An Adaptive and Parameterized Control Law for Campaign Finance Reform
Engineered control systems use feedback control and collections of parameterized and/or adaptive algorithms, called control laws. Control laws provide correct system response across the entire range of system environments. Our legislators, who are not, regrettably, computer programmers, should make more use of methods from system dynamics and computer science to write laws that account for, and are more robust in the face of, changing circumstances.
We are all familiar with control laws. Thermostats, the cruise control in a car and autopilot in a plane are familiar examples. The control law for an air conditioning thermostat is quite simple: if the air temperature climbs above the AC setting, blow chilled air into the room until the temperature drops to the setting. After that, turn off the power in the cooler, but keep blowing the reservoir of available chilled air while it is colder than the thermostat setting.
Even the simple control law for a thermostat shows the computer science (and military) principle of “divide and conquer”. Instead of creating a single temperature control system, which is mechanically difficult, engineers have divided the temperature control problem into distinct systems for heating and cooling. The same thermostat can be used for both heating and cooling, but there are different physical systems being controlled when we switch the thermostat from cooling mode to heating mode. That’s a parameterized control law.
In more complex systems, control laws typically divide and conquer the control problem through piecewise linear control. In each part of the control space, there is a simple linear control law with more variables. These control laws are parameterized by features of the system being controlled. The control law of the thermostat has one variable, temperature, and two parameters, the temperature setting and the mode (heat or cool). An airplane autopilot system has many variables, having to do with the yaw, pitch, roll, and airspeed and other measures of the plane in flight. For a given slice of all those variables (a given speed, yaw, etc.), there is a simple linear control law that translates small deviations from desired settings to modified actuators: just as a thermostat control law turns on the AC when the room gets too hot, an autopilot system will adjust the flap angles on the wings and tail if the plane banks too steeply in a turn. The difference for the autopilot control law is that those adjustments change the variables (the airspeed, etc), and a new linear control law comes into play. The autopilot control law has as many parameters as the plane has variables, plus the parameters the pilot sets, such as airspeed, altitude, and heading.
Biology is full of amazing examples of feedback control. There are numerous biochemical systems in cells where some chemical level is stabilized through feedback control. Substance A stimulates production of substance B which causes decreased production of substance A. When the B levels are steady, the cell is fine; when B levels drop, it makes substance A, which helps the cell re-stabilize. Moving up the genome, my favorite example is programable spinal reflexes. If you are sitting in the middle of a room and your chair unexpectedly tilts back (perhaps due to a prank or an earthquake) you will reflexively splay your arms and legs to maintain balance. However, if you are touching something solid that can prevent your fall, such as a sturdy table, you will reflexively grab the solid object instead of splaying your limbs. These distinct reactions are too fast to be anything but spinal reflexes. The (parameterized) reflexes are thought to be programmed into the spinal circuitry based on (the parameters of) your circumstances by the cerebellum.
Our future politics can use smarter approaches to control via adaptive structures that incorporate citizen input. Our government was designed as a feedback control system (via our votes for state and federal representatives), and we do have some systems thinking built into our institutions. We should do more of that.
One example of adaptation in law is the price-indexed Cost of Living Adjustment (COLA) to social security and various military benefits. The COLA is computed from a consumer price index, which is an average of prices of various consumer goods. As the price of consumer staples (bread, milk, etc) increases, so does the monthly social security payment. Conversely, the threshold income for the Alternative Minimum Tax was not indexed, and now a tax intended for small numbers of extremely wealthy families is applied annually, for better and for worse, to a larger than originally planned number of US households.
Let’s look at how we can use adaptive and parameterized feedback control to simplify the campaign finance reform problem.
Comprehensive campaign finance reform is hard to agree on. The Citizens United ruling in 2010 let more money flow into campaigns than individuals can compete with, and gerrymandering makes Congressional (House) districts predominantly non-competitive. The status quo gives a pronounced advantage to defenders of the status quo.
