MINDSET- Problem solving in uncertain times

Great problem solvers are made, not born. That’s what is found after decades of problem solving with leaders across business, nonprofit, and policy sectors. These leaders learn to adopt a particularly open and curious mindset, and adhere to a systematic process for cracking even the most inscrutable problems. They’re terrific problem solvers under any conditions. And when conditions of uncertainty are at their peak, they’re at their brilliant best.

Six mutually reinforcing approaches underly their success:

(1) being ever-curious about every element of a problem;

(2) being imperfectionists, with a high tolerance for ambiguity;

(3) having a “dragonfly eye” view of the world, to see through multiple lenses;

(4) pursuing occurrent behavior and experimenting relentlessly;

(5) tapping into the collective intelligence, acknowledging that the smartest people are not in the room;

(6) practicing “show and tell” because storytelling begets action

1. Be ever-curious

As any parent knows, four-year-olds are unceasing askers. Think of the never-ending “whys” that make little children so delightful—and relentless. For the very young, everything is new and wildly uncertain. But they’re on a mission of discovery, and they’re determined to figure things out. And they’re good at it! That high-energy inquisitiveness is why we have high shelves and childproof bottles.

When you face radical uncertainty, remember your four-year-old or channel the four-year-old within you. Relentlessly ask, “Why is this so?” Unfortunately, somewhere between preschool and the boardroom, we tend to stop asking. Our brains make sense of massive numbers of data points by imposing patterns that have worked for us and other humans in the past. That’s why a simple technique, worth employing at the beginning of problem solving, is simply to pause and ask why conditions or assumptions are so until you arrive at the root of the problem.

Natural human biases in decision making, including confirmation, availability, and anchoring biases, often cause us to shut down the range of solutions too early. 2 Better—and more creative—solutions come from being curious about the broader range of potential answers

One simple suggestion is to put a question mark behind your initial hypotheses or first-cut answers. This small artifice is surprisingly powerful: it tends to encourage multiple solution paths and puts the focus, correctly, on assembling evidence. We also like thesis/antithesis, or red team/blue team, sessions, in which you divide a group into opposing teams that argue against the early answers—typically, more traditional conclusions that are more likely to come from a conventional pattern. Why is this solution better? Why not that one? We’ve found that better results come from embracing uncertainty. Curiosity is the engine of creativity.

2. Tolerate ambiguity—and stay humble!

When we think of problem solvers, many of us tend to picture a poised and brilliant engineer. We may imagine a mastermind who knows what she’s doing and approaches a problem with purpose. The reality, though, is that most good problem solving has a lot of trial and error; it’s more like the apparent randomness of rugby than the precision of linear programming. We form hypotheses, porpoise into the data, and then surface and refine (or throw out) our initial guess at the answer. This above all requires an embrace of imperfection and a tolerance for ambiguity—and a gambler’s sense of probabilities.

The real world is highly uncertain. Reality unfolds as the complex product of stochastic events and human reactions. The impact of COVID-19 is but one example: we address the health and economic effects of the disease, and their complex interactions, with almost no prior knowledge. We have to be comfortable with estimating probabilities to make good decisions, even when these guesses are imperfect. Unfortunately, we have truckloads of evidence showing that human beings aren’t good intuitive statisticians. Guesses based on gut instinct can be wildly wrong. That’s why one of the keys to operating in uncertain environments is epistemic humility, which is defined as “the realization that our knowledge is always provisional and incomplete—and that it might require revision in light of new evidence

3. Take a dragonfly-eye view

Dragonfly-eye perception is common to great problem solvers. Dragonflies have large, compound eyes, with thousands of lenses and photoreceptors sensitive to different wavelengths of light. Although we don’t know exactly how their insect brains process all this visual information, by analogy they see multiple perspectives not available to humans. The idea of a dragonfly eye taking in 360 degrees of perception 6 is an attribute of “superforecasters”—people, often without domain expertise, who are the best at forecasting events.

Think of this as widening the aperture on a problem or viewing it through multiple lenses. The object is to see beyond the familiar tropes into which our pattern-recognizing brains want to assemble perceptions. By widening the aperture, we can identify threats or opportunities beyond the periphery of vision.

