Brian Walker's 2012 Krebs Lecture

What follows is a summary and time guide to Brian Walker's 2012 Krebs Lecture Learning how to change in order not to change: Lessons from ecology for an uncertain world (video available here).

Walker begins by asking his audience the following question: How much can you change and still be the same person? After how much change would you have a new identity? Walker's question aims to draw attention to the notion of 'dominant organizing principals' -- the central aspects responsible for maintaining the identity of a system. He illustrates the point with research connecting place, identity and resilience among Eritrean refugees: individuals who had spent their whole life in one location have a much harder time coping with being a refugee forced from their land than individuals who had lived a nomadic life.

Walker then turns to a brief description of four key elements of resilience thinking:

1. feedback: resilient systems are self-organizing systems governed by feedback (illustrated by the relation between the invasive fern salvinia molesta and a wevil used as a biological control): from around the 5:15 mark -  9:15
2. change: resilience is only maintained by disturbance and change (e.g., the forest that becomes less resilient as a result of being protected from fire): 9:15-10:15
3. scale: you cannot understand or manage a system at one scale: 10:15-11:25
4. tradeoffs: efforts to maintain resilience at one level often lead to declines in resilience at another scale (e.g., efforts to maintain the resilience of global scale economies and industries often has adverse effects on national level economies and industries): 11:25-12:40

Which he uses to describe the ball in a basin metaphor of resilience (with changes in the size and shape of the basin and resulting threshold effects, initially in terms of a simple system and then in systems with multiple organizing principals and multiple threshold effects): 12:40- 19:30

Walker then turns to adaptive capacity or the issue of how one maximizes the resilience of the system as a whole (19:30-20:40) as a means to introduce the central focus of the topic -- transformational change, that is change in the system such that it operates with different dominant organizing principals and has a different identity (e.g., the shift from feudalism to capitalism).

At the 23:15 point, Walker begins a discussion of three factors necessary for the transformation of a system and the complication of a rapidly changing environment:

1. getting beyond a state of denial
2. the existence or creation of options for change
3. the capacity to change (typically some source of higher level support or resources such as government financial resources)

At 27:00 Walker turns to a discussion of the Planetary Boundaries analysis. Particularly interesting is the discussion of the Arctic and the way the melting of ice is changing the alkalinity of the water leading to a scale decline in the size of plankton with potential implications for the entire food chain.

At 34:15 a discussion of the distinction between rapid and abrupt transformations of identity with slow and gradual transformations begins. Specifically, Walker contrasts the abrupt and cataclysmic change of the French and Russian revolutions with the enclosure movement in Britain. Walker uses it as an example of a fine grained change occurring at the level of the individual farm over a period of two generations that led to a gradual transformation of the entire British agricultural system.

The topic of combined bottom-up and top-down transformation begins at 37:30. In particular, Walker notes the need for transformation of both the energy system and the economic system at the global scale as a facilitating factor for necessary transformations at other levels.

At 39:00 the lecture turns back to the first factor necessary for transformation, getting past denial, with particular reference to the way risks are framed and perceived (as discussed by Kahneman in Thinking Fast and Slow).

At the 40:00 mark, Walker reprises the central point of the lecture: "We need to learn how to put in place continuing transformational change in order for us not to be subjected to the painful catastrophic change that will otherwise inevitably occur."

Battle of the Continental Titans: Harrison White takes on Niklas Luhman

In the 1960's, faced with a dramatic period of social change and a dominant Parsonian structural-functionalist view of social systems that emphasized stability, American sociology went through a theoretical crisis. Simplistically speaking, there were three major consequences: a rejection of functionalism (and, for the most part, systems theorizing), a re-emergence of Marxist political-economy, and an enhanced emphasis on the importance of meaning (evident in both symbolic interactionism and Garfinkel's ethnomethodology).

Percolating under the radar of these larger and more evident changes, was the work of Harrison White, a sociologist initially trained in physics, who combined a) Simmel's insight that social structure is based on patterns of relations instead of the attributes and attitudes of individuals with b) his ability to analyze those structural networks using highly sophisticated maths. Opaque to much of the discipline, White's work achieved cultish admiration among the cognoscenti and was, until comparatively recently, ignored by the bulk of American sociology.

Meanwhile, over in Europe,  Parson's student Niklas Luhmann continued the tradition of grand systems theorizing. His approach has been summarized as follows:

Luhmann's systems theory focuses on three topics, which are interconnected in his entire work.

1. Systems theory as societal theory
2. Communication theory and
3. Evolution theory

The core element of Luhmann's theory is communication. Social systems are systems of communication, and society is the most encompassing social system. Being the social system that comprises all (and only) communication, today's society is a world society. A system is defined by a boundary between itself and its environment, dividing it from an infinitely complex, or (colloquially) chaotic, exterior. The interior of the system is thus a zone of reduced complexity: Communication within a system operates by selecting only a limited amount of all information available outside. This process is also called "reduction of complexity." The criterion according to which information is selected and processed is meaning (in German, Sinn). Both social systems and psychical or personal systems operate by processing meaning.

Thus, Luhmann not only kept alive the grand tradition of social system theorizing, he did so in a manner that incorporated the emerging emphasis on meaning. In the recent "Order at the Edge of Chaos: Meanings from Netdom Switchings Across Functional Systems" (Sociological Theory, September 2011, 29(3): 178-198), White and his co-authors take direct aim at Luhmann's theory, arguing that it fails to recognize the indexical character of meaning identified by Garfinkel.

Here is the abstract:

The great German theorist Niklas Luhmann argued long ago that meaning is the central construct of sociology. We agree, but our scheme of stochastic processes—evolved over many years as identity and control—argues for switchings of intercalated bits of social network and interpretive domain (i.e., netdom switchings) as the core of meaning processes. We thus challenge Luhmann’s central claim that modern society’s subsystems are based on communicative self-closure. We assert that there is refuting evidence from sociolinguistics, from how languages are put together and how languages’ indexical and reflexive devices (e.g., metapragmatics, heteroglossia, genres) are used in social action. Communication is about managing indexicalities, which entail great ambiguity and openness as they are anchored in myriad netdom switchings across social times and spreads. In contrast, Luhmann’s concept of communication revolves around binary codes governed recursively and algorithmically within systems in efforts to reduce complexity from the environment. We conclude that systems closure does not solve the problem of uncertainty in social life. In fact, lack of uncertainty is itself a problem. Order is necessary, but order at the edge of chaos.

