Cognitive Bias


Base Rate Fallacy Is LIke An Illusion

I took a lot of statistics, quantitative analysis, and math courses in university. I think it was a function of going to three schools during every available semester and building up a boatload of credits before LSU gave me a diploma and made me leave the state. I still remember the day I learned partial differential equations (I finally could understand the footnotes in my economics texts). With all of that, I was not exposed to the idea of a base rate fallacy (known also as base rate bias) until several years later when I was working in the garment industry. Twice this week I have run into scenarios that are examples of base rate fallacies which suggest that many people either don’t understand the concept or are blinded by raw numbers (a shiny object phenomenon). 

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Not all puppies and kittens.

Cognitive biases are important decision-making tools.  The help to make snap decisions based on patterns of behavior that have been successful in the past. However cognitive biases are not all kittens and puppies.  Cognitive biases can also lead us to miss problems we are not trained to recognize or to ignore better solutions to problems we have solved before.  With some rules, effort, and support most of the problems caused by cognitive biases can be avoided. Tools to avoid the downsides of cognitive biases include:  (more…)

Need an extra set of eyes?

Paul Gibbons suggested in the introduction to Part One of The Science of Successful Organizational Change that every generation thinks it’s path is shaped by great upheavals.  Much of this perception is due to availability bias. Availability bias leverages the most relevant immediate example to evaluate a concept or idea.  The availability bias is only one of a myriad of cognitive biases that humans have developed to deal with the complexity of the world around us.  Steven Adams recently asked, “What biases/fallacies might a developer fall prey to when testing code that he or she developed?” If we broaden the question to which of the cognitive biases would affect anyone reviewing their own work (based on the 16 we have explored over the past two years), there are several cognitive biases that would suggest that reviewing your own work is less fruitful than getting a different set of eyes.  Some of the leading culprits are: (more…)

Teams thrive on reciprocity.

Biases affect everyone’s behavior in all walks of life.  In a recent Freakonomics podcast, The Stupidest Thing You Can Do With Your Money, Stephen Dubner described the impact of various cognitive biases on the behaviors of many well-known money managers (and nearly 70% of the investors in the world).  The people on teams involved in the development, support and maintenance of software products are not immune to the impact of biases.  After the publication of our essay A Return to Cognitive Biases, Steven Adams asked “What biases/fallacies might a developer fall prey to when testing code that he or she developed?” It is a great question that gets to the heart of why understanding cognitive biases is important for leaders and team members.  We will return to the question after we added two more biases to our growing pallet of biases that we have explored. (more…)

Creative thinking can help you combat cognitive biases.

Cognitive biases are shortcuts that people use in decision making.  The shortcuts generated by cognitive biases are typically helpful, which leads to people to internalize the bias. These internalized biases are therefore used unconsciously.  Any behavior that becomes an unconscious response can lead to actions and decisions that are perceived as irrational if the context or the environment has shifted.  For example, a colleague recently related a story about an organization with an emergent product quality problem that occurred after they had disbanded their independent test group. The response was to immediately reconstitute the test group based on the belief that if the independent testing had worked before it would work again. The response was based on a cognitive bias, not a root cause analysis or some form of mindfulness.   (more…)

HTMA

How to Measure Anything, Finding the Value of “Intangibles in Business” Third Edition


Chapter 12 of How to Measure Anything, Finding the Value of “Intangibles in Business” Third Edition is the second chapter in the final section of the book.  Hubbard titled Chapter 12 The Ultimate Measurement Instrument: Human Judges.  The majority of HTMA has focused on different statistical tools and techniques.  This chapter examines the human as a measurement tool.  Here is a summary of the chapter in a few bullet points:

  • Expert judgement is often impacted by cognitive biases.
  • Improve unaided expert judgment by using simple (sic) statistical techniques.
  • Above all else, don’t use a method that adds more error to the initial estimate.

