self-serving bias

At one company we know, for example, leaders were asked to estimate how much time they spent tiptoeing around other people’s egos: making others feel that “my idea is yours,” for instance, or taking care not to tread on someone else’s turf. Most said 20 to 30 percent. Then they were asked how much time they spent tiptoeing around their own egos. Most were silent. Psychology explains this dynamic as a very predictable, and very human, “self-serving bias.” It involves viewing our own actions favorably and interpreting events in a way beneficial to ourselves. This explains why 25 percent of students rate themselves in the top 1 percent in their ability to get along with others. It’s why, when couples are asked to estimate their contribution to household work, the combined total routinely exceeds 100 percent.

"Getting personal about change," by Scott Keller and Bill Schaninger. McKinsey Quarterly. August 21, 2019. 

beware of optimism bias

Beware of optimism bias: the expectation that the best possible outcome will emerge. This accounts for why divorce rates in the western world are around 40 percent, yet when you ask newlyweds to rate their likelihood of divorce they are most likely to put it at 0 percent…. It also explains why, as our colleagues Chris Bradley, Martin Hirt, and Sven Smit describe, “One of the most emblematic outputs of the dreaded strategic-planning process is the ‘hockey stick’ forecast – the line that sails upwards on the graph after a brief early dip to account for up-front investment. These hockey sticks, confidently presented by executives pitching their new strategy, are easy to draw but they don’t score many goals. What tends to happen in reality is that the strategy fails to meet the bold aspirations and is replaced by a new one. 

Being aware of such biases doesn’t help one avoid them. As Dan Ariely, one of the foremost thinkers in the field, declares, “I am just as bad myself at making decisions as everyone else I write about.” Fortunately, however, there are a number of proven and practical tools to minimize biases in decision-making. These include, among others, the following: the “pre-mortem” (generating a list of potential causes for failure of a recommendation and working backward to rectify them before they happen); “red team-blue team” (assigning one person/group to argue for, and one to argue against, a decision); “clean-sheet redesign” (developing a system from only a set of requirements, free from considerations related to current investments or path); and “vanishing options” (taking the preferred option off the table and asking, “What would we do now?”). Importantly, simply ensuring you are engaging a diverse team in decision-making will reap significant rewards – which research reveals can improve decision-making quality by more than 50 percent.

Scott Keller and Bill Schaninger

Beyond Performance 2.0: A Proven Approach to Leading Large-Scale Change. John Wiley & Sons, Inc. 2019