Serendipity

I’ve been following a heated discussion on twitter this past week about a planned protest on Sunday in the UK, in which environmentalists plan to destroy a crop of genetically modified wheat being grown as part of a scientific experiment at Rothamsted, in Hertfordshire (which is, incidentally, close to where I grew up). Many scientists I follow on twitter are incensed, calling the protest anti-science. And some worry that it’s part of a larger anti-science trend in which the science on issues such as climate change gets ignored too. In return, the protesters are adamant that the experiment should not be happening, no matter what potential benefits the research might bring.

I’m fascinated by the debate, because it seems to be a classic example of the principle of complementarity in action, with each group describing things in terms of different systems, and rejecting the others’ position because it makes no sense within their own worldview. So, it should make a great case study for applying boundary critique, in which we identify the system that each group is seeing, and then explore where they’ve chosen to draw the boundaries of that system, and why. I think this will make a great case study for my course next month.

I’ve identified eight different systems that people have talked about in the debate. This is still something of a work in progress (and I hope my students can extend the analysis). So here they are, and for each some initial comments towards a boundary critique:

1. A system of scientists doing research. Many scientists see the protests as nothing more than irrational destruction of research. The system that motivates this view is a system of scientific experimentation, in which expert researchers choose problems to work on, based on their expectation that the results will be interesting and useful in some way. In this case, the GM trials are applied research – there is an expectation that the modified wheat might lead to agricultural improvements (e.g. through improved yield, or reduced need for fertilizer or pesticide). Within this system, science is seen as a neutral pursuit of knowledge, and therefore, attempts to disrupt experiments must be “anti-knowledge”, or “anti-science”. People who operate within this system tend to frame the discussion in terms of an attack on a particular group of researchers (on twitter, they’ve been using the hashtag #dontdestroyresearch), and they ask, pointedly, whether green politicians and groups condone or condemn the destruction. (The irony here is that the latter question itself is, itself, unscientific – it’s a rhetorical device used in wedge politics – but few of the people using it acknowledge this). Questions about whether certain kinds of research are ethical, or who might yield the benefits from this research lie outside the boundary of this system, and so are not considered. It is assumed that the researchers themselves, as experts, have made those judgments well, and that the research itself is not, and cannot be, a political act.
2. A system of research ethics and risk management. If we expand the boundaries of system 1 a little, we see a system of processes by which scientific experiments are assessed for how they manage the risks they pose to be public. Scientific fields differ in their sophistication for how they arrange this system. In the physical sciences, the question often doesn’t arise, because the the research itself carries no risk. But in medical and social sciences, processes have arisen for making this judgement, sometimes in response to a disaster or a scandal. Most research institutions have set up Internal Review Boards (IRBs) who must approve (or prevent) research studies that poses a risk to people or ecosystems. My own research often strays into behavioural science, so I frequently have to go though our ethics approval process. The approvals process is usually frustrating, and I’m often surprised at some of the modifications the ethics board asks me to make, because my assessment of the risk is different to theirs. However, if I take a step back, I can see that both the process and the restrictions it places on me are necessary, and that I’m definitely not the right person to make judgements about the risks I might impose on others in my research. The central question is usually one of beneficence: does the value of the knowledge gained outweigh any potential risk to participants or others affected by the study? Some research clearly should not happen, because the argument for beneficence is too weak. In this view, the Rothamsted protest is really about democratic control of the risk assessment process. If all stakeholders aren’t included, and the potential impact on them is not taken seriously, they lose faith in the scientific enterprise itself. In the case of GMOs, there’s a widespread public perception (in the UK) that the interests of large corporations who stand to profit from this research are being allowed to drive the approvals process, and that the researchers themselves are unable to see this because they’re stuck in system 1. I’ve no idea how true this is for GMO research, but there’s plenty of evidence that’s it’s become a huge problem in pharmaceutical research. Medical research organizations have, in the last few years, taken significant steps to reduce the problem, e.g by creating registers of trials to ensure negative results don’t get hidden. The biotech research community appear to be way behind on this, and much research still gets done behind the veil of corporate secrecy. (The irony here is that the Rothamsted trials are publicly funded, and results will be publicly available, making it perhaps the least troublesome biotech research with respect to corporate control. However, that visibility makes it an easy target, and hence, within this system, the protest is really an objection to how the government ran the risk assessment and approval process for this experiment).
3. A system of ecosystems and contaminants that weaken them. Some of the protesters are focused more specifically on the threat that this and similar experiments might pose on neighbouring ecosystems. In this view, GMOs are a potential contaminant, which, once released into the wild cannot ever be recalled. Ecosystems are complex systems, and we still don’t understand all the interactions that take place within them, and how changing conditions can damage them. Previous experimentation (e.g. the introduction of non-native species, culls of species regarded as pests, etc), have often been disastrous, because of unanticipated system interactions. Within this system, scientists releasing GMOs into the wild are potentially repeating these mistakes of the past, but on a grander scale, because a GMO represents a bigger step change within the system than, say, selective breeding. Because these ecosystems have non-linear dynamics, bigger step changes aren’t just a little more risky than small step changes; they risk hitting a threshold and causing ecosystem collapse. People who see this system tend to frame the discussion in terms of the likelihood of cross-contamination by the GMO, and hence worry that no set of safeguards by the researchers is sufficient to guarantee the GMO won’t escape. Hence, they object to the field trials on principle. This trial is therefore, potentially, the thin end of the wedge, a step towards lifting the wider ban on such trials. If this trial is allowed to go ahead, then others will surely follow, and sooner or later, various GMOs will escape with largely unpredictable consequences for ecosystems. As the GMOs are supposed to have a competitive advantage of other related species, once they’ve escaped, they’re likely to spread, in the same way that invasive species did. So, although the researchers in this experiment may have taken extensive precautions to prevent cross-contamination, such measures will never be sufficient to guarantee protection, and indeed, there’s already a systematic pattern of researchers underestimating the potential spread of GMO seeds (e.g. through birds and insects), and of course, they routinely underestimate the likelihood of human error. Part of the problem here is that the researchers themselves are biased in at least two ways: they designed the protection measures themselves, so they tend to overestimate their effectiveness, and they believe their GMOs are likely to be beneficial (otherwise they wouldn’t be working on them), so they downplay the risk to ecosystems if they do escape. Within this system, halting this trial is equivalent to protecting the ecosystems from risky contamination. (The irony here is that a bunch of protesters marching into the field to destroy the crop is likely to spread the contamination anyway. The protesters might rationalize it by saying this particular trial is more symbolic, because the risk from any one trial is rather low; instead the aim is to make it impossible for future trials to go ahead)
