT:

When something bright or novel flashes, it tends to automatically win the competition for the brain’s attention, but that involuntary bottom-up impulse can be voluntarily overridden through a top-down process that Dr. Desimone calls “biased competition.” He and colleagues have found that neurons in the prefrontal cortex — the brain’s planning center — start oscillating in unison and send signals directing the visual cortex to heed something else.

These oscillations, called gamma waves, are created by neurons’ firing on and off at the same time — a feat of neural coordination a bit like getting strangers in one section of a stadium to start clapping in unison, thereby sending a signal that induces people on the other side of the stadium to clap along. But these signals can have trouble getting through in a noisy environment.

Reward and Recognition within our Educational System

In attending professional school, I felt surrounded by the kind of behavior that Walter Kirn describes of himself:

Early on, Mr. Kirn was a flash-card adept: "Ignoring three-quarters of the text to focus on the dates and numbers . . . this process involved neither reading nor comprehension. It was more like sifting sand for seashells." He tossed off a free-verse poem to win a contest. "I lived for prizes, plaques, citations, stars. . . . Learning was secondary, promotion was primary. No one ever told me what the point was, except to keep on accumulating points."

I note that Mr. Kirn's father was a patent lawyer:

Though his father was a patent lawyer and a Princeton graduate, the senior Kirn also prosecuted a longrunning war with convention; young Walter spent part of his childhood growing up on a subsistence farm in rural Minnesota, "Amish style."

Bill Watterson's Calvin wasn't raised Amish-style, but one can't help wondering if this is what becomes of Calvin after the system is finished with him.

Spontaneously Broken Continuous Symmetries in the Preference Functions of Economic Actors

Suppose we model an economy as a network of individual actors. Each actor is a node in the network. Each has a bundle of goods and a preference function associated with it. Consumption and production activities result in a transformation of the bundle. Exchanges result in a flow of goods through the network.

To a good approximation, at any given moment in time if we looked at the preferences for a particular good at each node, we might observe a continuous symmetry -- i.e., the relative preference for the good at each node might be approximately the same throughout the nework.

Now imagine that we watch what happens over time if we permit for a change in preferences to result from a (for now assume exogenous) new transormation discovered at a particular node. Suppose, for example, that one node in the network discovers a way to make a similar quality good using fewer inputs. At that node, the preferences for the particular good produced are going to diminish relative to other goods because the same (or similar good) can be produced at lower opportunity cost.

And now suppose that other nodes in the network can somehow (via mirror neurons or language) learn about this new means for producing the good so that both the knowledge and the change in preferences can propagate through the network.

Such propagation is arguably an example of the spontaneous breaking of a continuous symmetry in preferences. The observable result would be an oscillation -- a goldstone mode.

In general, not all continuous symmetries can be spontaneously broken to result in a goldstone mode. Under the Mermin-Wagner Theorem, continuous symmetries cannot be spontaneously broken at finite temperature in systems with sufficiently short-range interactions in dimensions less than or equal to 2. (Isn't this an explanation for why a one-dimensional series of price changes does not have a finite average?) But apparently, so long as the nodes in our network can interact in more than two dimensions (which they can since our preferences, in general, can have as many dimensions as there are goods), then there can be goldstone modes.

Let's take this a step further by imagining that instead of each node being connected only to a neighborhood of other nodes, each node is connected to all other nodes -- i.e., that our network is all-to-all. How might this be accomplished? By designating a central place for exchange -- a market. Now when a new preference is discovered, regardless of whether the new preference is communicated to any other node directly, the new preference will be reflected by a new kind of exchange in the market, which is observable (by construction) by all nodes in the network.

How will propagation of this new preference take place in an all-to-all network? It depends on whether the new transformation that resulted in the new preference can be "reverse-engineeered" by observing the new exchange alone. If so, then the new preference may be adopted instaneously, and a new equilibrium reached within the time needed for the new kinds of exchanges to be completed (although this will depend on the frequency of previous consumption, production, and exchange activities). If not, then the market mechanism will not result in a global approach to the new equilibrium. Rather, alternative mechanisms for communicating the new transformation will be required.

