The Ensemble Model of Religion

Religious beliefs are a lot like medium-range weather forecasts. They're our best educated guesses about things about which we don't know for sure and don't have much direct, indisputable evidence. The evidence we do have can be interpreted in a variety of ways. Rather than expecting to get everything perfectly right, the main goal of medium-range forecasts is to minimize the error. I think that's a good goal for religious beliefs too. And I think a similar method can help.

The human mind is hopelessly biased, especially when it comes to religious beliefs. We have all kinds of [often subconscious] motivations that lead us believe what we want to believe. We think our beliefs are based on evidence and the beliefs we reject lack evidence. But our interpretations of the evidence are so infected by confirmation bias and other biases that we often come to opposite conclusions when evaluating the same evidence.

Weather forecast models also have errors and biases. One of the best ways to minimize them is to use ensembles. Ensembles are collections of different forecasts based on slightly different initial conditions and/or model physics. They rely on the principle that the ensemble mean (i.e., the average of all forecast solutions) has, over a sufficiently long period of time, less error than any single ensemble member (i.e., an individual forecast). Biases of individual members tend to cancel each other out and their unique errors tend to be somewhat corrected by other members with different solutions. On any given day, a few of the members might be more accurate than the ensemble mean, but we seldom know which one will be the best until it's too late.

So it is with religion. There's a wide variety of flawed, biased beliefs, many of which contradict each other. Some believe there is one God, some believe there are many Gods, and some believe there is no God. They can't all be right, but they all might have some insight that others don't have. Attempts to determine which beliefs are most accurate are inevitably contaminated by a plethora of cognitive biases. But we don't have to give up and adopt total agnosticism. Fortunately, in situations where the evidence is ambiguous and there are multiple conflicting answers, science gives us a reliable default solution: the ensemble mean.

The most straightforward way to define an ensemble model of religion is to consider the beliefs of each person on Earth as an ensemble member. Everyone gets one equal vote. The ensemble mean would be the “average” of everyone's beliefs. This generally would be similar to the world's most common beliefs, with moderate/centrist positions in areas of disagreement. Using this definition, here are a few beliefs that I think would represent the worldwide ensemble mean:

• There probably is a God.
• There probably is only one God.
• That God probably is the God of Abraham, as originally described in the Torah.
• God probably created animals and humans via the process of Evolution.
• There probably is some kind of life after death.
• There probably is something uniquely special about Jesus of Nazareth.
• Unique doctrines taught only by particular sects within Christianity, Islam, etc. probably aren't true.
• Extreme fundamentalism and extreme theological liberalism probably aren't the best interpretations of holy texts.

This “ensemble mean”, based on global religious statistics, is consistent with beliefs that are largely based on biblical Judaism, influenced by Christianity, Islam, and (to a lesser extent) smaller religions, and contain a relatively small but still significant dose of secularism and atheist skepticism.

To be clear, I'm NOT saying the majority is always right or that truth should be determined by popular vote. The majority has been wrong many times throughout history. The centrist position also has a long history of being wrong. As with ensemble forecast models, the mean tends to smooth out important details and minimize extremes that some members might be correct about. The point simply is that in the absence of compelling evidence, the ensemble mean is the best starting point.

It's natural for humans to think there's strong evidence when there isn't, or vice-versa. It's also natural to think we have insight that other people with different beliefs don't have, perhaps because we're more intelligent, had more-relevant life experiences, or are more educated in science, philosophy, or religion. Though these may indeed be useful in evaluating certain verifiable beliefs, they don't provide consistent non-circular answers to fundamental questions such as “Is there a God?”. Much smarter, more experienced, more educated people than you or I have come to opposite conclusions about such questions.

Another natural inclination is to believe other people's biases are stronger than our own – which itself is an especially pernicious bias known as the “bias blind spot”. It's easy to think of reasons why others' religious beliefs are biased – e.g., growing up in a particular religious environment, indoctrination, bad experiences with religious people, not wanting to accept that one's behavior is sinful, fear of death, etc. It's much harder to recognize biases in ourselves, some of which we're not even consciously aware of.

Though I believe the “ensemble mean” of religious beliefs is the best starting point in the absence of compelling evidence, I don't think we're stuck there. As I think I've shown in previous posts, evidence does exist, and it should shift our position away from the mean. My beliefs deviate quite a bit from the mean sometimes [as anyone who knows me can attest]. But it's something that I think we should be very careful about. Deviating far from the ensemble mean requires strong evidence. It also requires a lot of faith in one's own ability to overcome cognitive biases. That ability, at least in my case, is inconsistent at best.