4. A system of intellectual property rights and the corresponding privatization of public goods. Some see GMO research as part of a growing system of intellectual property rights, in which large corporations gain control of who can grow which seeds and when. In Canada, this issue became salient when Monsanto tried suing farmers who were found to have their genetically modified corn planted in their fields, despite the fact that those farmers had never planted them (it turned out the seeds were the result of cross-contamination from other fields, something that Monsanto officially denies is possible). By requiring farmers to pay a licence fee each year to re-plant their proprietary seeds, these companies create a financial dependency that didn’t exist when farmers were able to save seeds to be replanted. Across developing countries, there is growing concern that agribusiness is gaining too much control of local agriculture, creating a market in which only their proprietary seeds can be planted, and hence causing a net outflow of wealth from countries that can least afford it to large multi-national corporations. I don’t see this view playing a major role in the UK protests this week, although it does come up in the literature from the protest groups, and is implicit in the name of the protest group: Take The Flour Back.
5. An economic system in which investment in R&D is expected to boost the economy. This is the basic capitalist system. Companies that have the capital invest in research into new technologies (GMOs) that can potentially bring big returns on investment for biotech corporations. This is almost certainly the UK government’s perspective on the trials at Rothamsted – the research should be good for the economy. It’s also perhaps the system that motivates some of the protesters, especially where they see this system exacerbating current inequalities (big corporations get richer, everyone else pays more for their food). Certainly, economic analysis of the winners and losers from GM technology demonstrate that large corporations gain, and small-scale farmers lose out.
6. A system of global food supply and demand, in which a growing global population, and a fundamental limit on the land available for agriculture, place serious challenges on how to achieve a better match of food consumption to food production. In the past, we solved this problem through two means: expanding the amount of land under cultivation, and through the green revolution, in which agricultural yields were increased by industrialization of the agricultural system and the wide-scale use of artificial fertilizers. GMOs are (depending on who you ask) either the magic bullet that will allow us to feed 9 billion people by mid-century, or, more modestly, one of many possible solutions that we should investigate. In this system, the research at Rothamsted is seen as a valuable step towards solving world hunger, and so protesting against it is irrational. The irony here is that improving agricultural yields is probably the least important part of the challenge of feeding 9 billion people: there is much more leverage to be had in solving problems of food distribution, reducing wastage, and reducing the amount of agricultural land devoted to non-foods.
7. A system of potential threats to human health and well-being. Some see GMOs as a health issue. Potential human health effects include allergies, and cross-species genetic transfer, although scientists dismiss both, citing a lack of evidence. While there is some (disputed) evidence of such health risks already occurring, on balance this is more a concern about unpredictable future impacts, rather than what has already happened, which means an insistence on providing evidence is irrelevant: a bad outcome doesn’t have to have already occurred for us to take the risk seriously. If we rely on ever more GMOs to drive the global agricultural system, sooner or later we will encounter such health problems, most likely through increased allergic reaction. Allergies themselves have interesting systemic properties – they arise when the body’s normal immune system, doing it’s normal thing, ends up over-reacting to a stimulus (e.g. new proteins) that is otherwise harmless. The concern here, then, is that the reinforcing feedback loop of ever more GM plant variants means that, sooner or later, we will cross a threshold where there is an impact on human health. People who worry about this system tend to frame the discussion using terms such as “Frankenfoods“, a term that is widely derided by biotech scientists. The irony here is that by dismissing such risks entirely, the scientists reduce their credibility in the eyes of the general public, and end up seeming even more like Dr Frankenstein, oblivious to their own folly.
8. A system of sustainable agriculture, with long time horizons. In this system, short term improvements in agricultural yield are largely irrelevant, unless the improvement can be demonstrated to be sustainable indefinitely without further substantial inputs to the system. In general, most technological fixes fail this test. The green revolution was brought about by a massive reliance on artificial fertilizer, derived from fossil fuels. As we hit peak oil, this approach cannot be sustained. Additionally, the approach has brought its own problems, including a massive nitrogen pollution of lakes and coastal waters, and poorer quality soils, and of course, the resulting climate change from the heavy use of fossil fuels. In this sense, technological fixes provide short term gains in exchange for a long term debt that must be paid by future generations. In this view, GMOs are seen as an even bigger step in the wrong direction, as they replace an existing diversity in seed-stocks and farming methods with industrialized mono-cultures, and divert attention away from the need for soil conservation, and long-term sustainable farming practices. In this system, small scale organic farming is seen as the best way of improving the resilience of the global food production. While organic farming sometime (but not always!) means lower yields, it reduces dependency on external inputs (e.g. artificial fertilizers and pesticides), and increases diversity. Systems with more diverse structures tend to be more resilient in the face of new threats, and the changing climates over the next few decades will severely test the resilience of our farming methods in many regions of the world.  The people who worry about this system point to failures of GMOs to maintain their resistance to pests. Here, you get a reinforcing feedback loop in which you need ever more advances in GMO technology to keep pace with the growth of resistance within the ecosystem, and with each such advance, you make it harder for non-GMO food varieties to survive. So while most proponents of GMOs see them as technological saviours, in the long term it’s likely they actually reduce the ability of the global agricultural system to survive the shocks of climate change.