In addition, the time required for frequency and phase matching a new exchange within the framework of existing exchanges among the nodes should not be overlooked. Especially if the new transformation occurs at low-frequency, it might be a long time before the network transitions to the new mode of operating through the necessary exchanges required.

Problems with the Draft Patent Bill Revisited

A number of blogs today have picked up on an email comment sent out by Hal Wegner on the drafting errors in the current reform bill. See here and here.

Just thought I'd point out that Broken Symmetry readers got this scoop over a month ago:

Section 102(a)(1)(B)

"who obtained the subject matter disclosed directly or indirectly from the inventor" -- shouldn't this be "the subject matter claimed"? This could have serious negative consequences if passed in this condition. We will have people claiming that ideas disclosed in their specification (that are wholly unrelated to what they've claimed) belong to them when others, using the information in the disclosure (and other information as well), make different, nonobvious claimed inventions. Isn't the point of this section to prevent the claimed invention from being preempted by a publication or patent application derived from its inventor (or inventors) within the one-year grace period? It's not the purpose to prevent the disclosure that supports the claimed invention from being used for other claimed inventions, right? Or do we want for the inventors to be able to preempt claims to entirely different inventions whose disclosures merely overlap with theirs? I don't think we want to open the door to that sort of dispute, even if the PTO wouldn't have to deal with it much because it wouldn't be obvious until publication at the earliest that there were applications made during the 1 year grace period to different inventions but with overlapping disclosures.

UPDATE: The problems identified by Hal are related to, but different from the problem pointed out a month ago. Hal's concern seems to be with (1) the fact that a later-filing applicant could overcome a rejection by showing how he or she had indirect access to the prior art cited against her application; and (2) the later-filed application could nonetheless serve as prior art for obviousness purposes against the first-filed application.

(1) seems to be correct, although I'm not sure how big a deal it would be in practice since the applicant would bear the burden of submitting evidence (by affidavit or declaration, for example) of indirect access to the alleged prior art. Either they have evidence of indirect access or they don't. It would seem hard to fake, especially with the penalty of unenforceability looming. And bear in mind that faking is pointless anyway if the cited prior art is filed more than a year before the later-filed application/patent.

(2) is harder for me to address. I don't know why Hal thinks that if something is not prior art under 102, it might still be prior art under 103.

The Transactions Costs of Frequency and Phase Mismatch

From today's WSJ:

The recession has exposed a harsh side effect of the supply-chain system. Because modern industry rewards suppliers with the leanest inventories and fastest reaction times, when economic crisis struck, tech companies up and down the line contracted as sharply as possible in hopes of being the ones to survive.

Forced to guess at demand for their products in a plummeting market, everyone hit the brakes, hard. An examination of the electronics supply chain -- from retailers all the way back to makers of factory machinery -- shows that, at almost every stage, companies were flying blind as they cut.

Paradoxically, the sudden drop in demand was amplified up the supply chain because (1) the response to new information at each stage in the supply chain was so fast, and (2) the delay as information was transmitted at each link up the supply chain. Had each link been slower to respond or had information about the frequency of demand been shared more quickly up the chain, then there would have been less amplification. Note that unless these frequencies are better matched, then we can expect surpluses to show up again within a few months.

Information about demand and responses to that information need to be frequency locked or oscillations will result.

Letter Comment to the FTC on the Evolving IP Marketplace

Here are a few excerpts:

According to systems theory, innovation emerges from iterated cycles in which data, theory, and the people who collect data and form theory constitute feedback loops, which evolve over time. Each cycle manifests as the gathering and exchange of information among a network of experts who act as gatekeepers to a set of symbolic rules and procedures. Following the terminology of Mihaly Csikszentmihalyi, I call the network of experts a “field,” and the set of symbolic rules and procedures a “domain.” To give examples, topology, physical chemistry, and constitutional law are domains. Each of these domains is associated, respectively, with the fields of mathematicians who specialize in topology; academic and practicing physical chemists; and academics, practicing lawyers, and judges who work on constitutional law.

