ROOT AND BRANCH

# Solve your next big problem at work or home with the help of a logic tree

When you can’t see the wood for the trees.
Image: Reuters/Jason Lee

How do you solve problems? Some of us set out for a walk, working the body and changing our surroundings as a way to achieve mental clarity. Others sit down to make a detailed list of pros and cons. Some people rely on instinct with snap decisions, or make incremental choices in part, perhaps, to avoid conceptualizing larger problems.

Charles Conn, co-author of the recently published book, Bulletproof Problem Solving, suggests a different approach. He believes most problems can be solved efficiently by first very clearly “formulating what the problem is,” and second, “showing the structure of the problem using logic trees.”

A logic tree is a visualization that captures all the component parts of a problem, in order to make it easier to identify a hypothesis that can then be tested with data and analysis. In their book, Conn and his co-author Rob McLean use examples of applying logic trees to problems ranging from the socially complex—how to think about climate change and begin to address it—to the minutely personal.

In one example, Conn’s family uses a logic tree to decide which new town to move to, narrowing nearly 30 possible candidates to just one: Ketchum, Idaho. “We lived there for 14 years. We really did that,” Conn says.

The logic tree for the Conn family’s decision looked like this:

Two things are notable in the “Where should we live?” tree. First, the variables have been captured, and then broken down into their constituent parts and weighted. Conn says he worked with his family, kids included, to decide what these elements should be, and how important they all were, assigning each a percentage. The other obvious thing is that the last variable—demographics and crime—has been crossed out. After some initial research, Conn writes that there wasn’t enough difference between the possible towns to make it a relevant factor. He also simplified all the travel variables into one metric: How long it would take him to get to the West Coast for work.

In this case, the variables were the result of family brainstorming sessions, and covered the things the family members felt to be important. A thornier problem could throw up many more variables, and it might not even be clear what they are at the beginning of the process. Conn and McLean advocate starting with a question and doing research to find out what impacts it, adding variables and removing them as it becomes clear what needs to be considered.

Even at an early stage, the creation of the tree can be instructive. (I tried it for a similar decision, whether to move house in the next year. Simply seeing the problem visualized and weighted made it much clearer where more research was needed, and clarified the motives for a possible relocation.) In order to move towards an actual decision, though, Conn did a fair bit more work. He gathered data on 28 college towns, finding metrics that would help to determine the answers to questions about the climate and the schools. The family visited the towns, helping create scores for subjective variables, and gauging their overall vibe. Conn then converted all the data to a scale from 1 to 100, so that variables could be compared to one another:

## Some problems are more complicated

Defining a problem and breaking it down using a logic tree are just the first two steps of seven that Conn and McLean developed via years of working as consultants to a range of businesses with McKinsey & Company, and subsequent experience in fields ranging from renewable energy investment to education and retail. Their book encourages those learning the “bulletproof” method to first define and then disaggregate the problem using a logic tree, and then go on through five more steps: prioritization, creating a work plan, using analytics, synthesizing information, and communicating findings, for example to a client (or to your family.)

Anyone keen to learn the whole method from start to finish will need to read the book, but creating even a simple tree forces you to understand the exact problem you’re trying to solve. In another example, McLean and his wife Paula, who are based in Australia, want to limit their carbon footprint and are considering putting solar panels on their roof. Will the return on investment actually pay off, and if so, when? Should they do it now, or wait until prices drop? Instead of the amorphous question, “How do we limit our carbon emissions?”, using a logic tree helps narrow the question down to something solvable, “Should we put solar panels on the roof now?” (Spoiler: They install the panels.)

There are plenty of business-specific examples in the book, but the personal decisions tend to stand out because they need less explanation. Should an injured runner have knee surgery? There’s a logic tree for that:

Design-thinking approaches like these might be familiar to people from a consulting, engineering, or startup background. But the importance of problem-solving as a specific skill, which might even be taught or learned separately from any particular discipline, is gaining recognition across fields. Last year, the World Economic Forum’s Future of Jobs report identified complex problem-solving as one of the crucial skills that humans working in the economy of the future would need.

Conn says that the logic tree approach is particularly useful for small teams whose members can question one another, and break up the work into manageable chunks. “It works especially well if you create teams to do it,” he says. That doesn’t have to be confined to a formal structure. “For stuff like, ‘Where should we live?’ make your partner a team. Make your extended family a team. Maybe they would call time on your confirmation bias.”