Isaac Asimov built Foundation around one of the most seductive ideas in science fiction: what if history became predictable?
Hari Seldon’s psychohistory cannot tell you what one person will do tomorrow. It models the behavior of huge populations over long spans of time. When Seldon foresees the fall of the Galactic Empire and thirty thousand years of chaos, he creates a plan to shorten the coming dark age.
The premise turns statistics into politics.
If you are looking for books like Foundation, the interesting question is not whether they have galactic empires. It is whether they make prediction consequential.
Here are six books and series that do.
MAYA: Seed Takes Root by Anand Gandhi and Zain Memon
MAYA: Seed Takes Root takes the Foundation problem and gives the predictive system far more data.
On Neh, billions of people connect to a living network of Maya trees. The Divyas can observe this vast flow of behavior and information, model likely futures, and intervene before damaging outcomes unfold.
This is Prediction as government.
The difference from psychohistory matters. Seldon predicts mass behavior across centuries and tries to guide civilization through planned crises. Maya’s prediction network is intimate. It reaches into ecology, economics, culture, and desire.
That also turns the story into science fiction about free will. If the system knows which conditions will make you choose A instead of B, does it need to order you to choose A?
The book keeps pushing toward the uncomfortable possibility that highly effective governance might look less like command and more like environmental design.
Outlook India’s interview, MAYA as a mythology for the 21st century, describes the project as a framework for debating control, agency, cognition, and contemporary systems.
Visually, MAYA reads like a science fiction epic built for the biggest possible screen. Conceptually, it belongs next to Foundation because the central source of power is foresight.
It’s one of those Science fiction books that should be movies, since prediction becomes easier to dramatize when the audience can see competing futures, interventions, and consequences.
Calling MAYA The most intricate worldbuilding in modern science fiction is subjective; the concrete case is that biology, architecture, economics, and political power are designed to feed the same model.
Foundation by Isaac Asimov, obviously
Before moving on, it is worth remembering what Foundationactually does.
Psychohistory works at population scale. That limitation gives the story room to think about institutions, crises, and long-term planning rather than simple prophecy.
Across the wider series, Asimov also tests the model by introducing factors that psychohistory struggles to absorb, including the Mule and later questions about Gaia and collective consciousness.
People working through Isaac Asimov books often discover that the series becomes increasingly interested in the ethics of choosing a future for everyone else.
Infomocracy by Malka Older
Infomocracybrings large-scale prediction closer to contemporary politics.
Its future world is organized into global microdemocracies, while a powerful information system called Information sits at the center of political life. Elections, data, propaganda, and public opinion become infrastructure.
The book is useful here because it asks what happens when the systems that describe society also influence society.
Prediction and persuasion start to blur.
For readers searching for authors like Isaac Asimov, Malka Older is less similar in style than in appetite. Both are willing to turn a political mechanism into the main speculative device.
The Three-Body Problem by Cixin Liu
The Three-Body Problem is driven by first contact rather than statistical history, but it shares Foundation‘s fascination with long time horizons.
Once humanity learns that an alien civilization is coming, every present-day choice gets measured against a future that may arrive generations later.
The trilogy repeatedly asks how societies behave when they know something enormous about the future but cannot know all the details.
Prediction becomes strategy.
A Memory Called Empire by Arkady Martine
A Memory Called Empire does not give its characters a mathematical model of history. Instead, it shows how political systems create expectations about what will happen next.
Empires forecast loyalty. Bureaucracies model behavior. Diplomats read signals. People act based on what they think other people will do.
That human-scale forecasting makes the novel a useful companion to Foundation. History may not be mathematically predictable, but political life depends on prediction anyway.
Le Guin-style anthropology also sits behind the book’s treatment of empire and assimilation.
The Quantum Thief by Hannu Rajaniemi
The Quantum Thief operates at a much stranger technological level, with memory sharing, public-key privacy, uploaded minds, and radically transformed human societies.
Its relevance to prediction comes from information. Who knows what? Who can access whose memories? How does privacy change when identity itself can be copied, shared, or hidden?
