Our group currently faces a large possibility space. We’re excited by the completion of the Exploratorium project. There’s an awesome network of people, and interesting projects that could come next. Before jumping ahead, I want to explore (and get feedback on) a few general strategies that a group can take to navigate a large possibility space. This post is less about our group in particular, and more about general strategy for making decisions. Later, we’ll discuss how to apply this approach to the cognitive technology field.
The early beginnings of most creative activities face a large possibility space, with many open-ended options. You might be looking for a good career, choosing a college major, designing a product for a start-up, or planning a new research project. You may also have very complex goals such trying to ensure humanity survives the next century, increasing the amount of information that can flow in and out of a brain, or being able to interpret the subtleties of the human genome.
There are many possible sub-goals, design decisions, and activities that could be explored, perhaps within some larger constraints.
When faced with many possibilities, a common failure mode is decision paralysis. Actual progress grinds to a halt. Mental resources are spent on “waiting to find more information” and “waiting for the pieces to crystallize in my head a bit more.”
These are important and necessary. The next step is to intentionally push forward the collection of information and crystallization of pieces in your head.
Eventually, you aim to reduce the space to more discrete options, where you are presented with path A, B, or C and can compare values between them. Once the problem is formalized, you can apply decision theory to decide between the options. In the large possibility space, we’re still defining what the options are.
I’ll start by looking at three (of many possible) general actions:
- A better definition of the game could come from:
- more information about the opportunities and constraints that you face
- a better understanding of your own possible moves and actions
Resources could include social or technical skills, a social network, money, a portfolio of completed projects, etc.
One way to approach this is to improve your tools/algorithms, and increase your speed/experience at using your set of tools/algorithms.
Click the image to expand to full size. You can, share, and modify the original XMind file for this flowchart here.
Chipping Away At Uncertainty
Question generation is especially interesting, so let’s explore that for a minute.
The general goal is to find questions that will give shape to the space of possibilities, and cast light on the constraints. The mental motion is: “Come up with a list of fairly specific questions, where if I knew the answer to those questions, I would have a clearer idea of what a better strategy would be.”
In other words, notice where there is uncertainty about your task or the task space. Come up with a list of specific questions that, when answered correctly, will make it better understood.
- What is the best possible outcome – what would be a “home run”?
- If you accomplished nothing else, what is the most important outcome at the end of this project?
- Are there metrics you can use that approximate the success of the project?
- What is the worst possible thing that can happen?
- What are the constraints? What directions can we definitely rule out?
- What are the fundamental units of the system? What are they made out of? What is the lowest level at which we can work?
- What activities would multiply / enable / build a springboard for projects in the future?
- Has someone tried approaching this kind of space before? Are there concrete examples of success or failure? (See reference class forecasting).
- Are there well-defined processes that could automate part of the plan?
- What hasn’t been done before, that ought to be?
– If you’re looking for more concrete examples, I wrote two more examples of using this method here as a followup note.
– What else would you add to the list of questions? What other actions would you take in an open possibility space? I would love your feedback: email stephenfrey5 at gmail dot com.
This was a first rough pass at describing the general approach to large possibility spaces. Specifically, I wanted to introduce the method of “generating and answering questions.”
(I’ll aim to provide more concrete examples in a followup post. This page may be updated once I have more feedback and information. If you know anything about decision theory and want to email me your thoughts, that would be lovely.)
Future posts will use method a tool to explore future actions the cognitive technology field.
Thanks to Madeeha Ghori for giving helpful feedback on this post.