The strongest arguments for atheism aren’t really related to atheism, but about how the human mind works and what makes something a good explanation.
We know why we have particular emotions. Anger protects us from harm, and thus dying. Feelings of friendship/bonding with others gives us access to resources and mates; it’s very hard to live alone without having had others to teach you how to do so or build the infrastructure that allows this. Many other animals have these same emotions, and for the same reasons: to not die and/or perpetuate their genes. The ones that don’t have these emotions usually die pretty quickly.
Why would a god have these emotions? There’s no underlying reason for a god to love or to get angry. A god that loves makes about as much sense as a god with a penis.
And then there are the reasons why we believe things in the first place. How are our beliefs formed? For many of the things we believe, we do so because of our feeling of certainty:
A newspaper is better than a magazine. A seashore is a better place than the street. At first it is better to run than to walk. You may have to try several times. It takes some skill, but it is easy to learn. Even young children can enjoy it. Once successful, complications are minimal. Birds seldom get too close. Rain, however, soaks in very fast. Too many people doing the same thing can also cause problems. One needs lots of room. If there are no complications, it can be very peaceful. A rock will serve as an anchor. If things break loose from it, however, you will not get a second chance.
Is this paragraph comprehensible or meaningless? Feel your mind sort through potential explanations. Now watch what happens with the presentation of a single word: kite. As you reread the paragraph, feel the prior discomfort of something amiss shifting to a pleasing sense of rightness. Everything fits; every sentence works and has meaning. Reread the paragraph again; it is impossible to regain the sense of not understanding. In an instant, without due conscious deliberation, the paragraph has been irreversibly infused with a feeling of knowing.
Try to imagine other interpretations for the paragraph. Suppose I tell you that this is a collaborative poem written by a third-grade class, or a collage of strung-together fortune cookie quotes. Your mind balks. The presence of this feeling of knowing makes contemplating alternatives physically difficult.
Did you get the same inability to explain the paragraph using some other concept? Take note of that: You really don’t have any control over how certain you feel about things. Just like other emotions, the feeling of certainty is generated unconsciously. The next obvious question would be “What sort of brain algorithm generates your feeling of certainty?” More on that below.
Experience teaches us what stimuli make us angry, or jealous, or happy, sad, etc. Sometimes, the feeling is unwarranted and using our self-reflection we can determine that feeling angry about a particular situation isn’t justified. What’s dangerous is this: Our feeling of certainty feels good. At least, it’s much more pleasant than the feeling of uncertainty. And in that sense, we generally never stop to reflect on why our feeling of certainty might not be correct. Unlike with, say, jealousy.
The rabbit hole of why we believe what we do goes a lot further than this. Books like Thinking, Fast and Slow about our cognitive biases go into a lot of this. The major premise of that book is that we have two types of thought engines. A “fast” engine (System 1) and a “slow” engine (System 2). These two engines are good at different tasks: the fast one is good at recognizing faces or voices, the slow one is good at math. The fast one is good at social interaction, the slow one is good for abstract/impersonal concepts.
Generally, the fast engine is the one that’s in charge, and is responsible for telling the slow engine to start up (the fast one is also the one responsible for the feeling of certainty). Problem is, the fast engine has to be trained on when a task should be handled by itself or when it should give a problem over to the slow engine. It’s not very good at doing this intuitively. But for many of us, a problem might have been answered already by the fast engine and when challenged that’s the only time the fast engine uses the slow engine: To defend the fast one’s conclusion. And a lot of the time, the fast one’s conclusion will be for some social goal: Status, friendship, not ending up dead, and so on.
Our brains are actually more complicated, or modular, than the System 1 and System 2 way of explaining it. There actually seem to be multiple modules in our brains, and the ones that use information don’t explain their “reasoning” to the ones that talk to the outside world. Our brains are more like Congress, with some congresspeople acting on behalf of the overall “fast” engine or “slow” engine. The you that you feel is “you”, speaking to the outside world, is more like the press secretary for Congress.
