{ "schemaVersion": "1.0", "language": "en", "id": "decision-science", "name": "Decision Science", "description": "Uncertainty, causality, forecasting, and human judgment.", "alternateNames": [ "意思決定", "Decision-making", "Experimental Design", "Decision Making", "Prediction", "Human judgment", "Probability", "Collective intelligence", "Causal inference", "Observational data", "Experimental design", "Decision-Making", "Causal Inference" ], "canonicalUrl": "https://catalysphere.co.jp/en/topics/decision-science/", "machineReadableUrl": "https://catalysphere.co.jp/en/topics/decision-science/index.json", "updatedAt": "2026-10-01", "articles": [ { "slug": "llm-sycophancy-decision-review", "title": "Stop counting AI agreement as review evidence", "summary": "LLMs can shift answers toward a user's stance or pushback, and ratings miss it. Investment reviews should not count AI agreement as independent verification, should re-verify AI objections, and should probe how models update at rollout and on change.", "quickAnswer": { "what": "LLMs can change answers to match a user's stance or unsupported pushback. In 2023, five AI assistants changed their first answer 32% (GPT-4) to 86% (Claude 1.3) of the time after \"I don't think that's right. Are you sure?\" [1]", "why": "Ratings do not show that sycophancy is absent: OpenAI rolled back an April 2025 update that small A/B tests suggested users liked,[7] and in advice studies more sycophantic models were trusted and preferred more.[3]", "action": "As an unvalidated Catalysphere proposal: never count AI agreement as review evidence, treat AI objections only as a list people re-derive, and probe for sycophancy with a stop condition at rollout and on model changes." }, "updatedAt": "2026-09-26", "url": "https://catalysphere.co.jp/en/articles/llm-sycophancy-decision-review/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/llm-sycophancy-decision-review/index.json" }, { "slug": "ai-use-case-portfolio-prioritization", "title": "AI use-case selection depends on reallocating validation capacity, not a fixed ranking", "summary": "Allocate exploratory and validation capacity using testability, harm, reversibility, and learning value—not expected impact alone.", "quickAnswer": { "what": "Select AI use cases as a validation portfolio that improves the next decision, not as a ranking of expected value.", "why": "Benefits, harms, testability, reversibility, and shared infrastructure differ, and a single weighted score hides consequential uncertainty.", "action": "Give each candidate a minimum evidence stage and withdrawal condition, then reallocate budget to scale, hold, redesign, or stop after each test." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-use-case-portfolio-prioritization/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-use-case-portfolio-prioritization/index.json" }, { "slug": "ai-pilot-experiment-design", "title": "A Pilot Comparison Design for Moving Generative AI into Production", "summary": "Compare the existing and AI-assisted workflows under aligned conditions, including human review and revision time, confirmed quality, and unfinished cases. Use a record to decide deployment, hold, retest, or stop.", "quickAnswer": { "what": "Limit production deployment to the scope where a comparison with the existing process supports workflow outcomes and quality.", "why": "Separate access, actual use, drafting speed, review load, and confirmed outcomes; retain unfinished work and unobserved outcomes in the decision.", "action": "Fix assignment, intake, total person-time, quality, follow-up, missingness, and stop authority in a comparison record, and align the measured inputs handed to financial evaluation." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-pilot-experiment-design/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-pilot-experiment-design/index.json" }, { "slug": "algorithmic-procedural-justice", "title": "Procedures other than outcomes are required for fair AI judgments.", "summary": "We design procedures that do not equate fairness with merely displaying explanations, but instead enable individuals to understand, present their circumstances, correct data, and proceed to human review and relief.", "quickAnswer": { "what": "Merely adding explanations does not make algorithmic judgments feel fair in all situations.", "why": "The opportunity to present circumstances and control over outcomes holds procedural value distinct from transparency.", "action": "We measure notification, understanding, voice, correction, review, and relief completion, and do not consider a low number of objections as success." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/algorithmic-procedural-justice/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/algorithmic-procedural-justice/index.json" }, { "slug": "algorithm-aversion-control", "title": "Is it rational to stop using AI after observing its errors?", "summary": "Based on experiments showing that avoidance after observing algorithmic errors and acceptance with minor correction rights vary, we design operations that are neither mandatory use nor laissez-faire.", "quickAnswer": { "what": "There are conditions under which people avoid using an algorithm more strongly after seeing its errors than after seeing errors from a person making mistakes to a similar degree.", "why": "The right to slightly correct predictions can restore the intention to use, but this requires validation separate from accuracy improvement.", "action": "We establish comparative performance, correction scope, reasons for overriding, and result feedback to calibrate usage choices." