There is no Natural Way of Life - Yuval Noah Harari - Sapiens - Joe Abittan

There is No Natural Way of Life

Are human beings naturally peaceful, or naturally violent? Are they naturally traders, or are they naturally competitors? Is it natural for them to pursue progress, or natural for humans to stick to tradition and avoid new ways of organizing the world around them? These questions rage every day in academic circles, on the news, in our offices, and everywhere that people gather. We like to believe that there are things that are simply natural for human beings, and things we consider natural are considered broadly good, while things that are unnatural are lumped in with everything bad and evil.
However, in his book Sapiens, Yuval Noah Harari argues that there is no natural way of life for Homo sapiens. Instead, according to Harari, there is a wide horizon of possibilities which includes, “…the entire spectrum of beliefs, practices, and experiences that are open before a particular society, given its ecological, technological, and cultural limitations.” Even for people living in the most remote, technologically limited, and culturally strict villages on Earth, there is a wide horizon of possibilities for what any individual or group could do. For those of us lucky enough to live in the United States, the horizon of possibilities is effectively endless. The ways in which we could live and experience the world are greater than what any of us could imagine, and all the different perspectives and permutations could be considered natural from a certain point of view – or unnatural from another. Trying to attach values such as good or bad, through labels of natural or unnatural, doesn’t really make sense for any given permutation chosen from the horizon of possibilities.
Harari continues, “The heated debates about Homo sapiens’ ‘natural way of life’ misses the main point. Ever since the Cognitive Revolution, there hasn’t been a single natural way of life for Sapiens. There are only cultural choices, from among a bewildering palette of possibilities.” Dating back at least 70,000 years ago, human tribes have varied and differed based on numerous factors. Looking at a single ancient tribe or group of humans and deciding that how they lived was natural gives us a misleading understanding of how we should live today. We can look back and find tyrannical leaders who conquered other tribes and sacrificed their victims to their gods, but this doesn’t mean it is natural for humans to be lead by a single genocidal tyrant. It is just as fair to look around today and see transsexual men and women cooperating and sharing virtual resources in a video game and make conclusions about what is natural for humans as it would be to look back at the genocidal tyrant, to look back at human groups from the days of the first books in the Christian Bible, or to look back at any other group of humans from any part of the globe since the Cognitive Revolution and decided that how people live, interact, behave, and interpret the world is ‘natural’. At each point in space and time there are options available to us based on the ecology of where we find ourselves, based on the technology and knowledge available to us, and based on many other factors we cannot enumerate. Some ways of living are more likely to help us and others survive, some ways of living are more likely to help us enjoy our lives, but that doesn’t mean they are natural, good, or will continue to help us survive and enjoy our lives indefinitely. There is no natural way of life for a human, only a staggeringly large set of possibilities.
A Bias Toward Complexity

A Bias Toward Complexity

When making predictions or decisions in the real world where there are many variables, high levels of uncertainty, and numerous alternative options to chose from, using a simple rule of thumb can be better than developing complex models for predictions. The intuitive sense is that the more complex our model the more accurately it will reflect the real complexity of the world, and the better job it will do with making a prediction. If we can see that there are multiple variables, then shouldn’t our model capture the different alternatives for each of those variables? Wouldn’t a simple rule of thumb necessarily flatten many of the alternatives for those variables, failing to take into consideration the different possibilities that exist? Shouldn’t a more complex model be better than a simple heuristic?

 

The answer to these questions is no. We are biased toward complexity for numerous reasons. It feels important to build a model that tries to account for every possible alternative for each variable, we believe that always having more information is always good, and we want to impress people by showing how thoughtful and considerate we are. Creating a model that accounts for all the different possibilities out there fits those preexisting biases. The problem, however, is that as we make our model more complex it becomes more unstable.

 

In Risk Savvy, Gerd Gigerenzer explains what happens with variance and our models by writing, “Unlike 1/N, complex methods use past observations to predict the future. These predictions will depend on the specific sample of observations it uses and may therefore be unstable. This instability (the variability of these predictions around their mean) is called variance. Thus, the more complex the method, the more factors need to be estimated, and the higher the amount of error due to variance.”  (Emphasis added by me – 1/N is an example of a simple heuristic that Gigerenzer explains in the book.)

 

Our bias toward complexity can make our models and predictions worse when high levels of uncertainty with many alternatives and relatively limited amounts of data exist. If we find ourselves in the opposite situation, where there is low uncertainty, few alternatives, and a plethora of data, then we can use very complex models to make accurate predictions. But when we are in the real world, like making stock market or March Madness predictions, then we should rely on a simple rule of thumb. The more complex our model, the more opportunities for us to misestimate a given variable. Rather than having one error be offset by numerous other point estimates within our model to reduce the cost of a miscalculation, our model ends up creating more variance and a greater likelihood that our prediction will be further from reality than if we had flattened the variables with a simple heuristic.