What do you do if your decision-making process in Systems Design is influenced by biases?

1 Anchoring bias

Anchoring bias is the tendency to rely too much on the first piece of information you receive or encounter when making a decision. For example, if you are designing a system and you start with a predefined budget, timeline, or architecture, you might be reluctant to adjust or change them later, even if new data or feedback suggests otherwise. This can limit your creativity and flexibility, and make you overlook better alternatives. To avoid anchoring bias, you should expose yourself to multiple sources of information, challenge your assumptions, and seek diverse perspectives. You should also avoid making hasty decisions based on incomplete or inaccurate information, and instead gather more evidence and test your hypotheses.

2 Confirmation bias

Confirmation bias is the tendency to seek, interpret, and favor information that confirms your existing beliefs or preferences, and ignore or dismiss information that contradicts them. For example, if you are designing a system and you have a preferred solution or technology, you might be more inclined to look for evidence that supports it, and disregard evidence that challenges it. This can lead to overconfidence, tunnel vision, and missed opportunities. To avoid confirmation bias, you should actively seek out opposing views, feedback, and criticism, and be open to changing your mind when faced with new or conflicting information. You should also avoid being emotionally attached to your ideas, and instead focus on the problem and the goals.

3 Availability bias

Availability bias is the tendency to judge the likelihood or importance of something based on how easily you can recall or access examples of it. For example, if you are designing a system and you have recently encountered or heard about a certain problem or solution, you might overestimate its frequency or relevance, and neglect other factors or options. This can lead to skewed priorities, inaccurate estimates, and flawed reasoning. To avoid availability bias, you should rely on data and facts, not anecdotes or opinions, and use objective criteria and metrics to evaluate your options. You should also broaden your knowledge and exposure to different scenarios and cases, and avoid being influenced by recency or salience.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?