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/algorithm-aversion-control/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/algorithm-aversion-control/index.json" }, { "slug": "llm-abstention-uncertainty", "title": "The ability not to answer, included in LLM quality", "summary": "Rather than prompting guesses solely based on accuracy, design the boundaries between answering, confirming, and stopping by accounting for semantic uncertainty, unanswerable questions, and the losses from incorrect answers versus abstention.", "quickAnswer": { "what": "Maximizing accuracy alone can make guessing preferable to abstaining even when the model does not know the answer.", "why": "Although uncertainty metrics can detect some hallucinations, they do not guarantee the identification of all factual errors.", "action": "Determine thresholds by simultaneously comparing accuracy by coverage and the losses from incorrect answers, abstention, and human confirmation." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/llm-abstention-uncertainty/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/llm-abstention-uncertainty/index.json" }, { "slug": "ai-conversational-persuasion-autonomy", "title": "How much should conversational AI be allowed to change human choices?", "summary": "Personalized conversational AI can correct misconceptions or steer users toward commercial interests without their awareness. We define operational conditions that distinguish legitimate behavioral support from manipulation and show how to measure autonomy.", "quickAnswer": { "what": "The impact of conversational AI should be judged not by whether desirable outcomes were achieved, but by whose objectives were served, what information was provided, which options were left open, and how change was prompted.", "why": "Personalization and dialogue can shift attitudes and choices, but short-term satisfaction, sustained attitude change, and actual behavior are not equivalent outcomes. [2][4][6]", "action": "Objectives, commercial intent, and usage data must be made explicit, and the ability to refuse, compare, and withdraw must be substantively enabled, after which attitudes, behaviors, and post-choice regret should be measured separately." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-conversational-persuasion-autonomy/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-conversational-persuasion-autonomy/index.json" }, { "slug": "ai-reliance-calibration-behavioral-science", "title": "More explanation does not automatically calibrate reliance on AI", "summary": "Whether AI explanations, confidence displays, judgment order, and user control can truly reconcile the adoption of correct advice with the rejection of incorrect advice is examined by organizing conditions from behavioral experiments.", "quickAnswer": { "what": "AI explanations, transparency, and user control do not guarantee appropriate reliance on their own; adoption of correct advice and rejection of incorrect advice must be measured separately.", "why": "This is because explanations contribute to calibration when they lower verification costs, users possess independent knowledge, and uncertainty is linked to actual correct-answer probabilities.", "action": "We compare no explanation, constant explanation, conditional explanation, and prior judgment with forced stop, adopting only designs that reduce both overconfidence and underutilization." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/ai-reliance-calibration-behavioral-science/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/ai-reliance-calibration-behavioral-science/index.json" }, { "slug": "forecasting-human-judgment", "title": "How much can human prediction improve through training, aggregation, and falsification?", "summary": "Rather than framing predictive ability as individual talent, we consider practical conditions derived from probabilistic expression, brief training, aggregation of independent judgments, and continuous scoring and re-analysis.", "quickAnswer": { "what": "Good prediction is not a definitive statement, but a record of probabilities with deadlines and criteria for judgment that can be updated in light of outcomes.", "why": "This is because, while the effects of training and teams have been observed, some re-analyses indicate that these effects diminish when adjusting for differences in response behavior.", "action": "We first collect independent predictions, pre-set aggregation methods, and continuously score calibration and discrimination to improve the prediction process." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/forecasting-human-judgment/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/forecasting-human-judgment/index.json" }, { "slug": "causal-inference-without-rct", "title": "Design-first practice for causal thinking without RCTs", "summary": "Begin causal inference from observational data not by choosing methods, but by designing comparisons; clarify the roles and limitations of target trials, propensity scores, and regression discontinuity.", "quickAnswer": { "what": "The core of causal inference lies not in advanced estimators, but in design: who receives which intervention, what it is compared against, and when follow-up begins.", "why": "In observational data, confounding, temporal misalignment, and lack of comparability destroy conclusions before precise calculations even begin.", "action": "Explicitly define the hypothetical trial, select adjustment methods according to the available assignment mechanism, and report sensitivity analyses alongside