Transformation, Vulnerability and Resilience

Emilio Moran poses an interesting question, what makes social ecological systems more vulnerable and less resilient?, in his recent article Transformation of social and ecological systems. The bulk of the article is a rather sweeping history of humanity aimed at emphasizing the unique nature of our current situation. In the conclusion, quoted after the break, Moran identifies the factors he sees as responsible for making current systems more vulnerable and less resilient: Hypercoherence, loss of redundancy, loss of trust and a sense of community, and the misuse of information by decision-makers.  Read on .....

Complexity and Geomagnetic Storms

A Perfect Storm of Planetary Proportions is a fascinating article linking two of my pet fascinations: complexity and catastrophe.The immediate prompt for the article is the expected 2012-13 peaking in solar activity and the potential it has for a geomagnetic superstorm that could disrupt power grids all over the world. The article provides lots of fascinating detail about the storms themselves. Here I focus on the connection to the disruption of power grids

From where we sit, the sun seems quiet enough. And yet it is constantly bombarding Earth with electrons, protons, and radio through X-ray waves. ... Under normal circumstances, this solar wind produces only negligible effects on Earth. Occasionally, though, the sun erupts violently, emitting solar flares or throwing out coronal mass ejections consisting of billions of tons of charged particles. ...

But how does all this space weather cause damage down on the ground? It's a multistep process. First, the intense magnetic field variations in the magnetosphere induce electric fields and currents over large areas of Earth's surface. In turn, this geoelectric field creates what are known as geomagnetically induced currents, or GICs, which flow in any available conductor, including high-voltage transmission lines, oil and gas pipelines, railways, and undersea communications cables. These interconnecting networks essentially act as giant antennas that channel the induced currents from the ground. Hit with a 300-ampere GIC, a high-voltage transformer's paper tape insulation will burn, its copper winding will melt, and the transformer will fail, either right then and there or in the future. High-voltage power grids are designed to withstand the loss of any single important element, such as a substation transformer, and then recover within a half hour or so. For a terrestrial storm like a hurricane or a tornado, this approach works well. But a severe geomagnetic storm covering an entire continent would cause multiple failures all at once.

The video below discusses the most recent major geomagnetic storm from 1989, a storm of roughly 1/10 the magnitude of those known to have occurred in the past. The last time we had a truly powerful storm was in 1921—decades before developed economies became utterly dependent on electrical infrastructure. So here we have a wonderful example of how complexity begets more complexity -- how the development of a complex technological infrastructure has led to the emergence of a new type of problem; a problem that requires yet more complexity (in the software and other aspects that manage the grid) in order to minimize the potential for widespread blackouts.

The limits of mechanistic understanding

Trials and Errors: Why Science Is Failing Us uses the story of a failed drug, torcetrapib, to illustrate issues involved with understanding complex systems. It begins with a critique of mechanistic reductionism.

The story of torcetrapib is a tale of mistaken causation. Pfizer was operating on the assumption that raising levels of HDL cholesterol and lowering LDL would lead to a predictable outcome: Improved cardiovascular health. Less arterial plaque. Cleaner pipes. But that didn’t happen.
....
Pfizer invested more than $1 billion in the development of the drug and $90 million to expand the factory that would manufacture the compound. Because scientists understood the individual steps of the cholesterol pathway at such a precise level, they assumed they also understood how it worked as a whole.

This assumption—that understanding a system’s constituent parts means we also understand the causes within the system—is not limited to the pharmaceutical industry or even to biology. It defines modern science. In general, we believe that the so-called problem of causation can be cured by more information, by our ceaseless accumulation of facts. Scientists refer to this process as reductionism. By breaking down a process, we can see how everything fits together; the complex mystery is distilled into a list of ingredients.

After a discussion of the problems involved in establishing causation, the article argues that science of the past few decades has pragmatically sidestepped these problems through the use of statistics and the substitution of establishing correlation for establishing causality.

But here’s the bad news: The reliance on correlations has entered an age of diminishing returns. At least two major factors contribute to this trend. First, all of the easy causes have been found, which means that scientists are now forced to search for ever-subtler correlations, mining that mountain of facts for the tiniest of associations. Is that a new cause? Or just a statistical mistake? The line is getting finer; science is getting harder. Second—and this is the biggy—searching for correlations is a terrible way of dealing with the primary subject of much modern research: those complex networks at the center of life. While correlations help us track the relationship between independent measurements, such as the link between smoking and cancer, they are much less effective at making sense of systems in which the variables cannot be isolated. Such situations require that we understand every interaction before we can reliably understand any of them. Given the byzantine nature of biology, this can often be a daunting hurdle, requiring that researchers map not only the complete cholesterol pathway but also the ways in which it is plugged into other pathways. (The neglect of these secondary and even tertiary interactions begins to explain the failure of torcetrapib, which had unintended effects on blood pressure. It also helps explain the success of Lipitor, which seems to have a secondary effect of reducing inflammation.) Unfortunately, we often shrug off this dizzying intricacy, searching instead for the simplest of correlations. It’s the cognitive equivalent of bringing a knife to a gunfight.

The piece ends with a paragraph that links back to an earlier discussion of the role of perception in establishing causation and hints at the importance of distinguishing between the known and the unknown.

And yet, we must never forget that our causal beliefs are defined by their limitations. For too long, we’ve pretended that the old problem of causality can be cured by our shiny new knowledge. If only we devote more resources to research or dissect the system at a more fundamental level or search for ever more subtle correlations, we can discover how it all works. But a cause is not a fact, and it never will be; the things we can see will always be bracketed by what we cannot. And this is why, even when we know everything about everything, we’ll still be telling stories about why it happened. It’s mystery all the way down.

Stability is Destabalizing

A post from a couple of months ago charted the course of various post-WWII US recessions and posed the question Was there a structural change in the economy in the late 1970s - early 80s?

Over the period from the late 1950's to 1980, US macro-economic policy became better and better at managing recessions. They continued to come at roughly the same frequency, but they were shorter and shallower than the earlier ones. This could, potentially, indicate an increase in rigidity over time as US macroeconomic policy attempted to 'smooth out' the business cycle.

