romainsimon/skills-for-decision-making0°

allocating-effort

仓库创建 2026年7月26日最近提交 10 小时前SkillHot 收录 2 小时前

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Splits scarce time, budget or traffic across competing products, channels, campaigns or variants by treating them as a multi-armed bandit - Thompson sampling over beta posteriors, with optimism for anything not yet tried enough to judge. Use when deciding what to work on next, how to divide a marketing budget, which product to prioritise, whether to kill something that is underperforming, or when picking a winner among test variants.

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# Allocating effort

Deciding what to work on next is a bandit problem: several options, each with an
unknown payoff rate, and every unit of effort spent on one is a unit not spent
learning about the others. The two failure modes are symmetric and both are common -
committing everything to the current leader before you know it is the leader, and
spreading effort evenly long after you do.

Thompson sampling resolves both without tuning, and its allocation has a plain-English
meaning: **each option gets the share of effort equal to the probability it is the
best one.**

## Workflow

```
- [ ] 1. Define the arms and what counts as a win
- [ ] 2. Count wins and losses honestly, including the zer

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