Behavioral Economics Foundations

Read each primitive's misuse boundary before applying it. The disclosure test is an ethical screen, not a legal definition: a technique that embarrasses the team on disclosure needs redesign even if it is lawful.

When to Apply

Apply behavioral-economics when:

• User-facing decision surface — pricing page, onboarding default, churn flow, retention nudge
• Habit-formation or cue-preservation in redesigns
• Loss-aversion / framing matters and downside is concrete
• Choice architecture — defaults, decoys, ordering, anchoring
• Conversion or activation experiment design where biases are exploitable ethically

Skip and use simpler alternatives when:

• Decision is between two AI systems or backend strategies (no human in the loop) — use foundations-decision-theory
• Causal "did the nudge work?" question — use foundations-causal-inference to measure
• Strategic multi-actor pricing — use foundations-game-theory (Bertrand, Vickrey)
• The proposed pattern requires deceiving the user about real value — fails the ethical gate; redesign, don't nudge
• Audience or market context is unknown — biases are not universal; lift bands won't generalise
• The required lift is larger than the evidence can support — diagnose the offer, audience, and friction before assuming framing can close the gap

Quick Reference

Each primitive has a full playbook: Definition / When to use / Misuse boundary / Inputs / Outputs / Failure modes / Worked example / Sources. Grades are explained in Expert Judgment.

#	Primitive	Core Effect	When to Use	Grade
1	Prospect Theory	Gains and losses are not mirror images; framing shifts choice	Pricing copy, offer framing, upgrade messaging	A (direction)
2	Loss Aversion	Loss framing can shift choices; magnitude varies by task, stakes, and elicitation method	Churn prevention, trial expiry, feature removal messaging	B
3	Anchoring	First number shown distorts all subsequent judgments	Pricing pages, salary negotiation, discount presentation	A (direction)
4	Defaults	People disproportionately stick with pre-set options	Onboarding, opt-in/out choices, plan pre-selection	A for enrolment-type, low-effort choices; B elsewhere. Organ donation is not an A case: in a 2025 PNAS Nexus panel of opt-out switches, deceased-donor rates rose a non-significant +7% and living-donor rates fell 29% (crowding out). Pooled evidence undecided after correction — see nudge-calibration.md
5	Social Proof	People infer correct action from others' behavior	Sign-up pages, review placement, usage statistics	B
6	Scarcity	Limited availability increases perceived value	Inventory counts, time-limited offers, waitlists	B
7	Hyperbolic Discounting	Present bias: immediate rewards are disproportionately preferred	Free trials, commitment devices, annual vs monthly pricing	A (direction)
8	Mental Accounting	People categorize money differently depending on source and label	Bundling, gift cards, credit framing, sunk-cost effects	B
9	Choice Architecture	How choices are presented alters which option is selected	Option ordering, menu design, default pre-selection	C for "choice overload" specifically (mean effect ≈0, Scheibehenne et al. 2010); B for ordering/default effects generally
10	Dual-System Cognition	Two cognitive modes with different speed/effort profiles influence which levers work	Copy tone, complexity of CTA, trust signals	B — a descriptive heuristic, not a literal neural dichotomy
11	Decoy Effect / Asymmetric Dominance	A dominated option shifts preference toward its dominator	Pricing tier design, plan comparison tables	C — fragile outside stylized numeric stimuli (Frederick, Lee & Baskin 2014; Yang & Lynn 2014: 11/91 reliable)
12	Habit Loop	Stable cue → routine → reward becomes automatic with repetition	Daily/recurring usage, retention beyond week 4, durable behavior change	A (direction); no fixed weeks-to-automaticity number — see the fixed-weeks trap below
13	Reinforcement Schedules	Schedule (FR/VR/FI/VI), not just reward, governs acquisition and resistance to extinction	Streaks, rewards, gamification, reactivation; auditing existing reward systems	B
14	Cognitive Load & Working Memory	Working memory holds ~4 novel chunks; extraneous load suppresses informed choice	Forms, dashboards, alerts, error displays, consent flows, onboarding step count	A
15	Context-Dependent Retrieval	Behaviors are bound to the cues present at encoding; performance can weaken when context shifts	Migrations, redesigns, cross-surface continuity, dormant-user reactivation	B — direction reliable in meta-analysis (Smith & Vela 2001), moderated by "outshining" from non-contextual cues; the flagship Godden & Baddeley-style replication failed (Murre 2021 + 2022 addendum)
16	Implementation Intentions	User-endorsed if-then plans bind a specific cue to a specific response, supporting goal completion	Goal-pursuit features, onboarding into habit-forming behavior, transition-state re-anchoring	B — outcome-specific effects; publication-bias adjustment reduces the overall estimate (Sheeran et al. 2024; see evidence notes below)