A recent post at Resilience Science draws attention to a similar matter and provides a nice set of links to analytic resources relative to the issue. Here is a key passage:

In complex adaptive systems, stability does not equate to resilience. In fact, stability tends to breed loss of resilience and fragility or as Minsky put it, “stability is destabilising”. Although Minsky’s work has been somewhat neglected in economics, the principle of the resilience-stability tradeoff is common knowledge in ecology, especially since Buzz Holling’s pioneering work on the subject. If stability leads to fragility, then it follows that stabilisation too leads to increased system fragility. As Holling and Meffe put it in another landmark paper on the subject titled ‘Command and Control and the Pathology of Natural Resource Management’, “when the range of natural variation in a system is reduced, the system loses resilience.” Often, the goal of increased stability is synonymous with a goal of increased efficiency but “the goal of producing a maximum sustained yield may result in a more stable system of reduced resilience”. The entire long arc of post-WW2 macroeconomic policy in the developed world can be described as a flawed exercise in macroeconomic stabilisation.

Network Analysis of A Complex Global System: the 147 Super-Connected Corporations that Run the World

Revealed – the capitalist network that runs the world
By wmw_admin on October 21, 2011

Andy Coghlan and Debbie MacKenzie – New Scientist October 19, 2011


[Caption: The 1318 transnational corporations that form the core of the economy. Superconnected companies are red, very connected companies are yellow. The size of the dot represents revenue.]

AS PROTESTS against financial power sweep the world this week, science may have confirmed the protesters’ worst fears. An analysis of the relationships between 43,000 transnational corporations has identified a relatively small group of companies, mainly banks, with disproportionate power over the global economy.

The study’s assumptions have attracted some criticism, but complex systems analysts contacted by New Scientist say it is a unique effort to untangle control in the global economy. Pushing the analysis further, they say, could help to identify ways of making global capitalism more stable.

The idea that a few bankers control a large chunk of the global economy might not seem like news to New York’s Occupy Wall Street movement and protesters elsewhere (see photo). But the study, by a trio of complex systems theorists at the Swiss Federal Institute of Technology in Zurich, is the first to go beyond ideology to empirically identify such a network of power. It combines the mathematics long used to model natural systems with comprehensive corporate data to map ownership among the world’s transnational corporations (TNCs).

“Reality is so complex, we must move away from dogma, whether it’s conspiracy theories or free-market,” says James Glattfelder. “Our analysis is reality-based.”

Previous studies have found that a few TNCs own large chunks of the world’s economy, but they included only a limited number of companies and omitted indirect ownerships, so could not say how this affected the global economy – whether it made it more or less stable, for instance.

The Zurich team can. From Orbis 2007, a database listing 37 million companies and investors worldwide, they pulled out all 43,060 TNCs and the share ownerships linking them. Then they constructed a model of which companies controlled others through shareholding networks, coupled with each company’s operating revenues, to map the structure of economic power.

The work, to be published in PloS One, revealed a core of 1318 companies with interlocking ownerships (see image). Each of the 1318 had ties to two or more other companies, and on average they were connected to 20. What’s more, although they represented 20 per cent of global operating revenues, the 1318 appeared to collectively own through their shares the majority of the world’s large blue chip and manufacturing firms – the “real” economy – representing a further 60 per cent of global revenues.

When the team further untangled the web of ownership, it found much of it tracked back to a “super-entity” of 147 even more tightly knit companies – all of their ownership was held by other members of the super-entity – that controlled 40 per cent of the total wealth in the network. “In effect, less than 1 per cent of the companies were able to control 40 per cent of the entire network,” says Glattfelder. Most were financial institutions. The top 20 included Barclays Bank, JPMorgan Chase & Co, and The Goldman Sachs Group.

John Driffill of the University of London, a macroeconomics expert, says the value of the analysis is not just to see if a small number of people controls the global economy, but rather its insights into economic stability.

Concentration of power is not good or bad in itself, says the Zurich team, but the core’s tight interconnections could be. As the world learned in 2008, such networks are unstable. “If one [company] suffers distress,” says Glattfelder, “this propagates.”

“It’s disconcerting to see how connected things really are,” agrees George Sugihara of the Scripps Institution of Oceanography in La Jolla, California, a complex systems expert who has advised Deutsche Bank.

Yaneer Bar-Yam, head of the New England Complex Systems Institute (NECSI), warns that the analysis assumes ownership equates to control, which is not always true. Most company shares are held by fund managers who may or may not control what the companies they part-own actually do. The impact of this on the system’s behaviour, he says, requires more analysis.

Crucially, by identifying the architecture of global economic power, the analysis could help make it more stable. By finding the vulnerable aspects of the system, economists can suggest measures to prevent future collapses spreading through the entire economy. Glattfelder says we may need global anti-trust rules, which now exist only at national level, to limit over-connection among TNCs. Bar-Yam says the analysis suggests one possible solution: firms should be taxed for excess interconnectivity to discourage this risk.

One thing won’t chime with some of the protesters’ claims: the super-entity is unlikely to be the intentional result of a conspiracy to rule the world. “Such structures are common in nature,” says Sugihara.

Newcomers to any network connect preferentially to highly connected members. TNCs buy shares in each other for business reasons, not for world domination. If connectedness clusters, so does wealth, says Dan Braha of NECSI: in similar models, money flows towards the most highly connected members. The Zurich study, says Sugihara, “is strong evidence that simple rules governing TNCs give rise spontaneously to highly connected groups”. Or as Braha puts it: “The Occupy Wall Street claim that 1 per cent of people have most of the wealth reflects a logical phase of the self-organising economy.”

So, the super-entity may not result from conspiracy. The real question, says the Zurich team, is whether it can exert concerted political power. Driffill feels 147 is too many to sustain collusion. Braha suspects they will compete in the market but act together on common interests. Resisting changes to the network structure may be one such common interest.

