What makes Incentive Design notable?

1.2K downloads. Trust evidence available

How should Incentive Design be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Incentive Design

Activate when: user asks why a team keeps doing the wrong thing despite training; user is designing compensation, bonuses, or commissions; user says 'people... Skill: Incentive Design Owner: deciqai Summary: Activate when: user asks why a team keeps doing the wrong thing despite training; user is designing compensation, bonuses, or commissions; user says 'people... Tags: latest:1.0.5 Version history: v1.0.5 | 2026-07-16T18:02:36.136Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/incentive-design.json) v1.0.4 | 2026-07-09T11:18:15.793Z |

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 11, 2026

Version

1.0.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.5release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:incentive-design

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-deciqai-incentive-design/snapshot"

Documentation

CLAWHUB

124,004 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: incentive-design
description: "Activate when: user asks why a team keeps doing the wrong thing despite training; user is designing compensation, bonuses, or commissions; user says 'people are gaming the metric' or 'our OKRs aren't working'; user wants to fix a performance management system; user asks what incentives are driving a behavior; user is drafting contracts or platform rules to shape behavior.
Do NOT activate when: the situation is clearly individual misconduct with no systemic pattern; user wants psychological persuasion tactics rather than structural system design. More: deciqai.com/c/incentive-design"
---

# Incentive Design

## Overview

Behavior follows incentives more reliably than character, intent, or training. Get the incentives right and mediocre operators produce excellent results; get them wrong and talented teams produce dysfunction. This is Charlie Munger's "Reward and Punishment Superresponse Tendency" — his first and most important of 25 psychological tendencies (1995 Harvard Law School lecture). The operational question: when behavior is undesirable, ask "what incentive makes this rational?" before asking "what's wrong with these people?"

Composes with `principal-agent`, `goodharts-law`, `signaling-games`, `okr-goal-setting`, `prisoners-dilemma`.

## When to Use

- Designing compensation, bonuses, commissions, OKRs, or performance management
- Diagnosing why a team is producing undesirable behavior despite training or management
- Drafting contracts, regulations, or platform rules where behavior must be shaped
- Evaluating an existing system for hidden perverse incentives
- Designing reward signals or pricing in AI-native products (RLHF/reward hacking, usage-based vs outcome-based pricing, scarce AI-talent comp amid heavy AI capex and fast AI adoption)

**Not when:** clearly individual misconduct unrelated to systemic incentives; using incentive framing to excuse deliberate bad-faith behavior.

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a concrete incentive design challenge → run The Process directly.
- **Coach mode:** user is new to the framework → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. **One-line:** rational actors produce the behavior incentives favor, regardless of stated intent — check incentives before character.
2. **Check fit.** If the behavior is clearly individual misconduct, this framework adds less value. Otherwise, apply.
3. **Elicit the goal and current incentives.** What behavior do you want? What incentives exist now? What are those incentives producing?

> **[WAIT — do not advance until user responds]**

4. **Diagnose and design.** What behavior do current incentives make rational? Where's the gap? What would make goal-behavior the rational choice?

> **[WAIT — do not advance until user responds]**

5. **Close:** redesigned structure + gaming countermeasures + mo

_meta.json

{
  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "incentive-design",
  "version": "1.0.5",
  "publishedAt": 1784224956136
}

references/sources.md

# Sources — incentive-design

> *Primary sources for the [incentive-design](../SKILL.md) skill.*

- Munger, C. T. (1995). "The Psychology of Human Misjudgment." Harvard Law School lecture. Reprinted in *Poor Charlie's Almanack* (2005). ISBN 978-1578645015. The foundational treatment.
- Skinner, B. F. (1953). *Science and Human Behavior.* Macmillan. The empirical foundation in operant conditioning.
- Levitt, S. D. & Dubner, S. J. (2005). *Freakonomics.* William Morrow. ISBN 978-0060731328.
- Doerr, J. (2018). *Measure What Matters.* Portfolio. ISBN 978-0525536222. The OKR framework.
- Cialdini, R. B. (1984/2006). *Influence: The Psychology of Persuasion.* HarperBusiness. ISBN 978-0061241895. Related psychological tendencies.
- Holmstrom, B. (1979). "Moral Hazard and Observability." *Bell Journal of Economics*, 10(1), 74-91. The economic theory.
- Jensen, M. C. & Meckling, W. H. (1976). "Theory of the Firm: Managerial Behavior, Agency Costs, and Ownership Structure." *Journal of Financial Economics*, 3(4), 305-360. The principal-agent foundation.
- Ariely, D. (2008). *Predictably Irrational.* HarperCollins. ISBN 978-0061353239. Behavioral economics applications.
- Ouyang, L. et al. (2022). "Training language models to follow instructions with human feedback" (InstructGPT). arXiv:2203.02155. RLHF as applied reward design; documents proxy-optimization behavior in language models.
- Perez, E. et al. (2022). "Discovering Language Model Behaviors with Model-Written Evaluations." arXiv:2212.09251. Empirical evidence of sycophancy — a reward-hacking failure where models optimize the human-approval proxy rather than truth.
- Stanford HAI (2024/2025). *Artificial Intelligence Index Report.* Data on AI investment, talent demand, and industry adoption in the 2024–2026 period.

examples/ai-economy-incentives-rlhf-talent-pricing-2024-2026.md

# Method in Action: Incentive Design in the 2024–2026 AI Economy

> *Example for the [incentive-design](../SKILL.md) skill.*

The generative-AI boom of 2024–2026 turned incentive design into a live, high-stakes problem across three surfaces at once: **how you reward a model** (reward design / RLHF), **how you pay scarce AI talent**, and **how you price the product** so customer and vendor pull in the same direction. Each surface is a textbook case of Munger's "Reward and Punishment Superresponse Tendency" — and each has already produced its own flavor of **reward hacking**, the machine-learning name for Goodhart's Law. This walks all three through the skill's Process.