Systems theory leads us to expect that these systems will interact in interesting ways, and indeed they do. For example, systems 6 and 8 can easily be confused as having the same goal, but in fact, because the systems have very different temporal scales, they can end up being in conflict: short-term improvements to agricultural yield can lead to long term reduction of sustainability and resilience. Systems 6 and 7 can also interfere – it’s been argued that the green revolution reduced world starvation and replaced it with widespread malnutrition, as industrialization of food production gives us fewer healthy food choices. Systems 1 and 4 are often in conflict, and are leading to ever more heated debates over open access to research results. And of course, one of the biggest worries of some of the protest groups is the interaction between systems 2 and 5: the existence of a large profit motive tends to weaken good risk management practices in biotech research.

Perhaps the most telling interaction is the opportunity cost. While governments and corporations, focusing on systems 5 & 6, pour funding and effort into research into GMOs, other, better solutions to long term sustainability and resilience, required in system 8, become under-invested. More simply: if we’re asking the wrong question about the benefit of GMOs, we’ll make poor decisions about whether to pursue them. We should be asking different questions about how to feed the world, and resources put into publicly funded GMO research tend to push us even further in the wrong direction.

So where does that leave the proposed protests? Should the trials at Rothamsted be allowed to continue, or do the protesters have the right to force an end to the experiment, by wilful destruction if necessary? My personal take is that the experiment should be halted immediately, preferably by Rothamsted itself, on the basis that it hasn’t yet passed the test for beneficence in a number of systems. The knowledge gain from this one trial is too small to justify creating this level of societal conflict. I’m sure some of my colleague will label me anti-science for this position, but in fact, I would argue that my position here is strongly pro-science: an act of humility by scientists is far more likely to improve the level of trust that the public has in the scientific community. Proceeding with the trial puts public trust in scientists further at risk.

Let’s return to that question of whether there’s an analogy between people attacking the biotech scientists and people attacking climate scientists. If you operate purely within system 1, the analogy seems compelling. However, it breaks down as soon as you move to system 2, because the risks have opposite signs. In the case of GMO food trials, the research itself creates a risk; choosing not to do the research at all (or destroying it if someone else tries it) is an attempt to reduce risk. In the case of climate science, the biggest risks are on the business-as-usual scenario. Choosing to do the research itself poses no additional risk, and indeed reduces it, because we come to understand more about how the climate system works.

The closest analogy in climate science I can think of is the debate over geo-engineering. Many climate scientists objected to any research being done on geo-engineering for many years, for exactly the reason many people object to GMO research – because it diverts attention away from more important things we should be doing, such as reducing greenhouse gas emissions. A few years back, the climate science community seems to have shifted perspective, towards the view that geo-engineering is a desperate measure that might buy us more time  to get emissions under control, and hence research is necessary to find out how well it works. A few geo-engineering field trials have already happened. As these start to gain more public attention, I would expect the protests to start in earnest, along with threats to destroy the research. And it will be for all the same reasons that people want to destroy the GM wheat trials at Rothamsted. And, unless we all become better systems thinkers, we’ll have all the same misunderstandings.

Update (May 29, 2012): I ought to collect links to thought provoking articles on this. Here are some:

• Rothamsted: things I’ve learned, things I want to know « tom chance’s blog
• New Statesman – Lessons from Rothamsted
• Caroline Allen for a Green London: Green Party and Science: The Truth…?
• Why protests against the GM foods field trials is pro-science | Liberal Conspiracy

Sometime in May, I’ll be running a new graduate course, DGC 2003 Systems Thinking for Global Problems. The course will be part of the Dynamics of Global Change graduate program, a cross-disciplinary program run by the Munk School of Global Affairs.

Here’s my draft description of the course:

The dynamics of global change are complex, and demand new ways of conceptualizing and analyzing inter-relationships between multiple global systems. In this course, we will explore the role of systems thinking as a conceptual toolkit for studying the inter-relationships between problems such as globalization, climate change, energy, health & wellbeing, and food security. The course will explore the roots of systems thinking, for example in General Systems Theory, developed by Karl Bertalanffy to study biological systems, and in Cybernetics, developed by Norbert Wiener to explore feedback and control in living organisms, machines, and organizations. We will trace this intellectual history to recent efforts to understand planetary boundaries, tipping points in the behaviour of global dynamics, and societal resilience. We will explore the philosophical roots of systems thinking as a counterpoint to the reductionism used widely across the natural sciences, and look at how well it supports multiple perspectives, trans-disciplinary synthesis, and computational modeling of global dynamics. Throughout the course, we will use global climate change as a central case study, and apply systems thinking to study how climate change interacts with many other pressing global challenges.

I’m planning to get the students to think about issues such as the principle of complementarity, and second-order cybernetics, and of course, how to understand the dynamics of non-linear systems, and the idea of leverage points. We’ll take a quick look at how earth system models work, but not in any detail, because it’s not intended to be physics or computing course; I’m expecting most of the students to be from political science, education, etc.

The hard part will be picking a good core text. I’m leaning towards Donnella Meadows’s book, Thinking in Systems, although I just received my copy of the awesome book Systems Thinkers, by Magnus Ramage and Karen Shipp (I’m proud to report that Magnus was once a student of mine!).

Anyway, suggestions for material to cover, books & papers to include, etc are most welcome.

I’ll be giving a talk to the Toronto section of the IEEE Systems Council on December 1st, in which I plan to draw together several of the ideas I’ve been writing about recently on systems thinking and leverage points, and apply them to the problem of planetary boundaries. Come and join in the discussion if you’re around:

Who’s flying this ship? Systems Engineering for Planet Earth

Thurs, Dec 1, 2011, 12:00 p.m. – 1:00 p.m, Ryerson University (details and free registration here)

At the beginning of this month, the human population reached 7 billion people. The impact of humanity on the planet is vast: we use nearly 40% of the earth’s land surface to grow food, we’re driving other species to extinction at a rate not seen since the last ice age, and we’ve altered the planet’s energy balance by changing the atmosphere. In short, we’ve entered a new geological age, the Anthropocene, in which our collective actions will dramatically alter the inhabitability of the planet. We face an urgent task: we have to learn how to manage the earth as a giant system of systems, before we do irreparable damage. In this talk, I will describe some of the key systems that are relevant to this task, including climate change, agriculture, trade, energy production, and the global financial system. I will explore some of the interactions between these systems, and characterize the feedback cycles that alter their dynamics and affect their stability. This will lead us to an initial attempt to identify planetary boundaries for some of these systems, which together define a safe operating space for humanity. I will end the talk by offering a framework for thinking about the leverage points that may allow us to manage these systems to keep them within the safe operating limits.