4. Pursue occurrent behavior

Occurrent behavior is what actually happens in a time and place, not what was potential or predicted behavior. Complex problems don’t give up their secrets easily. But that shouldn’t deter problem solvers from exploring whether evidence on the facets of a solution can be observed, or running experiments to test hypotheses. You can think of this approach as creating data rather than just looking for what has been collected already. It’s critical for new market entry—or new market creation. It also comes in handy should you find that crunching old data is leading to stale solutions.

Most of the problem-solving teams we are involved with have twin dilemmas of uncertainty and complexity, at times combined as truly “wicked problems.” 7 For companies ambitious to win in the great unknown in an emerging segment—such as electric cars or autonomous vehicles, where the market isn’t fully established—good problem solving typically involves designing experiments to reduce key uncertainties, not just relying on existing data. Each move (such as buying IP or acquiring a component supplier) and each experiment (including on-road closed tests) not only provides additional information to make decisions but also builds capabilities and assets that support further steps. Over time, their experiments, including alliances and acquisitions, come to resemble staircases that lead to either the goal or to abandonment of the goal. Problem-solving organizations can “bootstrap” themselves into highly uncertain new spaces, building information, foundational assets, and confidence as they take steps forward

5. Tap into collective intelligence and the wisdom of the crowd

Chris Bradley, a coauthor of Strategy Beyond the Hockey Stick, 8 observed that “it’s a mistake to think that on your team you have the smartest people in the room. They aren’t there. They’re invariably somewhere else.” Nor do they need to be there if you can access their intelligence via other means. In an ever-changing world where conditions can evolve unpredictably, crowdsourcing invites the smartest people in the world to work with you. For example, in seeking a machine-learning algorithm to identify fish catch species and quantities on fishing boats, the Nature Conservancy (TNC) turned to Kaggle and offered a $150,000 prize for the best algorithm. This offer attracted 2,293 teams from all over the world. TNC now uses the winning algorithm to identify fish types and sizes caught on fishing boats in Asia to protect endangered Pacific tuna and other species.

Crowdsourced problem solving is familiar in another guise: benchmarking. When Sir Rod Carnegie was CEO of Conzinc Riotinto Australia (CRA), he was concerned about the costs of unscheduled downtime with heavy trucks, particularly those requiring tire changes. He asked his management team who was best in the world at changing tires; their answer was Formula One, the auto racing competition. A team traveled to the United Kingdom to learn best practice for tire changes in racetrack pits and then implemented what it learned thousands of miles away, in the Pilbara region of Western Australia. The smartest team for this problem wasn’t in the mining industry at all.

6. Show and tell to drive action

We started our list of mindsets with a reference to children, and we return to children now, with “show and tell.” As you no doubt remember—back when you were more curious!—show and tell is an elementary-school activity. It’s not usually associated with problem solving, but it probably piqued your interest. In fact, this approach is critical to problem solving. Show and tell is how you connect your audience with the problem and then use combinations of logic and persuasion to get action.

The show-and-tell mindset aims to bring decision makers into a problem-solving domain you have created. A team from the Nature Conservancy, for instance, was presenting a proposal asking a philanthropic foundation to support the restoration of oyster reefs. Before the presentation, the team brought 17 plastic buckets of water into the boardroom and placed them around the perimeter. When the foundation’s staff members entered the room, they immediately wanted to know what the buckets were for. The team explained that oyster-reef restoration massively improves water quality because each oyster filters 17 buckets of water per day. Fish stocks improve, and oysters can also be harvested to help make the economics work. The decision makers were brought into the problem-solving domain through show and tell. They approved the funding requested and loved the physical dimension of the problem they were part of solving.

Rookie problem solvers show you their analytic process and mathematics to convince you that they are clever. That’s sometimes called APK, the anxious parade of knowledge. But seasoned problem solvers show you differently. The most elegant problem solving is that which makes the solution obvious. The late economist Herb Simon put it this way: “Solving a problem simply means representing it so as to make the solution transparent"

Thanks

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Excerpts borrowed from Mckinsey research