PMs continuously face ambiguous problems. It's your job to apply a structured approach to decompose the problem, and navigate the optimal path towards a solution.
Framing the Problem – Can you define the problem, and what information is needed to help solve it? skip to section
Thinking with Tools – Do you have a wide range of mental models to help you analyze a situation? skip to section
Avoiding common Mistakes – How do you avoid errors in reasoning to increase the quality of a decision? skip to section
Framing the Problem
PMs are asked to solve ill-structured problems – problems that don't have a right or wrong answer, just better or worse ones. Too often we start focusing on solutions before we evaluate if we're solving the most important problem.
Always separate the problem from the solution.
The problem space is your understanding and representation of the problem. The solution space is how you'll move from the initial problem to the end goal. While remaining separate, these areas should evolve together as new knowledge is gained.
Proper framing starts in the research phase when exploring the problem area.
Excerpt from Research gives us the problem, not the answer
This cuts to a fundamental question of where research belongs. If the team can’t decide which of multiple directions is better, it means the project wasn’t sufficiently shaped. There wasn’t enough problem definition.
When I’m just starting to shape something, I go through this checklist in my mind:
What’s the current way people do this, without the new solution?
When does the current way not work?
What are they trying to do when it doesn’t work?
How will they know if the new way is working better?
If I know the answers to those questions, I can start looking for solutions. I’ll have a frame I can plug ideas into to judge whether they are better than the old way and if they do what they should.
But don't stop at the first idea. Good problem-solvers try out many framings, exploring how each would affect the solution.
When faced with a complex problem, try to simplify your analysis down to the core, important tradeoffs.
Excerpt from Eigenquestions: The Art of Framing Problems
Aside from being an interesting snapshot from the internet historical archives, this story demonstrates an important learning: the power of a good frame — a critical success factor for teams and an important skill to master. By reframing the question as "consistency vs comprehensiveness," we made a clear decision and created a frame for a series of hard decisions down the line.
What is framing, and why does it matter? Framing is the process of breaking down a problem into a set of choices, tradeoffs, and options that enable a team to make a call and move forward.
At Coda, framing is an important skillーone we look for in candidates and teach to new employees. When done well, framing can steer the company, product, and team through tricky situations. When done poorly, we can feel stuck, frustrated, and like we're debating for an unnecessarily long period of time or are zeroed in on the minutiae of a decision.
First, articulate the why behind your actions. This should set the overall context and help you focus on what's important. Then it helps to write down what you know to be true.
A useful framework for this is First Principles. Rather than reasoning by analogy, you break down a problem to what you're sure is true and reason up from there. If you're not familiar with this concept, watch Elon explain how he used it to understand battery production.
Problem
Battery packs are really expensive
First Principles
What are batteries made of?
What is the cost of the individual materials?
Can we find a cheaper way to combine them?
When looking at a problem, don't start with a solution based on what you already know. Try to strip out existing biases and constraints to get to the core truth. Start your solution from there.
Proper framing of the problem, and the shared context it brings, helps your team to find a better solution.
Once you've asked the right questions and gotten to the core of the problem, you'll need tools to help you think through various scenarios assign probabilities to possible outcomes.
Mental Models are steriods for this process – they help us think smarter and faster.
Excerpt from Mental Models: The Best Way to Make Intelligent Decisions
A mental model is simply a representation of how something works. We cannot keep all of the details of the world in our brains, so we use models to simplify the complex into understandable and organizable chunks.
The quality of our thinking is proportional to the models in our head and their usefulness to the situation at hand.
The more models you have—the bigger your toolbox—the more likely you are to have the right models to see reality. It turns out that when it comes to improving your ability to make decisions variety matters.
The OG of mental models is a fellow named Charlie Munger who, along with his business partner Warren Buffet, invested in businesses. When he needed to make sense of the world, mental models proved superior to remembering isolated facts.
No one can know everything, but you can work to understand the big, important models across multiple disciplines – at least at a basic level. They collectively add value to both access the situation and come to a reasonable conclusion.
For Charlie, this meant understanding disciplines like biology, psychology, history, and engineering to become a better investor. For you, it means having a checklist of things to think about to help improve prioritization, scoping, and shipping.
Systems Thinking is another tool to add to your analytical toolkit.
A system is an interconnected set of elements that is coherently organized in a way that achieves something. If you look at that definition closely for a minute, you can see that system must consist of three kinds of things: elements, interconnections, and a function or purpose. –Donella H. Meadows
From a business perspective, one of the best examples was penned by Walt Disney back in 1957. It’s a masterpiece in visualizing the synergies among business units and the power of reinforcing feedback loops.
Systems help us understand how one part of system interacts with another, and the feedback loops that lead to network effects and economies of scale. Everything is interconnected. And understanding those connections – and points of leverage – can help deliver non-linear returns.
The best way to understand a system is to try and visualize it. Expose your system models to the light of day.
Excerpt from 📖 Thinking in Systems
You don't have to put forth your mental model with diagrams and equations, although doing so is good practice. You can do it with words or lists or pictures or arrows showing what you think is connected to what.
The more you do that, in any form, the clearer your thinking will become, the faster you will admit your uncertainties and correct your mistakes, and the more flexible you will learn to be.
Mental flexibility – the willingness to redraw boundries, to notice that system has shifted into a new mode, to see how to redesign structure – is a necessity when you live in a world of flexible systems.
From a product management perspective, almost everything can be framed as part of a system. Your team is a system. Your product is a system. Your organization is a system. If you want to improve these systems, you’ll need to understand the points of leverage.
For example, think about your team – a group of individuals working together to achieve a common goal. One of the leverage points is information flows. Are you withholding context from your engineering team that could help them better understand what they’re supposed to be building? Fix that, and watch how a simple action can create massive gains.
Think about your product – a group of technical capabilities designed and exposed to a human to help them do something better. What is the goal of your product? Could you reposition your value proposition and target a new segment of users? Simply changing the goal of your product bring exponential impact without much need to actually touch the underlying technologies.
You'll make less mistakes if you can learn to recognize dysfunctional decision-making processes. And as humans, we got a lot of those – they're known as cognitive biases.
Product teams are particularly susceptible to these, like confirmation bias (over-indexing on information that supports our belief) and sunk-cost fallacy (continuing to invest in something because of all the work we've already done).
It won't always be possible to clear up all the ambiguity around a certain decision. You'll need to get comfortable making educated assumptions based on data, and learn how to test those assumptions in the lowest cost way that still gives you reasonable certainty over the results.
