GDP's … something?

While the government is shut down, it seems like a good time for a rousing round of Alan Atkisson‘s GDP Song:

The shutdown means GDP measurements are on ice, which is not all bad, though we can expect a 15 basis point drag on GDP per week to include some real harm.

Shutting down our measurement systems strikes me as alarmingly close to turning off the instruments on the flight deck of a plane, due to a route dispute between the pilot and copilot.

Equity, equality and positive feedback

I’m reflecting on Deborah Rogers‘ presentation on equity/equality at the Balaton Group meeting, concerning the apparent evolutionary drivers of the transition from a long human prehistory of egalitarian societies to today’s extreme inequity. A key point of terminology is that equity and equality are not quite the same thing – equality implies similar wealth or resource access, while equity implies something more like Rawlsian justice. But you can’t have one without the other, because inequality leads the haves to tilt the tables of justice against the have-nots.

This might not be a deliberate choice to exploit the masses. It could occur as an evolutionary consequence of the inability to predict the outcome of dynamically complex decisions.

I once described a complex theory of the emergence of inequality to Donella Meadows. I no longer remember the details, but perhaps it was the ancestor of this. Her answer was characteristically simple and insightful, to the effect of, “it doesn’t matter what the specific dynamics are, because the rich control the decisions, so the question boils down to how much inequ(al)ity the elite will tolerate.”

Evidence indicates that high inequality is bad for growth, so a possible irony is that policies that transfer wealth to the wealthy in the short run are bad for them in the long run, because growth eventually dominates allocation, even for the richest.

So, for me, the key question for society is, how much positive feedback should a civilization build into its social organization?

A bit of positive feedback can be helpful, if it creates a gradient that guides individuals who aren’t making the best decisions to imitate the habits of their more successful peers.

However, this probably requires a relatively low level of inequality. As soon as there’s stronger positive feedback, it’s likely that dysfunctional feedbacks take hold, as the wealthiest institutions use their market power to block innovation and good governance in service of maintaining their exalted positions.

I think the evidence that this occurs today is probably fairly simple. Look at the distribution of IQs or any other metric that might be an input to productivity in the economy. It’ll be relatively Normal (Gaussian). But the distributions of wealth and power are heavy tailed (Zipf or Double Laplace). That’s a pretty clear indication that there’s a lot of reinforcing feedback at work.

Are there limits?

Several people have pointed out Erle Ellis’ NYT opinion, Overpopulation Is Not the Problem:

MANY scientists believe that by transforming the earth’s natural landscapes, we are undermining the very life support systems that sustain us. Like bacteria in a petri dish, our exploding numbers are reaching the limits of a finite planet, with dire consequences. Disaster looms as humans exceed the earth’s natural carrying capacity. Clearly, this could not be sustainable.

This is nonsense.

There really is no such thing as a human carrying capacity. We are nothing at all like bacteria in a petri dish.

In part, this is just a rhetorical trick. When Ellis explains himself further, he says,

There are no environmental/physical limits to humanity.

Of course our planet has limits.

Clear as mud, right?

Here’s the petri dish view of humanity:

I don’t actually know anyone working on sustainability who operates under this exact mental model; it’s substantially a strawdog.

What Ellis has identified is technology.

Yet these claims demonstrate a profound misunderstanding of the ecology of human systems. The conditions that sustain humanity are not natural and never have been. Since prehistory, human populations have used technologies and engineered ecosystems to sustain populations well beyond the capabilities of unaltered “natural” ecosystems.

Well, duh.

The structure Ellis adds is essentially the green loops below:

Of course, the fact that the green structure exists does not mean that the blue structure does not exist. It just means that there are multiple causes competing for dominance in this system.

Ellis talks about improvements in adaptive capacity as if it’s coincident with the expansion of human activity. In one sense, that’s true, as having more agents to explore fitness landscapes increases the probability that some will survive. But that’s a Darwinian view that isn’t very promising for human welfare.

