The Five Assumptions
Part I · The Engine — Chapter 4
The mechanism is only as sound as its premises. Five assumptions hold it up. Naming them precisely does two things: it tells you exactly when the mechanism applies, and it turns each "what if this fails?" into a concrete design requirement that the rest of the book addresses.
A1 — Shared reality
Claim. On most questions, a large fraction of resolvers eventually agree; their verdicts are positively correlated.
Why it's needed. Forecasters are paid for predicting resolver consensus. If there is no consensus to predict — if every resolver's verdict is idiosyncratic — then "consensus" is undefined and the target disappears.
What breaks if it fails. Forecasting degenerates into modeling each resolver separately; the shared world-model evaporates.
Where it's addressed. The architecture keeps the perception layer (forecasts, verdicts, world-model) global so the shared-reality population is as large as possible — see The Living System and Mathematical Core §5.
A2 — Dispersed capital
Claim. Reward budgets are not extremely concentrated.
Why it's needed. If one whale holds most of the budget, the money-optimal forecast is "predict the whale," not "predict consensus" — and aggregation collapses to the same failure as real-money markets under inequality.
What breaks if it fails. Capture: the world-model tracks one actor's beliefs.
Where it's addressed. This is the deepest cross-layer link in the stack: a demurrage currency structurally enforces dispersion, turning A2 from an assumption into a tunable inequality. See Value as Flow and the δ-dial in Mathematical Core §4.2.
A3 — Repeated game
Claim. Forecasters value a future stream of reward, so they keep their published beliefs current.
Why it's needed. Reputation only means something if stale beliefs cost you. A one-shot game gives no reason to update.
What breaks if it fails. Forecasts go stale; the world-model lags reality.
Where it's addressed. Inherent in the scoring design — reputation decays relative to peers who keep updating (Mechanism).
A4 — Tamper-evident timestamps
Claim. Forecasts cannot be backdated.
Why it's needed. The reward for being "surprising and early" is only meaningful if "early" can't be forged after the outcome is known.
What breaks if it fails. Anyone can claim, post hoc, to have predicted everything — the entire skill ranking becomes fiction.
Where it's addressed. A minimal timestamping primitive built from causal entanglement — no global blockchain required. See Mathematical Core §4.4 and the roadmap's shared-primitive analysis.
A5 — Non-reflexivity (the fragile one)
Claim. Forecasters' reports do not themselves change resolvers' verdicts.
Why it's needed. Proper scoring assumes you are predicting an outcome you can't move. If publishing a forecast causes the verdict it predicts, the rule rewards self-fulfilling prophecy instead of truth.
What breaks if it fails. The Keynesian beauty contest: forecasters predict an equilibrium they help create, and the system can converge on a stable, comfortable fiction.
Where it's addressed. This is the single most important open problem, and it gets a chapter of its own: Reflexivity and the Dark Room, formalized in Mathematical Core §2. The headline result is reassuring — linear influence is harmless; the danger is a sharp threshold that turns out to be measurable and controllable.