Failure modes addressed, one line each: #1/#2 treating gains and losses as symmetric; #3 letting the user anchor on a competitor's number; #4 opt-in flows that require active effort; #5 empty/invisible proof signals; #6 no urgency signal; #7 annual plans losing to a monthly default; #8 lump-sum pricing hiding true perceived size; #9 option overload or unguided menus; #10 System-2 copy aimed at a System-1 decision; #11 two-option pricing with no reference point; #12 attributing retention decline to "habit" without cue-conditional evidence; #13 a reward system that worked at launch then went dead; #14 mid-flow abandonment from working-memory overload; #15 a redesign silently breaking the cue that triggered a behavior; #16 generic reminders standing in for an authored if-then plan.

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Formal Supporting Theory

Use references/formal-theory-map.md when the task needs source assumptions, ethical boundaries, or a distinction between observed behavior and normative decision quality.

Canonical references: Kahneman & Tversky 1979 (prospect theory), Kahneman 2011 (dual-system), Thaler & Sunstein 2008 (nudge/choice architecture), Ariely 2008 (illustrative — anchoring, decoy, mental accounting; treat as a trade-book demonstration, not primary evidence), Cialdini 1984 (social proof, scarcity), Loewenstein & Prelec 1991 (mental accounting), Frederick, Loewenstein & O'Donoghue 2002 (time preferences), Wood & Rünger 2016 + Lally et al. 2010 (habit loop, time-to-automaticity), Ferster & Skinner 1957 + Schultz 1997 (reinforcement schedules), Miller 1956 + Cowan 2001 + Sweller et al. 2019 (working memory, cognitive load), Tulving & Thomson 1973 + Wood, Tam & Witt 2005 (encoding specificity, context-dependent habits), Gollwitzer & Sheeran 2006 (implementation intentions meta-analysis, updated by Sheeran, Listrom & Gollwitzer 2024). Numeric effect sizes are from primary papers; domain-specific applications may differ — always measure on your own population before treating published coefficients as guarantees.

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Ethical Bounds

The Harm Test (Thaler & Sunstein)

A nudge is legitimate if it: (1) steers people toward choices they would endorse on reflection; (2) can be easily overridden or opted out of; (3) does not exploit cognitive limitations to work against the user's interests.

A dark pattern fails one or more of these. The same psychological lever — scarcity, defaults, loss framing — can be a nudge or a dark pattern depending on whether the underlying offer is good for the user.

Dimension	Nudge	Dark Pattern
Transparency	Can be disclosed without changing its effect	Requires concealment to work
User benefit	Steers toward user's own goals	Overrides user's goals in favor of operator's
Reversibility	Easy to undo or override	Designed to make undoing difficult
Honest signal	Scarcity / proof / urgency is real	Signal is fabricated
Regulatory posture	Survives ASA/FTC/CMA scrutiny	Attracts regulatory action

Every primitive carries a "Misuse boundary" stating its specific manipulation risk and the required condition for ethical use — non-negotiable gates, not optional guidelines.

