Great point! Uncertainty really does explode the further we look ahead, and we need to handle that without giving up our long-term responsibilities.
How Time × Scope can deals with it
1. Separate “how much we care” (δ) from “how sure we are” (ρ).
δ tells the AI how strongly we want to value future outcomes.
ρ(t) is a confidence term that shrinks as forecasts get fuzzier — e.g. ρ(t)=e^{-kt} or a Bayesian CI straight from the model. Revised utility: U = Σ [ δ^t × ρ(t) × w_i × v_i(t) ].
2. Set a constitutional horizon (T_max).
Example rule: “Ignore outcomes beyond 250 years unless ρ(t) > 0.15.”
Stops the system from chasing ultra-speculative tail events.
3. In practice it’s already truncated.
Training usually uses finite roll-outs (say 100 years) and adds model-based uncertainty: low-confidence predictions get auto-discounted.
So, δ keeps our ethical aspiration for the long term alive, while ρ(t) and T_max keep us realistic about what we can actually predict. Experts can update the ρ(t) curve as forecasting methods improve, so the system stays both principled and practical.
Thanks. I think as time horizon gets bigger, uncertainty may also get bigger, and this should be calculated into the expected value formula.
Great point! Uncertainty really does explode the further we look ahead, and we need to handle that without giving up our long-term responsibilities.
How Time × Scope can deals with it
1. Separate “how much we care” (δ) from “how sure we are” (ρ).
δ tells the AI how strongly we want to value future outcomes.
ρ(t) is a confidence term that shrinks as forecasts get fuzzier — e.g. ρ(t)=e^{-kt} or a Bayesian CI straight from the model.
Revised utility: U = Σ [ δ^t × ρ(t) × w_i × v_i(t) ].
2. Set a constitutional horizon (T_max).
Example rule: “Ignore outcomes beyond 250 years unless ρ(t) > 0.15.”
Stops the system from chasing ultra-speculative tail events.
3. In practice it’s already truncated.
Training usually uses finite roll-outs (say 100 years) and adds model-based uncertainty: low-confidence predictions get auto-discounted.
So, δ keeps our ethical aspiration for the long term alive, while ρ(t) and T_max keep us realistic about what we can actually predict. Experts can update the ρ(t) curve as forecasting methods improve, so the system stays both principled and practical.