The top 50 of the 147 superconnected companies

1. Barclays plc
2. Capital Group Companies Inc
3. FMR Corporation
4. AXA
5. State Street Corporation
6. JP Morgan Chase & Co
7. Legal & General Group plc
8. Vanguard Group Inc
9. UBS AG
10. Merrill Lynch & Co Inc
11. Wellington Management Co LLP
12. Deutsche Bank AG
13. Franklin Resources Inc
14. Credit Suisse Group
15. Walton Enterprises LLC
16. Bank of New York Mellon Corp
17. Natixis
18. Goldman Sachs Group Inc
19. T Rowe Price Group Inc
20. Legg Mason Inc
21. Morgan Stanley
22. Mitsubishi UFJ Financial Group Inc
23. Northern Trust Corporation
24. Société Générale
25. Bank of America Corporation
26. Lloyds TSB Group plc
27. Invesco plc
28. Allianz SE 29. TIAA
30. Old Mutual Public Limited Company
31. Aviva plc
32. Schroders plc
33. Dodge & Cox
34. Lehman Brothers Holdings Inc*
35. Sun Life Financial Inc
36. Standard Life plc
37. CNCE
38. Nomura Holdings Inc
39. The Depository Trust Company
40. Massachusetts Mutual Life Insurance
41. ING Groep NV
42. Brandes Investment Partners LP
43. Unicredito Italiano SPA
44. Deposit Insurance Corporation of Japan
45. Vereniging Aegon
46. BNP Paribas
47. Affiliated Managers Group Inc
48. Resona Holdings Inc
49. Capital Group International Inc
50. China Petrochemical Group Company

* Lehman still existed in the 2007 dataset used

Source

Cesar Hidalgo on Economic Complexity

As evident from the voluminous discussion of the concept of biodiversity, the significance of diversity within ecosystems has long been recognized.  Generally speaking, this isn't the case in economics where the focus has typically been on identifying a small list of 'factors of production' (labor, capital, technology, etc.) and their transformation into a single comparable product ($ value). Taking a complexity view, Hidalgo and his collaborators argue that Adam Smith and Durkheim (with their emphasis on specialization and the division of labor) provide a more fruitful way of conceptualizing the economic realm.

Conceptually, as shown at the left, Hidalgo divides the countries of the world up into four groups: 1) Countries whose few major products are also produced by a number of other countries (e.g., the sugar producing countries of the Caribbean), 2) Countries with diversified economies, but all their major products are produced in a number of other locations, 3) Non-diversified economies that produce unique and exclusive products (such as Saudi Arabia and other oil based economies) and 4) Countries producing a diverse range of unique and distinct products.

Using a measure of economic complexity described in this paper, Hidalgo locates the economies of the world in a quantified representation of that basic conceptual space. Two points are worth of note. First, the countries locate themselves along a diagonal with almost every world economy being located in either the first or the fourth quadrant. As summarized by Ethan Zuckerman:

The nations that make only a few things all tend to make, more or less, the same things. Basically, we can divide the world into two sets of countries – those that have sufficient personbytes of knowledge to produce a wide range of goods, and those that can produce only a few simple things. The places that make everything make things that few others make. Hausmann explains that products require a specific set of personbytes to produce. When you gain additional personbytes of skill, it’s like getting new letters in Scrabble – you can produce a new set of words, but only within the constraints of the letters (skills, knowledge) you already have.

Second, this approach does a much better job than traditional economic analysis in addressing the classic question: "Why are some countries rich and other countries poor?" It explains 73% of the variance in income across nations.


Hidalgo describes his approach to complexity economics in the following two videos.



The same basic ideas are covered in more detail in this version.



His webpage is a wealth of information, including (among other things) pages with links to all his publications, to supporting materials for classes on complex systems, and access to the data sets used in his research.

Rifkin on Energy, Communications and Complex Societies

I still don't think that a distributed hydrogen grid could supply the same density of energy that fossil fuels does. I'm not convinced that distributed energy will run the kind of "global" economy that we have now (which, he is right, is in its death throes). If Rifkin followed Rifkin's own thinking, the third industrial revolution is distributed production, in which each locality produces what it needs and trades with other nearby localities. Instead of globalized agribusiness, we have small farms everywhere that produce food for a local market. I can't say with any certainty how a "distributed manufacturing system" would operate.

The biggest hole in Rifkin's thesis is that he's only talking about electricity production: he's not talking about transport energy. Electrified transportation is only good for short distances (compared with gasoline, diesel and jet fuel). Reliance on electricity for transport would be a limiting factor that would tend to localize production, and would discourage trading over long distances. And that's another reason why Rifkin's distributed energy grid won't fuel a globalized economy. But for him to leave the discussion of mobility and transport out of the discussion altogether seriously weakens his thesis. As Robert Hirsch said again recently, the peak oil crisis is a liquid fuel crisis that primarily impacts transportation.

Was there a structural change in the economy in the late 1970s - early 80s?

(Click on the graph for a larger view.) I really don't know what to make of this graph, other than it is really interesting. My thoughts are below, feel free to share yours.

The lines trace the impact of various recessions on employment. Each color refers to a particular recession. The individual lines trace both the extent of job loss (the deeper the drop, the greater the drop in employment) and the length of time to get back to the pre-recession rate of employment (the further to the right, the longer the recovery takes).

The striking thing about the graph is that something fundamental appears to have occurred around 1980/81. If you look at the lines for the seven recessions that occurred between 1948 and 1980, they all have the same basic V shape. There is also a fairly systematic relationship between the depth of the recession, the length of time until the bottom is reached and the time until employment returns to the pre-recession level. In systemic terms, all recessions up to 1980 behaved in a generally similar fashion.

There is also a noticeable difference between the earlier V shaped recessions and the later ones. The earlier ones (1948, 1953, 1957) have very similar profiles; they were deeper (job losses of 3.4-5.2%) and longer (with 13 months until the bottom) than the later V-shaped recessions. The recessions of 1960, 1969, 1974 and 1980 were shallower (job losses of 1.3-2.7%) and shorter (between 4 and 10 months to the bottom).

The three most recent recessions (1990, 2001, 2007) have a very different profile. Rather than the sharp drop and bounce back of the V shape, the whole process appears to have slowed down. The rate of decline and the rate of recovery are both much slower and the impact of the recession on employment is much longer. The profiles of the three most recent recessions look more like a river basin than the V-shaped valley of the earlier ones. It has taken longer to reach the bottom (24 months, or almost twice as long as the 48-57 recessions) and, once there, the percentage of job losses has flat-lined for close to a year before starting to recover. 

The transition appears to have occurred in 1980/81. This is the only case where there is a double-dip recession. Moreover, where the 1980 recession looks like the smallest of the V-shaped recessions, the 1981 curve has a transitional look. Unlike any of the previous recessions, the bottom comes a few months later and flatlines for a few months before the upsurge in employment occurs. The other point of significance. Where recessions had occurred on a crude 5 year cycle up to 1980, the cycle is more like 9 or 10 years since the 81 recession. This, like the shifting shape, suggests the overall process has slowed.

