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Check out Jamais Cascio’s direct (and very short) exposition of an important distinction between sustainability and resilience.

Sustainability is inherently static. It presumes there’s a point at which we can maintain ourselves and the world, and once we find the right combination of behavior and technology that allows us some measure of stability…Resilience, conversely, accepts that change is inevitable and in many cases out of our hands, focusing instead on the need to be able to withstand the unexpected.

- from The Next Big Thing: Resilience By Jamais Cascio

He lists some of the key attributes or values to honor when designing resilient systems: diversity, redundancy, decentralization, collaboration, transparency, graceful failure, flexibility, and foresight.

I often interact with people who are very attached to “getting it right” or at least to how others are “getting it wrong.” Getting it right is great; and I thoroughly enjoy the moments when I do. But a winning strategy is knowing what to do when you get it wrong.  ”If you want to increase your success rate, double your failure rate.” (Tom Watson) doesn’t just mean failing is okay, or even that the statistics are constant so endure the failures. It means you develop the ability to work off of the failures, understand the world through them and change the way you go at things.

One of the items in the list, fail gracefully, is a concept found in software development. Exception frameworks are common in most useful software languages and provide a mechanism for the developer to create a graceful path to failure for situations that are never supposed to happen like diving by zero, or addressing memory that doesn’t exist.  This idea has increased the usability of software many times over.

Also in software development practice, we have gone through a change of emphasis from foresight toward flexibility.  In software project management practices of 10 years ago, it was common to attempt to specify all details of a software project up front.  This ignored the inevitable education that comes from building a system and interacting with it over time.  Many software teams have moved to agile development methods–allowing later specification and more flexibility in the direction of software products as they are being built.  The potential problem with foresight is that seeing the future isn’t useful, it is the danger of committing to a particular (necessarily narrow) future and losing flexibility.

My experience pars with Cascio’s observations.  How to move these ideas into local community planning, city planning,  power grid evolution, communications grid evolution and other vital systems?

I have a friend who often admonishes people to hold their beliefs lightly. Our perceptions can be distorted by habit, framing, context, etc.  I like to think that with concentration and awareness, I can see what is. And so, this color illusion drives me nuts!  The problem? The “green” and “blue” spirals are actually the same color…

Colored spirals - not quite what it appears

Hat Tip: Bad Astronomy, TED Blog, James Cascio, (…nerds run in packs?)

I mentioned the collective effects foreshadowed by the Programmable LED paper and posted a related video in Coupling and Synchronization Video.  Here is the result of some great work by tinkerlog to make it happen.  He had to exchange the resistive sensor for a more senisitive photodiode and had circuit boards made for an Arduino version.  Nice work.

64 Fireflies Part II from tinkerlog on Vimeo.

In the February 2009 issue of SEED (no. 20), there is an interview the Bush administration science adviser John Marburger.   Marburger is reductive at the wrong level–to the point of seeming to believe in fundamental relativism.  He fails to make meaningful epistemological distinctions between science, policy, ethics and opinion.

His [Bush's] position on stem cells was attacked as a scientific position, when in fact it’s an ethical position.

This seems to convolve “what you can know” with “what you ought to do with it”.  Bush’s stem cell policy seemed to me to place significant restrictions on finding things out based on political/social squeamishness, rather than what one can do in the practice of reproductive or therapeutic medicine.  Science is about what we can know–ethics is about what we ought to do with it.

The biggest threat to that science is the inexorable growth of the mandatory budget for Social Security, Medicare, and other programs. The growth of mandatory budget is squeezing everything.  It is squeezing science, infrastructure, renewal.

This policy chip appears suddenly in the middle of an discussion of science policy. Is this the best forum for rolling out that discussion? Or is that an attempt to manipulate emotions? That statement is severely reductive.  It reaches straight to black and white bi-value thinking by positioning a stark opposition of science vs. society (cannot fund both!).  What about society vs. the military? Or any other reductive choosing of a single competing expense?  A more systemic approach would enable more workable policies.

Speaking of scientist Jim Hansen:

He’s a controversial person because he’s one of the few scientists who’s willing to state his opinion. It makes me a little nervous because of he authority as a scientist. Whenever science is recruited in the service of opinion, it makes me very nervous. Everybody wants to use the credibility of science to bolster their opinions.  And I don’t like that.  I try to avoid getting into that trap in this office.