What unites both the climate crisis and the financial crisis? What is it that has driven scientists and environmentalists to risk arrest in protests across the world? What is it that’s driven people from all walks of life to show up in their thousands to occupy their cities? In both cases, there’s a growing sense that the system is fundamentally broken, and that our current political elites are unable (rather than just unwilling) to fix them. And in both cases, it’s becoming increasingly apparent that our current political system is a major cause of the problems. Which therefore makes it even harder to discover solutions.

So how do we make progress? If we’re going to take seriously the problems that have led people to take to the streets, then we have to understand the processes that are steadily driving us in the wrong direction when it comes to things we care about – a clean environment, a stable climate, secure jobs, a stable economy. In other words, we have to understand the underlying systems, understand the dynamics within those systems, and we have to find the right leverage points that would allow us to change those dynamics to work the way we would like.

Failure to take a systems view is evident throughout discussions of climate change, and now, more recently, throughout mainstream media discussions about the Occupy protests. Suggestions for what needs fixing tend to focus on superficial aspects of the systems that matter, mainly by tinkering with parameters (emissions targets, stabilization wedges, the size of the debt, the bank interest rate, etc). If the system itself is broken, you can’t fix it by adjusting its current parameters – you have to look at the underlying dynamics and change the structure of the system that gave rise to the problem in the first place. Most people are focusing on the wrong leverage points. Even worse, in some cases, they are pushing in the wrong direction on some of the leverage points…

Perhaps the best analysis of this I’ve ever seen is Donella Meadows’ essay on leverage points. [If you're not already familiar with it, I highly recommend reading it before tackling the rest of this post]. Meadows has written some wonderfully accessible material on systems thinking, but only gives a very brief overview in this particular essay, because she’s focussing here on how to identify leverage points that allow one to alter a system. She identifies twelve places to look, and orders them, roughly, from the least effective to the most effective.

To illustrate the point, I’ll begin with a much simpler system than the ones we really want to fix. My example is the controllability of water temperature in a shower. The particular shower I have in mind is in a small hotel in Paris, and is a little antiquated, the result of old-fashioned plumbing. It takes time for the hot water to reach the shower head from the hot water tank, and there’s enough of a delay between the taps that control the hot and cold water and the temperature response, that you’re forever trying to adjust it to get a good temperature. It’s too cold, so you turn up the hot tap. The temperature barely seems to change, so you crank it up a lot. After a few minutes the water heats up so much it’s scalding. So you crank up the cold tap. Again, the temperature responds slowly until you realise it’s now too cold. You turn down the cold tap, and soon find it’s too hot again. And so on.

Does this remind you of the economy? Or, for that matter, the way the physical climate system works over the course of tens of thousands of years? More worryingly, it’s the outcome I expect if we ever try to geo-engineer our way out of extreme climate change. Right now, the human race is cranking up the hot tap. But the system responds very slowly. And by the time we’ve realized the heat has built up, we’ll have overshot our comfort zones. We’ll slam on the brakes and end up overcompensating. Because the system is just as hard to control (actually, a damn sight harder!) than that annoying shower in Paris.

Let’s look at how Meadows’ twelve leverage points might help us analyze the Parisian shower. At #12, we have what is usually the least effective place to seek change:

#12 Changes in constants, parameters, numbers. Example: Change the set point on the water tank thermostat. In general, such adjustments make no difference to the controllability of the shower. [There is an interesting exception, when we're prepared to make really big adjustments. For example, if we crank the thermostat on the hot water tank all the way down to 'pleasantly warm' we'll never have to balance the hot and cold taps again, we can just use the "hot" tap. Usually, such really big adjustments are unlikely to be made, for other reasons].

#11. Change the sizes of buffers and stocks relative to their flows. Example: Get a bigger hot water tank. This will make the energy bills bigger, but still won’t make the shower any more controllable.

#10. Change the structure of material stocks and flows. Example: Replace the water pipes with smaller diameter pipes. This might help, as it reduces the thermal mass of the pipes, and hence, may affect the lag between the water tank and the shower, leading to more responsive shower controls.

#9. Change the length of delays, relative to rate of system change. Example: Relocate the hot water tank closer to the bathroom; or Wait a little longer for temperature response to settle before touching the taps again. Such changes might be hard to achieve (hence they’re high up on the list), but very effective if we could do them.

#8. Increase the strength of negative feedback loops relative to the impacts they try to correct against. Example: Take a deep breath and calm down – you’re the negative feedback trying to keep the system stable. If you’re less impulsive on the taps, you’ll help to dampen the temperature fluctuations. If the system is changing too quickly, or is subject to instability, identifying the negative feedbacks, and working to strengthen them can often yield simple and effective leverage points. But when you’re in the shower getting scalded, it might be hard to remember this.

#7. Reduce the gain around positive feedback loops. Example: Replace the taps with ones that offer a finer level of control. This reduces the big temperature fluctuations when we turn the taps too quickly, and hence reduces the positive feedback loop that leads to temperature overshoot.

#6. Change the structure of information flows, to alter who does (or does not) have access to information. Example: Put an adjustable marker on the shower dial to record a preferred setting. Changing the flows of information about a system is generally much easier and cheaper than changing any other aspect of a system, hence, it’s often a more powerful leverage point than any of the above. For the shower, this one tiny fix may entirely cure the temperature fluctuation problem.

