The complexity feedback loop of modeling
Yesterday I was interviewed by a tech journalist about the concept of feedback loops in consumer-facing modeling. We ended up talking for a while about the death spiral of modeling, a term I coined for the tendency of certain public-facing models, like credit scoring models, to have such strong effects on people that they arguable create the future rather than forecast it. Of course this is generally presented from the perspective of the winners of this effect, but I care more about who is being forecast to fail.
Another feedback loop that we talked about was one that consumers have basically inheriting from the financial system, namely the “complexity feedback loop”.
In the example she and I discussed, which had to do with consumer-facing financial planning software, the complexity feedback loop refers to the fact that we are urged, as consumers, to keep track of our finances one way or another, including our cash flows, which leads to us worrying that we won’t be able to meet our obligations, which leads to us getting convinced we need to buy some kind of insurance (like overdraft insurance), which in turn has a bunch of complicated conditions on it.
The end result is increased complexity along with an increasing need for a complicated model to keep track of finances – in other words, a feedback loop.
Of course this sounds a lot like what happened in finance, where derivatives were invented to help disperse unwanted risk, but in turn complicated the portfolios so much that nobody understand them anymore, so we have endless discussions about how to measure the risk of the instruments that were created to remove risk.
The complexity feedback loop is generalizable outside of the realm of money as well.
In general models take certain things into account and ignore others, by their nature; models are simplified versions of the world, especially when they involve human behavior. So certain risks, or effects, are sufficiently small that the original model simply doesn’t see them – it may not even collect the data to measure it at all. Sometimes this omission is intentional, sometimes it isn’t.
But once the model is widely used, then the underlying approximation to the world is in some sense assumed, and then the remaining discrepancy is what we need to start modeling: the previously invisible becomes visible, and important. This leads to a second model tacked onto the first, or a modified version of the first. In either case it’s more complicated as it becomes more widely used.
This is not unlike saying that we’ve seen more vegetarian options on menus as restauranteurs realize they are losing out on a subpopulation of diners by ignoring their needs. From this example we can see that the complexity feedback loop can be good or bad, depending on your perspective. I think it’s something we should at least be aware of, as we increasingly interact with and depend on models.