Ellis glosses over the fact that technology is a stock (red) – really a chain of stocks that impose long delays:

With this view, one must ask whether technology accumulates more quickly than the source/sink exhaustion driven by the growth of human activity. For early humans, this was evidently possible. But as they say in finance, past performance does not guarantee future returns. In spite of the fact that certain technical measures of progress are extremely rapid (Moore’s Law), it appears that aggregate technological progress (as measured by energy intensity or the Solow residual, for example) is fairly slow – at most a couple % per year. It hasn’t been fast enough to permit increasing welfare with decreasing material throughput.

Ellis half recognizes the problem,

Who knows what will be possible with the technologies of the future?

Somehow he’s certain, even in absence of recent precedent or knowledge of the particulars, that technology will outrace constraints.

To answer the question properly, one must really decompose technology into constituents that affect different transformations (resources to economic output, output to welfare, welfare to lifespan, etc.), and identify the social signals that will guide the development of technology and its embodiment in products and services. One should interpret technology broadly – it’s not just knowledge of physics and device blueprints; it’s also tech for organization of human activity embodied in social institutions.

When you look at things this way, I think it becomes obvious that the kinds of technical problems solved by neolithic societies and imperial China could be radically different from, and uninformative about, those we face today. Further, one should take the history of early civilizations, like the Mayans, as evidence that there are social multipliers that enable collapse even in the absence of definitive physical limits. That implies that, far from being irrelevant, brushes with carrying capacity can easily have severe welfare implications even when physical fundamentals are not binding in principle.

The fact that carrying capacity varies with technology does not free us from the fact that, for any given level of technology, it’s easier to deliver a given level of per capita welfare to fewer people rather than more. So the only loops that argue in favor of a larger population involve the links from population to increase learning and adaptive capacity (essentially Simon’s Ultimate Resource hypothesis). But Ellis doesn’t present any evidence that population growth has a causal effect on technology that outweighs its direct material implications. So, one might much better say, “overpopulation is not the only problem.”

Ultimately, I wonder why Ellis and many others are so eager to press the “no limits” narrative.

Most people I know who believe that limits are relevant are essentially advocating internalizing the externalities that comprise failure to recognize limits, to guide market allocations, technology and preferences in a direction that avoids constraints. Ellis seems to be asking for an emphasis on the same outcome, technology or adaptive capacity to evade limits. It’s hard to imagine how one would get such technology without signals that promote its development and adoption. So, in a sense, both camps are pursuing compatible policy agendas. The difference is that proclaiming “no limits” makes it a lot harder to make the case for internalizing externalities. If we aren’t willing to make our desire to avoid limits explicit in market signals and social institutions, then we’re relying on luck to deliver the tech we need. That strikes me as a spectacular failure to adopt one of the major technical breakthroughs of our time, the ability to understand earth systems.

Update: Gene Bellinger replicated this in InsightMaker. Replication is a great way to force yourself to think deeply about a model, and often reveals insights and mistakes you’d never get otherwise (short of building the model from scratch yourself). True to form, Gene found issues. In the last diagram, there should be a link from population to output, and maybe consuming should be driven by output rather than capital, as it’s the use, not the equipment, that does the consuming.

There's just enough time

In response to the question, “is there still time for a transition to sustainability,” John Sterman cited Donella Meadows,

The truth of the matter is that no one knows.

We have said many times that the world faces not a preordained future, but a choice. The choice is between different mental models, which lead logically to different scenarios. One mental model says that this world for all practical purposes has no limits. Choosing that mental model will encourage extractive business as usual and take the human economy even farther beyond the limits. The result will be collapse.

Another mental model says that the limits are real and close, and that there is not enough time, and that people cannot be moderate or responsible or compassionate. At least not in time. That model is self-fulfilling. If the world’s people choose to believe it, they will be proven right. The result will be collapse.

A third mental model says that the limits are real and close and in some cases below our current levels of throughput. But there is just enough time, with no time to waste. There is just enough energy, enough material, enough money, enough environmental resilience, and enough human virtue to bring about a planned reduction in the ecological footprint of humankind: a sustainabil­ity revolution to a much better world for the vast majority.

That third scenario might very well be wrong. But the evidence we have seen, from world data to global computer models, suggests that it could conceivably be made right. There is no way of knowing for sure, other than to try it.