#5. Change the rules of the system (incentives, punishments, constraints). Example: Set limits on amount of time you can shower for. This might reduce the incentive to spend time fiddling with the temperature controls. But who will enforce the constraint?

#4. Nurture the power to add, change, evolve or self-organize system structure. Example: Teach yourself to tolerate a wider range of shower temperatures. or: Design a new automated temperature controller.

#3. Change the goal of the system. Example: Focus on getting clean quickly rather than getting the water to exactly the desired temperature. Of course, changing the goal of the system is hard, because it means changing people’s perceptions of the system itself.

#2. Change the mindset or paradigm out of which the system arises. Example: Is cleanliness over-rated? or: why stay in these antiquated hotels in Paris anyway? Paradigm shifts are hard to achieve, but when they happen, they have a dramatic transformative effect on the systems that arose in the previous paradigm.

#1. The power to transcend paradigms. Example: Learn systems thinking and gain the ability to understand a system from multiple perspectives; Realise that system structure and behaviour arises from the dominant paradigm; Explore how our own perspectives shape our interactions with the system.

Note that Meadows emphasizes the point that all twelve types of leverage point can be effective for changing systems, if you have a good understanding of how the system works, and can make good choices for where to make changes. However, in a self-perpectuating system, the dynamics that created the problem you’re trying to solve will also tend to defeat most kinds of change, unless they really do alter those dynamics in an important way.

Note that for many of these examples, I’ve chosen to include the person in the shower as part of my ‘shower system’. More importantly, some of my suggestions refer to how the person in the shower understands the shower system, and how her understanding of the system affects the system’s behaviour. This is to emphasize a mistake we often make when thinking about both the climate and the economy. In both cases, we have to understand the role that people play within these systems, and especially how our expectations and cultural norms themselves form part of the system. If people, in general, have the wrong mental model of how the systems work, it’s significantly more challenging to figure out how to fix things when they go wrong.

Let’s look at how the list of leverage points applies to the climate system and the financial system.

#12 Changes in Constants, parameters, numbers.

• Climate System: tighten pollution standards, negotiate stronger version of the Kyoto protocol, increase fuel taxes, etc.
• Financial System: change the interest base rates, increase size of stimulus spending, increase taxes, cut government spending, put caps on campaign contributions, increase the minimum wage, vote for the other party.

While changing the parameters of the existing system can make a difference, it’s rare that it does. Systems tend to operate in regions where small parameter adjustments make no difference to the overall stability of the system. If there’s a systemic effect that is pushing a system in the wrong direction (dependence on fossil fuels, financial instability, poverty, etc), then adjusting the system’s parameters is unlikely to make much difference, if you don’t also change the structure of the system.

None of these examples are likely to make much difference to the underlying problems. To understand this point, you have to understand the system you’re dealing with. For example, the whole problem of climate change itself might appear to be the result of a small parameter change – a small increase in radiative forcing, caused by a small increase (measured in parts per million!) in atmospheric concentrations of certain gases. But that’s not the real cause. The real cause is a systemic change in human activity that traces back to the industrial revolution: a new source of energy was harnessed, which then kicked off mutually reinforcing positive feedback loops in human population growth and energy use. A few more parts per million of CO2 in the atmosphere is not the problem; the problem is a new exponential trend that did not exist previously.

However, remember there’s sometimes an exception, if you make very large adjustments. For the climate system, you could increase fuel taxes so that gas (petrol) costs, say, ten times as much as it does today. Such an adjustment would be guaranteed to change the system (but not perhaps, when the mobs are done with you, in the way you intended). Here’s an interesting rule of thumb: if you change any parameter in a system by an order of magnitude or more, what you get is an entirely different type of system. Try it: a twenty-storey building is fundamentally different from a 2-storey house. A ten-lane freeway is fundamentally different from a single lane road. A salary of $1 million is fundamentally different from a salary of $100K.

#11. Change the sizes of buffers and stocks relative to their flows

• Climate System: Plant more forests to create bigger carbon sinks. Ocean fertilization and/or artificial trees to soak up carbon, etc.
• Financial System: Increase the federal reserve, require banks to hold larger reserves, increase debt ceiling limits.

In many systems these are hard to change, as they require large investments in infrastructure (the canonical example is a large dam to create a buffer in the water supply). This also means it can be hard to make frequent, fine-grained adjustments. More importantly, they tie up resources – keeping a large stock means that the stock isn’t working for you: your bigger water tank will make your energy bills much higher (and won’t affect your shower adjustment problem anyway). Making banks keep larger reserves will make them much less dynamic, and will reduce the funds available for lending.

All of these things might ease the problem a little, but none of them will make any significant difference to the cause of the problem. No matter how big you make the reserves, you’ll quickly be defeated by the exponential growth curves that you didn’t tackle.

#10. Change the structure of material stocks and flows

• Climate System: Carbon Capture and Storage (diverts emissions at the point they are generated, so they don’t enter the atmosphere).
• Financial System: Create a Tobin tax, which diverts a small percentage of each financial transaction to create a new pool of money to fix problems. Create new kinds of super-tax on the very rich. Separate the high street banks from their gambling investment operations.

Physical structure is also, usually, very hard to change, once the system is operating, although it’s sometimes easier to change how things flow than it is to create new buffers. However, both types of change tend to have limited impact, because the stocks and flows arise from the nature of the system – which means the system itself will find ways of defeating your efforts, for the same reason it ended up like it is now.

For example, separating high street banks from investment firms won’t really achieve much. People will find other ways to gamble bank money on foolish investments, if you haven’t actually addressed the reasons why such investments are made in the first place. Similarly for carbon capture and storage — diverting some percentage of the carbon that would go into the atmosphere via (expensive) CCS won’t help if our use of fossil fuels continues to grow at the rate it has done in the past. The fundamental problem of exponential growth in fossil fuel use will always outstrip our attempts to sequester some of it. There’s also the problem that on the timescales that matter (decades to centuries), it’s not clear the carbon will stay put. Oh, and CCS is only ever likely to be feasible on large, static sites like big power plants, so won’t make any different to emissions from transport, aviation, agriculture, etc.

