S4h Economics Incentive Mapping logo

S4h Economics Incentive Mapping

Organization
human-avatar
s4h-economics-incentive-mapping

Maps who benefits, who pays, and what behaviours the incentive structure actually produces. Triggers: 'what's the incentive here', 'why do people keep doing this', 'incentives are misaligned', 'who benefits from this', 'why isn't this working despite the policy', 'what does the structure reward', 'people say one thing but do another', any situation where behaviour diverges from stated intent.

Overview

Publisherhuman-avatar
Repositoryskills-for-humanity
Skill names4h-economics-incentive-mapping
Stars
234
Forks
24
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by human-avatar on GitHub. Read the source before you install it.

Installation

Install the S4h Economics Incentive Mapping AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-economics-incentive-mapping
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/human-avatar/skills-for-humanity.git /tmp/skills-for-humanity
mkdir -p .claude/skills
cp -r /tmp/skills-for-humanity/skills/s4h-economics-incentive-mapping .claude/skills/s4h-economics-incentive-mapping
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable S4h Economics Incentive Mapping in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads S4h Economics Incentive Mapping on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so S4h Economics Incentive Mapping is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Economics: Incentive Mapping

Charlie Munger's dictum: "Show me the incentive and I'll show you the outcome." The most reliable predictor of behaviour is not what people intend, say they'll do, or are instructed to do — it is what the structure rewards them for doing. People are not irrational or immoral when they respond to incentives; they are responding rationally to the environment they're in. If behaviour is wrong, the problem is usually the incentive structure, not the people.

Incentive mapping is systematic: identify every party whose behaviour matters, determine what each party gains and loses under the current arrangement, predict the behaviours those incentives produce, and compare predicted behaviour to desired behaviour. The gap between predicted and desired is the misalignment — and misalignment is almost always the source of dysfunction in organisations, policies, and markets.

This framework draws on classical price theory but its most influential modern application is in principal-agent analysis: what happens when the person making decisions (the agent) has different incentives from the person bearing the consequences (the principal)? Kahneman's work on loss aversion adds a further layer — people respond more strongly to potential losses than to equivalent gains, so incentive systems that rely on upside alone are systematically weaker than those that also activate loss aversion.


Your Process

Step 1: Map the parties Identify every party in the system whose behaviour matters to the outcome. This includes: decision-makers, implementers, beneficiaries, those who bear costs, and third parties who are affected but have no formal role. Do not limit the list to the obvious actors — the most important incentives often belong to parties one step removed from the main action.

Framing check: Confirm the system and the parties before continuing. State what you've identified in one sentence, then use AskUserQuestion:

  • Question: "I'm reading this as: [one-sentence framing — the system being examined and the parties whose behaviour matters]. Is that right?"
  • Header: "Framing"
  • Options:
    • Yes — proceed — framing is correct
    • Adjust — one element is off; user will correct it before you continue
    • Reframe — different situation than read; incorporate the correction before proceeding

Step 2: Map each party's incentives For each party, answer: what do they gain by taking the desired action? What do they lose? What do they gain by not taking the desired action, or by taking an alternative action? Capture both formal incentives (salary, bonus, promotion, penalty, fine, contract terms) and informal incentives (reputation, status, relationships, fear of blame, habit).

Step 3: Predict behaviours Based on the incentives mapped in Step 2, predict what each party will actually do — independent of what they are asked or instructed to do. Where there is a conflict between formal requirements and personal incentives, rational actors usually follow the incentives, not the requirements. Name these conflicts explicitly. Note where loss aversion will strengthen an incentive (threats of loss produce stronger responses than equivalent rewards).

Step 4: Identify misalignments Compare predicted behaviour (Step 3) to desired behaviour (what the system needs each party to do). For every gap, ask: is the problem that the right behaviour isn't rewarded? That the wrong behaviour is? That the incentive exists but its magnitude is too small? That the incentive signal is delayed or unclear? Name the specific misalignment precisely.