There are a few experiments that show that when communication is physically severed between the two halves of the brain, each side of the brain gets different information. Yet, the part of the brain that does the speaking might not be the part of the brain that has the information. So you end up with rationalizations like split brain patients grabbing a shovel with their left hand (since their left eye was shown snow) while their right eye sees a chicken. When asked to explain why they grabbed the shovel, they — well, the side of their brain that only sees the chicken — make up an explanation, like the shovel is used to scoop up chicken poop! That press secretary, pretty quick on his feet.
But this doesn’t just happen with split brain patients. It seems to happen a lot more than we think, in our normal, everyday brains.
So for example, there was one experiment where people were asked to pick their favorite pair of jeans out of four (unbeknownst to them) identical pairs of jeans. A good portion of the people picked the jeans on the right, since they looked at the jeans from left to right. But they were unaware that that was their decision algorithm, and they rationalized their decision by saying they liked the fabric or the length or some other non-discriminating fact about the jeans. Liking the fabric of one pair of jeans more than the others was demonstrably false since the jeans were identical, yet that was the reason they gave. There’s still no persistent across the isle partisanship in your fully functioning brain, so the press secretary has to still come up with a good, socially acceptable story about Congress’ decision for the general public’s consumption. The part of our brain that ‘reasons’ and explains our actions, neither makes decisions, nor is even privy to the real cause of our actions.
The tl;dr version is this. Our brains are good at social goals. And unless we’ve been trained on it, it’s not so good at forming true beliefs about the non-social world. If we had some machine was was designed to analyze electromagnetic radiation as seen in space and pointed that machine at its own circuitry, it would interpret everything about itself in the manner of cosmic rays. Similarly, if we had a machine (our brain) that interprets everything through the lens of social interaction, and pointed it at the universe, it would interpret everything in the universe as some manifestation of social rules.
And this is what happens. Our default is to treat a lot of non-social things as social. It’s why things like animism and magical thinking are prevalent. It’s why we call planets “planets” (Greek for wanderer), the Milky Way a galaxy (gala is Greek for milk. In our case, Hera’s milk). If someone “thinks really hard” about a problem, they’re more than likely using the tools meant for social problems, not the tools meant for solving non-social questions.
So if we don’t have control over our feeling of certainty, what’s a System 2 way of making sure that we have correct beliefs about non-social things? How can we be sure that we aren’t just defending a belief that we initially arrived at unconsciously? Or, more generally, what are some unbiased traits that good explanations share? What makes something a bad explanation? Since we’re operating under uncertainty (since we can’t trust our feeling of certainty), we have to use methods for explaining our uncertainty logically and consistently.
Let’s look at the:
Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.
Which is more probable?
- Linda is a bank teller.
- Linda is a bank teller and is active in the feminist movement.
What does this have to do with good explanations? Most people will say that it’s more likely that Linda is a bank teller and is active in the feminist movement. While that might seem true socially (i.e., being a feminist and a bank teller seems to tell a better story about Linda), it’s mathematically impossible; the population of people who are bank tellers is larger than the population of people who are bank tellers and in the feminist movement. That’s why this is called the conjunction fallacy. The conjunction of A and B is necessarily smaller than A individually (or B individually).
All else being equal, a good explanation has fewer unnecessary conjunctions than bad ones. Taken further, an explanation with two known facts as conjunctions is more likely than an explanation with one known fact and one unknown fact. The more unknown facts you use to support your explanation, the less likely it is. This is generally called Occam’s Razor.
So for example, a noise at night. A tree hitting your window at night has fewer assumptions that need to be true than an alien invasion in your house. Trees hitting windows only require things that we already know to be true. Alien invasions require a lot more things to be happening in the world that we don’t know to be true (e.g., the possibility of intelligent alien life, of interstellar/intergalactic travel) than just trees and wind. That goes into the next thing that good explanations have.
