Agent skill · vasilyu1983
foundations-decision-theory
Decision-theory primitives for uncertain choices, utility, Bayesian decisions, regret, value of information, MCDA, options, and bandits. Use when choosing under uncertainty.
What it needs
About 11k tokens when loaded.
What this skill does
Decision Theory Foundations 11 canonical decision-theory primitives for decisions under uncertainty. Each primitive is a formal tool with defined inputs, outputs, and failure modes. Primitives are domain-agnostic: the same expected-utility calculation that gates a product launch gates a capital investment; the same EVPI formula that sizes a market research study sizes a pre-launch pilot. When to Apply Apply decision-theory when: Single irreversible call under uncertainty (launch / kill / restructure) Value-of-information question — "is the next experiment worth running?" Real-options framing — staged investment with kill criteria Multi-criteria choice with explicit weights (MCDA, AHP) Multi-armed bandit allocation between treatments under regret minimisation Skip and use simpler alternatives when: Decision is reversible and low-cost — just try it; analysis paralysis costs more than the wrong choice Multiple agents with strategic interaction — use foundations-game-theory Causal "did X cause Y" question — use foundations-causal-inference A clear oracle exists (test suite, KPI threshold) — use the oracle All candidate options are dominated by one option on every criterion — no decision-theory needed EVPI is much smaller than the cost of acquiring info — skip the study and decide now Contents Quick Reference Primitive Index Formal Supporting Theory Misuse Boundaries When Expected-Value Reasoning Breaks Down Elicitation Failure Modes Machine-Elicited Probabilities Decision Checklist Anti-Patterns Composition Recipes Workflow ASCII Flow Related Skills Fact-Checking --- Quick Reference # Primitive When to Reach For It --- ----------- ---------------------- 1 Expected Utility (EU) Ranking risky options when outcomes are commensurable 2 Bayesian Decision Updating action after observing evidence; minimizing posterior expected loss 3 Minimax Regret Adversarial or ambiguous probability; Savage-style robustness 4 Value of Information Deciding whether to run an experiment, study, …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills vasilyu1983/foundations-decision-theory