Yoon Shwe Yee@yoon1466
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Regret Meter: A Behavioural Decision Audit
Every day, people make consequential decisions: quitting a job, sending an angry email, deploying an untested hotfix, making a big purchase while under emotional pressure, time constraints, or urgency bias. The core problem isn't a lack of intelligence; it's a lack of structured self-reflection at the exact moment a decision is being made. Most decision-support tools either give a vague "trust your gut" answer or produce an opaque AI score with no visible reasoning, leaving users unable to trust, challenge, or learn from the output. Regret Meter solves this by turning a user's dilemma, emotional state, and time horizon into a transparent, auditable regret-risk score. Rather than presenting an unexplained percentage, the system runs a deterministic decision-audit pipeline that calculates five measurable risk vectors: impulse, financial drag, irreversibility, opportunity cost, and social stakes and weights them into a final score (Safe Bet, Cautious Bet, or Critical Hazard). Crucially, every score is accompanied by a four-step "thinking pathway": sentiment/impulse detection, Type 1 vs. Type 2 door classification (how reversible the decision is), long-term opportunity-cost evaluation, and final vector weighting. This gives users the "why" behind the "what." Target users are anyone facing a high-stakes or emotionally charged decision under time pressure, from individuals contemplating a personal choice at 2 a.m. to teams debating whether to ship an unverified production change. Users interact by describing their dilemma in plain language, selecting their current emotional state and a time horizon (24 hours, 6 months, or 5 years), then receiving an instant score alongside a live reasoning summary. From there, they can dive into a full Forensic Report that connects each risk vector to detected cognitive biases (like present bias or action bias), a worst-case timeline, and a concrete safer alternative or revisit past decisions through audit history, export a PDF/JSON record, or copy a shareable brief. What makes Regret Meter creative and unique is its single source of truth architecture: one stored audit object powers every surface: the live gauge, the forensic report, history, PDF exports, JSON exports, and the copy brief, so the explanation a user sees immediately after scoring is guaranteed to match what they see in an exported report months later. This is paired with genuine explainability: rather than a black-box AI verdict, the pathway is a structured trace of input signals and a published scoring formula, so users can inspect and challenge individual inputs (was that really an "irreversible" decision?) rather than blindly trusting a number. This design makes Regret Meter effective and efficient at addressing decision paralysis and impulsivity: it reduces the chance a user treats an AI-driven number as an unsupported opinion, teaches decision quality concepts (urgency, reversibility, opportunity cost) through repeated use, and produces reviewable, auditable records that support team retrospectives and reduce undocumented, intuition-only calls, all without needing a live connection to an external prediction model, since the scoring engine is fully deterministic and self-contained.
27 Sep 2026
