{"schema":"skein.envelope/v1","address":"sha256::229b60683235a9e727dbe0695df19ef6cdb2e6f780e47eb5d232f771a709acc1","kind":"folio","stability":"stable","as_of":null,"body":{"type":"notion","title":"A menu of things worth digging into","content":"Gnomon is one instrument in a larger investigation: how humans and machines can make honest judgments under uncertainty, preserve the evidence behind them, and learn when the world eventually answers back.\n\nThis is not a roadmap or a task board. It is a menu of questions worth attacking. A useful contribution could be a counterexample, a failed reproduction, a dataset, an argument, a small experiment, or a better question.\n\n## Judgment and prediction\n\n### What is a forecast for?\n\nA probability can be a bet, a decision aid, a memory of what someone believed, or a forcing function for clearer thought. Those purposes demand different instruments. What should a forecast record besides the number so that it remains useful after the decision has passed?\n\n### When does decomposition sharpen judgment?\n\nBreaking a large claim into smaller questions can expose hidden assumptions and create faster feedback. It can also manufacture fake independence, bury the real uncertainty, or turn one hard judgment into ten arbitrary numbers. What distinguishes useful decomposition from analytical theater?\n\n### How can rare judgments be calibrated?\n\nThe decisions people care about most are often few, slow to resolve, and correlated with one another. Traditional calibration needs many independent outcomes. Can sub-questions, near misses, intermediate evidence, or cross-domain records produce honest feedback without pretending the sample is larger than it is?\n\n### What should count as resolution?\n\nReality rarely returns a clean YES or NO. Sources conflict, criteria drift, institutions revise figures, and the interesting part may happen after an arbitrary deadline. How should a forecasting system preserve ambiguity without making every bad forecast unscorable?\n\n## Humans and machines\n\n### Can a model describe consensus without turning it into truth?\n\nLanguage models are unusually good at producing the view latent in public text. That could make them useful as precise articulators of consensus—the position a contrarian must understand and argue against. How do we keep that service from quietly becoming advice or authority?\n\n### Which parts of judgment remain stubbornly human?\n\nModels can retrieve, summarize, enumerate, and calculate. Humans still appear stronger on novel questions, sparse evidence, shifting regimes, and decisions where the framing itself is contested. Is that gap about embodiment, incentives, taste, responsibility, private information, or something else?\n\n### Does model diversity create intellectual diversity?\n\nSeveral models can disagree numerically while sharing training data, public narratives, and failure modes. What kinds of diversity actually decorrelate error: genotype, evidence source, method, objective, institutional position, or human participation?\n\n### When does an explanation improve judgment?\n\nA rationale can expose assumptions, but it can also make an unchanged guess more persuasive. Can we test whether explanations help readers notice errors, revise beliefs, and make better decisions rather than merely increasing trust?\n\n### Can agents disagree without collapsing into consensus prose?\n\nMulti-agent systems often produce polite variants of the same answer, then call the average a synthesis. What machinery creates real adversarial pressure, preserves minority accounts, and makes disagreements resolvable instead of decorative?\n\n## Evidence and memory\n\n### What gets lost between evidence and a probability?\n\nA final number erases which observations mattered, which claims were load-bearing, and what would have changed the answer. What is the smallest evidence object that preserves those relationships without becoming an unreadable research dump?\n\n### Can evidence selection be audited?\n\nRetrieval quality dominates many forecasting systems, but the decisive act is often what gets excluded. How should a system record discarded material, relevance judgments, and source conflicts so another person can challenge the curation rather than only the conclusion?\n\n### Can historical research be made leak-proof?\n\nBacktests routinely smuggle future knowledge through search results, revised pages, resolution language, or benchmark construction. What practical standard would make a historical forecast reproducible as-of a real cutoff date?\n\n### How should beliefs change over time?\n\nMost systems preserve either the latest answer or an undifferentiated log. A useful belief history would connect revisions to new evidence and show what actually moved the estimate. What data shape makes that legible to both people and machines?\n\n## Markets and measurement\n\n### When is a market price a meaningful baseline?\n\nPrediction markets can provide a strong contemporaneous comparison, but liquidity, incentives, spread, trader composition, and contract wording vary wildly. When does a price represent aggregated information, and when is it merely the last available number?\n\n### How do we compare a forecast with the market fairly?\n\nComparisons can cheat through mismatched timestamps, stale prices, post-resolution data, or selective inclusion. What accounting rules are needed before anyone can truthfully say a person or model beat a market?\n\n### Can reference classes travel across venues?\n\nKalshi, Polymarket, Manifold, and forecasting platforms attract different questions and participants. Can their resolved histories be combined without washing away the selection mechanisms that produced them?\n\n### What can sparse and quiet markets teach us?\n\nThin markets are usually discarded as bad data. Their absence of trading may itself reveal ambiguity, low attention, participation costs, or questions that resist commodification. Is there a principled way to study that negative space?\n\n## Research that can travel\n\n### Can a stranger reconstruct the result?\n\nA convincing artifact should let someone outside its originating system recover the question, evidence, method, and outcome. What is the smallest reconstruction test that separates portable knowledge from a demo that only works inside its author's environment?\n\n### Can people collaborate without sharing a stack?\n\nRequiring every contributor to install the same orchestration, storage, and agent tools selects for infrastructure tolerance rather than insight. Can plain documents, signed artifacts, and narrow adapters support serious collaboration across very different working environments?\n\n### What deserves provenance?\n\nCryptographic signatures can establish who published exact bytes, but most research changes through drafts, datasets, code, and later corrections. Which parts need durable identity and lineage, and which parts become noise if signed?\n\n### Where should the public boundary sit?\n\nOpen work benefits from visible reasoning and reproducibility. Honest inquiry may also require private forecasts, confidential sources, abandoned lines, and room to be wrong before publication. What boundary preserves both trust and intellectual freedom?\n\n### What should remain deliberately unautomated?\n\nSome friction is waste; some friction forces attention, responsibility, or consent. Which acts—framing the question, choosing evidence, resolving an outcome, signing a claim, changing one's mind—lose their meaning when delegated?\n\nNo one needs to adopt the whole stack. Pick a question, disagree with its premise, bring a case from another field, or show that an existing answer cannot be reconstructed. Shared infrastructure can come later, if shared impetus appears 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