As noted in an earlier post, there are other indicators that the global economy significantly changed at this time, a period linked with the early phases of economic globalization.

So, to take a shot at translating this into panarchy terms, recessions are a product of the adaptive cycle. Over the period from the late 1950's to 1980, US macro-economic policy became better and better at managing recessions. They continued to come at roughly the same frequency, but they were shorter and shallower than the earlier ones. This could, potentially, indicate an increase in rigidity over time as US macroeconomic policy attempted to 'smooth out' the business cycle. This national level process, around 1980, confronted a different dynamic -- resulting from changes to the higher (global) level cycle associated with economic globalization. In other words, the shift from one form of recession (V-shaped) to another (river-shaped) involves a cross-scale interaction where changes in the global economy (outsourcing of manufacturing and the increasing financialization of the US economy, for example) affect the ability of the US to rebound from recessions and, in particular, to create jobs.

A brief meditation on science, democracy and complexity

A recent article in the Ventura County Reporter, Our Ocean: As Healthy as it Looks?, does a nice job of contrasting public perception (the ocean looks great from Highway 101, the fishing is good, altogether it seems pretty healthy) with a series of scientific reports predicting environmental catastrophe due to ocean acidification, rising global carbon emissions, overfishing, pollution and a variety of other factors.

What accounts for the differing views of scientists and the public? And why is the situation likely to get much worse? Read on after the jump ...

Bodies, Big Brains and Regulatory Reform

When I started this post, it was just a listing of two things I found interesting. Then I realized they were connected. So... what are the two articles?

1. A Body Fit for a Freaky-Big Brain, summarizing research on the anatomical adaptations necessary to accommodate our over sized brains -- which use 20 times as much energy per pound as muscle tissue. Among the factors identified: reduction in the amount of gut tissue (also very energy intensive); shifting of diet to a higher energy cuisine based on seeds, tubers and meats; and a genetic adaptation in glucose transporters that resulted in extra molecular pumps to funnel sugar into the brain, while starving muscles by giving them fewer transporters.
2. Individuals interested in takes on the financial collapse will want to check out Capital Inadequacies The Dismal Failure of the Basel Regime of Bank Capital Regulation. Put out by the libertarian Cato Institute, the paper provides 40 pages or so of analysis aimed at a) documenting that regulatory solutions to financial matters are misplaced because regulatory apparatus is subject to capture and b) advocating a solution based on financial laissez faire.

The solution is free banking or financial laissez faire. The state would withdraw entirely from the financial system and, in particular, abolish capital adequacy regulation, deposit insurance, financial regulation, and the central bank, as well as repudiate future bailouts (and especially the doctrine of Too Big to Fail). ... Such systems have worked well in the past, and reforms along these lines would take the United States a long way back to its banking system of a century ago, in which banks were tightly governed and moral hazards and risk taking were well controlled because those who took the risks bore their consequences.

Now we can debate the empirical validity of these claims -- the individuals who lost all their savings in the bank runs of the Great Depression probably wouldn't agree that "those who took the risks bore their consequences" -- but that isn't the point.

Compare the view of systems and adaptation in the two scenarios. In the first article the "system" is the human body. The basic argument is that modification of one major subsystem (the brain) necessitated modification to other parts of the system in order for the "big brained" version of humans to survive. Contrast this with the view of the economic system advanced in the Cato Institute analysis. Over the past century the economy has changed dramatically. The growth of financial services as the mainstay of many advanced economies is the equivalent of the emergence of big brains -- one particular part of the system is becoming unusually important. A century ago, advanced economies were based on manufacturing and agriculture. Today, these sectors play a comparatively minor role and financial services (conventionally rendered as Wall Street) rule. But, rather than recognizing that change in one part of the system requires an adjustment in other parts of the system, the Cato paper argues for stability in the other aspects of the system (as expressed in the desire for a banking system similar to what was in place in 1910).

There is also a confusion Cato Institute paper about the role of organization (regulation) as it characterizes complex systems, but getting into that would be another (lengthy and necessarily technical) post.

World's strongest banks

There are two basic explanations for the recent global financial collapse. The first, the Marxist account, emphasizes the role of capitalist accumulation and, in particular, the ability of the financial classes to profit from asset bubbles. The second account emphasizes complexity and uncertainty. According to this account, lax regulation was a major contributing factor. In light of these competing accounts it is interesting to look at the information below, identifying banks in Singapore and Canada as the world's strongest. Both locations came through the crisis comparatively unscathed and both are noted for their relatively high level of bank regulation.

Paleoclimate evidence on rapidity of climate change

How rapidly will climate change occur? Traditionally, the changes were expected to be linear -- that as the amount of carbon in the atmosphere went up, the temperature would rise proportionately. Over the past few decades, this view has increasingly been replaced with the recognition of tipping points and the possibility of comparatively rapid change. James Hansen and Makiko Sato have recently produced a pair of papers -- one technical and the other focused on a more general audience -- describing their recent findings suggesting that the bulk of change from certain feedback processes will occur in a period of decades rather than centuries. Here is the introduction from the non-technical version: Earth's Climate History: Implications for Tomorrow

The past is the key to the future. Contrary to popular belief, climate models are not the principal basis for assessing human-made climate effects. Our most precise knowledge comes from Earth's paleoclimate, its ancient climate, and how it responded to past changes of climate forcings, including atmospheric composition. Our second essential source of information is provided by global observations today, especially satellite observations, which reveal how the climate system is responding to rapid human-made changes of atmospheric composition, especially atmospheric carbon dioxide (CO2). Models help us interpret past and present climate changes, and, in so far as they succeed in simulating past changes, they provide a tool to help evaluate the impacts of alternative policies that affect climate.

Paleoclimate data yield our best assessment of climate sensitivity, which is the eventual global temperature change in response to a specified climate forcing. A climate forcing is an imposed change of Earth's energy balance, as may be caused, for example, by a change of the sun's brightness or a human-made change of atmospheric CO2. For convenience scientists often consider a standard forcing, doubled atmospheric CO2, because that is a level of forcing that humans will impose this century if fossil fuel use continues unabated.

