A Theory of Complexity Aversion

Abstract

Abstract

This paper proposes a tractable model of “complexity aversion”. The key ingredient is “first order complexity aversion”: when people know they’re making a mistake (because the situation is complex) they experience dread, which is a utility loss proportional to the expected size of their error. This adds a new complexity aversion term to the traditional utility function. This simple enrichment has a host of consequences, which I illustrate in five examples. (i) If complexity aversion is high enough, the price of a good will be constant over time, even though the marginal cost might be variable, to avoid annoying the consumer with a complex price system. (ii) In the theory of optimal taxation, if complexity aversion is high enough, the optimal tax system is “simple”, e.g. just features a uniform tax rate rather than a different tax rate for each good, as recommended by the traditional Ramsey model. (iii) Whereas the traditional model predicts that contracts should be indexed aggregate factors (e.g. on inflation, GDP, or the stock market), with enough complexity aversion, contracts are non-indexed, “simple”. (iv) As higher inflation leads to a more complex planning process, complexity aversion leads first order cost of inflation, which much larger than the second order costs of the traditional model. (v) This in turn changes optimal monetary policy, which de facto should ensure zero inflation (more generally, zero deviation from the inflation target), to the exclusion of other goals, except in rare extreme circumstances such as an extreme recession. I finally discuss how using this model of complexity aversion will lead to a useful “behavioral mechanism design” theory, and more realistic—simpler—mechanisms.