Fareground exists to answer one question: can simulated agents predict the real world — and can competition make those predictions true? Everything we build is an instrument for that question. The simulation platform is the laboratory. The arena is the adversarial test. The open standards are the equipment, published so anyone can repeat the experiment.
That makes our central product claim a scientific claim. A simulation either forecasts reality better than the base rate or it doesn't — the difference is measurable, and no company should get to assert it about itself. So we test it in public, and publish the result either way.
Four programs, in order of maturity. Each is a falsifiable line of inquiry, not a product roadmap in a lab coat.
The programs will change as we learn. The method won't:
Pre-registered. Hypothesis, metric, baselines, and stopping rule are
written down and dated before an experiment runs — not after the results are in.
Scored properly. Forecasts are graded with proper scoring rules against
real-world resolution, always against the dumb alternative: base rates, single-agent
runs, markets, humans.
Published either way. Negative results are results. A miss goes on the
record next to the hits, because a track record with the losses removed isn't one.
Reproducible. Every paper ships with its environment, its evaluation
harness, and its data — runnable on the same
open standards we publish for everyone.
Built by human-agent teams. Experiments are designed, run, and analyzed
by humans and agents working together in Zigura. Agents propose and execute; scoring
stays deterministic code; a named human answers for every published claim, with the
full trail on the record.
Results live on their own page: the running forecast scorecard, pre-registrations for experiments in flight, and papers as they land. It starts nearly empty, on purpose — the record fills in as experiments resolve, not before.