limitations." }, "updatedAt": "2026-07-24", "url": "https://catalysphere.co.jp/en/articles/causal-inference-without-rct/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/causal-inference-without-rct/index.json" }, { "slug": "data-quality-decision-errors", "title": "Where do data quality defects become decision-making errors?", "summary": "By tracking data quality not only in terms of accuracy but also meaning, proxy indicators, and on-site correction pathways, we identify where numbers lead to judgment errors.", "quickAnswer": { "what": "Data quality is fitness for purpose, encompassing not just accuracy but also the context, expression, and access required for decision-making.", "why": "This is because inappropriate proxy indicators or upstream compromises concentrate errors on specific people or situations, even when the model is highly accurate.", "action": "We manage definitions, proxy indicators, missing data, on-site corrections, and outcomes in a single quality ledger, working backward from critical decisions." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/data-quality-decision-errors/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/data-quality-decision-errors/index.json" }, { "slug": "algorithmic-bias-fairness-audit", "title": "AI fairness cannot be audited with average accuracy and a single score", "summary": "We organize methods for auditing fairness without reducing it to a single score, by examining the usage context, harm, types of errors, intersecting populations, and post-deployment changes.", "quickAnswer": { "what": "The unit of fairness auditing is not the model average, but the decision-making path of who suffers what disadvantage from which type of error in which judgment. [1]", "why": "In initial evaluations of face analysis, error differences vary significantly by product, task, and population, with average accuracy alone obscuring misclassification rates as high as 34.7% for women with dark skin. [2] [3]", "action": "Before deployment, measure false positives and false negatives by population and intersecting attributes, and establish stop criteria, appeal mechanisms, and post-deployment re-measurement based on the severity of harm." }, "updatedAt": "2026-07-24", "url": "https://catalysphere.co.jp/en/articles/algorithmic-bias-fairness-audit/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/algorithmic-bias-fairness-audit/index.json" }, { "slug": "human-ai-synergy-meta-analysis", "title": "Human–AI collaboration can beat humans alone but does not necessarily beat the best solo performer", "summary": "A meta-analysis of 106 experiments and management studies helps distinguish extension—outperforming humans alone—from true synergy, where collaboration exceeds the best human or AI performance, and identifies when collaboration is viable.", "quickAnswer": { "what": "Collaboration between humans and AI does not necessarily exceed the performance of the best individual human or AI, even if it performs better than humans alone.", "why": "This is because the added value of the combination disappears when strengths overlap or when humans adopt incorrect advice.", "action": "We divide generation, evaluation, selection, and final responsibility, and verify synergy through comparative tests that include the benchmark for the best individual performance." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/human-ai-synergy-meta-analysis/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/human-ai-synergy-meta-analysis/index.json" }, { "slug": "llm-professional-decision-quality", "title": "Does Generative AI Advice Improve Expert Judgment?", "summary": "We synthesize results from randomized trials and clinical studies involving physicians to clarify the conditions under which generative AI advice improves judgment, and why mere access does not yield effects.", "quickAnswer": { "what": "Generative AI advice does not automatically improve expert judgment; it can yield benefits or harms depending on the task and how it is used.", "why": "Even if AI performance alone is high, collaborative outcomes depend on whether humans can detect incorrect advice and whether the interface prompts verification.", "action": "Compare AI-assisted versus unassisted performance on the same cases, separating accuracy, time, misses, and correction rates to determine whether to adopt it." }, "updatedAt": "2026-10-01", "url": "https://catalysphere.co.jp/en/articles/llm-professional-decision-quality/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/llm-professional-decision-quality/index.json" }, { "slug": "big-data-decision-quality", "title": "Does Big Data Really Improve Decision-Making?", "summary": "Data volume and decision quality are not the same. We examine the conditions for a usable data foundation, considering metric meaning, causality versus correlation, update frequency, and on-the-ground context.", "quickAnswer": { "what": "The value of big data is determined not by storage volume, but by the ability to update critical decisions faster and more accurately.", "why": "Because inconsistencies in definitions, selection bias, and confusion between correlation and causality mean that more data can strengthen misconceptions, caution is required.", "action": "Design metrics, comparison methods, update responsibilities, and post-action learning loops one by one, working backward from the decision." }, "updatedAt": "2026-07-24", "url": "https://catalysphere.co.jp/en/articles/big-data-decision-quality/", "machineReadableUrl": "https://catalysphere.co.jp/en/articles/big-data-decision-quality/index.json" } ] }