Most people routinely entertain futures, model scenarios, and attempt to forecast just to get through it all. It’s the wavering calculus we use to derive the world, mostly autonomic and instinctual but often focused and procedural, an adaptive tick, enabled by a meaty forebrain and the capacity to anticipate outcomes. Along the steepening curve of civilization, we’ve now scaled-up to evaluate enormously large complex structures in hopes that we can tune and modify and pre-determine their behaviors. From cellular pathways to ecosystems, economies to weather, we humans seem compelled to grow our dominion over the future ever larger and wider.
Most organizations use different types of strategic foresight to anticipate change. Digital book-keeping helps business owners project revenues and inventory needs. Larger companies deploy sophisticated Enterprise Resource Planning (ERP) services, business intelligence (BI) suites, and teams of competitive intelligence experts to log and analyze operational data and characterize the shifting landscape. Sophisticated government algorithms crunch impossible volumes of data for agent-based simulations and regression analyses, hoping to predict the next smoldering threat to freedom. Strategic forecast takes many shapes but all organizations use some form.
And yet, we routinely fail to predict the most important stuff. The inevitable Nostradamic lapses of futurism say less about the methodologies of foresight and more about our tragic aspiration to know the un-knownable. Face it: the future cannot be predicted. Models are fine but the real world is rife with the humors of non-linearity, shivering across vast ecosystems that make nice spreadsheet charts turn into run-away hockey-sticks with the flap of a butterfly’s wings. Like stock market crashes, riots, and earthquakes, discontinuities warp linear projections with sudden disruptions that often defy prediction. As they say, change is the only constant.
For millennia, we’ve grappled with “things” pretty well but systems are really different. Systems are complex interactions of interdependent parts that give rise to emergent and often-unexpected (“non-deterministic”) behaviors. If you’ve ever kept an aquarium, you have a sense for the delicate equilibrium necessary to a healthy aquatic system. Add a new fish or trim too much of the macroalgae and you can suddenly veer into an ecosystem crash. Small changes can have large results, so you have to be very deliberate in how you manage the tank.
For the most part, an aquarium is a closed system, but natural systems are embedded in higher and lower orders, possibly to infinity, so defining their boundaries is very challenging. Consider the Pacific Ocean. Managing salmon populations, for example, ought to consider regional krill disposition, which is dependent upon currents and temperatures, which in turn are affected by atmospherics and land runoff and emissions, and on and on. Where do you define the boundaries in order to manage such a system? Meaningful analysis requires careful scoping: what to include and what to dismiss.
Typically, we think in terms of linear cause and effect. Add too much salt to your aquarium and things start to die. You could plot this relationship as a straight line: salt concentration vs. number of living fish. In a saltwater reef, however, many actors participate in the ecosystem. Corals build reefs that harbor diverse species. Snails and crabs clean detritus. Sea plants filter the water and lower nitrogen. Fish eat algae and organisms, excreting waste that raises nitrogen levels. In this type of living system, changes can quickly propagate across interdependent actors towards a sudden tipping point. Overfishing can allow algae populations to bloom and kill off the corals that support reef health, leading to a population crash. But so can nutrient flows from farming along rivers that dump into coastal waters, or changes to water temperature from regional warming trends. In complex, inter-dependent ecosystems, changes can happen suddenly when equilibrium is pushed to its edges. Such systems are considered non-linear. They take little changes and turn them into large effects.
For the reef model, regional warming demonstrates the nested nature of systems. To really understand why the fish are dead you probably need to include the weather. Similarly, the impact of upstream farm runoff enters the equation as an adjacency–a factor not typically associated with the system but one that, nevertheless, impacts it greatly. For organizations, adjacencies and externalities are the things they rub against and the things that are left out of the accounting because they’re so far down the chain. They introduce friction and costs and unexpected outcomes. One of the defining shifts in the paradigm of globalized economics is that the rise of near-universal data collection and modeling is revealing the impact of these externalities. Nature is being priced in.
So how do we come to terms with all this complexity and non-linearity? The first step is to remove any hope of ever really predicting outcomes. Foresight is an exercise in probability, not prediction. Believing otherwise is a set-up for failure. Scenario modeling is a tool for mapping current trends into a set of most-likely outcomes. Typically these take the form of a positive linear scenario, business as usual with steady growth; a positive non-linear scenario, things get way more optimized much more quickly than expected; a negative linear scenario, things get steadily worse; and a negative non-linear scenario, the system crashes into chaos. The central forecast becomes a beacon shining from the present into the future, refracted through these possible scenarios. It’s not an oracular light, but it shows the possible futures as distinct shadows on the wall of the cave.
Having abandoned the hope of prediction in favor of probability, the next cognitive shift requires that we embrace uncertainty and use scenarios to build resilience and agility. Like treating the back porch or upgrading your home utilities, smart organizations use scenarios to guide the distribution of expenditures across resilient infrastructure, human resources, and innovation in order to prepare for discontinuities. If scenarios allow us to model the impact of pesticide use on the health of coral reefs, funding bioremediation along the Mississippi delta, for example, is an investment in the resiliency of fish populations (and human food stocks).
The third shift requires that we learn to think in terms of systems rather than parts; that we think beyond the visible edges; and that we wrap our heads around non-linearity. Or if that’s too hard, we can offload the effort to machines. Cloud-scale computing helps the models edge closer to reality, enabling better prediction of likely outcomes.
As has been said, you may not be able to predict the future but you can build it. The best way to manage the vast probability field is to collapse it into a reality of your own construction. Foresight and futurism are activist pursuits. For all our focus on futurism as being about the future, it’s actually a lens on the present, a snapshot of the way we deal with time itself and how we prioritize our actions in line with some sense of legacy and impact. Strategic forecast challenges us to act proactively to avoid pain and capitalize on opportunities. Futurism calls to the heart and spirit with visions of how things might come to pass if our will is strong enough, or too weak. In times of global change and shifting paradigms, all organizations should be embracing these tools to evolve and adapt in the dynamic environment.