SDS 532: Mutable vs Immutable Conditions

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Recently, I had dinner with my wonderful friend Jake Zerrer, who’s a Senior Software Engineer at Flexport, a logistics and supply chain start-up based in San Francisco. Conversation with Jake is never dull, but I particularly enjoyed a part of the conversation where he brought up an idea for framing problems: He described this framework on the basis of mutable versus immutable conditions.

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The idea is that immutable conditions just are and tend to be relatively fixed, whereas mutable conditions are variable, can be defined by humans such that they may only exist in our collective imaginations, and they tend to change continuously over time.

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Examples of immutable conditions — things that just are and tend to be relatively fixed — include: Much studied by physics, such as light and gravity; How DNA stores information and is read by biological systems to create the functional proteins that sustain life; The behavioral response of animals to cues and rewards, such as dogs salivating when they hear a bell if the dog has become accustomed to the bell being rung when they’re given food.

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Right? So those are examples of immutable conditions. Now, when we — as an individual or even more broadly as a society — study phenomena with immutable conditions like these, by and large we are making progress monotonically toward a deeper and better understanding of the phenomenon. The phenomenon might not be easy to understand but once we make a breakthrough, it can be recorded, and then we or others can continue to understand the phenomenon more deeply. Since these immutable conditions are relatively fixed, society as a whole understands them much better each passing year, decade, or generation.

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In contrast, mutable conditions are variable, they can be defined by humans such that they may only exist in our collective imaginations, and they tend to change continuously over time. Examples of phenomena with mutable conditions include corporate hierarchies, the stock market, the electricity grid, and the healthcare system.

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When we — as an individual or again even more broadly as a society — study phenomena with mutable conditions such as these, it can be a tricky, sometimes quixotic task. The way a stock market behaves or a healthcare system works over time is continuously shifting. A model we have for trading profitably on the stock market today might be a losing strategy a month from now. Even if we have all the historical data possible on a given mutable problem and design a model that works perfectly for all of those historical data, it may be wholly inapplicable tomorrow.

(03:06):
How does this context on immutable versus mutable conditions provide us with a framework for solving problems? Generalizing broadly, Jake has the interesting idea that there is great opportunity for technologists like data scientists at the intersection of immutable and mutable conditions. By developing a deep understanding of some immutable phenomenon, there can be tremendous opportunity applying that understanding to a mutable phenomenon, even if only for a period of a few months or a few years.

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For example, we can apply an understanding of the animal cue-response reward systems (an immutable condition) to design effective marketing campaigns or devise a financial model of a stock (those are mutable conditions). As a second example, we can apply an understanding of the physics of light (an immutable condition) to develop a mechanism for storing solar power and provide that power to an electricity grid (a mutable condition). And as a third and final example, we can apply an understanding of genetics (an immutable condition) to engineer an mRNA-based vaccine for a particular coronavirus strain (a mutable condition).

(04:17):
So perhaps next time you’re tackling a problem, considering where it sits on the spectrum from immutable to mutable conditions will help you frame the problem — and maybe even identify a novel solution or a novel commercial opportunity. To read more about these ideas on mutable and immutable conditions, Jake has written a blog post on the topic. We’ve provided a link to it in the show notes.

(04:39):
All right, that’s it for today. Keep on rockin’ it out there folks and catch you on another round of SuperDataScience very soon.