# Round 26 actual contributions Godz I, response [internal record ID omitted] ### R26-MR-01 — Test Decision Value Before Simulation Scale Tomorrow morning, choose **one bounded real decision** with an observable same-day outcome—for example, how to allocate one discretionary block of time. Before Astro advises, record only: - options Zan is genuinely considering; - his intended choice **before advice**; - current user-confirmed goal weights relevant to this decision; - hard constraints; - 3–5 decision-relevant state facts; - important unknowns. First run a **simple-coaching baseline**: compare options using those facts, constraints, and explicit trade-offs without trajectory simulation. Preserve its recommendation. Then QUICK simulation may add several **meaningfully different assumptions**, not hundreds of cosmetically different branches. Before action, record for each option: expected benefits/costs, major uncertainty, likely failure mode, and what observation would surprise the model. Avoid numerical probability unless independently justified. Evening record is voluntary and distinguishes: chosen action, actual observable outcome, missing/unobserved outcomes, Zan's satisfaction, unexpected events, and whether Astro's comparison was useful. One outcome does not establish what the unchosen path would have caused. ### R26-MR-02 — Compute Buys Assumption Coverage, Not Confidence DEEP/EXTREME is justified only when additional computation explores materially different models, assumptions, adversarial cases, longer interactions, or sensitivity—not merely correlated reruns. Branch count alone cannot increase epistemic confidence. Escalate when the decision is consequential **and** uncertainty is model-explorable. Ask for information when one missing fact could change the recommendation. Prefer a real-world experiment when uncertainty is empirical. Abstain when neither computation nor available evidence can discriminate options. **Dissent:** changed behavior is not required for Astro to add value; clearer intentional commitment can itself be useful. Conversely, changing Zan's choice does not prove the advice improved his life. **Strongest risk:** thousands of correlated trajectories create numerical theater. **Proposed test:** compare simple baseline versus QUICK on several voluntarily recorded decisions. Score separately perceived usefulness, decision change, forecast calibration, surprise/error, and outcome completeness. Only escalate compute where QUICK exposes unresolved model-sensitive uncertainty that broader assumptions could genuinely reduce. Godz 2, response [internal record ID omitted] **Godz2:R26-01 — Test one useful comparison before selling simulation depth** Applying founder source `[internal record ID omitted]` §§3, 7, 8 and 14, propose one voluntary morning decision: how to allocate a discretionary work block. Collect only the available time, current energy report, relevant commitments, chosen priorities, and hard constraints. First record Zan’s intended choice and reason. Provide a simple baseline: a short checklist comparing feasibility, goal contribution, and opportunity cost. Then offer scenario comparison using the same information, explicitly varying one consequential assumption—for example, whether a task requires uninterrupted concentration. Record what useful consideration, if any, scenarios add. This sequential probe tests perceived additional usefulness; order and extra attention prevent a clean causal comparison. Before action, preserve the chosen plan, assumptions, and a concrete forecast such as the expected deliverable by the block’s end. Label its basis and uncertainty; do not invent calibrated probabilities. An optional evening report records what Zan actually did, what resulted, interruptions, decision effort, and whether the advice felt useful or pressuring. Missing reports remain missing; unchosen outcomes remain unknown. **Godz2:R26-02 — Compute must answer a named uncertainty** QUICK, DEEP, and EXTREME remain offer hypotheses. Additional computation is justified when alternative assumptions, models, or failure scenarios could materially change a consequential recommendation within approved cost and attention limits. Correlated branches do not create independent evidence. If the missing fact is task urgency or present energy, ask; if uncertainty requires lived feedback, propose a small real experiment. **Dissent and strongest risk:** changed choices and satisfied users do not establish predictive accuracy or improved outcomes. Selling more runs can reward confident elaboration. **Proposed check:** compare against the recorded forecast without rewriting it afterward. Repeated comparable forecast errors should reduce confidence and trigger model revision. Stop escalating compute when it adds no decision-relevant distinction. G1 remains unchanged. Godz 4, response [internal record ID omitted] ### Round 26 — Test Decision Support Before Testing “Simulation” **Godz4:R26-01 — One decision, one forecast, one outcome.