--- 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." --- # 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`](../principal-agent/SKILL.md), [`goodharts-law`](../goodharts-law/SKILL.md), [`signaling-games`](../signaling-games/SKILL.md), [`okr-goal-setting`](../okr-goal-setting/SKILL.md), [`prisoners-dilemma`](../prisoners-dilemma/SKILL.md). ## 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 + monitoring plan. > **[WAIT — do not advance until user responds]** ## The Process **Step 1 — Goal and actors:** desired outcome · required behavior · actors · time horizon. **Step 2 — Map current incentives:** rewards (financial, status, autonomy) · penalties · timing · observability. **Step 3 — Diagnose alignment gap:** what behavior do current incentives rationally produce? where's the mismatch (metric, magnitude, timing)? **Step 4 — Design new structure (7-item checklist):** (1) alignment (2) measurability (3) timing (4) threshold structure (5) anti-gaming predictions (6) long-short balance (7) tampering defense. **Step 5 — Anticipate Goodhart's Law:** whatever you incentivize will be optimized — map the most-likely gaming pattern and close it. **Step 6 — Implement and monitor:** pilot first · monitor 3-6 months · review cycle every 6-12 months · build in actor feedback. ## Output Template ``` Incentive Design: Goal/actors | Current incentives | Alignment diagnosis Redesign (7-item) | Pilot scope / Monitoring / Review cycle ``` *→ Method in Action: [Munger 1995 + FedEx + Modern Applications](examples/munger-1995-fedex-modern-applications.md)* *→ 2026 lens: [Incentive Design in the AI Economy — RLHF reward design, scarce-talent comp, usage-based pricing (2024–2026)](examples/ai-economy-incentives-rlhf-talent-pricing-2024-2026.md)* ## Pack: Application Patterns | Domain | Common misalignment | Aligned design | |---|---|---| | Sales | Pay on closed deals only | Mix acquisition + retention + customer-fit | | Executive comp | Options vest 1-4 years | Multi-year vesting + clawbacks + risk-adjusted metrics | | Engineering | Promote on velocity | Add quality + on-call + cross-team metrics | | Customer support | Pay on tickets closed | Add reopen rate + satisfaction | | Recruiter | Bonus per hire | Add 12-month retention + performance ratings | | Health system | Fee-for-service | Outcomes-based; bundled payments | ## Applying It Well - Look at the incentives before you look at the people - Incentive-caused bias is invisible to the actor — they sincerely believe they are doing the right thing - The most important incentive design is at the founding moment; embedded structures become hard to change - "Roughly right" incentives often produce dramatically wrong behavior at scale — precision matters *→ Primary sources: [references/sources.md](references/sources.md)* ## Common Rationalizations **[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.** | Fake move | Reality | |---|---| | [D] "Our people just need more training" | Training rarely fixes incentive misalignment. Fix the incentives. | | [D] "We hire for character" | Even good character bends under bad incentives. Incentives reliably dominate character in systematic behavior. | | [D] "The incentive structure is industry-standard" | Industry-standard structures produce industry-standard dysfunctions. | | [D] "Our team understands the goal" | Understanding the goal doesn't override misaligned incentives. | | [D] "We can't change comp mid-year" | Real constraint — but plan the change for next cycle, don't accept the misalignment indefinitely. | | [D] "Goodhart's Law is overstated" | Wells Fargo, standardized testing, gaming KPIs say otherwise. Pre-commit anti-gaming defenses. | | [D] "We can't anticipate gaming" | You can. Spend time pre-launch imagining how it'll be gamed. The probabilities are higher than you think. | | *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* | ## Red Flags - Behavior attributed to character/capability rather than incentives - New people hired or fired without examining the incentive structure - Compensation or KPI system not reviewed in 2+ years - Previously-functioning incentive system starting to produce gaming - Anti-gaming countermeasures absent from a metric-driven system ## Verification - [ ] Goal-behavior explicitly specified - [ ] Current incentives mapped - [ ] Alignment gap diagnosed - [ ] Redesigned incentives address the alignment - [ ] 7-item checklist applied - [ ] Goodhart-style gaming anticipated - [ ] Monitoring and review cycle in place --- *Part of **deciqAI Knowledge Skills** — 225 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. **See it run → https://www.deciqai.com/s/incentive-design** · Built by deciqAI · github.com/deciqAI · Contributions welcome.*