Thinking Companion, Companion for Thinking




  • there is no doubt that humans often fail to behave rationally (View Highlight)
  • would like to suggest that AI provides a unique opportunity to address the issues of decision biases in a new way. After all, many of these biases come from us being stuck in our own mind. But now we have another (strange, artificial) mind we can turn to for help. AI can assist us as a thinking companion to improve our own decision-making, helping us reflect on our own choices (rather than simply relying on the AI to make choices for us). (View Highlight)
  • The Abilene Paradox is a phenomenon in which a group of people collectively decide on a course of action that is counter to the preferences of any individual member of the group (View Highlight)
  • This occurs because each member mistakenly believes that their own preferences don’t fit the group consensus and therefore, they do not want to voice their opposition. So everyone decides on an action that each individual group member hates (View Highlight)
  • Groupthink, on the other hand, happens when the desire for harmony or conformity leads to irrational or dysfunctional decision-making. Members of the group prioritize agreement over critical evaluation of alternative viewpoints, often suppressing dissenting opinions and overlooking potential problems. (View Highlight)
  • , conducting a pre-mortem, where you tell stories about how a project might fail, is linked to higher success rates, but requires acknowledging failure (View Highlight)
  • there are also many biases that suggest that worrying too much about failure can paralyze us from taking action. Among these biases are loss aversion (our tendency to avoid losses beats our willingness to seek gains), hyperbolic discounting (our tendency to consider short-term awards more than long-term benefits), and the status-quo bias that causes us to avoid making changes, even when they might be good. (View Highlight)
  • AI for Considering Alternatives There are also biases that limit our view of alternative options, and blind us to the true costs of our action (View Highlight)
  • The concept of opportunity cost is notoriously difficult to use in practice, since it requires actively considering uncomfortable and annoying information that undermine the joy of acting on our choices. For example, to consider opportunity costs, you could list other alternatives you would want to spend money on, put costs in terms of hours of work, or even imagine yourself when retired. Not only are these tedious to consider, but they can be tedious to calculate. Again, this is where AI comes in. You can ask the AI: I am 25 and making 2,800. How could I think of the opportunity cost of making the purchase both in terms of my work now, and in terms of my eventual retirement. Make whatever assumptions you need. (View Highlight)
  • Given that the process of considering opportunity cost is designed to rob the joy out of purchases, it is not surprising that asking an AI to consider these issues is not fun. But research suggests that considering opportunity costs leads to better decisions, and it is very hard to communicate these topics in other ways (trust me, I have been involved in multiple efforts to build games to teach financial literacy). (View Highlight)
  • Most importantly, AI is not meant to replace human expertise but rather to augment it. By leveraging AI’s analytical capabilities, decision-makers can make more informed choices while still relying on their intuition, experience, and judgment to navigate complex situations. (View Highlight)