What is he talking about here? Which scientists don’t express their opinions? Which scientists have not had their opinions informed by their science? Which scientists haven’t fallen into the natural pattern of allowing their opinions to dictate what scientific questions they find most compelling?  How does Marburger think science works?

Science is a human endeavor.  People have values.  People have creative sparks, some of which are born out through the process of science and some which are proven to be non-sense or un-falsifiable.  That is why the discovery-explanation-review process is vital to science. Open communication, sharing results, “arguing it out” is the only way to get science done.  This quote betrays a very naive model of the workings of scientific discovery, the nature of scientific creativity and a disappointing disregard for the people and process of science.

Sending out a quick ping to prove I am still alive.   I picked this from the garden this morning…

The term “Smart Grid” is losing meaning.  With the stimulus package, highly funded startups, government pilot projects and renewed national energy awareness, the term is used everywhere and used to justify almost everything energy-related.

What is a “Smart Grid?” Or nationally speaking, what is THE “Smart Grid?”  What is smart about it?  I am not going to survey the smart grid, but rather hope to point to and contrast two fundamental ideas behind the smart grid.

To start, I propose drawing a distinction between two concepts behind the Smart Grid :

(1) information access and transparency
(2) centralized control structures

Behind idea (1), people know more about energy consumption, load, demand, pricing and they know it faster and cheaper.

The idea underlying (2) is a bit more abstract.  The concept here is that only a central player has knowledge of the effects of the collective behavior of separate energy consumers and that this central player can make decisions about individual behaviors to make the system more efficient. This is often the illusion of centralized planning and control of complex systems. It usually results in systems that are optimized until they become brittle.

Our experience is pretty consistent that sufficiently complex systems are rarely understood and managed successfully in terms of what engineers think of as (linear) feed back and control.  We like to think that free markets are the proven answer to centralized and planned economies and that the history of the last 100 years has proven our side.  I think of these systems as “top-down.”

I propose that what we want from our national power grid is a system that is efficient enough and resilient.  And that that resilience will come from a bottom-up smart grid. A key attribute of resilient systems is that at various scales of the system, decisions contribute to the survival of the local and global system at the same time.

Notice that we violated this idea with a mortgage system in which mortgage brokers provided mortgages to consumers and sold them to investors on the same day, assuming no risk of default. Not surprisingly, mortgage brokers stopped being concerned with default, putting the entire system in peril.

These two ideas regarding information access and central control should be seen as entire Worlds apart.  The repeated conflation of the two ideas in the current thinking on energy distribution and consumption is dangerous. It seems clear from a systems engineering perspective that central control ought to be based on adequate information.  Adequate information coming in the form of appropriate models of system behavior and relevant access to real-world data.  From repeated experience, it is also clear that people and companies will hide information to maintain control and power as defined by success outside the system the information is about (e.g. Enron).

Robust, emergent, adaptive systems, on the other hand, may rely on the similar looking “information access and transparency” structures to the top-down systems, but have none of the centralized, coordinated control structures of the top-down system. Instead, these bottom-up systems rely on essentially local (in both proximity and meaning) decisions, where the resilient structure of the system emerges from the behavior of individual agents.

The “smarts” in the Smart Grid will come from agents acting on readily accessible (cheap, timely, relevant) information (#1 above) and not centralized, top-down control (#2 above).  Mixing the two ideas is confusing us, distracting us and slowing us down. A smart grid will emerge as a robust system relying on alignment between local and global objectives of the energy consumers and produces.

Earlier this year, Rod Beckstrom released a research paper “A New Model for Network Valuation.”  In his model Beckstrom proposes that the value of a network is the sum of the benefits to networked individuals minus the cost to networked individuals.  From a perspective, this is obviously true.

However, I have some criticisms of this model:

(1) He proposes this be called “Beckstrom’s Law” analogously to Metcalfe’s Law Law and Reed’s Law Laws (discussed here previously here and here).