AI Agents as Decision Subjects

When LLMs or AI agents make decisions on behalf of users (shopping, booking, form completion), choice-architecture manipulations apply to the agent, not the human — and with dramatically larger effect sizes. Under a default-option nudge, most tested LLMs shifted their choices far more than humans did, though a few models stayed close to human sensitivity. The pattern held for suggested alternatives and information highlighting too (Cherep, Maes & Singh, "AI agents are sensitive to nudges," PNAS 123(25), 2026, DOI 10.1073/pnas.2537030123; preprint arXiv:2505.11584). Standard remediation (chain-of-thought prompting, in-context human examples) shifts the distribution but does not reliably resolve this sensitivity; reasoning-optimized models partially restore human-level sensitivity, but inconsistently and at substantial inference cost — treat per-model susceptibility as something to test directly, not a property newer models resolve for free.

Awareness does not confer resistance. A separate 2025 study of LLM-powered GUI agents across 16 dark-pattern types (Tang et al., arXiv:2509.10723) found agents frequently fail to recognize manipulative interfaces at all, and — critically — even when they do recognize one, they prioritize task completion over protective action. Human oversight improved avoidance but added attentional tunneling and supervisor load, so human-in-the-loop is a mitigation with a price, not a solution. Design implication: agent-facing guardrails must be structural (constrained action scope, explicit confirmation gates on irreversible steps) — detection prompts alone do not change agent behavior.

Conversational dark patterns are a distinct surface: manipulation enacted in dialogue rather than layout — exaggerated agreement (sycophancy), biased framing of options, privacy-intrusive probing. Users detect these through conversational signals but frequently accept them as normal helpfulness (Shi et al., "The Siren Song of LLMs," CHI 2026; arXiv:2509.10830). If your product ships an LLM interface, the harm test applies to generated turns, not just to screens.

AI as a persuasion source (distinct from AI as a nudge target, above): as reported in Salvi et al. 2025 (Nature Human Behaviour, DOI 10.1038/s41562-025-02194-6), GPT-4 with access to a debate opponent's demographics had 81.2% higher odds of higher post-debate agreement than the Human–Human baseline (Abstract), an odds comparison rather than a completion-rate multiplier. A later Author Correction reportedly changed the personalised-vs-non-personalised comparison (unsourced — verify before citing); until checked, do not claim that personalisation specifically adds persuasion. Separately, Hackenburg et al. 2025 ("The levers of political persuasion with conversational artificial intelligence," Science 390, DOI 10.1126/science.aea3884; three experiments) found post-training and prompting raised persuasiveness more than personalisation or scale did, and that where persuasiveness rose, factual accuracy fell (magnitudes not re-verified). That accuracy trade-off is the concrete harm test for an AI sales or support agent: measure factual accuracy alongside persuasion, not persuasion alone. There is no established method here for auditing agent-as-persuader risk beyond ad hoc accuracy checks; treat this as an open gap.

When AI/ML systems deliver nudges (recommendation engines, personalisation, chatbots): (1) disclose the targeting model as an autonomy safeguard, not a general duty; (2) algorithmic personalisation can amplify heterogeneous treatment effects, raising equity concerns when it disproportionately steers vulnerable segments; (3) primitives #4, #9, #10 that produce small average effects in static designs can produce much larger effects when a personalisation model is tuned to exploit individual susceptibility. Apply the standard harm test to the targeting logic, not just the surface.

Regulatory Context

Before a legality claim about pricing, defaults, cancellation, or AI manipulation, read references/regulatory-context.md. Follow its official-law and regulator links to check territorial scope, current amended text, rule status, and application dates; cached enforcement examples do not establish the governing obligation. Qualified counsel determines the legal conclusion.

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Expert Judgment: Reading Evidence Strength

A non-expert treats every named effect the same way — "behavioral economics says X, so X is true." An expert grades each effect by replication status, sample, and design before recommending it. Behavioral economics has an unusually high rate of famous, textbook-cited findings that later failed to replicate or were built on fabricated data (ego depletion, social priming/"elderly walk," the Ariely–Gino "sign-at-top" honesty studies retracted in 2021, power-posing's hormonal claims). None of those are load-bearing evidence in this skill's primitives — but the discipline expects you to ask the same question of every claim brought in from outside it.