From the point of view of an economist, the most important difference between systems theory and neoclassical theory may be that systems theory permits for economic actors (the field) to have preference functions (the domain) that evolve over time. As a result, although neoclassical theory is still a useful model of economic activity when changes in preferences are small within the window of time in which economic activity is observed, more generally no stable equilibrium may be reached because preferences may change during the period of observation. In addition, because systems theory need not assume that economic actors know how their preferences have or will change over time, an assumption that actors will try to match their allocation of resources to their preferences can be called “rational” only in a weak sense. Under systems theory, whether an economic actor will engage in an activity of consumption, production, or exchange is a function of: (1) their history of preferences; (2) their history of consumption, production, and exchanges with others; and (3) the frequency of their exchanges with others. Whereas within neoclassical theory institutions are designed to promote allocative efficiency, within systems theory institutions are designed to promote the discovery of information about preferences, and thereby the synchronization and frequency-matching of consumption and production activities through exchange and cooperation.

For example, the systems theory makes explicit the spatial and temporal dynamics underlying the knowledge problem described by F.A. Hayek in “The Use of Knowledge in Society.” With the systems theory in mind, we can identify the function of markets with their facilitation of the exchange of information and coordination of economic activity independent of central planning. This is in stark contrast with neoclassical theory, which often identifies the function of markets with the maximization of aggregate utility based on an assumption of stable preferences — a function that cannot be carried out by a central planner beyond a small scale. The systems theory therefore identifies markets as a mechanism for rewarding information exchange and cooperation, thereby facilitating economic growth.

Download Letter Comment to FTC on the Evolving IP Marketplace (Final)

Characterizing Individual Communication Patterns by Malmgren, Hofman, Amaral, and Watts

Available here:

The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for characterizing individual behavior and, by extension, identifying latent structure in human populations. Here, we propose a model of individual e-mail communication that is sufficiently rich to capture meaningful variability across individuals, while remaining simple enough to be interpretable. We show that the model, a cascading non-homogeneous Poisson process, can be formulated as a double-chain hidden Markov model, allowing us to use an efficient inference algorithm to estimate the model parameters from observed data. We then apply this model to two e-mail data sets consisting of 404 and 6,164 users, respectively, that were collected from two universities in different countries and years. We find that the resulting best-estimate parameter distributions for both data sets are surprisingly similar, indicating that at least some features of communication dynamics generalize beyond specific contexts. We also find that variability of individual behavior over time is significantly less than variability across the population, suggesting that individuals can be classified into persistent “types”. We conclude that communication patterns may prove useful as an additional class of attribute data, complementing demographic and network data, for user classification and outlier detection—a point that we illustrate with an interpretable clustering of users based on their inferred model parameters.

Via the Physics arXiv Blog, which has been in sync with my interests very well lately.

Folks, this is how economics looks without the rational hypothesis.

(UPDATE 8/12/9: This is how economics looks with a more granular model of preferences.)

Looking at the two clusters in parameter space, I can't help wondering whether the one is for staff and administrators and the other for students and faculty.

Small Steps Toward Solving Hayek's Knowledge Problem through the PTO

From the consistently interesting Computational Legal Studies blog:

The United States Patent and Trademark Office patent classification scheme organizes 3 million patents into about 160,000 distinct patent classes. This visualization by Katy Börner, Elisha Hardy, Bruce W. Herr II, Todd M. Holloway, & W. Bradford Paley considers the organizational schema used to classify patents at the US Patent Office. Their article Taxonomy Visualization in Support of the Semi-Automatic Validation and Optimization of Organizational Schemas was published in the Journal of Informetrics in 2007.

Revisiting Robert Ellickson's Critique of Functionalism

An excerpt from Order Without Law:

[E]arly functionalists had a tendencey to consider a human group as a single organizm whose "health" could be examined. It was then easy for them to surmise that Darwinian evolutionary processes would help favor the survival of socially adaptive norms. In the following passage the libertarian scholar Friedrich Hayek lapsed into this sort of analysis:

It is not only in his knowledge, but also in his aims and values, that man is the creature of civilization: in the last resort, it is the relevance of these individual wishes to the perpetuation of the group or the species that will determine whether they will persist or change. It is, of course, a mistake to believe that we can draw conclusions about what our values ought to be simply because we realized that they are a product of evolution. But we cannot reasonably doubt that these values are created and altered by the same evolutionary forces that have produced our intelligence. All that we can know is that the ultimate decision about what is good or bad will be made not by individual human wisdom, but by the decline of the groups that have adhered to the "wrong" beliefs. [The Constitution of Liberty at 36]

(Before nonlibertarian scholars dismiss these thoughts, they should be aware that both Lon Fuller and Thomas Schelling have also harbored suspiciously functionalist sentiments.) [Citing Fuller's "Human Interaction and the Law," and Schelling's Micromotives and Macrobehavior at 124-133].

