Bulletproof Problem Solving

Put simply, the world no longer rewards people just for what they know—Google knows everything—but for what they can do with what they know. Problem solving is at the heart of this, the capacity of an individual to engage in cognitive processing to understand and resolve problem situations where a method of solution is not immediately obvious.”6

companies will only hire people who can see problems and organize responses.”5

Confirmation bias is falling in love with your one‐day answer. It is the failure to seriously consider the antithesis to your thesis, ignoring dissenting views—essentially picking low hanging mental fruit.
Anchoring bias is the mistaken mental attachment to an initial data range or data pattern that colors your subsequent understanding of the problem.
Loss aversion, and its relatives, the sunk cost fallacy, book loss fear, and the endowment effect, are a failure to ignore costs already spent (sunk) or any asymmetric valuing of losses and gains.
Availability bias is use of an existing mental map because it is readily at hand, rather than developing a new model for a new problem, or just being influenced by more recent facts or events.
Overoptimism comes in several forms including overconfidence, illusion of control or simply failure to contemplate disaster outcomes.

availability bias (drawing only on facts at hand), anchoring bias (selecting a numerical range you have seen already), or confirmation bias (seeing only data that aligns with your prejudices)

Failing to link conclusions with a storyline for action. Analytically oriented teams often say, “We're done” when the analysis is complete, but without thinking about how to synthesize and communicate complex concepts to diverse audiences. For example, ecologists have pointed to the aspects of nature and urban green spaces that promote human well‐being. The message has frequently been lost in the technical language of ecosystem services—that is, in describing the important role that bees play in pollination, that trees play in absorbing particulate matter, or water catchments play in providing drinking water. The story becomes so much more compelling when, in the case of air pollution, it has been linked to human respiratory health improvements in asthma and cardiovascular disease.10 In this case, by completing the circle and finding a way to develop a compelling storyline that links back to the “hook” of human health makes all the difference in capturing an audience and compelling action.
Treating the problem solving process as one‐off rather than an iterative one. Rarely is a problem solved once and for all. Problems we will discuss often have a messiness about them that takes you back and forth between hypotheses, analysis, and conclusions, each time deepening your understanding. We provide examples to show it is okay and worthwhile to have second and third iterations of issue trees as your understanding of a problem changes.

Neglecting team structure and norms. Our experiences in team problem solving in McKinsey and other organizations highlight the importance of a diversity of experience and divergent views in the group, having people who are open‐minded, a group dynamic that can be either competitive or collaborative, and training and team processes to reduce the impact of biases. This has been underscored by recent work on forecasting.8 Executives rank reducing decision bias as their number one aspiration for improving performance.9 For example, a food products company Rob was serving was trying to exit a loss‐making business. They could have drawn a line under the losses if they took an offer to exit when they had lost $125 million. But they would only accept offers to recover accounting book value (a measure of the original cost). Their loss aversion, a form of sunk‐cost bias, meant that several years later they finally exited with losses in excess of $500 million! Groupthink amongst a team of managers with similar backgrounds and traditional hierarchy made it hard for them see the real alternatives clearly; this is a common problem in business.
Incomplete analytic tool set. Some issues can be resolved with back of the envelope calculations. Others demand time and sophisticated techniques. For example, sometimes no amount of regression analysis is a substitute for a well‐designed, real‐world experiment that allows variables to be controlled and a valid counterfactual examined. Other times analysis fails because teams don't have the right tools. We often see overbidding for assets where teams use past earnings multiples rather than the present value of future cash flows. We also see underbidding for assets where development options and abandonment options, concepts akin to financial options, are not explicitly valued. How BHP, an Australian resource company, addressed these issues is developed in Chapter 8.

There are a number of pitfalls and common mistakes that many make. These include:
Weak problem statements. Too many problem statements lack specificity, clarity around decision‐maker criteria and constraints, an indication of action that will occur if the problem is solved, or a time frame or required level of accuracy for solving the problem. Rushing into analysis with a vague problem statement is a clear formula for long hours and frustrated clients.
Asserting the answer. The assertion is often based on experience or analogy (“I've seen this before”), without testing to see if that solution is really a good fit for the problem at hand. Answers like this are corrupted by availability bias (drawing only on facts at hand), anchoring bias (selecting a numerical range you have seen already), or confirmation bias (seeing only data that aligns with your prejudices).
Failure to disaggregate the problem. We see few problems that can ever be solved without disaggregation into component parts. A team looking at the burden of asthma in Sydney got the critical insight into the problem only when they broke it down along the lines of incidence and severity. In Western Sydney the incidence of asthma was only 10% higher than Northern Sydney, but deaths and hospitalization were 54–65% greater. The team was familiar with research that linked asthma with socioeconomic status and tree cover. It turns out that socioeconomic status is significantly lower in Western Sydney, tree cover is about half Northern Sydney, and daily maximum particulate matter (PM 2.5) is 50% higher. By finding the right cleaving point to disaggregate the problem, the team was able to focus on the crux of the issue. This led to them proposing an innovative approach to address respiratory health through natural solutions, such as increasing tree cover to absorb particulate matter.

a seven‐step framework for creative problem solving, Bulletproof Problem Solving, starting with these critical questions:
How do you define a problem in a precise way to meet the decision maker’s needs?
How do you disaggregate the issues and develop hypotheses to be explored?
How do you prioritize what to do and what not to do?
How do you develop a workplan and assign analytical tasks?
How do you decide on the fact gathering and analysis to resolve the issues, while avoiding cognitive biases?
How do you go about synthesizing the findings to highlight insights?
How do you communicate them in a compelling way?

World Economic Forum labeled complex problem solving its number one skill for the twenty‐first century

At McKinsey there is no greater compliment than to have your reputation as a problem solver described as “bulletproof.”