Hi! Welcome to the 27th edition of 💡learnshiv. Here, you will find three practical ways to future-proof your life to adapt and thrive in a rapidly changing world — every week.

I have once again changed the structure of this newsletter because straight up: I’m burning out.

Instead of writing about all three topics every week - I’m going to focus on sharing three practical things I’ve learned (that may fall into many different topics) and how I think they’ll help you future-proof yourself.

Here’s what we’re talking about in this week’s newsletter:

• 👉🏽 Decision Intelligence: Are you a good decision-maker?

  • I recently took this quick course on LinkedIn: Decision Intelligence by Cassie Kozyrkov (Former Chief Decision Scientist at Google)

  • I’ll share the three most practical learnings from the course and why I think they’re helpful for future-proofing

  • At the end, you’ll find my mini-review of this course

If there’s one thing I know for sure, the future of work, the world and our well-being will require A LOT of decision-making. When I saw this course on LinkedIn I had to take it and, of course, share the useful stuff with you.

Here are three of the most practical lessons from this course and how they relate to future-proofing yourself.

1) Mitigating Outcome Bias
Lesson: Always evaluate decisions based on the information available at the time, not on the eventual outcome.

Explanation: Outcome bias occurs when we judge a decision based on its result rather than the decision-making process. This can lead to poor learning and future decision-making. To mitigate this:

• Before making a decision, document the information you have and your reasoning.

• After the outcome, evaluate your decision based on this documentation, not the result.

• Remember that good decisions can sometimes lead to bad outcomes due to factors beyond your control.

Practical application: Next time you face a significant decision, write down your reasoning and the information you're using. Review this later, regardless of the outcome, to improve your decision-making process.

Here’s the example she gives in the course: Imagine a game where you can choose between flipping a coin (50% chance of winning $100) or rolling a die (1/6 chance of winning $100). The objectively better decision is to choose the coin flip, as it has a higher probability of success. However, if you choose the coin and lose, while the die roll would have won, it doesn't mean you made a bad decision. You made the best decision based on the information available at the time.

2) Framing Decisions Effectively
Lesson: Use the “no information,” “full information,” and “partial information” progression to frame decisions.

Explanation: This approach helps clarify your thinking and identifies when you need additional data. Put yourself in the following scenarios:

• No information: Ask yourself, "What would I do if I had to make the decision right now with no new information?" This establishes your default action.

• Full information: Imagine you had access to all possible information. Describe the scenarios where you would take your default action versus not taking it. This helps clarify what information is truly relevant to your decision.

• Partial information: Assess what information you actually have available. If you have all the information you need, make your decision. If not, you're dealing with uncertainty and may need to involve a statistician or expert.

Practical application: Before making your next important decision without all the data, carefully go through these scenarios. They will help you understand what you know, what you need to know, and how to proceed.

Using the "Career-Making Question"
Lesson: Always ask decision-makers, "What would it take to change your mind?"

Explanation: This question is about understanding the decision-maker's current position and what might influence their decision. This should be asked early in the decision-making process, ideally in a one-on-one or small group setting with the key decision-maker. Specifically:

• It helps determine if there's actually a decision to be made. If nothing would change their mind, the decision has already been made.

• It reveals the decision-maker's default action (what they're planning to do if nothing changes).

• It uncovers the decision criteria - what factors or information would be significant enough to alter their current stance.

• It helps identify what additional information or data analysis would be truly valuable for the decision.

Practical application: In your next meeting, ask, "What would it take to change your mind about [the specific decision at hand]?" This question will help focus the conversation on what really matters for the decision and can prevent wasted effort on irrelevant data analysis.

I found this one a little hard to understand, so I asked Claude to give me an example: Imagine you're a data analyst at a retail company, and your CEO is considering expanding into a new market. Before you start a complex data analysis project, you schedule a meeting with the CEO and ask, "What would it take to change your mind about this expansion?" The CEO might respond, "If the data showed that our target demographic in the new market has less disposable income than we thought, or if the competition is stronger than we anticipated, I'd reconsider." This response gives you clear direction on what data to focus on, ensuring your analysis will be directly relevant to the decision at hand.

By applying these lessons, you'll be able to make more rational decisions, frame your decision-making process more effectively, and have more productive conversations about decisions in your workplace and in your life.

👉🏽 COURSE REVIEW

✏️ Course Content and Quality: A 
The course was highly interesting and useful. Its content was applicable to all types of decision-making, not just data-related decisions, demonstrating comprehensive coverage of the topic.

✏️ Relevance: A 
The course proved highly relevant due to its applicability to numerous daily workplace decisions. It effectively addressed biases and decision-making with incomplete information, enhancing its practical value.

✏️ Usability and Accessibility: B+ 
Subtitles and a full transcript were available, enhancing accessibility. However, limited additional attempts were made to improve overall accessibility, indicating room for improvement in this area (which is LinkedIn’s responsibility).

✏️ Support and Resources: N/A 
As a short, non-live course, it did not provide additional documents or opportunities for direct instructor interaction. The course content stood alone without supplementary resources.

✏️ Engagement and Delivery: A 
The instructor demonstrated great teaching skills, presenting information clearly and in an organized manner. Slides were easy to read and understand, and concepts were explained exceptionally well, contributing to high engagement.

✏️ Overall Grade: A 
The course is valuable, concise, and timely. While there is potential for improving accessibility, this is primarily a platform issue rather than a course-specific concern. The overall quality and relevance of the content justify the grade.

Thanks for reading our 27th newsletter. How do you feel about this new format? It’s much more doable for me - so I hope you still find it valuable.

If you’re reading this, reply, and I might give you a sweet treat.

Have a great week, and stay curious,

Shiv 💁🏽‍♀️