Grade legend: A = replicates across labs, populations, elicitation methods; direction reliable even where magnitude varies. B = direction replicates but the coefficient swings ≥2× by design, population, or stimulus. C = frequently invoked in product folklore, but the pooled/meta-analytic estimate is near zero or the mechanism is disputed — do not drive a design decision on a C-grade effect without a local pilot. Grades per primitive are in the Quick Reference table above; do not treat "A" as license to skip local measurement, and do not treat "B" as license to quote a specific number (λ, d) to a stakeholder as a forecast.

Famous but not credible as evidence — do not import even if asked by name: ego depletion as a standalone effect, social/behavioral priming (Bargh-style), power posing. Contested, not settled: scarcity-mindset-as-cognitive-tax (failed Colombia replication, null registered report); do not use it as a design driver. If asked, explain the replication failure rather than silently complying.

Nudge calibration and sample-size arithmetic (Maier 2022, DellaVigna & Linos 2022, Szaszi 2022 — replacing a single low-quality second-order synthesis as the primary calibration layer): references/nudge-calibration.md. Read this before promising a board a forecast lift or sizing a test.

Loss aversion coefficient: Brown, Imai, Vieider & Camerer, JEL 62(2), 2024 (607 estimates, 150 papers) places mean λ at 1.955 [1.820, 2.102]; Walasek, Mullett & Stewart, J. Econ. Psych. 103, 2024 (risky choice) at 1.31 [1.10, 1.53]. The canonical λ ≈ 2.25 (Tversky & Kahneman 1992) is a historical task-specific estimate, not a population bound or copy-response multiplier — never use it as a lift multiplier on copy or forecasts. A 2025 re-meta-analysis of the Brown et al. dataset (84 papers, 163 estimates, N=149,218) finds λ ≈ 1.07 (non-significant) for symmetric unordered gain-loss designs.

Nudge effect sizes at scale: see references/nudge-calibration.md for the full calibration set (Maier, DellaVigna & Linos, Szaszi, Hu). Do not treat any single published nudge effect size as a deployment guarantee.

Implementation intentions: Sheeran, Listrom & Gollwitzer 2024 — the author manuscript, p. 4, reports d = 0.27 for behavior and d = 0.66 for affect, not a format/rehearsal range. Its 642-test overall estimate is d = 0.36, reduced to d = 0.15 [0.08, 0.22] by Robust Bayesian Meta-Analysis adjusting for publication bias. Format, motivation, and rehearsal moderate effects; none is a product-lift guarantee.

WEIRD-sample generalization limits: the primary literature underlying most of these primitives (Kahneman & Tversky's original studies, classroom-recruited replications, Prolific/MTurk convenience samples) draws overwhelmingly from Western, Educated, Industrialized, Rich, Democratic populations. Effect direction travels reasonably well across cultures; effect magnitude does not — collectivist vs individualist framing changes social-proof and loss-aversion magnitudes, financial literacy changes anchoring susceptibility. Treat a published coefficient as a hypothesis to test locally before applying it to a non-US/UK, non-English-speaking, or non-online-panel population.

When a nudge backfires — recognize these before shipping, not after:

• Reactance: a default or scarcity signal that feels coercive triggers deliberate defiance, especially in populations primed to distrust the operator. Symptom: the "losing" option's selection rate rises after the nudge ships.
• Overjustification / crowding-out: adding an extrinsic reward to a behavior the user already did for intrinsic reasons can reduce long-run engagement once the reward is removed or becomes routine.
• Negative social proof: showing a low-adoption base rate to encourage adoption normalizes the low rate instead (Schultz et al. 2007).
• Spillover to an adjacent behaviour: measure spillover to an adjacent behaviour before declaring a win. A lift in the targeted behaviour can be offset by substitution in a related one (opt-out donation defaults: living donation fell). Name one adjacent outcome that could fall, and track it.
• Trust cliff on discovery: any fabricated or exaggerated signal produces a below-baseline outcome once discovered — the downside is asymmetric and often larger than the nudge's upside, because it taints future claims from the same source.
• Diagnostic tell: if a nudge's projected lift depends on the user not looking closely, it is already a backfire waiting for a discovery event.

