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Psychohistory: Can we predict the future of humanity?

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Isaac Asimov’s Foundation Series envisions a world where the future of humanity can be predicted through an interdisciplinary science called psychohistory. Let’s explore how this fictional concept relates to data science and what it means for our future, especially in the era of generative AI. 

Disclaimer: I will focus only on the concept of psychohistory and ignore all the specific events in the books to avoid spoilers. 

Foundation series and psychohistory

Imagine knowing what will happen to humanity 300 years from now. Will we spend portion of our lives in virtual reality? Will the production of toilet paper be optimized for unexpected events like pandemics? 🌏🦠🤒

The Foundation series, a classic in science fiction, introduces psychohistory—a fictional science combining sociology and mathematics to predict future events in large societies. This concept, spanning hundreds of years, raises intriguing questions about our ability to foresee the future. Without spoiling anything, let’s have a look at all of the axioms of psychohistory.

Original:

(1) The populations whose behavior was modeled should be sufficiently large

(2) The population should remain in ignorance of the results of the application of psychohistorical analyses because if it is aware, the group changes its behavior

Added later:

(3) There would be no fundamental change in the society

(4) The human reactions to stimuli would remain constant

(5) Humans are the only sentient intelligence in the galaxy

You may have already picked up on some craziness in these assumptions. They highlight the challenges of predicting human behaviour. What is a sufficiently large population? What if you predict something terrible? Wouldn’t it be better to share such a prediction? In the real world, psychohistory would face challenges related to three main areas – scale, limits of current knowledge, and ethics.

The issue of scale

There are physical challenges regarding the number of population members and, of course, the enormous time scale. The theory is inspired by the emergence of gas from billions of atoms, and similar amounts are considered when the first axiom talks about sufficiently large populations. As you might guess, one Earth cannot hold so many people, and we don’t have the technology to travel and live on different planets. 

Limits of current knowledge 

We can’t describe mechanisms underlying changes in societies mathematically with the required level of precision. We just don’t understand them well enough. Some would argue that we will never understand societal patterns enough because, unlike physical systems, social sciences deal with more uncertainty. However, some of that uncertainty could be understood with relevant data. 

Imagine a future where we spend a significant amount of time in a virtual environment, and our activity could be tracked. Wait, you don’t need to imagine it. How much does Google or Facebook know about you? 

This is also a question of consumer privacy. Do we want the data to be collected? This brings us to the last issue.

Ethics 

Hiding information about dangers sounds unethical, but there can be a situation when it’s justified to take such a risk. For instance, when you need to collect evidence to prove wrongdoing.  

Besides, it’s unlikely that any future iteration of our society would allow a group of scientists to cover disastrous predictions and deceive the public.

Science is about openness. 

Additionally, you can make a prediction and share it with people, but they must believe it to change their behaviour. How many people still doubt the existence of climate change or even Covid-19? 

Is psychohistory possible?

Depending on who you ask, you can get quite different answers. However, considering the points raised above, the short answer is no; as described by Asimov, psychohistory is not possible. 

Can we at least predict events in societies in the next 50 or 100 years? Even though many social scientists would disagree, I think the answer is “Maybe”. The amount of relevant data is growing every day and with the advancements in AI, who knows. Our knowledge of societies and patterns of their evolution is growing too. For example, weather forecasting wasn’t always as accurate as it is now. It was better models and data integration that led to the current success. With focused efforts, social sciences could try to do the same.

Data science vs. psychohistory

As all of the original Foundation stories were written when computers weren’t in everyone’s pocket, how exactly psychohistory works is not evident. For the sake of the comparison, I will assume that psychohistory is a combination of mathematical modelling and currently unavailable social science research. 

Mathematical modelling is when you develop a theoretical understanding of how something works and then translate that into the mathematical formulation.

This then allows you to simulate different scenarios and likely outcomes. We often call it prescriptive modelling because we can determine, a.k.a prescribe, the best course of action without living through all of the options. This approach is often used in decision science and was used extensively during the COVID-19 pandemic.

My favorite definition of data science says that ‘data science is the discipline of making data useful’. Data science is an umbrella term and if psychohistory was real, it would probably be covered by that umbrella.

Conclusion

So to conclude, predicting societal events for hundreds of years ahead is probably out of the question. Still, predicting societal events spanning between one or two lifetimes – well, that’s not too crazy. The question, however, won’t be whether we can do it but whether and when we should.

In the context of generative AI, predicting the future of how will the society operate and how we will work becomes even more intriguing. Introduction of Large Language Models and similar new techniques cause such a shift that it is difficult to imagine all the possible consequences. However, we can closely follow the AI developments to understand AI capabilities and invest into skills that cannot be replaced. In essence, when it comes to work, we need to focus on the changing landscape now rather then trying to predict the 50 years.

P.S. For those interested, there’s now an Apple TV adaptation of the Foundation series!

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Originally published on Hackernoon.com

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