We show from paleoclimate data that the eventual global warming due to doubled CO2 will be about 3°C (5.4°F) when only so-called fast feedbacks have responded to the forcing. Fast feedbacks are changes of quantities such as atmospheric water vapor and clouds, which change as climate changes, thus amplifying or diminishing climate change. Fast feedbacks come into play as global temperature changes, so their full effect is delayed several centuries by the thermal inertia of the ocean, which slows full climate response. However, about half of the fast-feedback climate response is expected to occur within a few decades. Climate response time is one of the important 'details' that climate models help to elucidate.

For those who want the technical details, here is the abstract of the scientific publication.

Paleoclimate data help us assess climate sensitivity and potential human-made climate effects. We conclude that Earth in the warmest interglacial periods of the past million years was less than 1{\deg}C warmer than in the Holocene. Polar warmth in these interglacials and in the Pliocene does not imply that a substantial cushion remains between today's climate and dangerous warming, but rather that Earth is poised to experience strong amplifying polar feedbacks in response to moderate global warming. Thus goals to limit human-made warming to 2{\deg}C are not sufficient - they are prescriptions for disaster. Ice sheet disintegration is nonlinear, spurred by amplifying feedbacks. We suggest that ice sheet mass loss, if warming continues unabated, will be characterized better by a doubling time for mass loss rate than by a linear trend. Satellite gravity data, though too brief to be conclusive, are consistent with a doubling time of 10 years or less, implying the possibility of multi-meter sea level rise this century. Observed accelerating ice sheet mass loss supports our conclusion that Earth's temperature now exceeds the mean Holocene value. Rapid reduction of fossil fuel emissions is required for humanity to succeed in preserving a planet resembling the one on which civilization developed.

Padgett, Part II: Emergence of Partnership

A previous post described the basic outline of one of the most important ideas I've come across in the past decade: Padgett's use of multi-network perspective to explain organizational invention. This post provides a concrete illustration of that perspective in action through the emergence of a system of economic partnership in Renaissance Florence. Future posts, exploring the theory in more abstract terms, will refer back to this material as a way of making the ideas more understandable. For more details .... read on.

Padgett, Emergence of Organizations and Markets, Part I

I'm an idea junkie. I have fairly diverse interests and scan lots of things. But, for the most part, the things I come across strike me as either generally similar to this or that idea I've come across before or interesting and novel but focused on a matter that isn't terribly consequential.

It is very rare that I come across something that strikes me as fundamental -- in the sense that it addresses a deep problem in a novel way that makes intuitive sense to me. I count three instances in the past decade:
1) Holling's conceptualization of the operation of systems in terms of panarchy.
2) Abbott's description of the process of knowledge change.
3) The ideas discussed in the rest of this post.

How do new organizational forms originate? As a visit to the Oil Drum, or virtually any other major environmental website shows, you see lots of calls for dramatic social transformation. And you can also find good studies documenting historical change in organizational form, for example Chandler's analysis of capitalist managerial organization. But these works, in a Darwinian sense, focus on the selection process -- why a novel form gets perpetuated -- rather than the innovation process -- how the novel form originated. While there is some discussion of where novel ideas come from, the focus tends to be on technological innovation. In many ways it makes sense to speak of bureaucracy as a technology, but it is profoundly problematic to think that the process of social innovation is just a mirror of the process of technological innovation. So, to summarize, a general recognition of the need for social innovation -- that is the development of new forms of social organization -- coupled with virtually no understanding of how novel forms of social organization are generated.

Into this gaping hole walks John Padgett and his collaborators with their forthcoming Princeton University Press title The Emergence of Organizations and Markets. In a work of stunning scope, they lay out a general theory of organizational innovation and then proceed to provide a diverse set of illustrative examples covering the sweep of modern history -- from early capitalism (the emergence of merchant banks, the partnership system, global markets and the joint stock system), thru studies of communist economic transition (China, USSR, Hungary), to the emergence of new linkages between science and capitalism (e.g., the emergence of Silicon Valley and other high tech research network clusters). While the book focuses on economic organization, there is no obvious reason the theory wouldn't apply to other types of organization.

In essence, Padgett translates the biochemical theory of autocatalysis, which Stuart Kauffman has argued provides a model for abiogenesis (the emergence of biological life out of inorganic matter), into a form useful for understanding the emergence of organizations. There are three key parts. First, there is the emergence of novelty. In the abiogenesis example, this would be the emergence of life out of inorganic chemicals. Second, there is the requirement of persistence through time. If life emerged, but failed to persist over many generations, then its development would be transient and not terribly important. Third, the new development must be consequential. The emergence of a single-celled living organism that persisted for millions of years but remained the only form of living organism, as significant as that example of life may be, is not as consequential as the development of a wide variety of diverse living organisms (cats, dogs, whales, people, birds, insects, etc.). I'm not talking about the value of the different organisms themselves, but rather the importance of a process which spills over into other areas and generates greater and greater variety.

In the process, Padgett comes up with a new way of thinking about individual humans: not as static, bounded objects but, rather, as dynamic flows of chemicals, energy and information embedded in networks. The implications for scholars interested in understanding conjoined socio-ecological systems, which have been plagued by the attempt to integrate ecology's focus on flows (energy flows and biogeochemical cycles) with approaches to human systems that emphasize the primacy of individuals and their actions, are hard to overestimate.

Here is how they organize the theoretical discussion:

First, we describe the problem of organizational novelty in the context of multiple social networks. Next, we explain our core dynamic motor of autocatalysis, at the levels of both chemical and economic production. Then, we extend the biochemical concept of autocatalysis to encompass the social production of persons. Fourth, we describe six network mechanisms of organizational genesis that we have discovered in our case studies. Fifth, we identify seven mechanisms of multiple-network catalysis that turned these organizational innovations into systemic inventions. Finally, we discuss the important outstanding issue of structural vulnerability to network tipping. This question of poisedness, for us, is the next research horizon.

There is way too much here for one post. So, there will be several spread out over time. If you can't wait, the key theoretical chapter is here. Padgett's homepage has links to other material, both published and unpublished, including several other chapters from the forthcoming book.  For an extended overview, including description of the multi-network perspective,  read on ....