#9. Change the length of delays, relative to rate of system change

• Climate System: Speed up widescale deployment of clean energy technologies. Speed up the legislative process for climate policy. Speed up implementation of new standards for emissions. Use a faster ramp up on carbon pricing. Lengthen the approval process for new fossil fuel plants, oil pipelines, etc.
• Financial System: Lengthen the approval process for risky loans, mergers, etc. Speed up implementation of government jobs programs. Slow down economic growth (to remove the boom and bust cycles).

Often, these kinds of change can be very powerful leverage points, but many of the delays in large systems are impossible to shorten – things take as long as they take. Also, as Meadows points out, most people try to shift things in the wrong direction, as many of these changes are counter-intuitive. For example, reducing the delay in money transfer times just increases chance of wild gyrations in the markets. Governments around the world usually seek to maximize economic growth, when often they should be trying to dampen it.

#8. Increase the strength of negative feedback loops relative to the impacts they try to correct against.

• Climate System: Make the price of all goods reflect their true environmental cost. Remove perverse subsidies to fossil fuel companies (the cost of extraction & processing should be a negative feedback loop on dependence on fossil fuels); introduce better monitoring and data collection for global carbon fluxs, to more quickly assess impacts of different actions.
• Financial System: More transparent democracy to allow people to vote out corrupt politicians more quickly; Remove subsidies, bailouts, etc (these distort the negative feedbacks that keep the financial system stable); protection for whistleblowers; more scrutiny of boardroom pay rises by unions and shareholders.

Negative feedbacks are what tend to keep a system stable. If the system is changing too quickly, or is subject to instability, identifying the negative feedbacks, and working to strengthen them, can often yield simple and effective leverage points. All of the examples here a likely to be more effective at fixing the respective systems than anything we’ve mentioned so far. Some of them rely on people acting as negative feedbacks. For example, by offering better protection for whistleblowers, you create a culture in which the people are less likely yield to corrupting influences.

#7. Reduce the gain around positive feedback loops

• Climate System: Higher energy efficiency standards (this dampens the growth in energy demand); Green development – mechanisms that allow people to improve their quality of life without needing to increase their use of fossil fuels; wider use of birth control to curb population increases.
• Financial System: Punish bankers who make reckless investment decisions (discourages others from following suit). Use a more progressive tax structure and introduce very high inheritance taxes (these prevent the rich from getting ever richer). High quality & free public education (to prevent the rich from forming privileged elites). Forgive all student loans on graduation (ends the cycle of individual indebtedness)
• Both Systems: Slower economic growth (above, I described this as a “length of delay” issue – as slower growth can allow other processes, such as clean energy technology to keep up; but more importantly, economic growth is itself a positive feedback loop that drives ever more resource consumption, financial fluctuations and environmental degradation.

Runaway feedback loops inevitably destroy a system, unless some negative feedback loops kick in. In the long run, a negative feedback always kicks in: we use up all the resources, we kill off most of the population, we bankrupt everyone. But by that time the system has already been destroyed. The trick is to dampen the positive feedbacks long before too much damage is done, and this is often much more effective than trying to boost the strength of countervailing negative feedbacks. For example, if we want to address inequality, using tax structures that stop the rich getting ever richer is much more effective than creating anti-poverty programs aimed at mopping up the resulting inequalities.

Economic growth is an important example here. Remember that economic growth is a measure of the change in GDP over time. And GDP itself is a measure of the volume of financial transactions, or in other words, how fast money is flowing through the system. Accelerating these money flows makes all the instabilities in the financial system much worse. Worse still, one of the primary ways that GDP grows is by ever accelerating consumption of resources, so you get a self-reinforcing positive feedback loop between over-consumption and economic growth.

#6. Change the structure of information flows, to alter who does (or does not) have access to information

• Climate System: Get IPCC assessment results out to the public faster, and in more accessible formats. Put journalists in touch with climate scientists. Label products with lifecycle carbon footprint data. Put meters in cars showing the total cost of each journey.
• Financial System: Publish pay and benefit rates for all employees of private companies. Publish details of all political donations. Increase government oversight of financial transactions.
• Both Systems: Open access to data, e.g. on campaign financing, carbon emissions, etc; Require all lobbyists and think tanks to publish full details on funding sources.

Changing the flows of information about a system is generally much easier and cheaper than changing any other aspect of a system, which means these can be very powerful leverage points. Many of these examples are powerful enough to cause significant changes to the underlying behaviour in the system, because they expose problems to the people that shape the behaviour of the system.

Providing people with full information about the true cost of things at the point they use them is a very powerful inducement to change behaviour. Meadows gives an example of electricity meters in the front hall of a home, rather than in the basement. Another one that bugs me is the information imbalance between different transportation choices. We always know how exactly how much a trip will cost by public transit, but the cost of driving is largely invisible (paying to fill up, paying the insurance and maintenance bills, etc are too far removed from the actual per-journey decisions). Some potential fixes are very simple: Google maps could show the cost, as well as the distance & time, when doing journey planning.

#5. Change the rules of the system (incentives, punishments, constraints)

• Climate System: Government grants for energy efficiency projects. Free public transit. Jail-time for executives whose companies break carbon emissions rules. Mandatory science comprehension testing for anyone standing for public office.
• Financial System: Give workers the right to vote on boardroom pay rates. New regulations on what banks can and cannot do with investors’ money. Remove immunity from prosecution for politicians. Ban all private funding of political campaigns.
• Both Systems: Strict limits on ownership of media outlets. New ethics rules for journalists and advertisers.

