Decision Intelligence
My notes from the decision intelligence class
Table of contents
Quick Summary
Decision Intelligence
Smarter decisions for better results
What is Decision Intelligence?
Think of your life as a car ride. You can’t control the weather (luck), but you can steer the wheel (your decisions). Good decision-making compounds over time, shaping the direction and quality of your life. This course helps you turn information into better actions, no matter the scale or situation.
The best part? Decision-making is a skill. You can improve it.
Key Takeaways
Decisions vs. Outcomes
- A decision is more than just picking between options. It’s how you spend your time, money, and energy.
- An outcome is what happens afterward. A good decision doesn’t always lead to a good outcome (luck plays a part).
- Avoid outcome bias. Don’t judge decisions based on what happened after. Focus on what was known at the time.
How to Spot a Quality Decision
- What info did you gather?
- Where did you get it?
- How much info did you need, based on the stakes?
- Pro tip: Write down what you knew right after making a big decision. It saves you from hindsight bias later.
Why Some Decisions Are Hard
- Too many options – The more choices, the harder it is.
- Similar options – If they all seem the same, picking one feels impossible.
- No clear goal – What are you actually trying to achieve?
- High stakes – When mistakes are costly, decisions feel heavier.
- Reversible or not – Can you undo this decision?
- Emotion – Sometimes, we feel too much about a decision.
- Risk vs. Ambiguity – Is the chance of failure known (risk) or unclear (ambiguity)?
- Group decisions – When others are involved, it gets complicated.
- Social pressure – What will people think?
- Internal conflict – Are you getting in your own way?
When a decision feels overwhelming, slow down. Re-evaluate.
Setting Better Goals
- Start with what isn’t a priority.
- Then, look at your real opportunities.
3 Types of Goals:
- Outcome goals – Big-picture wins (e.g., “Be healthier”).
- Performance goals – Specific targets (e.g., “Run 5 miles in 45 min”).
- Process goals – Daily actions (e.g., “Run every other day”).
Trusting Intuition vs. Data
- Small decisions? Trust your gut.
- Big, high-stakes decisions? Use data and structure.
Visualize the best and worst-case outcomes. How much is this decision worth to you?
Human Factors in Decisions
- Tired? Hungry? Stressed? Your brain doesn’t work as well.
- Hack your brain. Make big decisions when you’re well-rested and calm.
- The “two selves” problem – Long-term you (future goals) fights short-term you (temptation). Set rules to stay on track.
Being Decisive
- Time matters. Don’t rush, but don’t overthink.
- Focus on important decisions. Don’t waste energy on trivial ones.
- When all options seem bad, pick the least worst and move forward.
- If two options seem equal, stop wasting time – just choose one.
Staying Objective
- Your brain loves to prove itself right (confirmation bias).
- Data helps, but even data can be misleading. Stay aware.
- Ask: “Am I just looking for info that agrees with me?”
Better Questions, Better Answers
- Don’t jump straight to answers. First, ask:
- What are my options?
- What assumptions am I making?
- What questions should I actually be asking?
Data-Driven Decisions
- Define your goals before you look at the data. Otherwise, you’ll just cherry-pick info that fits your pre-made choice.
- Use separate data sets for different stages of analysis.
- Key step: Ask yourself, “Would this new info actually change my decision?”
Automating Decisions
- Repetitive decisions? Automate them. Use data, machine learning, or simple rules to save time.
- Examples:
- Use analytics for open-ended exploration.
- Use AI/ML for frequent decisions.
Group Decisions
- Pros: More perspectives, less bias.
- Cons: Takes longer, and responsibility gets blurry.
- Fix it: Limit decision-makers. Use the rest as advisors.
- Clarify: Is the group here to decide or just to approve?
Career-Making Decisions
- Ask decision-makers: “What would change your mind?”
- If nothing could change their mind, it’s not a real decision – it’s persuasion.
- Follow-ups:
- How nervous are you?
- How important is this?
- How bad could it go?
Delivering Value & Driving Culture
- Find key decision-makers. What do they care about? What data do they trust?
- Improve their decisions. Bring better data, ask better questions, and align with their goals.
- Automate small, repeatable decisions so you can focus on the big ones.
- Remember: Good leadership = good decision-making.
- Final tip: Most problems (even technical ones) are people problems. Work well with others, and you’ll go far.
Recommendation: This course is a game-changer. If you want to level up how you think, act, and lead – sign up. You’ll thank yourself later!
