Imagine a group of people with a reasonably large amount of resources woke up tomorrow and decided to solve some of our big problems. For the sake of argument, let’s say that they’ve landed on the following goal: Reduce atmospheric warming and ocean acidification to within certain levels, while minimizing the loss of human lives and ensuring economic prosperity. Let’s call this “saving the world”. What is the best way for this group to achieve their goal?
Obviously, this is not a hypothetical scenario. There are many well-meaning and competent organizations out there, whether governments, corporations or non-profits, devoting substantial resources to solving these problems through different approaches. However, it is widely accepted that tackling such massive goals takes a combination of efforts — there is no silver bullet or grand savior. Further, resources, whether money or time, are always limited, and there is a fair risk that they can be squandered in misguided efforts that achieve no substantial benefit or even cause harm. So, if these well-meaning organizations intend to solve these massive problems, they must coordinate and deploy their precious resources wisely, to maximize the likelihood of actually achieving the desired outcome.
Large companies are constantly faced with exactly this problem (which we’ll call the problem of coordinated strategy), and over the years, the craft of portfolio management has evolved to help answer it. While there are many different portfolio management methods and frameworks in wide use, they are all founded on a set of key economic and psychological insights, to which I’ll refer as the portfolio mindset. As we’ll see, adopting this mindset in the context of tackling global challenges sheds light on some remarkable deficiencies in our current approaches, and can provide very useful (if counterintuitive) insights and recommendations.
Simple systems: The portfolio as a machine
Of course, the world is not simple, not even just complicated, but complex. However, even the most simple portfolio analyses can be extremely enlightening.
The first principle of the portfolio mindset is to focus on outcomes, not inputs or outputs. (If you’re familiar with this mantra from product development or design, guess what — it’s been a thing since cybernetics in the 1940s, if not earlier!) By this we mean simply: “spending trillions of dollars on climate change” is nothing to be proud of per se; even “mobilizing millions of people” is irrelevant. The portfolio mindset asks us to be laser-focused on one thing: have we moved the needle towards the goals (in our example, reducing warming and acidification)?
In a simple world, this means merely that different efforts (let’s call them “projects” here) can have enormously different rates of return, e.g., actual degrees of warming per bucket of dollars spent. (By “enormously” I mean several orders of magnitude different.) Further, the rate of return is always uncertain to some extent: in a simple world, this mostly means that projects have a risk of falling short of their promise. So you can see that projects can be categorized into four archetypes:
- White elephant: probable wastes of money — high risk and low return.
- Bread and butter: sure-fire impact, but low return.
- Oyster: high expected return if you can crack the high risk.
- Pearl: the rarest of them all — sure-fire and high return.
Of course, these categorizations are relative, and depend on what choices are on the table: unless a project has literally zero chance of positive impact, it could very possibly be a pearl if all other alternatives are worse. Conversely, a pearl could turn out to be a white elephant if new choices are discovered or unlocked. This illustrates another principle of the portfolio mindset: all alternatives must be put on the table.
In our example context, the most common examples of oysters are difficult-to-crack technologies such as nuclear fusion or fuel cells; bread-and-butter are things like energy efficiency improvements. Most contemporary nuclear fission reactors are pearls. And at the risk of making some of my readers angry, I suggest that most “awareness campaigns” aimed at creating behavioral change among individuals (e.g., reducing wastefulness in household energy consumption) are indeed white elephants, at least after a certain level of “low-hanging fruit” improvements.
So, given these insights, what can our hypothetical group do to maximize the likelihood of saving the world? The answer (unsurprising to cybernetics fans) is in the form of the ubiquitous perception-action cycle or OODA loop. In our case, it looks like this:
- Get as much information as reasonable about your alternatives. There is a whole sub-discipline called information economics, which is all about figuring out what constitutes a “reasonable” amount of information to make decisions. But the principle is simple: if you have a project that might be a white elephant, you want to find out about it as soon as possible so that you stop investing in it. Conversely, if you’re holding an oyster, you want to take a peek inside to see if there’s a pearl hiding in there. In both cases, the information-gathering process usually involves a combination of small-scale experiments and input from domain experts. (On that topic: domain experts should never be asked to judge absolutes — “this is the best idea!” — only relatives — “this is better than that”. Same applies for everyone, by the way: people can only reason accurately via comparisons.)
- Prioritize the highest-impact projects. Put all the alternatives in a matrix such as the above, and pick the highest-return projects for each given risk level. Focus your resources first on these projects. It’s not that the others are necessarily bad — but if you want to reach your goals, you have to pick the best efforts. Of course, for a variety of real-world reasons (e.g., limited scalability), it may turn out that you do need to spend money on the “next-best” picks, but at least you’re doing it consciously.
