There is usually at least a tiny grain of truth hidden somewhere behind even the most absurd belief. For example, if you lend a book to someone, you might as well kiss it good-bye. You won’t get it back without asking for it back, and maybe not even then. If you’re a library, you’ll probably get it back; but for anyone else, lending books is about the same as giving them away.
What is the belief? People don’t return borrowed books when they borrow them from other people.
There is a grain of truth here. Some people aren’t good about returning borrowed books and perhaps that’s most people, depending on who you lend your books to.
Is this true on average? I don’t know and doubt that you know either. Even so, I believe it. How about you? Do people return books other people lend to them?
Now suppose that I ask you to lend me a book. If you think people don’t return borrowed books, you might give the book to me, not expecting it to be returned. However, if you think people are good and usually return borrowed books, you still may not lend it to me, depending on the value of the book to you. But why?
When we make choices involving other people, we are always playing the odds, using our own idiosyncratic computational system. We attach odds to their doing or not doing something, the likelihood of their behaving one way or another, the probability of their reacting as we expect or surprising us instead. Our action, behavior or involvement with them then depends on those calculations. First comes the calculation and then we act, based on our nearly instantaneous decision process.
When dealing with other people, we may occasionally stop and think, do some research, carefully consider why we will or will not make a particular choice, and otherwise be more deliberate than we typically are. Even then, we mostly focus on our side of the equation. Will we be better off or less well off, what is or is not in our interest, what will be the consequences for us if we do or do not proceed? Most of the time though, we just go with our first calculation. We make a flash judgment about the person and the situation and then do or not do whatever the calculation calls for.
Do we often get burned or disappointed? If not, we are definitely toward the safe end, running the risk of being too skeptical, too mistrusting. If we often are disappointed or get burned, we are too far toward the other end where naive and gullible come to mind.
As we see, whether borrowing a book or having any other interaction with people, we infrequently give much if any consideration to the criteria we use with respect to the other person, especially if we haven’t spent much time and effort in getting to know them. — And we have actually invested that level of time and energy in only a very small minority of people we know or come into contact with. – For most people, most of the time and in most situations, we put them into our sorting algorithm, assigning them to “average,” on whatever sorting criteria we are used to using.
How do we know what is average? Well, we usually don’t. It’s like people who borrow books from other people. On average, do they return the borrowed books without needing to be reminded? We don’t actually know; but nevertheless, we likely have a quick criterion we instantly apply whenever anyone asks to borrow a book, particularly if they want to borrow our first edition of a rare book or perhaps our checkbook.
Here’s the rub. It’s that idiosyncratic computational system and its algorithm. The automatic criteria we use to judge people and to make decisions and choices is based on averages, as we understand them. The problem is twofold. First, “average” is a tricky concept. If on average, men are more violent than women, knowing that tells us nothing about most men or most women, because most men are not more violent than most women. It’s only at the extreme that men are more violent than women. If we drop the extremes from our algorithm, men and women have about the same tendency toward violence. Along with being true for men and women, it’s also true for whites and non-whites, citizens and emigrants, younger people and older people, poor people and the more affluent, or most any other way you tend to classify people.
The same fallacy of average slips into our computational algorithm in far more insidious ways. Let me repeat an earlier sentence. “Along with being true for men and women, it’s also true for whites and non-whites, citizens and emigrants, younger people and older people, poor people and the more affluent, or most any other way you tend to classify people.” Is it still slipping past you?
When talking about violence, the point was made that, at the extreme, men tend to be slightly more violent than women. In the same context, there is an unspoken implication that extreme violence somehow applies to whites and non-whites, citizens and emigrants, younger people and older people, poor people and the more affluent, when it only applies to men and women as classifications. Whites are no more violent than non-whites, citizens no more than emigrants, younger people no more than older people, poor people no more than affluent people.
We could spend an hour or so just enumerating the classifications we use in our computational algorithms for relating to and interacting with other people. For each classification, we have automatic criteria we use to signal us about who they are, how and what they think, how they do and do not behave, what they will and won’t do, what they can and can’t be trusted with, where they do and don’t fit in relation to us, and on and on. For a few of those criteria, we know why we use them, how valid they are, when they do and do not apply, and when we tend to misuse them. Think of these as our carefully curated criteria for judging others. We also know that they are based on more solid ground than tradition, culture, group norms, and personal belief and preference. They have a strong claim to reasoned and validated truth.
Along side our relatively small stash of carefully curated criteria for judging and relating to other people, we have a much larger stash of non-curated criteria that we regularly use on a day to day basis. We are not aware of most of those criteria; and for the ones we are aware of, we don’t know much about them or where they came from. We just have them and use them with confidence. Those non-curated criteria are the foundation of our prejudice and more often than not, are that prejudice itself.
Are we prejudiced? Most definitely – every last one of us. We all have our supplies of non-curated criteria we use when judging and interacting with other people. The good news is that most of our non-curated criteria serve us well enough, are usually reasonably in sync with reality and are seldom harmful or counterproductive. They serve us and our interests without impinging on the rights, interests and well being of others.
The bad news is this. Far too many of us do have non-curated criteria that are harmful to the needs, interests and well being of others. Many of us also have what we think are curated criteria that are little more than inherited beliefs and values that we think are real and valid but are not. They represent what we can think of as our cognitive and cultural blind-spots. Collectively these represent prejudice in its worst sense. When this level of prejudice spreads too far for too long, as it has, we end up with xenophobia, institutional racism, ideological schisms and pervasive divisiveness.
What to do? Try to initially relate to and interact with everyone through a default filter, using the same set of criteria for each person with whom you have contact. Avoid classifying anyone. Force or at least encourage yourself to withhold judgment until you know enough and have had enough experience with the individual or group to have an opinion and perspective informed by their actions and behavior, not by your non-curated criteria. It’s pretty simple, although not all that easy.
Please relate to me and judge me based on who my actions and behavior tell you I am and not on who you assume I am. If you’ll do that for me, I’ll for sure make my best effort to do the same for you.