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Misuse and Anti-Patterns

One merged table. Every primitive's "Misuse boundary" subsection restates its own row in more detail — read that before applying the primitive.

Pattern	Diagnosis	Required Fix
Fabricated scarcity or social proof	The lever works by deception, not better choice architecture	Use only verifiable constraints and real cohort data; specific numbers ("50 teams") beat vague claims
Hidden opt-out or cancellation	Defaults become coercive when exit is costly	Make override and reversal as easy as entry (#4)
Loss framing on trivial events	Manufactures anxiety where no real value is at stake	Reserve loss framing for decisions where real value is genuinely at stake (#2)
Anchoring with no informational value	The anchor distorts without informing	Anchor with plausible, comparable, explained values (#3)
Decoy used to push overbuying or mislead	A dominated option can manipulate plan choice; the mechanism is fragile outside numeric-attribute contexts (Grade C)	Ensure the target is genuinely best for the modal user; use the corrected asymmetric-dominance definition and fragility caveat in the decoy template (#11)
Choice overload used to justify option-cutting	Meta-analytic mean choice-overload effect ≈0 across 63 conditions (Scheibehenne, Greifeneder & Todd, JCR 2010, N=5,036); real only under specific moderators	Treat option reduction as a testable hypothesis; measure abandonment with N vs N−3 options before attributing drop-off to overload (#9)
Treating lab effect sizes, or any single published coefficient, as a production guarantee	Nudge effects shrink under publication-bias correction and in government at-scale deployments (see nudge-calibration.md)	Always measure on your own population; use published effects as priors, not forecasts (#4, #9)
Assuming loss aversion is a universal 2× constant	λ is design-dependent; the constants and their sources live in the Loss aversion coefficient paragraph above (the single home)	Always measure on your own population and design; do not apply λ as a fixed multiplier to any copy or forecast number (#2)
Variable-ratio schedules on non-essential or compulsion-prone actions	Maximises engagement by exploiting prediction error, not by serving the user's goal	Default to fixed schedules; rate-cap any VR component; require explicit harm-test sign-off (#13)
Streak counters standing in for the whole habit design	Substitute reward — counter inflation rather than the user's real outcome; collapses on a missed day	Bind the routine's reward to a real user-visible outcome; treat the streak as instrumentation, not the reward (#12, #13)
Engineering habits for behaviors the user did not endorse	Cue→routine→reward design used to drive metrics, not user-stated goals	Bind habits to user-acknowledged goals; make cues legible and disable-able (#12)
Fixed "N weeks to form a habit" claims	Time-to-automaticity is highly variable (Lally et al.: 18–254 days to 95% of asymptote, 96 volunteers) — no fixed week count generalises	Instrument the cue-conditional completion rate per cohort week instead of committing to a weeks-to-habit number (#12)
Material decisions buried under cumulative cognitive load	Late-flow decisions may undermine informed choice; consent validity depends on the full interaction context	Move material decisions to early/low-load positions; strip extraneous load (#14)
Major redesign shipped with no cue audit	Behavior weakens when the icon, entry path, or layout cues that triggered it move — moderated by "outshining" from other in-product cues	Catalogue cues per load-bearing behavior pre-redesign; preserve the dominant cue across ≥1 full retention cycle (#15)
Generic reminders substituted for implementation intentions	"Don't forget to review" is not a plan — no cue→response binding, no rehearsal	Replace with user-authored if-then ("When [cue], I will [response]"); require confirmation, not pre-fill (#16)
Reactivating dormant users on an untested cue	The prior cue may have lost relevance or accessibility; dormancy alone does not establish extinction	Test whether the cue still works; offer re-onboarding when it fails, using a threshold matched to the product's use cycle (#12, #15)
Underestimating trivial friction in high-value flows	As reported in Grieder et al., Results: 91.7% orders with consultant ordering vs. 51.7% with self-service ordering among 173 Swiss SMEs	Audit ordering friction, then measure local uptake; this field contrast is not a universal per-step halving rule (#9, #14)
Telling an AI agent to "avoid dark patterns" and calling it mitigated	Agents that recognise a manipulative interface still prioritise task completion over protective action — detection is not resistance (Tang et al. 2025)	Constrain structurally: narrow action scope, hard confirmation gates on irreversible/spend steps, spend ceiling
"Neuroscience-based" retention claim with no named mechanism	Marketing veneer — habit loops dressed up as neuroscience without specifying cue, schedule, or capacity	Force every retention claim to name the primitive: cue (#12), schedule (#13), capacity (#14), cue stability (#15), or planned action (#16)
Picking a winner without comparative confirmation	Selecting the largest noisy estimate can exaggerate its effect under either sequential or simultaneous testing	When delivery is scalable and arms can be powered, compare several interventions against a common control and shared outcome; adjust for multiple comparisons and confirm the selected winner (megastudy approach; Duckworth & Milkman, PNAS Nexus 2022)
Business model whose margin IS the bias	The primitive is the revenue line, not a retention technique. Revenue concentration rising in the top decile is a leading indicator of both whale churn and duty-of-care exposure	Track share of revenue from users whose spend exceeds plausible disposable income as a risk metric; see references/patterns-scenarios-traps.md § Edge-over-appetite business models