What is a “law” good for?  In the case of other soft laws–the most famous being Moore’s Law–the value of the law comes from a scaling relation that gives some rough predictive capabilities independent of scale.  Moore’s law is a soft law because there isn’t anything about the physics of the problem that allows deriving Moore’s scaling law.  But the social/business network has behaved this way for decades.  Moore’s law is widely believed to break down as we reach the power and frequency limits of conventional integrated circuits manufacturing methods.   The physics of current circuit manufacturing techniques says that either Moore’s law will stop being an accurate predictor of the scaling of computation power, or we will make a significant shift in the physics we are using.

Thus a law provides predictive and analytical power:  How does this change as that changes?  Even a law being violated (i.e. a model breaking down) gives us signals indicating when important shifts in the relevance of a model to a social or physical system are occurring.

“Beckstrom’s Law” isn’t a law in this sense.  It derives no scaling relations.  Further, you get an idea of how difficult it is to make estimates of value in this accounting system when Beckstrom starts his discussion of phone network value by discussing the “upper limit” as GDP.

(2) Beckstrom’s Rules of Accounting. Useful laws provide an “abstraction jump.”  With powerful models, can accumulate the particulars of a system and use this leverage to understand and manipulate at a different scale, i.e., we can derive laws. For example, we sum up the location and amount of all the little masses in the earth and calculate the acceleration of falling objects at the surface of the earth–the theory allows us to think about acceleration of falling objects at the surface of the earth without worrying about the location and mass of all the little grains of stuff that make up the earth.  Keeping track of all the pieces of the earth is “accounting” (vital to the theory) but the power of the law comes from the leverage of the abstractions derived.  Beckstrom’s law doesn’t seem to have this quality. It doesn’t derive high-leverage abstractions from underlying structure.  It is an accounting method.

(3) Beckstrom’s valuations are essentially relative.  In particular, benefits and costs are calculated compared to the prevailing contextual way of performing the transactions.  They depend on the path through history rather than depending on form. In his example of Amazon.com, for example, the benefits of for a user of Amazon.com are realized in savings over driving to the local bookstore. He points out that Amazon.com also gets a “network benefit” from the transaction because they have wholesale and operational costs less than the purchase price of the book.  These are “benefits” of Amazon.com over the local bookseller based on cost savings for a commodity assumed to be available through both channels.

Before you discount this problem with a “what else is there?” consider that the system of agents and interactions may reach a novel state through losses–a new state not used in the relative benefits calculation.  The network may enable new states of the system that were not contemplated before the existence of the network.  This has important implications for the network security arguments in Beckstrom’s paper.  Losses may exceed benefits.  More importantly, losses may come from reaching system states that were not visited on the way to realizing benefits.

Backing this thinking up a bit, we realize that relative gains pose a problem too: the system of agents and interactions may enable transactions that were not possible in the prevailing contextual way of performing transactions.  Then the benefits can no more be calculated than the losses using an accounting method.

(4) Transactions don’t seem to neatly belong to a single network. In the Amazon.com example, presumably, Beckstrom was calculating the value of the Internet, or maybe just the World-Wide Web. Or maybe the value of the network of the network of roads allowing me to drive to the store vs. the value of the Internet?  Or maybe the network of all booksellers (since they all buy from the same publishers)?  Which network does the transaction belong to?  Or do we allocate a fraction of the transaction to each of the overlapping networks that can be argued to enable the benefits of the transaction?

(5) Very carefully (!) add historical benefits to predicted losses. (I think Taleb would argue never!) These are apples and oranges. See multiple explanations of the current state of the world’s banking system.  Beckstrom argues that “optimal security investment occurs on the loss function line where it is tangent to the 45 degree line, or where one dollar of security investment equals one dollar of decrease in expected losses.”  This is true in two places on Beckstrom’s curve–once for very low security investment and again for moderate security investment.  In fact, my losses are bounded by “everything I value” for $0 security spending (I can lose everything, and no more). So the existence of two inflexion points seems accurate and how we choose between them based on Beckstrom’s arguments isn’t clear.

How do we know the curve doesn’t look like this all the way down?

"Pathelogical" risk-cost curve. Risk Investment on the x-axis; Cost of losses on the y-axis.

More troubling is that security investment is historical accounting while the loss curve represent one of the infinite possible world lines.  What if security spending creates value rather than merely stemming losses–then we have more to lose than when we started security spending.  Maybe the curve looks like this?