- Hedge your bets. Given that rates of return vary by orders of magnitude, and that projects are almost always uncertain, you will always have to have a mix of efforts in your portfolio. The exact mix will depend on what you consider an acceptable level of overall risk (probability of failure); in our example, as we want to be very sure that we do end up saving the world somehow, the recommendation is to diversify greatly, exploring as many avenues as necessary to cover the ground of reasonable alternatives. This competition between approaches can be perceived as “wasteful”, but it pays off in risk reduction.
- Rebalance frequently. Portfolio management is an iterative process; as we achieve interim results and learn, we then reprioritize efforts according to the new state of knowledge. In our example, “saving the world” is a definite end-state criterion, and so in theory, rebalancing looks like a filtering process: we start off with many possibilities and gradually narrow it down to a relatively small set of very certain approaches that can be followed through to cross the finish line.
This is the “traditional” portfolio approach, prescribed by every business luminary and guru (though the specific metaphor of the machine is most recently associated with Ray Dalio in his book Principles). As I said, it is very enlightening and fundamentally correct; however, to solve “wicked problems”, we need to go beyond mechanical metaphors and into the biological.
Complex systems: The portfolio as a forest
I don’t think I need to convince readers that our economy and our natural environment form a complex system — that’s already become commonplace. However, does this complexity mean that all of the above tools are useless? Quite the opposite, but it does mean we need to hold them a bit differently. Recapping the key features of complex systems:
- Emergence: The whole is more than the sum of its parts; global outcomes are dictated by the interaction of subsystems, and so analyzing each subsystem in isolation has very limited predictive power.
- Self-organization: An example of emergence, where stable macro behaviors or structures emerge from the micro interactions between subsystems.
- Nonlinearity: Also known as context-sensitivity; what works in one setting may not work in others, and putting in 10 times the money or effort may not give you 10 times the results, but maybe 100 times — or maybe minus infinity.
- Feedback loops: Closely related to nonlinearity; each subsystem provides the context for others, creating cycles where disentangling cause and effect is difficult.
So, how do we think about portfolios in a complex system? The basic paradigm of the OODA cycle remains the same, but it’s augmented with a few extra principles:
- Light touch. Almost anything is fair game to experiment, but at large scales, interventions should happen in a distributed, context-sensitive manner, avoiding the “fatal conceit” (central planner’s hubris) and ruin. This can be thought of as a more measured version of the the “precautionary principle”.
- Fail fast. I personally hate this buzzword, but the spirit of it is spot on: be humble and objective about how well you’re doing; if there are signs that you need to change approaches (“pivot”), do so as quickly as possible.
- Shared understanding. The system is too complex for any single person to understand, but by connecting the insights of a diverse set of knowledgeable people, you can have a distributed reasoning that covers all the critical nuances well enough. Note that this isn’t portfolio management by committee or consensus — we’re still after an objective goalpost, and dissenting opinions should be encouraged.
- Critical paths and chains. Big changes are usually the results of “domino effects” caused by lots of small changes. Strategy in a complex world often boils down to creating multiple domino chains, taking advantage of feedback loops and leverage points wherever you can find them, and building up your chains further from the ones that already exist.
- Redundancy. Nonlinearity makes it even more important to hedge your bets; distributing a function into multiple redundant alternatives, all slightly different in the relevant dimensions, eliminates single points of failure (SPOFs).
- Leverage points. These are places where a small intervention can produce a big effect. Note that a given point can be both a leverage point and a SPOF — so as always, a light touch is recommended!
- Optionality. Given the importance of critical paths, it’s not surprising that most of your effort is going to be spent on the unglamorous middle of the chain (capabilities). Therefore, your main consideration for any given effort is usually its option value, i.e., the additional chains of possibilities and capabilities that it unlocks. The real power of optionality is that it is combinatorial: capabilities will almost always “mash up” in exponentially useful — and often unexpected — ways.
Thus, an effective portfolio for tackling a complex system must be like a managed forest. Such a portfolio is made up of a very large number of individual efforts of various “categories” (say: energy production, efficiency, carbon capture, etc…), with many “species” in each category. Individuals compete with each other for resources, and yet the system benefits from all of their efforts. Such a portfolio isn’t dependent on any single “big bet”, but is kept thriving by the presence of “keystone species” which together provide fundamental infrastructure services (IT, materials, logistics, finance, etc.) and which act in conjunction to distribute resources, with no central planning (although the forest manager is giving it small nudges all the time). Variations are absorbed and put to good use — a “specimen” that fails and leaves more room for its neighbors, while providing valuable material for decomposers; a controlled fire that clears out the undergrowth; new species that emerge and create whole new niches into which the whole portfolio grows. Taken as a whole, this portfolio achieves antifragility — it’s even better than robust, because it actually benefits from unpredictability and disorder.
