Taxes and Algorithms

I'll continue with my walk through my bookshelf shortly, but as tax day just came and went, a thought or two on taxes, with an algorithmic bent...

Ideally, the computation of taxes is a (relatively simple) algorithm. Ostensibly, there should be a set of numbers one gives as inputs, and then an output-- the amount of tax you owe or are owed -- is produced. This is the basis for tax software programs -- you plug in the numbers, and off you go.

In real life, things are much worse. The required inputs are, at best, vaguely defined -- leading to such fun questions as when I make a charitable deduction by buying a ticket to a charity dinner, how much am I not allowed to deduct because of the value of the dinner -- and subject to interpretation. In computer-speak, garbage in, garbage out. I'm amused by reports like the one where Money magazine sends the same information to 50 tax professionals and ends up with 50 different tax returns (and a large spread in what is owed), or reports that in tests of IRS call-in helplines that the wrong answer is given over a third of the time. And when exceptions or odd cases occur, as they do often with the way the tax code is set up (AMT, anyone?), things rapidly become incomprehensible.

Naturally, I think there are lessons here. I actually find many tax forms are simple to understand, when I understand what the end goal and result is supposed to be. That is, it's much easier to understand an algorithm when one understands the goal of the algorithm, and there's some natural connection on how the steps of the algorithm are getting you to that goal. The problem with many tax forms is that they give the steps of the process without clarifying the goal, so I can't tell if my situation is a standard case or a special case that requires more time and attention. When theorists explain algorithms to outsiders, we ought to keep in mind that they might not understand special cases or detours in the argument that seem natural to us. We should keep the end goal clear throughout our explanation. And I admit I'd be thrilled if the US moved toward a tax system that was algorithmically speaking comprehensible (at least to me).

Another way of viewing this lesson is that there are (or should be) significant differences between algorithms designed for a computer to use, and algorithms designed for a human to use (with or without the help of computers). Humans, for instance, often like to understand the output produced, for their own satisfaction or so they can explain it to others. Computers don't care. In my past foray into human-guided search systems, this was one of the lessons I learned. In theory, we often think of an algorithm as a set of instructions transforming an input to an output. Sticking too closely to that worldview can be quite limiting; algorithms can be objects that humans -- or other agents -- interact with, leading to different design goals than we commonly consider. Even if you could just plug numbers into a computer and have your tax bill pop out, I think many people (myself included) would be unhappy, if we didn't have some understanding of how that final number was derived.