Defining Uncertainty, Difficulty, and Complexity

A useful taxonomy from Scott E. Page's Uncertainty, Difficulty, and Complexity (Journal of Theoretical Politics 20(2): 115–149 DOI: 10.1177/0951629807085815)

My point is that uncertainty differs from difficulty and complexity and that they too matter. ... (U)ncertainty refers to the absence of information about some relevant variable or what some call the state of the world – tomorrow’s weather, to give one example, is uncertain. Difficulty refers to problems that have many interacting variables. Developing a workable form of fusion is difficult as is designing an office building or writing a piece of legislation. Difficult problems have many possible solutions and no obvious best one a priori. When faced with a difficult problem, most problem solvers prove incapable of finding the optimum. They must rely on perspectives and heuristics (Page, 2007). Complexity in turn differs from difficulty. Complexity refers to dynamic environments that contain multiple actors who interact with one another. The response to a natural disaster like a hurricane creates a complex web of interactions whose outcomes are unpredictable. Complex environments cannot be solved in any sense. At best, the complexity can be harnessed (Axelrod and Cohen, 2000).

The relevance of the definitions is outlined in the abstract below:

In this article I clarify the often muddled distinctions between uncertainty, difficulty, and complexity and show that all three can enhance our understanding of institutional performance and design. To cope with uncertainty, institutions align incentives for information revelation; to handle difficult problems, institutions create incentives for diverse problem-solving approaches; and to harness complexity, institutions adjust selection criteria, rates of variation, and the level of connectedness. The distinction between complex systems and equilibrium systems also necessitates a discussion of the differences between the existence, stability, and attainment of equilibria and why, despite often being neglected, the latter two concepts are important to our understanding of institutions.

Google+ goes Social: Why Should You Care?

Wired's Steven Levy has a long, but very insightful article, Inside Google+ — How the Search Giant Plans to Go Social, describing the company's recent rollout of Google+ and the company's long term plans to transform itself and its products in light of Facebook's success. This may well be great for Google's bottom line, but bad for humanity. Here's why.

Without going too deeply into the details of the new product vision (you'll have to read the article for that), there are two main components to Google+. The stream looks a lot like a Facebook feed: a stream of social information from friends and others -- but with a significant tweak. Google thinks they have solved the privacy issue by allowing you to easily create different 'Circles' of friends and, hence, share different information with different people. The second element, Sparks, is a stream of information on topics of interest to the user. But, unlike the results of the standard Google search, the Sparks search algorithms have been tweaked to deliver content that is fresh, visual and viral. In other words, there is a designed synergy: the Sparks stream brings things to your attention that you will want to share with one or more of your Circles.

Overall, the stream and Sparks are indications of how the need to respond to the social challenge has already changed Google’s philosophy. It’s almost as if the Emerald Sea team is creating an anti-Google. Before starting the company in 1998, Page and Brin had tried to sell their technology to portals like Excite and Yahoo, whose execs refused because the Google search engine was deemed too effective: It would fulfill a users’ requests and then briskly send them on their way, taking their lucrative eyeballs with them. Google insisted that search quality trumped stickiness, and built a business on the premise that users were best served by getting results that sent them off to preferred destinations.

But with these streams, Google is changing direction. Right now, the content from Sparks and the social stream is not intermingled, but it’s reasonable to assume that before long, the company will use its algorithmic powers to produce a single flow that skillfully mixes those apples and oranges. Google has already pulled off a much more complicated version of that trick with Universal Search, which includes web pages, images, videos, books, Tweets, news items and other formats among its results. And that’s only the beginning. With its deep resources of information about its users, Google is capable of delivering a comprehensive collection of information, tailored exactly to one’s needs and interests. “It’s the long-term vision that we have for that newsfeed, that stream,” Gundotra says. “We think long-term, four to five years from now, the system should be putting items in there not just from your friends, but things that Google knows you should be seeing.”
...
This mother of all streams would be the equivalent of an intravenous feed of information, with inclusion of all the vital content from our social graph and the world at large (Google calls this the “interest graph”). It would scroll forever, and everything would be relevant. If Google’s original goal was to expeditiously dispatch us elsewhere, with this near-clairvoyant stream, Google could turn us into search potatoes who never leave.

So, Google is changing its business model. Why should we care? To appreciate the significance of this, we need to take a short trip into the world of evolutionary anthropology. Conventional wisdom over the past 160 years in the cognitive and neurosciences has assumed that brains evolved to process factual information about the world. Over the past decade, that view has largely been displaced by the social brain hypothesis. Crudely put, the traditional view held that human information processing capabilities (our brain and related language skills) evolved in relation to adaptive pressures that favored the sharing of technical information and the ability to socialize came as an added benefit. The social brain hypothesis turns this explanation on its head: the selective adaptation was sociability -- specifically the ability to use language in order to bond in groups larger than than those of other primates which bond by touch through sequential grooming. According to the social brain hypothesis, the ability to share technical information is an extra bonus, not the primary purpose of language. This model corresponds much better with how people actually act: we spend 3-4 times as much of our day using language to socialize (How are you?) as we do using it to exchange technical information (That will be $29.95, please.)

In short, Google is recognizing that the primary focus of human communication is social rather than technical and adapting their product to take that into account. This could well be a good move for Google's bottom line, but it is a potentially bad development for humanity. We are facing a set of complex and interconnected challenges -- climate change, population growth, biodiversity loss, improperly regulated economies, new diseases, increasing inequalities in the distribution of income, the end of cheap energy supplies, the list goes on and on -- that require innovative thought and action to successfully address. And pretty much everyone who studies the process of innovation (see, for example, the clip below), argue that innovations come from places that allow ideas to have sex -- that is to interact with one another in ways that will generate something new.



In sum, innovation typically depends on connecting ideas that weren't previously seen as related to each other. The new Google framework, however, is designed to do the exact opposite -- to take all the information and knowledge that they have about you as an individual and deliver to you only those things they think will be of interest. I don't want to be a Luddite here. People are creative and will find new and interesting ways to use Google's social turn to their advantage.

But, everything considered, this development seems to negatively impact the likelihood that ideas will have sex in any individuals head in several ways. First, it turns an efficient search engine into another time suck. Second, while nominally facilitating connectivity (a good thing) it will actually tend to limit the breadth of information that the person receives and the types of connections their mind makes. Think, for example, of the difference between a) receiving a newspaper, scanning all the headlines and deciding to read something totally unexpected and b) receiving a feed limited to specific topics that, as a result, doesn't bring you the article in an area outside your interest. Third, it will take development dollars away from Google's previous focus -- technical search algorithms -- and, potentially, limit future developments in that area.