These changes tend to impact the behaviour of the system immediately (as long as they’re actually enforced), and hence can have very high leverage. Unfortunately, one of the problems with fixing both the climate and financial systems is that our systems for changing the rules (e.g. legislative processes) are themselves broken. Large corporations (especially fossil fuel companies) have, over a period of many years, deliberately co-opted legislative processes to meet their own goals. Where once it might have been possible for governments to pass new laws to address climate change, or to change the way governments allocate resources, now they cannot, because these processes no longer act in the interest of the people. Similarly, the mainstream media has been co-opted by the same vested interests, so that people are fed little more than propaganda about how great the system is.

#4. Nurture the power to add, change, evolve or self-organize system structure

• Climate System: Evidence-based policymaking. Resilient communities such as Transition Towns (these empower individual communities to manage their own process of ending dependence on fossil fuels).
• Financial System: Switch from private companies to credit unions and worker-owned cooperatives.
• Both Systems: Change to proportional representation for elections (this gives a more diverse set of political parties access to power, and helps voters feel their vote counts). Celebrate social diversity and give greater access to political power for minorities (this removes the tendency to have one dominant culture, and hence helps build resilience).

Systems gain the ability to evolve because of diversity within them. In biology, we understand this well – the diversity of an ecosystem is essential for evolutionary processes to work. Unfortunately, few people understand this matters just as much for social systems. Societies with a single dominant culture tend to find it very hard to change, while societies that encourage and promote diversity are also laying the foundations for new ideas and new forms of self-organization to emerge.

The political culture in the US is case in point. US politics is dominated by two parties that share an almost identical set of cultural assumptions, especially to do with the capitalist system, the role of markets, and the correct way to manage the economy. This makes the US particularly resistant to change, no matter how much the evidence accumulates that the system isn’t working.

#3. Change the goal of the system

• Climate System: Don’t focus on emission reduction, focus on eliminating each and every dependency on fossil fuels. Don’t focus on international negotiations towards a treaty, focus on zero-carbon strategies for each city or region.
• Financial System: Stop chasing short-term corporate profits as the primary goal of the economy. Corporations should aim for sustainability rather than growth. Instead of measuring GDP, measure gross national happiness.
• Both Systems: Recognize and challenge the hidden goals of the current system, such as: the tendency for large corporations to maximize their power and influence over national governments; the desire to control access to information via media conglomerates; the desire of ruling elites to perpetuate their control.

Of course, changing the goal of the system is hard, because it means changing people’s perceptions of the system itself. It requires people to be able to step outside the system and see it from a fresh perspective, to identify how the dominant goals of the system shape its structure and operation. In short, it requires people to be systems thinkers.

There’s one kind of goal change related to the climate system that troubles me: instead of trying to prevent climate change, we could instead focus on survival strategies for living on a hotter planet. Given what we understand of the impacts on food, water and habitable regions, this would only be possible for a much smaller human population, and so it entails giving up trying to save as many people as possible. But it’s a very simple leverage point. The problem is that there are both ethical and practical reasons to reject this approach. The ethical reasons are well understood. The practical problem is that humans are very effective at fighting like crazy over diminishing resources, so it’s hard to see how this approach would work in the face of growing waves of climate refugees.

#2. Change the mindset or paradigm out of which the system arises

• Climate and Financial Systems: Triple-bottom line accounting (forces companies to balance social and environmental impact with profitability). A shift in mindset from consumerism to living in harmony with the environment. A shift from material wealth as a measure of success to (say) social connectedness. A shift in mindset from individualism to community. A shift from individual greed to egalitarianism.

Paradigm shifts are hard to achieve, but when they happen, they transform the systems that arose in the previous paradigm. Much of the root cause of current problems with climate change and financial instability are due to the dominant paradigm of the last thirty years: blind faith in the free market to fix everything, along with accumulation of wealth and material assets as a virtue.

#1. The power to transcend paradigms

• Climate and Financial Systems: Learn systems thinking and gain the ability to understand a system from multiple perspectives; Realise that system structure and behaviour arises from dominant paradigm. Explore how our own perspectives shape our interactions with the system…
• … And then take to the streets.

Postscript: Notice that as we proceed down the list, and look at more fundamental changes to the systems, the solutions for climate change and the financial crisis start to merge. Also notice that in both cases, many of my examples aren’t what climate scientists or economists generally talk about. We need to broaden the conversation.

I introduced some ideas from systems thinking last month, and especially the idea of second order cybernetics: the study of how people’s perceptions of systems affect their ability to understand and control them. I want to pick up on this idea, because I think it’s crucial to understanding the predicament we’re now in with respect to climate change. The system of systems that we have to understand in order to grasp the challenges of climate change is so complex that naturally, everyone sees it a little differently.

When describing relatively simple systems, most people’s descriptions coincide to some degree. Typically, one person will give more detail than another, such that the simpler description is completely subsumed in the more detailed one. However, for more complex systems, different people’s descriptions tend to diverge more. For any reasonably complex system, it will be impossible to completely derive any one person’s description from another persons – each will offer unique details that the other missed. Weinberg dubs this the principle of complementarity in his book on General Systems Thinking: any two descriptions of a complex system are likely to be complementary.

Here’s a simple example – these two photos are of the same lake, but are complementary views:

The principle applies whenever we have partial descriptions of the world from our observers, and may disappear if we ask the observers to make increasingly detailed observations. Assuming they really are describing the same system, it should eventually be possible to reconcile their descriptions completely. For example, with a little effort, you can match up the peaks in the two photos above, and even some of the trees (it’s a little easier with the enlarged photos – click on them for bigger). Unfortunately, if the systems are complex enough, the descriptions can only ever be partial, and it may be infeasible to trace down every last detail in order to reconcile them.

When it comes to climate, the principle of complementarity works overtime. People end up talking past one another because they don’t even realise they’re describing the same systems – their descriptions appear to have no common ground. For example, one person might talk in terms of atmospheric carbon concentrations, and emphasize the need to stop using fossil fuels. Another person might talk in terms of the costs of climate policies, and the risk to the economy if we place a price on carbon. Because they don’t stop to explore how the systems they are describing inter-relate, they don’t understand that they are each focussing on just one part of a much larger system of systems.

