Cognitive biases are a pattern of behavior that reflects a deviation in judgment that occurs under particular situations. Biases develop as filters or shortcuts that help us perceive information quickly in a manner that turns out to be beneficial to a decision-making process. Biases that help us recognize patterns helped early humans stay alive by recognizing predators. The shortcut kept our ancestors alive even if there were false positives (you can survive lots of false positives, but you aren’t likely to survive a false negative). Project teams (Agile or not) use or fall prey to a wide range of biases that affect perceptions and decisions. A sample of common biases that affect project teams in this category include:
Anchor bias refers to the tendency to rely heavily on one piece of information when making decisions. This bias is often seen when early estimates for a project or a tasks are made. The instant they are placed on the table they become a reference point to which all changes will be compared.
Clustering illusion (or clustering bias) is the tendency to see patterns in clusters or streaks of in a smaller sample of data inside larger data sets. For example, a team I recently worked with had an average velocity of 30 story points per sprint (ranged between 20 – 36) had three sprints in a row that delivered 40+ story points. While nothing significant had changed with how the team was working, outsiders saw a pattern and believed something out of the ordinary was occurring. (FYI – if there is no statistical significance to the data what we are seeing is “common cause” variance.)
The curse of knowledge bias generates a filter that blocks the ability think about a topic from a different and generally less informed perspective. In many cases being an expert on a topic makes it very difficult to see an out-of-the-box solution. This is one of the reasons significant changes in IT platforms or concepts come as new players enter the organization that have less experience with current tools and techniques. In a similar manner to the curse of knowledge, the status quo bias or the tendency to want things to stay relatively the same, creates a perception filter that helps the individual or team seek out and fixate data and concepts which makes them comfortable so they do not need to change.
An availability cascade causes a concept to become more and more plausible the more it is repeated publicly. An availability cascade generates a self-reinforcing feedback loop. Perhaps that is why Pokemon became more popular the more it was shown on the Cartoon Network. Daniel Pink, author of To Sell Is Human, in a Salesforce.Com webinar on July 9th pointed out that repetition increases process fluency, which makes the repeated item seem to be more true through repetition. Sales, marketing and 24 hour news channels understand and use the availability cascade bias to great effect.
A final example of biases that affect behavior and perception is optimism bias. Optimism bias is the tendency to be overoptimistic about favorable outcomes. This bias is often exhibited in status reports or in promises made early in a project. These are generally not lies, but rather due to optimism bias we believe that we can deliver. Dr. Ricardo Validri in Software Process and Measurement Cast 84 suggests that optimism bias is one of major reasons IT personnel are poor estimators.
This is a sample of cognitive biases that affect how we perceive information and then how we make decisions. Each of the biases reflects some basic component of human psychology and has been found to be generally beneficial. However all biases can create blind spots. A good coach or leader will first be aware of his or her biases and then help the team understand their blind spots while not abandoning the shortcuts that can help us perceive what is important and make timely decisions.