Check references/patterns-scenarios-traps.md before applying primitives to production user flows.

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Decision Checklist

• Mechanism and outcome: Select primitives from the Quick Reference, name the cue/reference point/default being changed, and specify the user's desired outcome plus a control comparison.
• Informed and reversible choice: Verify truthful anchors/proof/scarcity, an easy override, legible total cost, and low-load material decisions. For recurring behavior, confirm the user's goal, cue, reward, and editable if-then plan.
• AI-agent deployment: Has a nudge-susceptibility audit run against the choice architecture the agent will encounter? Are irreversible actions gated structurally, not by prompt instruction? → AI agents as decision subjects
• Generated-turn harm test: If the product ships an LLM interface, do generated responses avoid sycophantic agreement, biased framing, and privacy-intrusive probing, and is factual accuracy checked alongside persuasion? → AI as a persuasion source
• Ethical gate: Does each technique pass the harm test? Would disclosure embarrass the team? → Ethical Bounds

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Composition Recipes

Read references/composition-recipes.md for pricing, onboarding, churn, retention, or migration stacks.

Recipe 6: AI-Assistant UX — Ethical Nudge Guardrails

Goal: build onboarding flows, pricing pages, and conversational UX for AI-assistant / AI-agent products without crossing into manipulation or EU AI Act Art. 5 violations.

Context: Test human-facing choice architecture and the specific AI agent acting on it separately; susceptibility differs by model (see AI Agents as Decision Subjects).

Stack — for the human-facing UX:

1. Defaults (#4): Pre-select the most privacy-protective and least surprising configuration. For AI features (data retention, training opt-in, autonomous action scope), apply the disclosure test for UX, then separately assess the legal conditions in EU AI Act Art. 5(1)(a).
2. Cognitive load (#14): Consent and scope-setting flows must strip extraneous load. Material decisions (what data the AI accesses, what actions it can take autonomously) must appear early at low cognitive load.
3. Social proof (#5): Usage statistics are valid. Claims about AI accuracy or reliability require accurate sourcing — fabricated precision is a dark pattern with higher stakes (users may over-rely).
4. Dual-system (#10): AI-assistant UX often triggers deliberate System-2 attention because the capability is unfamiliar. Lean into deliberate framing at launch; shift to lighter cues only after the user has formed a mental model.

