Cognitive Biases and their influence for Effectiveness of Business problem solving

 

Introduction

Bias is the tendency to favor or disfavor certain perspectives, individuals, or groups. Problem-solving is a critical component of Lean methodology, which is a data-driven approach to process improvement. The quality of problem-solving can be negatively impacted by different types of biases.

 Types of biases in problem-solving

There are many types of biases that impact the quality of problem solving. Following are a few key biases and how they influence the problem-solving process. As problem solvers it is crucial to understand these biases and  the damage, they make to the quality of the outcome of systematic problem solving.

A. Confirmation bias

Confirmation bias is the tendency to search for, interpret, and favor information that confirms one's preconceptions, values and beliefs.

Confirmation bias can lead to a narrow or incomplete problem-solving  that only considers evidence that supports one's initial hypothesis or what the problem-solving team initially had in their mind as the problem, causes or countermeasures. This can result in a failure to fully explore alternative explanations or possibilities, leading to solutions that are incomplete or ineffective. The quality of problem-solving can suffer when data is selectively analyzed or when alternative explanations are not fully considered.

For example,  a team collects data on a process and only looks for evidence that supports their hypothesis, rather than exploring all possibilities.

Confirmation Bias occurs because humans tend to seek out information that confirms their existing beliefs and discount information that contradicts them. This is often driven by a desire for consistency and the need to maintain a positive self-image.

To address confirmation bias, it's important to encourage a culture of open-mindedness and to actively seek out evidence that contradicts initial hypotheses. Some specific actions that can help include: encouraging teams to challenge assumptions and ask probing questions to explore alternative explanations.

Using data visualization techniques, such as scatter plots or histograms, to help identify patterns that may contradict initial assumptions. Bringing in outside experts or diverse perspectives to help identify blind spots or assumptions that may be influencing the problem-solving approach.

For example, if a team is tasked with reducing defects in a manufacturing process, they may start with the assumption that the problem is caused by a faulty machine. However, by using data visualization techniques, they may discover that the problem is actually caused by human error. By actively seeking out evidence that challenges their initial assumption, they can develop a more complete and effective problem-solving approach.

 

B. Availability bias

Availability bias is the tendency to prioritize or overemphasize information that is more readily available or memorable.

Availability bias can lead to an incomplete or ineffective problem-solving approach that focuses on less important issues while overlooking more critical ones. This can result in a failure to prioritize the most important issues and can lead to solutions that do not fully address the root cause of the problem. The quality of problem-solving can suffer when important issues are overlooked or when less important issues are prioritized.

For example, a team may prioritize a problem because it is top of mind, even if there are more critical issues that require  immediate attention.

Availability  bias occurs because humans tend to overestimate the importance of information that is easily available to them. This is often driven by a desire for simplicity and the need to process information quickly.

To address availability bias, it's important to use a structured approach that prioritizes critical issues and to actively seek out diverse perspectives. Some specific actions that can help include: using a problem-solving methodology, such as Lean Six Sigma, to prioritize critical issues and to systematically analyze root causes. Encouraging teams to seek out diverse perspectives, including those of customers or end-users, to help identify the most important issues. Using data-driven decision-making, rather than relying on intuition or anecdotal evidence, to prioritize issues.

For example, if a team is tasked with reducing defects in a manufacturing process, they may be tempted to focus on the most recent issues or the ones that are most salient. However, by using a structured approach and analyzing actual data, they can identify the most critical issues and prioritize their efforts accordingly.

 

C. Hindsight bias

Hindsight bias is the tendency to overestimate one's ability to predict an outcome after it has occurred.

Hindsight bias can lead to an overconfident or inaccurate problem-solving that fails to account for the possibility of unexpected outcomes. This can result in a failure to anticipate and prepare for future challenges, leading to solutions that are ineffective or unsustainable. The quality of problem-solving can suffer when teams fail to recognize the limitations of their knowledge and experience.

For example, a  team may look back at a failed project and overestimate their ability to have predicted the outcome.

Hindsight  bias occurs because humans tend to overestimate their ability to predict an outcome after it has already occurred. This is often driven by a desire to feel in control and to minimize uncertainty.

To address hindsight bias, it's important to use a structured approach that encourages continuous learning and improvement. Some specific actions that can help include: encouraging teams to reflect on their previous experiences and to identify what they learned from them. Using post-mortem analysis to identify what went well and what could have been improved in previous problem-solving efforts. Encouraging teams to stay open to new information and to continuously re-evaluate their assumptions and approaches.

For example, if a team has previously attempted to reduce defects in a manufacturing process and was unsuccessful, they can use a post-mortem analysis to identify what went wrong and to learn from their mistakes. By staying open to new information and continuously re-evaluating their assumptions and approaches, they can develop a more effective problem-solving approach.

 

D. Anchoring bias

Anchoring bias is the tendency to rely too heavily on initial data or assumptions when making decisions.

Anchoring bias can lead to an incomplete or inaccurate problem-solving approach that fails to adjust based on new information. This can result in a failure to adapt to changing circumstances or to consider alternative approaches. The quality of problem-solving can suffer when teams fail to adjust their approach based on new information or when they rely too heavily on initial data or assumptions.

For example, a team may rely too heavily on initial data or assumptions, rather than adjusting their approach based on new information.

Anchoring  bias occurs because humans tend to rely too heavily on the first piece of information they receive when making decisions. This is often driven by a desire for efficiency and the need to make quick decisions.

To address anchoring bias, it's important to actively seek out new information and to be open to alternative approaches. Some specific actions that can help include: encouraging teams to challenge their initial assumptions and to consider alternative explanations. Using data-driven decision-making, rather than relying on intuition or anecdotal evidence, to identify new trends or patterns.

For example, if a team is tasked with reducing defects in a manufacturing process and initially assumes that the problem is caused by a specific machine, they can challenge this assumption by testing alternative hypotheses. By using a structured approach and actively seeking out new information, they can develop a more effective problem-solving approach that is not limited by initial assumptions.

 

In summary, different types of biases can impact the quality of problem-solving in various ways. Biases can lead to incomplete or incorrect problem-solving approaches, a failure to prioritize important issues, overconfidence, and a failure to adjust based on new information. To ensure effective problem-solving, it is important to be aware of biases and to use objective data and a structured approach, such as PDCA or Eight steps method of problem solving, to mitigate their effects.