Introduction
- Two things determine the quality of your life: luck and the quality of your decisions. You can control only one of them
- Good decision-making compounds over time
- Decision skills are the steering wheels of your life
- Decision intelligence is turning information into better action at any scale, under any setting
- Decision-making is a skill you can get better at
Decision Maker In Isolation
This section goes over - the differences between a decision and an outcome, outcome bias, and its pitfalls
Decisions vs Outcome
- Decision is more than a choice between options. It is an irrevocable allocation of resources. Money, time, opportunity cost
- Outcome is how things turn out later. A good decision might or might not lead to a good outcome.
- If you show an “outcome bias,” i.e., sometimes good decisions lead to bad outcomes, you will walk away learning the wrong lessons.
- Outcome has two components, the quality of decision and luck. Please keep in mind the conditions under which a decision was made.
Dangers of outcome bias
- Always evaluate decisions based on what was known at the time the decision was made.
- Dont judge decision makers by what was not known at the time a decision was made
- Writing down the things known at the time of a decision is critical to learn the right lessons from the decisions.
- Do this immediately after the decision is made. Saves for from hindsight bias
- Key ways to evaluate a decision quality -
- which factors did the decision maker consider?
- how did they gather information?
- which sources did they use?
- did they get the right amount of information based on what was at stake?
- Key lesson - for important decisions document the decision-making process
Difficult Decisions
- what makes certain decisions harder than others?
- Options: Large number of options and particularly combinations of options
- Similarity between options: No clear winner among options
- No clear objectives: Have you considered how a decision is being made? Criteria?
- High Cost: Evaluation and execution cost. How expensive are the mistakes?
- Reversibility: Is it possible to reverse a decision that goes against the definition of a decision above
- Cognitive Load: high cognitive function. A lot of effort.
- Emotion: Does the decision trigger you emotionally?
- Information: Do you have full and reliable information?
- Risk & Ambiguity: Both make a decision much harder
- Ambiguity - Probability not known. Risk - probability is known
- Timing: Amount of time you have to make a decision
- Group decisions: where the decision has a large impact on others
- Social consequences & social effects: scrutiny and consequences
- Internal conflicts: Conflict of incentives
- Adversial effects: competition
- when a decision seems complex, pause, slow down and re-evaluate
How to set Goals
- Before setting goals, think about the priorities and opportunities
- when thinking about priorities,
- Start by thinking of non-priorities
- Some of these might be other people’s priorities, but not yours
- Next start thinking about your opportunities
- Two classic mistakes while setting goals are - being too concrete & being too vague
- The trick is to have layers of goals that serve different purposes for you. Three kinds of goals -
- Outcome Goals: The win that you are interested in. It might be hard to measure and hence vague. Ex: Be more healthy
- Performance Goal: Measurable and under your control. Can be aspirational. Ex: run 5 miles in under 45 mins
- Process Goal: Measurable and fully under your control.Ex: Run 45mins every other day
- Never allow the process goal to take over the most important
Intuition
- How much effort, data & information to put towards your decision The key is not to overspend or underspend on a decision
- Visualize the best and worst-case scenarios of what could come out of your decision
- what are you willing to pay for the perfect decision? When the value isn’t too high - use intuition
- Intuition works best for low-value decisions.
- Practice using rigor to make low-value decisions.
- Intuition also works best under time pressure. Expertise helps here
- Unstructured decision making
- use the most amount of time on decisions which are -
- High importance
- Plenty of time
- Lack expertise
- needs structure
Human Elements In Decision Making
- Humans are influenced by biological factors: Our decision-making can be suboptimal due to factors like lack of sleep, low blood sugar, stress, and strong emotions.
- Pre-hacking yourself: Like athletes create optimal conditions for performance, you should create optimal conditions for important decisions. Avoid making critical decisions when you’re not at your best.
- Principal-agent problem analogy: Long-term goals (principal) and short-term desires (agent) can conflict. Setting constraints can help align short-term actions with long-term goals.
- These points highlight the importance of understanding and managing the biological and psychological factors that affect our decision-making
Being Decisive
- Timing of Decisions: Make decisions at the right time to avoid wasting resources.
- Prioritization: Indecisiveness often stems from being distracted by lower-priority decisions. Focus on high-priority decisions to avoid cognitive overload.
- Handling Emotions: When all options seem bad, choose the least worst one and execute it. Avoid getting stuck in emotional grief over suboptimal choices.
- Similar Options: If options are very similar in value, don’t over-optimize. Save your energy for more significant decisions.
Objectivity
- Confirmation Bias: This is a psychological effect where your pre-existing beliefs influence how you perceive and interpret information, making it difficult to remain objective.
- Impact of Cognitive Biases: Your brain can play tricks on you, affecting how you process information. Even the wording of information can change your decisions.
- Data and Objectivity: Data does not automatically equal objectivity. It’s important to be aware of your cognitive biases and how they might affect your interpretation of data.