Tipping points

The concept of tipping points is powerful, but sometimes a bit muddled. Things that get described as tipping points often sound to me like mere dramatic events or nonlinear effects, simple thermodynamic irreversibilities, or exponential signals emerging unexpectedly from noise. These may play a role in tipping points, and lead to surprises, but I don’t think they capture the essence of the idea. You can see examples (good and bad) if you sift through the images describing tipping points on google.

I think of tipping points as a feedback phenomenon: positive feedback that amplifies a disturbance, such that change takes off, even if the disturbance is removed. The key outcome is a system that is stable or resistant to disturbances up to a point, beyond which surprising things may happen.

A simple example is sitting in a chair. The system has two stable equilibria: sitting upright, and lying flat on your back (tipped over). There’s also an unstable equilibrium – the precarious moment when you’re balanced on the back legs of the chair, and the force of gravity is neutral. As long as you lean just a little bit, gravity is a restoring force – it will pull you back to the desirable upright equilibrium if you pick up your feet. Lean a bit further, past the unstable tipping point, and gravity begins to pull you over backwards. Gravity gains leverage the further you lean – a positive feedback. Waving your arms and legs won’t help much; you’re going to be flat on your back.

A more generalized explanation is given  in catastrophe theory. The interesting twist is that a seemingly-stable system may acquire tipping points unexpectedly as its parameters drift into regimes that create new stable and unstable points, leading to surprises. Even without structural change to the system, its behavior mode can change unexpectedly as the state of the system moves from locally-stable territory to locally-unstable territory, which occurs due to shifting loop dominance from nonlinearities. (Think of the financial crisis and some kinds of aircraft accidents, for example.)

Anyone know some nice, simple tipping point models? I think I’ll have to mine my archives for some concrete examples…

Fortress USA

The Fortress World scenario came up in Bert de Vries’ presentation at the Balaton meeting today. It’s a dystopian global future in which the rich retreat into safe havens (a macro version of gated communities) while the rest of the world degenerates into some combination of feudal subsistence, resource extraction and chaos.

On dark days, looks increasingly to me like this is already playing out in the US with the disappearance of the middle class.

The drivers of rising inequity in the US seem fairly simple. With globalization, capital has become mobile while labor remains tied to geography. So, capital investment flees high wage countries (US) and jobs follow. Asset income goes up, because capital is leveraged by cheaper labor and has good bargaining power among hungry host countries. There’s downward pressure on rich world wages, because with less capital per capita employed, the marginal productivity of labor is lower.

It’s not all bad for the rich world working class, because cheaper goods (WalMart) offset wage losses to some degree. If asset and wage income were uniformly distributed, there might even be a net benefit.

However, asset income and wages aren’t uniformly distributed, so income disparity goes up. Pre-globalization, this wasn’t so noticeable, because there was an implicit deal, in which wage earners knew that, even if they didn’t own all the capital in their country, at least they’d be the beneficiaries of it in some sense through employment and trickle down. Free trade and mobile capital turns the deal into a divorce, which puts a sharp point on questions of property rights allocations that were never quite fair, and sows the seeds of future discontent among the losers.

So far, everyone appears to be committed to pursuing this thread to its logical conclusion. Probably most are unwitting participants; workers are as enthusiastic about offshoring of capital in their pension funds as are the captains of industry.

However, it seems to me that there are several corrosive effects. The asset-owning rich appear convinced that their windfall has arrived because they’re smart, that the misfortunes of the masses are due to laziness. Their incentive to invest in services like education for labor they don’t need is no longer palpable. Uneducated masses are easier to manipulate anyway. Meanwhile the masses are desperate (if misguided) to lower tax burdens in order to compete with offshore labor.

The ultimate effect seems likely to hollow out the human capital of the rich world, leaving only tycoons and serfs, with perhaps a few protected sectors of the economy (pilots for tycoons’ jets). But is that a plausible end-state for this game?

If I were an American tycoon endowed with a little enlightened self interest, I’d be worried about several ways things could go wrong:

  • Increasing income disparity and loss of human capital cause a loss of civility at home, requiring wealthy enclaves to become desperate armed camps.
  • Political turbulence abroad leads to loss of control of all that capital that went overseas.
  • The global economy reaches such a vast physical scale that no amount of personal wealth provides adequate insulation against its side effects.

