Upside-down Decision Making Framework

 


My default framework for decision making is what I call upside-down because it focuses on upside and downside risk. It involves answering the following questions and has served me well across a variety of situations, including day to day in startups, investing and in my personal life.

  • What are the options? Brainstorming sessions routinely bring out non-obvious options. Even for small decisions, a few minute session with two other people can be fruitful. Remember that there is an always an option of inaction.

  • What are the possible scenarios that might unfold from these options? This is an abbreviated version of scenario analysis, which is generally used for more long-term planning. For larger decisions, it helps to write these out.

  • How plausible are they? Plausible != true. Based on the actual data/experience you have, estimate the likely probabilities for the possible scenarios.

  • What are the potential upside and downside risks? Next assess what you might gain or lose from the scenarios. Try your best to check your emotions, sunk costs, etc. at the door. After this step you have a decision tree of sorts.

  • How reversible is this decision? If you pick something, determine how easily can you change it later. That is, how much optionality for the various scenarios can you preserve?

  • What is the minimally viable decision? There may be something smaller you could decide, or it might be that the original framing is really an atomic decision point. If there is something smaller, you could decide just that piece for now.

  • Is there any impactful information to gather? Is there anything you can do before making the decision that would give you data to significantly impact your plausibility or upside/downside estimates? For now, you could decide to just gather that information.


This simple framework works for very small and very large decisions. On the small side, you may choose to write nothing down and just run the logic in your head or out loud in a meeting. On the larger side, you can assemble all the data and get more formal.

It's also very flexible in that it doesn't impose any value judgements in terms of what you should do. It is up to you to interpret the results. For example, startups generally look for high upsides and, depending on the situation, are less sensitive on the downside. Whereas bigger companies may have more loss aversion or desire to lock in smaller gains. Startups also often make reversible decisions with great speed.

When framing scenarios and plausibility for larger decisions, I like to list out the assumptions on which they are based. That gives you the opportunity to asses the strength of those assumptions when thinking about probabilities.

 

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