Why social simulations need uncertainty

In the real world, life is rarely predictable.

Policies have unintended consequences, people misinterpret events, and societies react in ways that defy neat cause-and-effect logic. If a social simulation is too clean, too deterministic, it risks becoming nothing more than a scripted play-through — informative perhaps, but stripped of the messy realism that makes its insights truly valuable.

The Illusion of Certainty

Traditional simulations often take a straightforward path: feed in the data, run the model, and watch events unfold in a fixed sequence. While these models can be useful for testing a narrow hypothesis, they don’t reflect how real communities behave.
In reality, public perception can diverge dramatically from factual events. A successful policy might still be met with resistance if it’s framed poorly. A rumour can spread faster than an official statement. The “truth” is filtered through bias, personal history, and incomplete information.

Enter Uncertainty as a Feature, Not a Flaw

In Sundial Sim, we address this through the Ambiguity Engine — a system designed to model the gap between reality and perception. It allows events to be seen differently by different personas, injects misinformation into media streams, and distorts memories under stress.
The result? Two runs of the same scenario can evolve in very different ways, even when starting from identical conditions.

Why This Matters:

• Realistic outcomes: You see the kinds of partial successes, misunderstandings, and overreactions that occur in the real world.

• Robust planning: Policymakers and strategists can test how ideas survive under imperfect communication.

• Better storytelling: Writers and game designers can create worlds where uncertainty drives plot twists and character decisions.

Modelling Human Behaviour Means Modelling Imperfection

Human beings are not perfect information processors. We make decisions based on limited data, gut feeling, or trust in certain sources over others. In social simulations, modelling only the logical “ideal” choice strips away the very behaviours we often want to understand — fear, hope, overconfidence, or confusion.

By integrating uncertainty, simulations become tools for exploring ranges of outcomes, not just a single “most likely” path. This makes them more valuable for:

• Risk assessment — Understanding the spectrum of possible futures.

• Resilience testing — Seeing how systems adapt to misinformation or misperception.

• Education — Showing students how history could have taken different turns.

Predictable Is Boring. Variable Is Powerful.

If you can run a simulation twice and get exactly the same result, you’re not really simulating a society — you’re replaying a script.
Uncertainty transforms a simulation from a static report into a living experiment, where cause and effect are shaped by human complexity. For researchers, strategists, and storytellers, that’s where the real insights — and surprises — live.