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Product: Meet Economics

What thinking about economics does for designers

Designers and product people don’t think about economics all that often. Sure, a product manager thinks about ARR or CAC, a design lead looks at their project budget, someone talks about burn rates or material design costs, but none of those are really economics. That’s just accounting. Economics is, bear with me here, actually pretty interesting.

Economics might seem like the study of numbers, but really it’s the study of decisions at scale. There’s some very relevant things that go into deciding that are relevant to product and design.

First: information. Decisions aren’t made without information of some kind and no information is still information. If someone says “I don’t have the information” they are at least aware that there is information that they do not have. They might have past information (shout out to Thomas Bayes). They almost certainly have incomplete information. Markets are, fundamentally, information sharing mechanisms. More open and better market, more information. How much does that bicycle cost? Is that bicycle a good bicycle? How much should it cost? How long will it last? Do other people like it? Do I trust those people? Is there a better one available for less? Will it get cheaper if I wait? What kind of information people have is a powerful determinant of how rationally and predictably they might act, what decisions they might make, and what values they might assign to products. Friedrich Hayek, the archly free-market mid 20th century economist (about whom I have somewhat mixed feelings frankly but whatever), wrote:

“The economic problem of society is thus not merely a problem of how to allocate ‘given’ resources – if ‘given’ is taken to mean given to a single mind which deliberately solves the problem set by these ‘data’. It is rather a problem of how to secure the best use of resources known to any of the members of society, for ends whose relative importance only these individuals know. Or, to put it briefly, it is a problem of the utilization of knowledge which is not given to anyone in its totality.”

Basically, when economists are talking about ‘markets’ what they’re talking about is open information sharing mechanisms that help people figure out what’s important. Good markets means good information (emphasis on the good not just the more). So what do people know your type of product? What do they know about your product in particular? What do they know about how much it should ‘cost’ (even if it’s actually free) and when they know that, how do they make decisions? What information do they trust? What entities do they trust and what happens when that trust gets broken?

Second: strategies. Economists love game theory and game theory loves economics. John Von Neumann invented game theory to try to understand how actors in a constrained scenario might act when only one of them could win. That quickly expanded to ways of thinking about how people might collaborate, compete, adjust or update strategies, or evolve new ones if the game has multiple stages. Game theory is helpful for designers and products particularly on the service and platform side of things because these aren’t just one time exchanges, they’re relationships. A service is not ‘a good’, it’s both ephemeral and it’s time demarcated (as in, it has a duration to it). When you’re designing a service or a product where people interact, you want to understand how both the folks on your side of things (providers) are going to act and how the folks on the other side of things (consumers) are going to act. How are they incentivized to act? How do they test their strategies and update them? How do they decide to collaborate, compete, defect, or align? When do they find equilibrium and what breaks that equilibrium? These are all surprisingly relevant for product and service design and especially when you have complex relationships between providers and consumers and the parent organization. Organizational design itself is a part of the design and the way you make the making of the product happen does in fact affect the product. Providers competing with one another will have a dramatically different outcome than providers collaborating. Pitting consumers against one another sometimes works and sometimes backfires spectacularly. Being able to think through the strategies that agents in your scenario might employ gets very clear when you have some familiarity with game theoretic concepts. Even more valuable, game theory can help you anticipate how your product might evolve.

Third: Causation. Economists don’t have a lock on thinking about causation. In fact, some of the most significant work on causal inference in the past few decades was done by a computer scientist, Judea Pearl. Economists do however think a lot about causation and they tend to do so with much worse data than a biostatistician or a physicist. They work to tease out theories of causation from random historical data, observational data where two similar entities got slightly different treatment by random change (the term ‘natural experiments’ might ring a bell), or by using measurements that affect something only indirectly and non-intuitively but turn out to have powerful predictive capability (instrumental variables). This kind of “what does the data actually mean” is vital for meaningful product thinking across strategy, design, management, and operations. If you can’t think causally, you’re going to chase all kinds of random spurious correlations and find yourself down innumerable dead ends because you will commit the cardinal sin of thinking that (you know what’s coming), correlation is causation. Many of the techniques that sociologists, anthropologists, behavioral psychologists, in fact social scientists of all kinds, have for figuring out whether X caused Y and to what degree came from economics. Now that doesn’t mean that there isn’t some bad math and poor science in econ, far from it. The overlap between economics and monetary and public policy means that there’s no shortage of political pet theories masquerading as economics and questionable conclusions used to justify poor public policy. But the fundamental techniques of causal inference are plenty powerful when used for good as well. There’s more to understanding causation than just A/B tests and there’s plenty of powerfully predictive things we can do even when we can’t run an experiment.

Economics can certainly be boring and the minutiae of monetary policy or how someone did or didn’t structure as a Difference in Differences study is not exactly what a designer might want to spend their time wading through but there’s real value in thinking about some of the core principles of economic thought that anyone on the product side of the house should explore a little.

A short reading list:

Causal Inference


Good Economics for Hard Times

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