These outcomes could be triggered or amplified by financial or ecological stress. Even if you don’t care about equity or social justice per se, these possibilities seem like a great reason to invest in human and social capital at home and abroad.

The Insidious Dynamics of Driving to School

When I passed by my old high school a few years ago, I was astonished to see that they’d paved over a nice grass field to make room for a vast parking lot, which must be for students. There’s really no excuse for driving to school in Palo Alto, CA – the weather is great, it’s flat, and no one lives more than a couple miles away.
Most of the responsibility falls to this nest of positive feedback loops:

I’ll start with a perception: parents worried about the safety of their kids start driving them to school (or, in Palo Alto, buy them a BMW so they can drive themselves). All that extra driving adds to traffic density, reinforcing the perceived danger on the roads. Over the long haul, all that traffic demands more lane space, so bike lanes and sidewalks get crowded out. And who wants to bike next to a bunch of hot, smelly tailpipes?

The more students drive, the less fit they get, which diminishes the fun of riding. They also become less tolerant of weather – in spite of Gore Tex, a lot of people react to a little water falling from the sky like the Wicked Witch of the West.

The result of all this is a kind of phase transition – at some point, conditions are right for all these positive loops to kick in, and everyone shifts from bike-dominated transport to driving.

This transition should not be irreversible, if one is patient. One can move the point at which the phase transition occurs, to encourage bicycling. I think there are two leverage points. First, a society that can afford cars for kids can afford to provide Dutch- or Danish-style traffic separation, breaking the safety loop and decreasing the attractiveness of driving by removing traffic lanes, which causes congestion until people go back to bikes. Second, make the cars pay for the infrastructure they use and the environmental and safety externalities they cause. Once people are back on bikes, they’ll get fitter and healthier, and the positive loops will help lock in a more sustainable mode.

Inspired by a comment in Bert de Vries’ talk this morning at the 30th Balaton Group meeting.

The GDP Song

In SD, we often talk about the pitfalls of managing systems with delays and feedback while paying attention to the wrong indicators. The classic example is navigating a car at high speed in the fog on an icy road by looking in the rearview mirror.

A related problem is managing your system to maximize the wrong goals, e.g. running the economy by a problematic metric like GDP. Here’s Alan AtKisson’s musical take on that:

Sharing Systems

I’m at the 30th Balaton Group meeting this week. A group of us just put our heads together to think about online approaches to teaching and sharing systems thinking and systems modeling. The basic question was, if you needed thousands of systems thinkers in a hurry, how could you scale up systems education quickly?

My list of interesting things people might want to do online:

  • Model building
    • Group model building (in the spirit of SUNY Albany work)
    • Collaborative modeling (e.g., a distributed team working on federated modules of a model, but not necessarily involving the client and group conceptualization processes)
    • Collaborative causal loop diagramming
    • Model code sharing and reuse
  • Model consumption
    • Online games (playing through a simulation in real time) – possibly multiplayer
    • Online simulations (interactive experimentation with a model) – possibly with a social aspect as at Climate Colab

Much can already be done through online model services like Forio and other means. However, I think there’s a lot more to be done. In particular, we’re weak on providing shared model transparency and quality control for any but the simplest models.

Some interesting systems & sustainability online learning links that came up in the conversation:

Tim Jackson on the horns of the growth dilemma

I just ran across a nice talk by Tim Jackson, author of Prosperity Without Growth, on BigIdeas. It’s hard to summarize such a wide-ranging talk, but I’d call it a synthesis of the physical (planetary boundaries and exponential growth) and the behavioral (what is the economy for, how does it influence our choices, and how can we change it?). The horns of the dilemma are that growth can’t go on forever, yet we don’t know how to run an economy that doesn’t grow. (This of course begs the question, “growth of what?” – where the what is a mix of material and non-material things – a distinction that lies at the heart of many communication failures around the Limits to Growth debate.)

There’s an article covering the talk at, but it’s really worth a listen at