Critically, a portfolio for a complex system is tightly coupled to the system itself. There is no clear boundary where the “managed” part starts and ends, no easy divestment.
How to (probably) not save the world
If you’re thinking “wow, this sounds a lot more complicated than putting together a risk-return model on a spreadsheet”, you’re spot-on. Solving challenges in complex systems is extraordinarily different from anything we’re trained to do, whether in business or science. Indeed, if our hypothetical world-saving group is guided by traditional or intuitive approaches, it is probably taking some large missteps.
To start with, most groups attempting to solve big challenges don’t even bother to apply the first set of (simple-system) principles, relying instead on bad heuristics like over-indexing on specific technologies, failing to review progress and processes often enough, and making decisions driven by “expert judgment”, personal persuasion, consensus or optics. Ironically, this is particularly true of “legacy” large and well-funded organizations, where old habits die hard and incentives to optimize for outcomes are often not strong enough.
Unfortunately, this doesn’t mean that new and small organizations always get it right. For starters, these small groups often work in echo chambers: picking arbitrary domains, driven by their founders’ passion, and tackling them with great enthusiasm, but no coordination or question of whether they are solving for the right problem in the grand scheme of things. (Not that there is anyone to tell them otherwise, as the métier of “impact entrepreneurship” is often uncritical of initiatives and unwilling to prioritize.) Additionally, all too often, these “contenders” read the startup playbook a bit too literally, and find themselves focusing exclusively on short-term growth or publicity, trying to outcompete and become leaders, over-promising and under-delivering, etc.
End game: The global portfolio
Of course, this doesn’t mean that nothing impactful is getting done — just that it’s being done very inefficiently, as the sum of local optimizations doesn’t add up to a global optimum. So, what if our hypothetical group of world-savers wants to avoid those pitfalls and do the absolute right thing with their wads of cash?
Thankfully, there are some systemic enablers (or, in Donella Meadows’s language, high-effectiveness leverage points) that help make world-scale distributed portfolio planning more feasible. These enablers are already in play and we can see them being put to good use in many contexts.
- Outcome-oriented mindsets. Beyond the buzzwords, the growth of “lean startup”, “agile” and “design thinking” in public awareness has enormously enhanced the level of conversation about “what to do”, shortcutting numerous excuses for local optimization and resistance at an organizational level. This can be truly transformational for global challenges, when coupled to the other mindsets and enablers mentioned here. A nice example of this is the book Lean Impact, which shows how to apply outcome-oriented mindsets and practices to non-profit contexts, with several encouraging examples.
- Open ecosystems. As I’ve written about before, it’s hard to overstate the power of open sociotechnical systems, founded on shared commons of knowledge and resources. Not only can they enable collaboration and shared understanding across billions of individuals (an extreme version of “given enough eyes, all bugs are shallow”); just as critically, open ecosystems enable the combinatorial explosion of mash-up capabilities to transcend arbitrary organizational boundaries. My favorite example of this so far, although very incipient, is Transatomic’s open source nuclear reactor design. (Imagine how much faster the energy industry would move if even a small fraction of it went open!)
- The meaning revolution. We are currently undergoing a global shift to a new paradigm of business —AKA “conscious capitalism” — one of whose main hallmarks is the ability for individuals and organizations to intentionally and credibly align on common purpose (what I’ve called agentic alignment). As it passes the straits of skepticism and acquires the stature of “common knowledge”, the new paradigm enables us to move past ad hoc, local optimization (at the per-organization or even per-nation level) and meaningfully base our day-to-day decisions on long-term shared goals, protected by a deep social infrastructure of mutual trust.
These three enablers, combined, create the conditions for us all to collaborate on a world scale, going beyond myopic incentives, local solutions, and siloed approaches. In doing so, we allow all of our resources to be deployed into a single, massive and coherent global portfolio of solutions, playing together in complementary ways to tackle complex challenges holistically, yet leveraging local knowledge at its heart. By plugging into the global portfolio, each group or organization can deploy resources towards the most effective, timely uses; and its capabilities are instantly made available to leverage (and be leveraged by) those of our entire civilization.
In other words, this set of upgrades to our world supermind enables it to understand, diagnose and tend to itself and its environment, in a way that’s exponentially more effective and sustainable — more intelligent — than anything previously possible.
All paradigm shifts are exponential: a slow start, seemingly “sudden” acceleration, and by the time people notice something is happening, they’ve already happened. Eventually, most of us will just think “this is the way we’ve always done things”.
As with all of my other articles, I’m not the originator of any of those ideas — they have all been floating around, many for quite some time now. But I hope that this way of putting them together will be useful to the reader. A big thanks to my many friends who have recommended fantastic books, and special thanks to David and Ralph at SmartOrg, experts in portfolio management for innovation.