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Turkey Trot

Turkey Trot

Empathy is defined as understanding what another person is experiencing from their frame of reference. Empathy is more than mere understanding; requiring more of a cognitive connection between two people or a group. Empathy is a very valuable concept because it forms a basis for trust, enables communication and possibly facilitates the development of altruism. However, poorly practiced empathy can lead to problematic behaviors. The first is reinforcing boundaries and defining outsiders, which makes it hard to teams of teams to interact. Secondly is misapplied empathy when there is no basis of trust; the illusion of empathy can be perceived as manipulation. (more…)

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The Software Process and Measurement Cast 345 features our essay on Cognitive Biases and two new columns. The essay on cognitive bias provides important tools for anyone that works on a team or interfaces with other people! A sample for the podcast:

“The discussion of cognitive biases is not a theoretical exercise. Even a relatively simple process such as sprint planning in Scrum is influenced by the cognitive biases of the participants. Even the planning process itself is built to use cognitive biases like the anchor bias to help the team come to consensus efficiently. How all the members of the team perceive their environment and the work they commit to delivering will influence the probability of success therefore cognitive biases need to be understood and managed.”

The first of the new columns is Jeremy Berriault’s QA Corner.  Jeremy’s first QA Corner discusses root cause analysis and some errors that people make when doing root cause analysis. Jeremy, is a leader in the world of quality assurance and testing and was originally interviewed on the Software Process and Measurement Cast 274.

The second new column features Steve Tendon discussing his great new book, Hyper-Productive Knowledge Work Performance.  Our intent is to discuss the book chapter by chapter.  This is very much like the re-read we do on blog weekly but with the author.  Steve has offered the SPaMCAST listeners are great discount if you use the link shown above.

As part of the chapter by chapter discussion of Steve’s book we are embedding homework questions.  The first question we pose is “Is the concept of hyper-productivity transferable from one group or company to another?” Send your comments to spamcastinfo@gmail.com.

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The Re-Read Saturday focus on Eliyahu M. Goldratt and Jeff Cox’s The Goal: A Process of Ongoing Improvement began on February 21nd. The Goal has been hugely influential because it introduced the Theory of Constraints, which is central to lean thinking. The book is written as a business novel. Visit the Software Process and Measurement Blog and catch up on the re-read.

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What you see is dependent on what you expect.

What you see depends on what you expect.

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.

Many of us have a cognitive bias toward eating scorpions!

Many of us have a cognitive bias toward eating scorpions!

Cognitive biases are patterns of behavior that reflect a deviation in judgment that occurs under particular situations. The phrase cognitive bias was introduced by Amos Tversky and Daniel Kahneman in 1972. Biases can affect how information is perceived, how teams and individuals behave and even our perception of ourselves. Biases are a part of nearly every human interaction, so we need to understand the potential biases that are in play if we are going to help teams grow and evolve.

Project teams make decisions on continuous basis. Most decisions are made based on how the decision maker perceives the information he or she has at hand. One bias that can affect how information is perceived is the illusory correlation. The illusory correlation is the perception of a relationship between two or more variables when no relationship exists. An example would be that a team that works more hours a week has higher productivity because working longer gives the perception of creating more output. The perception of a relationship causes you to pay less attention to other factors, such as the higher level of effort they are expending. There are numerous biases that affect how information is perceived, and these biases can impact the outcome of decisions or even whether we make needed decisions at all.

Biases can affect behavior. Neglect of probability is a type of cognitive bias common in IT organizations that are planning and estimating projects or in risk management. For example, most estimates should be represented as a range based on probability. Techniques like Monte Carlo analysis can be used to generate a range of probability based estimates to address type of bias. However, almost all estimates are represented as a single number and regardless of all the caveats attached, and the continuum of probability is ignored. Lottery ticket sales are another reflection of the neglect of probability bias; buying one or 10 doesn’t materially affect the probability of winning, but that does not stop those who think buying ten tickets increases their chances of winning. In both cases neglecting probability affects how we behave and make decisions.

Biases can affect our motivation. For example, a self-serving attributional bias, occurs when success is attributed to internal factors and failures are attributed to external factors. This type of bias can occur at an individual level or at the team level. While self-serving bias can improve self-esteem (or a team self-esteem) it can also cloud judgment by causing an overestimation of capability. For example, if a team is able to deliver more than their long-term productivity or velocity would predict, the team might then perceive that they have increased their capability to deliver. If no fundamental changes have occurred such as an infusion of knowledge, training or new tools, the higher velocity may not be attributable to the team. A good coach will help teams examine these types of biases during retrospectives.

Bias are powerful psychological filters that affect how both individuals and teams perceive the world around them and then how they behave. Biases reflect shortcuts in how we interpret and react to stimuli. In many cases these reactions are valuable; however they can also cause problems (as many shortcuts can). Understanding how biases impact how individuals and teams perceive the world around them can help team make better decisions and therefore deliver value more effectively.