Good explanations are more commonplace (more mundane) than bad ones. If you’re walking down the street and hear hooves clicking on the street, it’s probably a deer or a horse. Not a zebra or a cow. Or a hooved alien from Jupiter. The corollary for commonplace is that extraordinary claims require extraordinary evidence. Hearing hooves isn’t unlikely enough to suggest that what you hear is a hooved alien from Jupiter. You need evidence that’s a lot less likely to happen than that.
Another facet of good explanations is that they explain only what they intend to explain and very little else. It’s the difference between using bug spray to kill a spider over setting fire to your house to kill it; good explanations are precise in what they explain.
As an example, let’s say you’re a student at uni. You know one of your TAs, Anna, only uses red ink when grading papers. But the other TA, Jill, uses a variety of colored ink (red, blue, green, black, orange, purple, etc.) to grade the papers. You get your grade on a quiz back one day and it’s a grade you disagree with. The ink on it is red. Based on only this information (e.g., assume they’ve graded equal amounts of papers at this point and they have similar handwriting), which TA was more likely to have graded your paper? It’s certainly possible that Jill did, after all, she has been known to use red, but it’s more likely that Anna was the one who graded it since she only uses red. The lesson here is that, the more possible things your explanation can explain, the less likely it is to explain a particular instance.
Now notice the words I’m using: Likely, probably, possible. I’m not reinventing the wheel by saying that we need a logical framework for dealing with uncertainty, and one has already been created: Probability theory. For the conjunction fallacy, this works. The conjunction of 90% and 50%, or 90% * 50% is less than both 90% and 50% (it’s 45%). Commonplace is another way of saying prior probability. And when we talk about prior probability, we’re usually talking about.
Now, Pr(Claim | Evidence) reads “the probability of claim given evidence”. The short formulation of Bayes Theorem (BT) is Pr(Claim | Evidence) = Pr(Evidence | Claim) * Pr(Claim) / Pr(Evidence). An extraordinary claim, that is, a low prior probability claim, needs a correspondingly low probability evidence. And if you have some equation that is 100 * 4 / 5, the result will be a lot closer to 100 than it is to 4 or 5.
BT also explains why Anna was more likely to have graded the paper than Jill. Let’s say Anna is represented as dice that is 1s on all sides, and Jill is normal 6 sided dice (it’s the reason I picked 6 colors for Jill above). Let’s further say you have a jar filled in equal amounts with the normal 6 sided dice and the 1 sided dice; the jar is 50 / 50 of each. You’re blindfolded, told to pull a die from the jar and roll it. You’re told that you rolled a 1. What’s the probability that you grabbed the Anna dice (the 1s on all sides) or you grabbed the Jill dice (normal 1 – 6 dice)? The probability of rolling a 1 given the 1 dice is 100%. The probability of rolling a 1 given the 1 – 6 dice is 1 / 6, or around 16%.
For this we use the long form of BT: Pr(Anna | One) = Pr(One | Anna) * Pr(Anna) / [ Pr(One | Anna) * Pr(Anna) ] + [ Pr(One | Jill) * Pr(Jill) ]. What we end up with is around an 86% chance that you grabbed the Anna dice. If you follow this, you can tell that the more possible numbers the Jill dice has, the less likely it is that it can account for rolling a 1. Another way of phrasing “precision” is that there’s a punishment for spreading yourself too thin, of trying to hedge all bets, when trying to explain something.
So, tl;dr the qualities of good explanations are that they are on the likelier side of Occam’s Razor, are mundane, and precise. There are others, but this is probably (heh) getting too long.
Notice that I hardly ever mentioned god or atheism in these sections. Especially the second part. That’s because I think the strongest arguments for atheism aren’t about atheism per se, but are in general strong arguments for good thinking. They take into account our imperfections as human beings, especially in regards to how people think and act, and attempts to account for those failings. It seems to me that god(s) are what happen when social brains are trying to explain a fundamentally impersonal universe. And when that happens, those personal explanations for impersonal events tend to fail the logic of dealing with uncertainty.