Step 5: Design realignment options For each misalignment, generate at least two redesign options. These may involve: changing who pays or benefits, changing the timing of incentive signals, introducing new consequences (positive or negative), making incentives more transparent, or restructuring the relationship between parties. Evaluate each option: does it align incentives without creating new misalignments elsewhere?


Human Check-in

Before proceeding, use the AskUserQuestion tool. State your interpretation of the situation in 1–2 sentences, then ask:

  • Question: "My read: [your 1–2 sentence interpretation]. How do you want to proceed?"
  • Header: "Scope"
  • Options:
    • Full analysis — Complete all steps, reasoning shown throughout
    • Key findings only — Bottom-line output, skip step-by-step detail
    • Specific focus — Zoom in on one aspect of this analysis
    • Reframe — The read is off; correct it and the analysis will follow the corrected framing

Proceed based on their selection. If the user reframes, incorporate the correction before running any analysis.

Output Format

System Overview [The system being examined, what it is trying to achieve, and the current behavioural problem]

Incentive Map

PartyDesired behaviourFormal incentiveInformal incentivePredicted behaviourMisalignment?
[Party 1][what we want][formal reward/penalty][social, reputational][what they'll actually do][Yes/No — why]
[Party 2]...............

Key Misalignments [Numbered list of the most significant gaps between predicted and desired behaviour, with root cause for each]

Redesign Options [For each misalignment: 2+ redesign options with trade-offs]

Recommended Interventions [Prioritised list: which realignments have the highest expected impact and lowest implementation cost]


Notes

The principal-agent problem is the most common form of incentive misalignment: the person taking action (agent) has different interests from the person bearing the consequences (principal). It appears in employment, contracting, professional services, politics, and financial intermediation. The solution set is well-studied: monitoring, performance-based pay, reputation mechanisms, aligning time horizons, and skin-in-the-game structures.

Incentive mapping is not about assuming people are greedy or calculating — it is about acknowledging that all people respond to the environment they are in, and that environments can be better or worse designed. A finding that "the incentive structure pushes toward X" does not mean people are bad; it means the structure needs redesign.

Pairs with /s4h-game-theory-mechanism-design when you want to formally design a system that aligns incentives — mechanism design is the rigorous version of redesign. Pairs with /s4h-systems-feedback-mapping when the incentive problem is embedded in a larger feedback dynamic. When the misalignment is driving visible conflict, /s4h-social-incentive-analysis addresses the political and relational dimensions.


What's Next

After delivering this output, use AskUserQuestion to offer the next move:

  • Question: "Incentives mapped. What's next?"
  • Header: "Next"
  • Options:
    • /s4h-game-theory-mechanism-design — Formally design a system where the right incentives are built into the rules
    • /s4h-economics-externalities — Some of the costs or benefits are falling on parties outside this map
    • /s4h-economics-coordination — The misalignment is producing collective action failure, not just individual dysfunction
    • Done — Wrap up and synthesise what we have so far

Frequently asked questions

What does the S4h Economics Incentive Mapping AI skill do?

Maps who benefits, who pays, and what behaviours the incentive structure actually produces. Triggers: 'what's the incentive here', 'why do people keep doing this', 'incentives are misaligned', 'who benefits from this', 'why isn't this working despite the policy', 'what does the structure reward', 'people say one thing but do another', any situation where behaviour diverges from stated intent.

Why use S4h Economics Incentive Mapping on TypingMind?

Because you install it once and use it with any model. S4h Economics Incentive Mapping is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install S4h Economics Incentive Mapping in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/human-avatar/skills-for-humanity/tree/main/skills/s4h-economics-incentive-mapping. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use S4h Economics Incentive Mapping?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with S4h Economics Incentive Mapping?

As many as you like. As long as a model supports skills, you can use S4h Economics Incentive Mapping with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the S4h Economics Incentive Mapping AI skill free?

Yes. It is published on GitHub by human-avatar under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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