To apply an adaptive algorithm to campaign finance reform, let’s first divide and conquer: break the problem down into smaller pieces. Let’s start by tackling blow-out districts in which there is either no out-party candidate (IE the incumbent is the only major party candidate) or the victory margin in the previous general election was greater than say, 40%. Whereas it is exceedingly difficult to pass campaign finance reform for all districts, it may be relatively easy to put laws in place to cover the blow-out districts, particularly if we do not too-greatly threaten the incumbent.
Like the COLA, we need some measured values that will control the amount of money provided instead of just writing that dollar amount into law. Instead of some fixed amount, like $100,000, the out-party nominee will receive funds based on some measured values. The social Good to be stabilized here is the ability of the voters to hear competing messages. So, the measures should reflect the ability of the out-party candidate to get his or her message to the public.
We’ll base the adaptive and parameterized campaign finance law for the blow-out districts on the cost of running a given number of ads in the largest media market that reaches into the district. There were 64 districts without competitors in 2016 and another 95 with victory margins of 40% or more. Just as the thermostat has distinct modes for heating and cooling, we’ll try to pass an adaptive campaign finance reform that only applies to these 159 blow-out districts (about 1/3). In these districts, an out-party candidate will receive enough public money to make a showing, where “enough money” depends on the district. By defining the amount by political impact instead of dollars, we will tend to give more dollars to republicans than democrats, because the democrats tend to come from urban areas with expensive media markets.
Suppose the law provides funds to run 25 60-second ads reaching 200,000 viewers. In 2018, the law would provide $135,800 to a republican opponent for Rep Maloney in D-NY-12, who had a 66% margin in 2016 in the New York media market. The same law would provide $57,500 to a democratic opponent for Rep Walden R-OR-02 who had a 43% margin in 2016 in the Yakima media market. This seed money is unlikely to tip the balance, but it should at least help voters in a few more blow-out districts hear an opposing point of view.
Next, let’s tackle the public financing of the slightly competitive races, such as the elections with 20% — 40% victory margin in the previous general election. There is often an out-party competitor in those districts, but the competitor lacks the stature of his or her incumbent opponent. The problem in these districts is not the existence of competition, but rather the existence of viable competition. There are enough out-party voters for the out-party candidate to be known, but their political work will inevitably lack the visibility of a member of congress. The campaign finance law in these districts, which would be much more contentious to pass, could perhaps center on providing out-party candidates with loyal opposition visibility. For example, a tax on campaign expenditures in these districts could be used to fund some public project to be chosen by the voters and managed by the losing candidate in the 1–2 years following the election.
Finally, in the districts with margins less than 20%, the incumbent representatives probably feel that the margins are too close for comfort. They will resist any assistance to their competitors. Efforts to control the spending in these districts also run up against first amendment protections. And, by the way, what exactly is the social Good in reducing the dollar amount of campaigns in which both sides can raise enough money to compete? In these districts, I believe, the social Ill to be combated is the overall degradation of civility by campaign ads. Perhaps we can build a legislatively viable feedback control law around objective measures of civility. Assuming we can construct appropriate measures, perhaps we can set a negative tax rate around them. For example, if voters are better able to correctly answer factual questions about an issue after viewing your ad, you get x% per factual question back on the ad buy. If a random panel of independent reviewers consistently agree that your ad shows your opponent and his/her ideas respect, you get some multiple of y% back on the ad buy. The voters of the district would have some say in the relative balance of x and y, so they can decide what’s more important.
By dividing difficult problems into distinct sub-domains, we may be able to find sub-domains where passage of a law is more tractable than is whole-domain, comprehensive reform. Within any domain, we should take a moment to consider what social Good we are attempting to promote, define measures of that Good, and then construct the law via feedback control on those measures. Within these (and other) laws, we should use computed values rather than fixed values to define the impact. Parameterized and adaptive feedback control laws are the way of the world, except for our actual laws. We should fix that.
