Affirmative Action

Affirmative ActionYesterday I talked about how playing chess against a wide variety of players in the internet age improved my game at a much faster rate than when I played against the same opponent again and again. This led me to the conclusion that variety of experience leads to a better life and improved skill. I want to take this argument and apply it to the idea of affirmative action.

As usual, I think it is a good idea to actually define what we are talking about in order to fully understand it and come to accurate conclusions with our critical thinking skills.

Affirmative action is a relatively simple idea. A particular group of people is underrepresented in a situation and laws are created so that this group must be given an equal opportunity to participate. For example; a study reveals that while Martians represent 8% of the total population of Utopia City they account for 1% of students at Utopia University. A law is passed that forces Utopia U. to make certain 8% of its incoming class is of Martian descent.

It seems a quite reasonable solution to the problem and becomes even more reasonable when the problem is related to active discrimination against the party in question.

In the United States the original affirmative action laws, signed by John F. Kennedy on March 6, 1961, were created to counteract racial bias against black U.S. citizens. It originally prohibited discrimination against people based on race, creed, color, or national origin.

The advantage to creating such laws is that the Martians get a fair chance to participate at Utopia U. Another advantage is that we expose all our students to a wider array of cultural ideas and this makes them a more rounded and essentially better people.

The disadvantages are that such laws work against institutions that are not practicing discrimination. If I run Utopia U. and my only criteria for admission is the students with the best grades then I’m forced to enroll Martians with lower scores at the expense of a potential students who have a better chance to succeed. This is, in itself, discrimination.

So, what’s the solution?

To my way of thinking there should simply be laws against discrimination but everyone should be able to hire, enroll, or otherwise deal with people as they see fit. If a case of discrimination can be proven then the violator should face whatever punishment the law suggests, fine or prison. The idea that we must have 8% Martians at Utopia U. as a way of trying to monitor discrimination is fine but it is not actual proof. We might have only 1% Martians because only 1% are qualified to get in.

The advantage of experiencing life more fully is not one the government can solve. We must actively try to experience life more fully and meet different types of people as I discussed yesterday. If we do this we become better and our friends and relatives will copy the behavior. I just don’t think the government can legislate this solution as well-intentioned as the idea might be. 

It’s fine to use statistical analysis to look for anomalies and then investigate potential discrimination but I think it’s a mistake to insist upon particular numerical values. The Supreme Court of the U.S. largely agrees with this point of view.

I’m of the opinion that affirmative action should largely be phased out although discrimination laws should certainly be kept in place. I see the racism problem as largely, although certainly not completely, solved in the United States. If we can instill a Meritocracy based system then all such nonsense can finally be put to rest.

One of the ways to do this is to always critically analyze a situation and make the best decision. The best decision is blind, like justice, of things like race, creed, sex, handicap, or other potential discriminations. Keep in mind that what is best for you and your future involves making good decisions. You want to surround yourself with people who are best equipped to handle the job regardless of any other factor.

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Tom Liberman
Sword and Sorcery fantasy with a Libertarian Twist

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