Too many variables to make a decision.

A step-by-step approach for decision making, synthesising insights from titans and thought leaders like Gates, Jobs, Musk, Karpathy and Dalio.

Decision making is hard, and what’s harder is that as you grow in your career, you need to make big decisions more and more frequently.

Whether you're a startup founder trying to achieve product-market fit or a seasoned executive navigating market disruptions, the ability to make sound decisions can make or break your success.

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What makes decision making complex?

Too many interconnected variables:

Problems rarely exist in isolation. Each scenario is part of a larger, intricate system with numerous variables influencing outcomes. Understanding these connections is crucial for effective decision-making.

Example: When you’re deciding which feature to prioritise next, and if you start thinking top down (or from the first principles), the variables are:

1. Who are we building for?

2. What’s the user’s fundamental need?

3. What is the reach and impact of this feature going to be? And how do we optimise that?

4. What is the effort required, and do we have those many developers?

5. Are there any current or potentially upcoming market trends that might impact this release?

An even scarier example is Investing

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1. Fundamentals of the company you’re interested in

2. Is the market or the industry in the company seeing ANY strong tailwinds?

   1. Will the impacts of these tailwinds be sustainable?

3. What numbers in the fundamentals are impacted by current market trends that won’t sustain for long?

4. Where in the cycle of growth over time is the company currently in?

5. And SO MANY MORE

Any problem statement that is part of a system that’s affected by larger number of variables is harder to make decisions for than any other problem statement

To top it off, there’s always also the uncertainty of whether your decisions would even make sense for a future that you cannot accurately imagine today. Plus, your cognitive biases that can lead you astray to take decisions that might not be objectively right for the situation.

Frameworks for managing complexity:

Let’s dive into some frameworks built to manage complexity, by some of the greatest thought leaders that we know of today.

1. Ray Dalio’s framework of ‘Principles based Decision Making’
   Ray Dalio, founder of Bridgewater Associates, advocates for establishing clear principles to guide decision-making. In his book "Principles," Dalio writes, "Principles are fundamental truths that serve as the foundations for behavior that gets you what you want out of life."

   How to apply it: Develop a set of core principles that align with your goals and values. Use these as a filter for your decisions. For example, if one of your principles is "Always prioritize long-term sustainability over short-term gains," it can help you navigate tough choices about resource allocation or growth strategies.

2. Bill Gates’ framework of ‘Thinking in Systems’

   Both Gates and Nadella are known for their ability to see the bigger picture. Systems thinking involves understanding how different parts of a system interrelate and how systems work over time and within the context of larger systems.

   How to apply it: When faced with a decision, map out all the elements involved and how they interact. Consider both immediate and long-term consequences. Ask yourself, "How will this decision affect other parts of our business or market?"

3. Jobs’ framework of “Simplicity and Focus”
   Steve Jobs was famous for his ability to cut through complexity and focus on what truly matters. As he said, "That's been one of my mantras - focus and simplicity. Simple can be harder than complex: You have to work hard to get your thinking clean to make it simple."

   How to apply it: When faced with a complex decision, try to identify the core issue. What's the one thing that matters most? Strip away everything else and focus your decision-making process on that key element.

4. Musks’s framework of “First principles thinking”
   This involves breaking down complex problems into their most fundamental truths and reasoning up from there.

5. Andrej Karpathy’s framework of “Data driven decision making”
   As an AI researcher and former director of AI at Tesla, Andrej Karpathy emphasizes the importance of data in decision-making. In the age of big data and machine learning, we have more tools than ever to inform our choices.

   How to apply it: Whenever possible, base your decisions on solid data rather than gut feeling alone. Use A/B testing, user analytics, and predictive models to estimate the outcomes of different choices. But remember, as Karpathy would likely caution, data should inform decisions, not make them for you.

While the frameworks we've discussed offer valuable insights, we can distill their core principles into a practical, step-by-step approach for decision-making:

1. Drill down to the core issue: Break down the problem into smaller, more fundamental questions until you reach the root of the matter. This aligns with first principles thinking, allowing you to see the issue clearly without extraneous details.

2. Prioritise key questions, and simplify your problem: Identify the most critical questions that need immediate answers to solve the problem at hand.

3. Map the systems: Identify all the systems these fundamental questions belong to. Document these connections to maintain a holistic view of the problem.

4. Solve the prioritised questions: Address the key questions you've identified. As Ray Dalio astutely notes, some situations may be unique, requiring you to find your own solutions rather than relying on existing frameworks. Don't force-fit a framework if it doesn't suit your specific circumstances. Instead, go and look for a solution. You WILL find one.

5. Validate with data: Embrace Karpathy's data-driven approach:

   • Take Dalio's example at Bridgewater Associates. He built tools to aid decision-making in the complex world of investing, incorporating variables from as far back as the 1800s to inform almost all his decisions - right from investing to promoting and firing people

   • For novel situations without historical data, conduct experiments to gather your own data. Over time, you'll build a database that helps you identify patterns and make informed decisions without constant experimentation

6. Execute.

This synthesised approach combines the strengths of each framework we've discussed. It encourages thorough analysis (Dalio and Gates), simplicity and focus (Jobs), data-driven validation (Karpathy), and systems thinking (Nadella).

We’ll learn about executing some other time, but for now remember, the goal isn't to make perfect decisions - that's impossible. The goal is to have a robust process that leads to generally good decisions over time. As Ray Dalio puts it, "I believe that the key to success lies in knowing how to both strive for a lot and fail well."

I’d love to hear your stories of managing complexities around decision making! Do leave a comment and help me improve my framework!