Stack — for the AI agent acting on behalf of users: 5. Choice architecture audit (#4, #9): Before deploying an AI agent in any choice-architecture-heavy environment (marketplaces, booking flows, multi-vendor pricing), run behavioral tests against the nudges present, per model — susceptibility is model-specific, not universal. 6. Structural guardrails over detection prompts: Do not rely on instructing the agent to "watch for manipulative interfaces" — detection is not resistance. Constrain what the agent can do: narrow action scope, hard confirmation gates on irreversible or spend-incurring steps, a spend/commitment ceiling the agent cannot exceed without the user. 7. Human oversight, costed honestly: Oversight improves avoidance but adds attentional tunneling and supervisor load. Escalate selectively on irreversible actions — a review surface the user stops reading is not oversight. 8. Transparency disclosure: explain relevant highlighting/default mechanisms to the user as an autonomy safeguard. Article 5 is a prohibition with specific conditions, not a general duty to disclose every nudge. Assess applicable legal notices separately with qualified counsel.

Ethical-bound check: (1) Is the default AI action scope the least-surprise, least-invasive option? (2) Can the user find and operate the scope controls without assistance? (3) Is the AI's behavior disclosure legible without reading a privacy policy? (4) Is factual accuracy checked whenever a persuasive generated turn is shipped (see AI as a persuasion source)?

Fail signal: high initial activation but user complaints of "the AI did something I didn't expect" — the agent's autonomy scope default was set too wide; cognitive load at consent was too high for informed choice.

Inputs: AI feature list with autonomy scope per feature (read / suggest / act), default state per feature (on/off), cognitive load estimate of consent/scope-setting flow (steps × decision complexity), target user's technical familiarity level. Rules: Default autonomous actions to off or narrowest scope unless user has explicitly expanded; material scope decisions shown before permission is granted, with comprehension tested in the actual flow; social proof claims must cite verifiable data; agent deployment in third-party environments requires a nudge-susceptibility audit before launch; irreversible and spend-incurring agent actions require a hard confirmation gate. Outputs: Default state table per AI feature (on/off, scope level, reversibility) + consent flow redesign with cognitive load score per step + nudge-susceptibility checklist for any third-party environment the agent operates in + EU AI Act Art. 5 issue list for counsel review.

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Workflow

1. Identify the decision point you are designing for (pricing, onboarding, retention, feature adoption, copy).
2. Use the Decision Checklist to identify which primitives are relevant.
3. Open the per-primitive playbook in assets/templates/behavioral-economics/ for the full definition, misuse boundary, and worked example.
4. Apply the Ethical Bounds harm test to each technique before implementation.
5. For compound design problems, use the Composition Recipes as starting stacks.
6. Check Misuse and Anti-Patterns to confirm you are not inadvertently shipping a dark pattern.

Local Validation Artifact

• Operational record, worked case and regression checks. Read before translating a mechanism into a deployment recommendation.

Navigation

• Per-primitive playbooks: assets/templates/behavioral-economics/ (one file per primitive)
• Composition guide: assets/templates/behavioral-economics/README.md
• Recipes 1–5: references/composition-recipes.md
• Nudge calibration and sample-size arithmetic: references/nudge-calibration.md
• Regulatory context (UK/EU/US): references/regulatory-context.md
• Formal theory map: references/formal-theory-map.md
• Patterns, scenarios, and traps: references/patterns-scenarios-traps.md
• Edge-over-appetite business models: references/patterns-scenarios-traps.md#edge-over-appetite-business-models
• Domain-agnostic primitives overview: references/primitives-overview.md
• Sources: data/sources.json

Related Skills

Consumer applied recipe layers that use these primitives link here.

• marketing-cro — conversion rate optimization applied recipes
• startup-business-models — pricing and packaging applied recipes
• marketing-content-strategy — copy and messaging applied recipes
• product-management — onboarding and feature adoption applied recipes
• marketing-paid-advertising — ad copy and landing page applied recipes

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Learnings Loop

When prior decisions or pitfalls are relevant, consult learnings.consolidated.md if present; use learnings.md only for needed history or as the available fallback. Otherwise skip both.

After applying it, if you encountered a pattern worth remembering, a mistake worth preventing, or a domain fact that surprised you, append one dated bullet to learnings.md via agents-skills-feedback-loop/scripts/append_learning.py. Do not modify SKILL.md itself.