Risk-cost region. Risk Investment on the x-axis; Cost of losses on the y-axis.

What will a law describing network value look like?

• Scaling laws from underlying size, structure and dynamics
• It is highly likely that the values of constants essential to practice will be missing from the law.  Actual risk and benefit calculations will depend on the particulars of the system we are analyzing.
• Provide some level of causal explanation for the generative, emergent qualities we observe from real networks every day.  It seems clear that networks enable new transactions. So using relative transaction value seems to miss the most exciting source of value from networks. A useful network value law will say something about the way that the future is not always a simple extrapolation of the past.
• (There may be no such law–then maybe Beckstrom’s “Rules of Accounting” are the best we can do.)

Where's the USB jack on this thing?

WattsGoingDown requires a low cost solution for putting energy measurements on the Internet. Our short-term plan is to use off-the-shelf hardware to create and deploy systems that send energy use data to our servers every minute or two.  A key driver of the software-as-a-service (SaaS) energy monitoring business model will be the balance of the savings realized by customers offset by the cost of the initial installation and WGD’s monthly monitoring fees.

When the costs are justified purely by energy savings, monitoring system costing a few hundreds of dollars per monitored circuit only makes sense for higher-voltage, high current systems, or for circuits that are representative of many in the building or facility.

This is because, for a single circuit, the power used scales as voltage and current,  P= V x I . This is why business models centered on home energy monitoring are difficult to justify based on energy savings alone–customers have to be data geeks to make them work.  A typical home circuit runs at 120 volts and up to 20 amps.  This gives a maximum capacity of 2400 Watts per circuit (about two hair dryers).

Over a year, a home owner might run this circuit at capacity a quarter of the time (365 days x 6 hrs/day = 2200 hours).  At $0.10 per kWH, the total cost of running this example circuit is 2200 hours x 2400 Watts x 0.10 cents/kWH = $528.  If energy monitoring enables savings of 25% (aggressive!), you can pay no more than $132 per circuit for the hardware and monitoring to reach 1-year break even.

Without utility or government subsidy, the Tendril system costs quite a bit more. The Kill-A-Watt can be purchased for about $35 but provides no logging, network or analytics.  There is a clever hack, the tweet-a-watt, that adds a wireless Xbee module to the Kill-A-Watt, but you’ll need to be comfortable with soldering and analyzing the raw data yourself. The typical commercial network/analytic-enabled solutions run $750+ (e.g. WattNode + PLC).

So one challenge will be to create low-cost, dependable web-enabled measuring devices, devices so cheap that there will be no hesitation to install them and no motivation to remove them.

I am a fan of all three: I identify with Priestley’s addiction to coffee, his outlook on discovery (and he was actually chased down by a mob with pitchforks and torches that burned his house down). Johnson’s book is a good read and a little different than your average science biography. And Colbert…

It is more efficient to move information than energy.

One of the challenges (and advantages!) of our ubiquitous use of energy in the form of electricity is the separation of energy generation from consumption.  We generate electricity where there is readily available gravitational, wind, chemical etc. energy then  transport it to high concentrations of people.

For the system to be efficient and robust, the network must have generation, routing and consumption optimizations at many scales.  More on this later, but this requires all parties–including end users–to have appropriate and complete data about energy flows.

Watts Going Down’s tag line is “Energy Intelligence.”  We think energy intelligence lets the users of electrical power self-organize to use energy more efficiently and effectively for their businesses.  Here are some principles we hold to that will enable energy intelligence:

• Transparency.  It has been lacking in our banking system to dire consequences.  It is lacking in the details of your monthly power bills.  One number for energy and one number for the price is not transparent information.  How can you make better usage decision on that?
• Moving information is more efficient than moving energy.  This is true today, and WDG will widen the gap in the coming months.
• Decision support.  Numbers are worth much if they can’t be assembled into a meaningful story.  We don’t learn by concentrating harder on numbers, we learn by integrating what numbers mean into our experience.
• Energy monitoring is infrastructure. Current solutions are meant to be spot checks.  They are temperamental, expensive and designed to be temporary.  WGD is building a system that is hosted by WGD, uses low-cost Internet-connected sensors and provides the up-time, security and continuously growing capability of a hosted ASP solution dedicated to energy intelligence.