The difficulty with this sort of organic analysis is that evolutionary processes, as most biologists understand them, select either genes or individual organisms, not groups, for survival. [Citing Dawkins's Selfish Gene and E.O. Wilson's Sociobiology at 106-129; and noting V.C. Wynne-Edwards's dissent.] Assume, for example, that honesty is a functional norm in the sense that, if all the members of a group were consistently honest, they would each be better off than if everyone were consistently dishonest. Would the Darwinian struggle favor the survival of the groups of honest persons? Not necessarily. A dishonest individual living in an environment surrounded by honest people might especially prosper, surrounded by easy marks. As a result, the Darwinian process would tend to select dishonest people for survival. In short, to support a Panglossian scenario of group evolutionary progress, functionalists could not rely simply on the biological theory of natural selection. Instead, they had to develop theories to explain how social-control systems would evolve within groups to prevent successful invasions by deviants who would subvert group welfare.

The punchline is that group selection theory is now enjoying a resurgence among some evolutionary biologists, notably including E.O. Wilson, whom Ellickson cites in support of the proposition that the theory has been rejected by evolutionary biologists. Wilson and Holldobler recently published, The Superorganism, which lays out a group selection theory for how cooperation evolved among insects.

The other objections to functionalism raised by Ellickson are also moot now in view of the improved theoretical tools for being more quantitative about functionalism. In particular, associating a group with a network, and individuals with nodes therein, permits evolution of group behavior to be modeled using mathematical models for network growth and decay. Such models help to eliminate much of the conceptual circularity to which Ellickson objects in this passage.

How Silicon Valley Came to Be by Timothy J. Sturgeon

A chapter of this extraordinary history available here:

The animosity between RCA and Globe Wireless was quite strong. As Ralph Heintz put it: "We [Globe Wireless] were a monkey on [RCA's] back, because we had a mechanism of handling [radio] traffic across the Pacific that they seemed unable to stop. They tried to harass us by having Western Union refuse our traffic in every conceivable way" (Heintz 1982). In 1937 RCA again filed suit against Globe Wireless for patent infringement. Heintz, fed up with RCA harassment, constructed a series of "breadboard" demonstration sets to compare various vacuum tube technologies that could be used to send and receive radio signals. One set was based on the deForest patents, which RCA controlled. The other sets were based on technology that RCA did not own the patents on, including those owned by Larson (Norway), Vreeland (USA), Simpson (USA), and Goddard (USA). These patents, plus others held by Heintz, covered the technology on which Globe's system was based.

Heintz asked his friend and classmate from Stanford, Frederick Terman, to appear in court as a technical expert. Donald Lippincott provided legal advice. When the day in court came, the request to have Heintz's "breadboard" sets entered as evidence was accepted by the judge. When the six RCA patent attorneys realized that losing the case could jeopardize RCA’s entire patent position, they immediately approached the bench and asked that the suit be withdrawn (Heintz 1982).

Notice how complex the interplay of patent rights is in this story.

Early Real-time Estimation of Infectious Disease Reproduction Number

From Davoudi, Pourbohloul, Miller, Meza, Meyers, and Earn:

We map a collection of N individuals to a network in which each vertex represents an individual and each edge shows a pathway of possible infection transmission between two individuals.

Here's a figure:

What I learned from the Internet this week

Within academia, us/them distinctions are drawn based on precommitments to particular methodologies. Some of you in academia are smacking your foreheads and saying, "Well duh!" It takes some of us longer to catch on.

From a systems theory view, it would be better for people to organize around the problems to be solved. But given how complex and important some problems are, I guess that would require a group too large for most people to feel comfortable in psychologically.

Territorial behavior over metholodical approaches is very counterproductive to learning to say the least!