Decision Intelligence
- Data as a Tool: Data is a powerful tool for decision-making, but it’s important to remember that data itself is just a means to an end.
- Electronic Storage: Storing information electronically enhances memory and accessibility, making it easier to analyze and use.
- Limitations of Data: Data is not inherently objective or true; it is influenced by the choices and biases of the people who collect and interpret it.
- The value of data is memory, not objectivity
Better Questions
- Analytics is taking a look at information and enables us to frame the questions
- what are the options
- what assumptions are reasonable -
- what what questions are relevant and which ones are worth asking
- Analytics augment the decision makers’ abilities but is not decision-making by itself
- Managing analytics is investing time in exploration
Data Driven Decisions
- When using data for decisions watch out for confirmation bias i.e. when you are looking for data to back the decision you already made
- Avoiding Confirmation Bias: To make a truly data-driven decision, it’s crucial to set your goals and criteria before looking at the data to avoid confirmation bias.
- Separate Data Sets: Use separate data sets for analytics and statistics to ensure that the same data points do not influence your questions and answers.
- Pre-Decision Homework: Decision-makers should do their homework and define what the data means to them before analyzing it to improve decision quality.
Better Answers
- Steps to frame a data-driven decision
- what would you do with no new information? We are selecting the default action
- Does the information truly change my mind
- what would do with all the information you needed?
- Stats is helpful when you don’t have all the information and there is uncertainty
- Deal with information in this order -
- No information. This leads to the default action
- Full information. Which pieces of information would you consider
- Partial information. Stats can help here
Decision Automation
- Data science is an umbrella term used to make data useful and involves 4 different flavors depending on the number of decisions
- analytics: no idea. Open minded and exploring
- Stats: used for a few important decisions
- Machine Learning / AI - Many decisions
- Persuasion: Data storytelling.
- Decision Automation: Machine learning and AI are essential for automating decisions at a large scale, using patterns in data to create models that execute decisions automatically.
- Machine learning boils down to optimise ___(objective) on __( Examples )
Data Driven Leadership
Group Decisions
- Critical to diagnose who the key decision makers are and how is the decision making responsibility shared among them
- Benefits of group decision making
- Protects against individual blind spots
- Balance extreme tendencies
- Foil tiredness
- Guardrails against highly unwise decisions
- Balances individual incentives
- The most fool-proof way of avoiding the principal-agent problem is to introduce constraints
- Downsides to group decision making
- Increases difficulty
- Increases time to make a decision
- Lost independence
- Note takers in a group setting have outsized influence
- watch out for diffusion of responsibility.
- One way is to decrease the number of actual decision-makers while increasing the number of advisors
- Also key to understanding whether the group is convened to ratify a group decision or to make a decision
- Decision responsibility is unequal among a group
Career Making
- Once we know the decision maker, a powerful question to ask is “What would it take to change your mind?”
- Helps in identifying if the individual is responsible for the decision
- If no information can change someone’s mind, there is no decision to be made. It is mere persuasion
- The above question also helps in -
- Understanding the decision makers default action
- tells you the decision metric and criteria
- A few good follow-up questions are -
- Identify Decision-Makers: -Understand who the decision-makers are at a higher level than you. If there are none, you will be setting the priorities. -Knowing their priorities helps you align your efforts to support them effectively.
- Understand Information Sources:
- Learn about the information sources your senior decision-makers rely on, including conversations and electronic data.
- Supplement these sources with better information and analysis to inspire valuable and relevant questions.
- Evaluate decision-maker’s Mindset:
- Assess what it would take to change your decision-maker’s mind. Perform evaluations to see if they are open to new decisions and how you can assist.
- Automate Repetitive Decisions:
- Identify decisions that are made repeatedly and consider automating them using data-driven methods, machine learning, and AI.
- Understand Organizational Needs:
- Work closely with decision-makers to understand their needs and the organization’s needs. Think outside the box to find ways to help them.
- Evaluate Data Sources and Infrastructure:
- Assess your organization’s data sources, engineering, and infrastructure. Ensure you have the necessary components for effective data-driven decision-making.
- Hire and Protect Data Experts:
- As you grow in seniority, hire data experts and protect their careers. Align them with important decisions and automation challenges to ensure their impact and career growth.
- Consider Incentives and Group Decision-Making:
- Always take into account the principle-agent problem and think about incentives. Help make group decision-making effective.
- Perform Good Work and Advocate for It:
- Deliver high-quality work and advocate for it within the organization. Collaboration skills and strong decision fundamentals are crucial.
- People-Centric Solutions: -Remember that most challenges, even technical ones, often boil down to people. Solid collaboration and decision-making skills will help you tackle various challenges effectively.
Written on December 25, 2024