Ada Palmer

They reveal when the atmosphere is in a predictable state, ensemble members cluster together, versus a turbulent one, ensemble members diverge widely.

Ensemble prediction doesn't defeat chaos, it works along with chaos.

It accepts that specific trajectories cannot be predicted beyond a certain horizon, but reveals that the distribution of trajectories can be.

It's a fundamentally different kind of knowledge.

Not it will rain Tuesday but there's a 70% chance of rain Tuesday, with high uncertainty.

Asterisk asterisk asterisk.

Palmer's papal election simulation exhibits exactly the same structure, though she arrived at it independently and for different reasons.

Each run of the simulation starts from the same historical situation.

The date is 1492.

There are the same cardinals with the same resources, the same European powers with the same constraints.

But Palmer populates these roles with different students, each bringing their own judgment, risk tolerance, and strategic thinking.

Run the simulation once and you get a history.

One specific pope elected, one specific pattern of alliances, one specific set of cities burned.

Run it ten times and a pattern emerges that no single run could reveal.

Certain outcomes consistently occur, a powerful cardinal wins, war breaks out, Italian city-states suffer, while others vary widely, which specific cardinal, which specific alliances, which specific cities.

The simulation generates not a single counterfactual but a probability distribution across possible 1492s.

What emerges is a probabilistic model of the political situation of 1492.

Not Florence will be sacked but Florence survives in 70% of runs.

Not France will invade but French intervention occurs with near certainty, though the target varies.

This is the kind of knowledge ensemble prediction provides.