And the problem is that most people are so embedded in a particular worldview, they are incapable of understanding the systems in the way that others see them. To illustrate the depth of this problem, consider this story from Bill Tomlinson’s book “Greening through IT“:

One day, when I was in graduate school, I was walking along a paved bicycle path near my Davis Square apartment in Somerville, Massachusetts, on the way to the T station (the Boston area subway). A father and son were walking a few yards in front of me. The boy was about four years old. He was running back and forth across the path, looking under rocks and investigating things. I saw him find something small, pick it up, and carry it over to his father. I heard the father say, “Oh, you found a snail!” I could feel a life lesson about to ensue. “Let’s see how far you can chuck that snail, Bobby!” (p109)

I feel a strong sense of revulsion towards this father, because my values are very different from his. I see the snail as a fascinating creature, to be studied and admired for its behaviours, and it’s interaction with the urban environment in which it lives – my kids and I have spend ages admiring how they wave their feelers and how they move. The father in the story sees the snail as part of a system of objects that can be hefted and thrown in sport. But this is just the principle of complementarity at work: we’re focussing on very different systems, which overlap. If we can’t step back and understand how our different values cause us to have complementary views of the ‘same’ system, then we’ll never manage to reach agreement on the broader goals of tackling a problem as complex as climate change.

Here’s an interesting article entitled “Decoding the Value of Computer Science” in the Chronicle of Higher Education. The article purports to be about the importance of computer science degrees, and the risks of not enough people enrolling for such degrees these days. But it seems to me it does a much better job of demonstrating the idea of computational thinking, i.e. that people who have been trained to program approach problems differently from those who have not.

It’s this approach to problem solving that I think we need more of in tackling the challenge of climate change.

I went to a workshop earlier this week on “the Future of Software Engineering Research” in Santa Fe. My main excuse to attend was to see how much interest I could raise in getting more software engineering researchers to engage in the problem of climate change – I presented my paper “Climate Change: A Software Grand Challenge“. But I came away from the workshop with very mixed feelings. I met some fascinating people, and had very interesting discussions about research challenges, but overall, the tone of the workshop (especially the closing plenary discussion) seemed to be far more about navel-gazing and doing “more of the same”, rather than rising to new challenges.

The break-out group I participated in focussed on the role of software in addressing societal grand challenges. We came up with a brief list of such challenges: Climate Change; Energy; Safety & Security; Transportation; Health and Healthcare; Livable Mega-Cities. In all cases, we’re dealing with complex systems-of-systems, with all the properties laid out in the SEI report on Ultra-Large Scale Systems - decentralized systems with no clear ownership; systems that undergo continuous evolution while they are being used (you can’t take the system down for maintenance and upgrades); systems built from heterogeneous elements that are constructed at different times by different communities for different purposes; systems where traditional distinctions between developers and users disappear, as the human activity and technical functionality intertwine. And systems where the “requirements” are fundamentally unknowable – these systems simultaneously serve multiple purposes for multiple communities.

I’ve argued in the past that really all software is like this, but that we pretend otherwise by drawing boundaries around small pieces of functionality so that we can ignore the uncertainties in the broader social system in which it will be used. Traditional approaches to software engineering work when we can get away with this game – on those occasions when it’s possible to get local agreement about a specific set of software functions that will help solve a local problem. The fact that software engineers tend to insist on writing a specification is a symptom that they are playing this game. But such agreements/specifications are always local and temporary, which means that software built in this way is frequently disappointing or frustrating to use.

So, for societal grand challenge problems, what is the role of software engineering research, and what kinds of software engineering might be effective? In our break-out group, we talked a lot about examples of emergent successful systems such as Facebook and Wikipedia (and even the web itself), which were built not by any recognizable software development process, but by small groups of people incrementally adding to an evolving infrastructure, each nudging it a little further down an interesting road. And by frequently getting it wrong, and seeking continual improvement when things do go wrong. Software innovation is then an emergent feature in these endeavours, but it is the people and the way they collaborate that matters, rather than any particular approach to software development.

Obviously, software alone cannot solve these societal grand challenges, but software does have a vital role to play: good software infrastructure can catalyze the engagement of multiple communities, who together can tackle the challenges. In our break-out group, we talked specifically about healthcare and climate change – in both cases there are lots of individuals and communities with ideas and enthusiasm, but who are hampered by socio-technical barriers: lack of data exchange standards, lack of appropriate organizational structures, lack of institutional support, lack of a suitable framework for exploratory software development, tools that ignore key domain concepts. It seems increasingly clear that typical governmental approaches to information systems will not solve these problems. You can’t just put out a call for tender and commission construction of an ultra-large scale system; you have to evolve it from multiple existing systems. Witness repeated failures of efforts around shared health records, carbon accounting systems, etc. But governments do need to create the technical infrastructure and nurture the coming together of inter-disciplinary communities to address these challenges, and strategic funding of trans-disciplinary research projects is a key element.

But what was the response at the workshop to these issues? The breakout groups presented their ideas back to the workshop plenary on the final afternoon, and the resulting discussion was seriously underwhelming. Several people (I could characterize them as the “old guard” in the software engineering research community) stood up to speak out against making the field more inter-disciplinary. They don’t want to see the “core” of the field diluted in any way. There were some (unconvincing) arguments that software engineering research has had a stronger impact than most people acknowledge. And a long discussion that the future of software engineering research lies in stronger ties between academic and industrial software engineering. Never mind that increasingly, software is developed outside the “software industry”: e.g. open source projects, scientific software, end-user programmers, community engagement, and of course college students building web tools that go on to take the internet world by storm. All this is irrelevant to the old guard – they want to keep on believing that the only software engineering that matters is that which can be built to a specification by a large software company.

I came away from the workshop with the feeling that this community is in the process of dooming itself to irrelevancy. But then, as was pointed out to me over lunch today, the people who have done the best under the existing system are unlikely to want to change it. Innovation in software research won’t come from the distinguished senior people in the field…