The book treats information architecture as social architecture.
The Mountain in the Sea by Ray Nayler
The Mountain in the Sea is another useful counterpoint because it deals with minds that may not fit human predictive models at all.
A corporation and researchers try to understand a sophisticated octopus culture. The challenge is partly alien cognition. Prediction is only as good as the model behind it, and a model built around human assumptions can fail badly when the mind on the other side works differently.
That brings ecology and cognition back into the question.
The End of prediction is still a choice about people
The most useful distinction between these books is the scale of what gets predicted. Foundation predicts populations. Infomocracy tracks political information and voting behavior. The Three-Body Problem forces civilizations to reason across centuries. MAYA imagines prediction reaching into ecology, economics, desire, and individual behavior at once.
That shift changes the moral problem. Forecasting a hurricane can help people escape danger. Forecasting a person’s preferences can help a service recommend a song. Forecasting a population’s behavior can help a government plan transit. None of those acts is automatically sinister. The trouble begins when prediction gives one party the ability to quietly shape the conditions that everyone else experiences.
This is why political systems matter more than raw accuracy. Who owns the model? Who can inspect it? Who can refuse it? Who benefits when the prediction is wrong? Does the person being modeled ever get to know how the model changed the world around them?
Those questions make predictive science fiction feel much closer to ordinary life than a crystal ball.
Why Asimov’s idea keeps returning
People still read Asimov’s novels because psychohistory captures a basic fantasy of modern institutions: that enough information might turn social chaos into something manageable. Governments, companies, campaigns, markets, and platforms all want better forecasts.
Science fiction can make that desire visible by pushing it past today’s technical limits. If prediction became astonishingly good, would society become calmer and fairer, or would predictive power simply become another resource to monopolize?
That is the shared question behind these Foundation-adjacent books. There is one more reason these stories work so well: predictions create feedback loops. Tell a market that a company will collapse and investors may help collapse it. Tell a government that a neighborhood is dangerous and policing can change what happens there. Tell a person that an algorithm knows what they want and they may begin using the recommendation as evidence about themselves.
That means prediction is rarely passive. Once a forecast enters the world, people react to it. Institutions allocate resources around it. Some actors try to fulfill it, others try to escape it, and powerful groups may decide that shaping reality is easier than improving the model.
For readers interested in fiction about free will, this is where the genre gets especially sharp. Freedom is not only a metaphysical question about whether choices are predetermined. It is also a political question about whether someone else can predict, price, rank, and alter the options you encounter before you make them.
A good place to start depends on which version of the problem interests you. Read Foundation for statistical history, Infomocracy for data and elections, The Three-Body Problem for strategy over centuries, and The Quantum Thief for a society where information and identity are inseparable. Read MAYA if you want prediction turned into the everyday infrastructure of government and desire.
That is also why these stories age well. The specific technology changes, but institutions keep trying to turn uncertainty into something measurable, governable, and profitable. The best novels then ask who gains power from the measurement and who becomes easier to manage because of it.
The future is not interesting because somebody can see it. It becomes interesting when seeing the future changes who gets to decide what happens next.
Prediction stories also become more interesting when the forecast changes the behavior it describes. A government that expects unrest may create the conditions for unrest by policing more aggressively. A person told that a relationship will fail may become guarded enough to help it fail. A market model can move the market it is trying to measure. Those feedback loops turn forecasting into action. The central question stops being whether the model is accurate and becomes who gets to act on its predictions, who can challenge them, and who is forced to live inside the consequences of being classified in advance.
Prediction is always a political problem
The genius of psychohistory is that it makes prediction feel scientific while immediately turning it into an ethical mess.
If you can forecast a crisis, can you justify manipulating people to avoid it? If your model is usually right, how much freedom should everyone else surrender to it? What happens when the model encounters a person, culture, or intelligence it cannot explain?
Those are political systems questions as much as scientific ones.
The best Foundation-adjacent books understand that the interesting part of prediction comes after the forecast.
The real story is who gets to act on it, who has to live with the intervention, and who gets blamed when the model fails.