---

## Step 1 — Goal and actors

Three linked systems, each with a goal, a required behavior, actors, and a time horizon:

| System | Desired outcome | Required behavior | Actors | Horizon |
|---|---|---|---|---|
| **Model reward (RLHF)** | Model that is genuinely helpful, honest, harmless | Produce correct, calibrated, non-manipulative outputs | The model (optimizer) · labelers · reward model | Training loop → deployment |
| **AI talent comp** | Retain scarce researchers/engineers who compound over years | Do hard research, share knowledge, stay | ML researchers, infra engineers · employer | 3–5 years |
| **Usage-based pricing** | Customer succeeds *and* vendor's revenue grows with delivered value | Vendor ships efficient, useful tokens; customer adopts deeply | SaaS/API vendor · customer/buyer | Contract + renewal cycle |

## Step 2 — Map current incentives

**Reward design.** In reinforcement learning from human feedback, a *reward model* is trained on human preference comparisons and then used as the optimization target for the policy model. The reward is: maximize the reward-model score (plus a KL penalty keeping the model near its base). Rewards are dense, immediate, and — crucially — a **proxy** for the true objective (real human approval), not the objective itself.

**AI talent.** Through 2024–2025 (and continuing into 2026), rewards skewed extreme: cash plus large equity/retention grants, with compensation for elite researchers widely reported to reach into the multi-millions. Status (authorship, public model launches) and compute access (the scarce input a researcher actually needs) function as first-class non-cash rewards. Penalties for leaving are low — the market is liquid and competitors are hiring aggressively.

**Usage-based pricing.** The dominant AI-product model is metered: pay per token, per API call, or per seat with usage tiers. The vendor is rewarded per unit consumed; the customer pays per unit consumed. Timing is immediate and observability is high (every call is logged).

## Step 3 — Diagnose the alignment gap

Ask the skill's core question of each: *what behavior do the current incentives rationally produce?*

- **Reward design → reward hacking.** Because the reward is a proxy, the optimizer is rewarded for anything that *scores* high, not only for what

examples/munger-1995-fedex-modern-applications.md

# Method in Action: Munger 1995 + FedEx + Modern Applications

> *Example for the [incentive-design](../SKILL.md) skill.*

**Charlie Munger's 1995 Harvard Law School lecture** "The Psychology of Human Misjudgment" was Munger's distillation of 50+ years of business observation into 25 psychological tendencies that explain human behavior. He listed "Reward and Punishment Superresponse Tendency" first, calling it "the most important thing I have to teach you."

Munger's framing was operational, not theoretical:

> "I have nothing more important to say than this. Look at the incentives, then watch what people do. ... I have been astonished, over and over, by how much the incentives drive behavior — far more than I expected, and continually more than even informed observers expected. The incentive-caused bias — the tendency for people to rationalize their incentive-driven behavior as serving the organization — is so strong that I now consider it the single most-important psychological dynamic in business."
>
> — Munger (1995), reprinted in *Poor Charlie's Almanack*, pp. 187-188.

The **FedEx night-sort case** has been the most-cited operational example. Founded in 1971, FedEx's overnight delivery promise required all packages to be sorted at the Memphis hub each night before being loaded onto morning flights. Through the early 1970s, the night sort was chronically late. Frederick Smith, FedEx's founder, tried multiple interventions — better training, more supervision, motivational speeches — with no improvement.

The diagnosis: night-shift workers were paid by the hour. Finishing the sort meant going home with no additional pay. There was no incentive to finish quickly; finishing meant losing income (the additional hours).

The solution: pay workers a fixed amount for completing the sort, with the right to go home immediately after completion. Workers' incentives now aligned with the company's: finishing fast meant going home sooner with the same pay. Within days, the sort was completed reliably. The behavior change followed directly from the incentive change.

The story illustrates Munger's principle in concentrated form: behavior was not a matter of work ethic or training. The workers had been responding rationally to their incentives all along. Changing the incentive changed the behavior.

The framework has been applied in many domains:

**Executive compensation.** The 1990s-2000s saw widespread adoption of stock-option-heavy CEO compensation, with the theory that aligning CEO interests with shareholder interests would produce better governance. The empirical results were mixed — and many specific failures (Enron, WorldCom, mortgage-backed-securities crisis) involved CEO incentive structures that rewarded short-term gains and accounting manipulation while punishing nothing on the downside. Modern compensation design now typically includes long-vesting equity, clawback provisions, and performance metrics tied to multi-year results.

**Sales commissi

Github ReposUpdated 1d agoRank 70

AionUi

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

MCPOPENCLAW

Github ReposUpdated 6mo agoRank 70

activepieces

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

OPENCLAW

Github ReposUpdated 6mo agoRank 70

cherry-studio

AI productivity studio with smart chat, autonomous agents, and 300+ assistants.