In a McLuhanesque sense, a new technology creates a new environment. My fear is that, everything considered, the environment created by Google+ encourages protected sex over unprotected sex and, as a result, is less likely to produce new offspring. In a world that desperately needs innovation, this is not a positive development

On the Royal Wedding, Emergence and Political Discourse in the US and Britain

I had no plan to watch the royal wedding this morning, but it happened to coincide with my morning coffee. And, as I watched, I was struck by a peculiar philosophical difference between American and British political discourse and its relation to the concept of emergence.

While the wedding had lots of pomp and ceremony, I was surprised at the clear, explicit and didactic nature of the statements made by the Cardinal. He literally provided numbered reasons for the benefits of marriage ... one, to have children; two ...; etc. It was not just a ceremony for those involved, it was also a message to the British about the intended role of the Royals. And, looking at the larger structure of the discourse, here is what was articulated:

1) The wedding ceremony involved a 'fusion' of William, Kate and God. Significantly, the point was made explicitly and repeatedly that the result of this process -- making God an operative element in their lives -- should not be seen as forcing them to 'conform' to a particular moral or ethical code. Rather, fusing themselves with God was meant to be transformative -- to provide them with access to the Holy in a way that allows them to navigate novel situations with a moral compass informed by God about what is good and what is not.

2) Later in the ceremony, there was some explicit discussion of the future and, in particular, the obligations of the Royals in facilitating it. This was, as one would expect in a nation where formal political decisions rest with the elected officials in Parliament, a bit more subtly phrased -- but the intended meaning was still clear. The Royals, using their moral compass informed by God, have an important role to play in defining the direction of British society and, interestingly, its relation to nature.

I don't know squat about Anglican doctrine, previous royal weddings or any particular role that Charles (and his well known green bent) might have had in crafting the reference to preserving nature. But the general philosophical tone of the ceremony was clear: rather than leading through adherence to a rigid code, the Royals are expected to channel God and provided enlightened and emergent leadership that addresses novel problems in novel ways.

This view is, interestingly, in stark contrast with American political discourse where the dominant theme has to do with the infinite wisdom of the founding fathers and the merit in finding solutions that conform to their ideas. This orientation is most explicitly present in the legal philosophy of 'strict constructionism' but the invocation of the founding fathers is a standard trope in Congress as well.

So this is where the English speaking world currently finds itself. On one side of the pond, moral leadership is invested in an in-bred group of Royals who are expected to channel God in order to help Britain find its way. But they have no formal political power. On the other side of the pond, God has been replaced by a group of long dead men -- the founding fathers -- as the source of enlightened governance. Moreover, rather than adaptively addressing new problems, solutions are found though conformity to the ideas of the founders. Personally, I don't find either of those options -- adaptive leadership based on the Divine right of Kings and the spiritual connection of the Royals to God or rigid doctrinal adherence to a civil authority constrained to the ideas of a group long dead men -- particularly attractive. Can't we some how find a way to get adaptive governance integrated into a civil authority structure?

Complexity and Forecasting the Future

Predictions of the future are notoriously prone to error. Up to this point, two major accounts have dominated discussions of why that is the case.

The classic explanation, offered by William Ascher in Forecasting: An Appraisal for Policy Makers and Planners provides detailed evidence documenting 1) that the core assumptions incorporated into the forecasting model are far more important than the methodology used, 2) that 'assumption drag' (the continued reliance on old and outdated core assumptions) is responsible for many of the most spectacular failures of prediction and 3) that failure to incorporate expert judgment (e.g. through the use of a method that mechanically applies past trends and structural relations without incorporating 'intuition') typically decreases the accuracy of the forecast.

The second account, presented in Phillip Tetlock's Expert Political Judgment: How Good Is It? How Can We Know?, extends the emphasis on subtlety and complexity implicit in Ascher's recognition of the importance of expert judgment. Tetlock divides experts into two types -- hedgehogs who are poor at making accurate forecasts and foxes who are more successful. Low scorers look like hedgehogs: thinkers who “know one big thing,” aggressively extend the explanatory reach of that one big thing into new domains, display bristly impatience with those who “do not get it,” and express considerable confidence that they are already pretty proficient forecasters, at least in the long term. High scorers look like foxes: thinkers who know many small things (tricks of their trade), are skeptical of grand schemes, see explanation and prediction not as deductive exercises but rather as exercises in flexible “ad hocery” that require stitching together diverse sources of information, and are rather diffident about their own forecasting prowess.

Here is more from Tetlock himself:



Aside from the emphasis on complexity and subtlety, both these accounts share a materialist philosophy. They see forecasting as the act of understanding and predicting the empirical world of human activity and the institutions created, reproduced, or destroyed by that activity. The success of the forecast is wrapped up in the match between the sophistication and 'core assumptions' of the forecasting model and the material operation of the world.

The flip side of materialist philosophy is idealism -- the notion that ideas cause behavior rather than the other way around. This is the fundamental way in which complexity theorist John Casti's Mood Matters distinguishes itself from previous assessments of the practice of forecasting.

Here is some material from the book's website, followed by Casti's summary of the book's main thesis. There is an interesting correspondence between Casti's argument and the emerging recognition within the environmental community that effective climate change policy can't be forged around a doom and gloom philosophy (if you don't enact cap-and-trade, we're doomed) discussed here a few weeks ago.

Most people think that the outcome of elections causes the mood of the country to change. The opposite is true: The mood of the country determines the outcome of elections.

Most people think that a plethora of happy popular music on the charts makes the public happy and that a plethora of depressing popular music on the charts makes the public depressed. The opposite is true: A happy public pushes happy songs up the charts, and a depressed public pushes depressing songs up the charts.

Most people think that a productive economy makes people optimistic and that an unproductive economy makes people pessimistic. The opposite is true: Optimistic people make a productive economy, and pessimistic people make an unproductive one. Most people think that peace makes people content and tolerant while wars make people angry, fearful and patriotic. The opposite is true: Content and tolerant people make peace, while angry, fearful and patriotic people make war.

It sounds so simple, yet no one in the social sciences has made this case-until now. Mood Matters tells the story of why human events happen the way they do and not some other way, showing how it is the collective mood of a population, its social mood, that biases the events that we can expect to see. If you want to understand how information flows from the individual human impulse to herd together in groups to the overall social mood in a population that gives rise to events, read this book!