Here on Earth, who serves in public office depends a handful of factors -- some of which are unsettling. In the US there is the infamous Wall Street-Washington Corridor where financial regulators are former bankers (and vice versa). Lobbyists from finance and other industries are able to influence legislation. In the grand scheme of things, the US is still an interesting example but by no means the worst of the bunch. Here is Transparency International's 2017 Corruption Perception Index:
World Building Premise
Now I know what you're thinking: "This isn't about world building!" (Shakes fist)
Well, buckle your seatbelts, because this is where we leave Earth behind! You see, I'm trying to build a world whereby only the most robust to corruption are able to hold office. And to achieve this element for my world, I will need a set of scientifically backed heuristics. If you are wondering why I would take such an approach, let's look at one quick analog:
Air Pilot Analogy
Air Pilots must meet demanding requirements, you have to be:
- at least 64" tall
- at least 160 lbs
- at least 20/70 vision
The air force doesn't impose these requirements out of spite or anything; it simply comes down to the fact that the pilot will have an unacceptable risk of failing to execute his job without fulfilling the requirements. This is the same philosophy and level of rigidity I want to have for my world's public office holders. Now this analogy is not intended to be taken to an extreme, but it will nonetheless provide a few useful concepts to apply for corruption.
Corruption Robust Indicators
Corruption robust indicators are the crucial missing ingredient in my world. Again drawing inspiration from the air force analogy, I want these indicators to be quantifiable. So instead of quantifiable requirements to become a pilot like height, weight, eyesight, etc., we need quantifiable metrics for tendency for corruption. Of course tendency for corruption is a latent variable and not easily measured directly, but that doesn't mean we can't find unbiased estimators for tendency of corruption.
There are many forms of corruption, so to keep the scope within reason, let's limit corruption to Transparency International's definition:
Corruption is the abuse of entrusted power for private gain. It can be classified as grand, petty and political, depending on the amounts of money lost and the sector where it occurs.
My Progress Thus Far
In contrast to Transparency International where we look at an index that reflects the public's perception of corruption, what I have to do is estimate how corrupt the public office holders are directly, which is a different beast. The best idea I had thus far was replicating the Stanford Prison Experiment. This way neutral parties can evaluate prospective public office holders and how well they handled being in power (if they were the guards). The trouble with this idea, is if this methodology were ever to be revealed, then people could just be on their best behavior for the duration of the study to defeat the analysis. Afterwards, when they take office, they could still exhibit corrupt behavior.
According to known science, what is one but no more than three possible heuristics working in concert to gauge what an individual's tendency for corruption is? To keep it short, you may just refer to such heuristics as CRI: Corruption Robust Indicators.
Quality Metric: Answers that provide solutions using studies/evidence that are already in existence and/or well-documented are weighted higher. Conversely, answers that are mostly speculative will not score as high.
We are not trying to model tendency for corruption; that will be far too difficult. To model something outright means we must explain all the variation in the data perfectly. That is unlikely to be possible with tendency for corruption because of the complexity involved. We are just trying to give decent heuristics / estimations, much like meteorologists -- meteorologists can't model something as complex as the weather and so sometimes they get it wrong. But we can all agree that we are better off with their scientifically-based guesses.