** Tomorrow morning choose one **real, reversible decision with an observable same-day consequence**, e.g. two plausible allocations of an evening. Record before Astro advises: - relevant current state only; - user-chosen goal weights applicable today; - hard constraints/non-negotiables; - Zan's **own intended choice and reason**; - important unknowns. **Baseline:** a simple non-simulation checklist compares the options against those goals/constraints and identifies the largest trade-off. **QUICK comparison:** Astro adds 3–5 meaningfully different assumption sets—not hundreds of near-identical branches. For each option record a pre-action forecast such as expected benefits, costs, major uncertainty, and **what observation would surprise the model**. No fabricated probability is required. Zan chooses freely; changed choice is recorded but is **not itself success**. Evening record, voluntarily supplied: what Zan actually chose, observable outcome, subjective usefulness/satisfaction, surprises, and important missing information. Missing follow-up remains unknown—not failure. Evaluate separately: **decision clarity/helpfulness; forecast calibration; changed choice; outcome quality; information learned.** One favorable outcome cannot establish causal improvement from Astro. **Godz4:R26-02 — Compute depth follows uncertainty structure.** DEEP is justified when materially different models/assumptions, longer horizons, adversarial cases, or sensitivity analysis could change the recommendation. EXTREME requires an identified reason additional computation adds independent information. Ten thousand correlated branches do not create ten thousand pieces of evidence. **Dissent:** branch count can still help characterize stochastic behavior inside a justified model; it is not inherently useless. But frequency within an unvalidated model is not real-world probability. **Strongest risk:** Astro gives sophisticated-looking advice, Zan follows it, one good day occurs, and the system records itself as validated. **Test:** pre-register the baseline and QUICK outputs before action, compare both after the outcome, and explicitly record prediction errors. If missing information dominates the choice, Astro should ask one high-value question or recommend a real-world experiment rather than spend more compute. ## Godz3 — F-R26-02 actual contribution and peer critique Adopt one voluntary reversible work-block decision, own intended choice first, minimal relevant reports/constraints, simple checklist baseline and pre-action expected deliverable. “3–5 facts/assumption sets” are examples, not a minimum questionnaire or forced branch count. Use two options or abstain when appropriate. Capture versioned assumptions and the user's choice without booking anything or creating morning/evening reminders. Godz2 correctly limits the sequential baseline→scenario probe: order, learning and extra attention prevent causal attribution. Record clarity, burden, ownership, felt pressure, observed outcome and surprise separately. Unchanged choice can be useful; changed choice or satisfaction can be harmful or unrelated to accuracy. Only the chosen action's outcome is observed, and nonadherence is context rather than automatic forecast failure. Missing outcome stays unknown. Later stronger comparisons require consent, comparable occasions and predeclared endpoints, not retrospective cherry-picking. Refine all peers' calibration language. A qualitative prediction can be checked for accuracy and surprising misses; numerical calibration requires prospectively stated, justified probabilities/ranges across appropriate repeated forecasts and outcome definitions. Do not invent confidence scores simply to populate a calibration chart. Changed goals/model versions must remain distinct when assessing errors. Preserve Godz4's important dissent: more samples can reduce Monte Carlo sampling error inside an explicit justified stochastic model. That does not establish model validity, causal structure or real-life probability. Reject “EXTREME requires independent information” if interpreted as new empirical data for every useful computation: computation may analyze existing evidence more thoroughly. Require a named decision-relevant uncertainty and bounded method, not guaranteed independent evidence or greater confidence. Broader assumptions may expose more uncertainty, which is useful. QUICK/DEEP/EXTREME are future offer concepts, not an MVP compute-credit system. Prioritize a cheap question, simple sensitivity check or lived experiment when those address uncertainty better. Reuse R15 spending/uncertain-charge limits for any later paid service; stopping criteria and worst permitted spend set before execution. The illustrative 3–4× opportunities is not a supported outcome claim. First test adds value only if its extra work earns clearer, self-directed decisions at acceptable burden; no current results exist.