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Nudge Theory

Activate when: user says 'nudge,' 'default,' 'opt-in vs opt-out,' 'choice architecture,' or 'why do people know they should but don't?'; user has a behavior... Skill: Nudge Theory Owner: deciqai Summary: Activate when: user says 'nudge,' 'default,' 'opt-in vs opt-out,' 'choice architecture,' or 'why do people know they should but don't?'; user has a behavior... Tags: latest:1.0.5 Version history: v1.0.5 | 2026-07-16T18:09:07.891Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/nudge-theory.json) v1.0.4 | 2026-07-09T11:19:39.363Z | user Re

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Rank

Safety

Downloads

1.1k

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. 1.1K 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: 1.1K 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:nudge-theory

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-nudge-theory/snapshot"

Documentation

CLAWHUB

125,714 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: nudge-theory
description: "Activate when: user says 'nudge,' 'default,' 'opt-in vs opt-out,' 'choice architecture,' or 'why do people know they should but don't?'; user has a behavior gap between intent and action; user is designing product onboarding, policy enrollment, or public health interventions and wants to change behavior without mandates or incentives.
Do NOT activate when: the gap is informational (people genuinely don't know what to do — education precedes nudging); the designer's goal is to serve their own interests rather than the chooser's (that is a dark pattern, not a nudge). More: deciqai.com/c/nudge-theory"
---

# Nudge Theory

## Overview

People procrastinate on retirement savings, skip vaccine appointments, and leave privacy settings on dangerous defaults — not from ignorance, but because the choice environment works against them. Nudge theory (Thaler & Sunstein) treats *choice architecture* — defaults, framing, social norms, friction — as the decisive variable. A nudge alters behavior in a predictable way without forbidding options or changing economic incentives; it must be easy and cheap to avoid. The foundational result: switching 401(k) enrollment from opt-in to opt-out raised participation from ~49% to ~86% — a 37-point lift from changing only the default.

Composition: use status-quo-bias before nudge design to know where inertia points; use probabilistic-thinking to estimate effect size; use second-order-thinking to catch downstream consequences (e.g., a low default rate that anchors people).

## When to Use

Apply when: (1) intent-action gap exists; (2) mandates or financial incentives are infeasible or unacceptable; (3) the choice environment can be redesigned; (4) you are setting defaults, opt-in/opt-out flows, or model-selection and data-sharing settings in an AI-native product where choice architecture steers millions of users amid rapid AI adoption and AI-native competition.

**When NOT to use:** gap is informational (educate first); deep values at stake; expert deliberate decision-makers (System 2); no defensible claim one outcome is better for the chooser.

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** user has a concrete behavior gap → run The Process directly.
- **Coach mode:** user is unfamiliar or has no concrete case → 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 what-it-is: a nudge is any small change to the environment — a default, a framing tweak, a social comparison — that steers people toward a better choice without forcing or paying them.
2. Check fit against When to Use / When NOT to use. If the gap is informational, redirect to communication design.
3. Elicit their real behavior gap. "We want users to engage more" is not a case; "63% never complete their first savings transfer despite signing up" is.
> **[WAIT — do not advance until user responds]**
4. Run The Proce

_meta.json

{
  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "nudge-theory",
  "version": "1.0.5",
  "publishedAt": 1784225347891
}

references/sources.md

# Sources — nudge-theory

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

- **Thaler, Richard H. & Sunstein, Cass R.** *Nudge: Improving Decisions About Health, Wealth, and Happiness.* Yale University Press, 2008. **Primary source** for the nudge definition and the libertarian-paternalism framework. Verbatim quote above from p. 6. https://yalebooks.yale.edu/book/9780300122237/nudge/
- **Thaler, Richard H. & Benartzi, Shlomo.** "Save More Tomorrow: Using Behavioral Economics to Increase Employee Saving." *Journal of Political Economy*, Vol. 112, No. S1 (2004), pp. S164–S187. **Primary source** for the SMarT plan results. Verbatim quote above from p. S165. https://doi.org/10.1086/380085
- **Madrian, Brigitte C. & Shea, Dennis F.** "The Power of Suggestion: Inertia in 401(k) Participation and Savings Behavior." *Quarterly Journal of Economics*, Vol. 116, No. 4 (November 2001), pp. 1149–1187. The foundational empirical demonstration of the 401(k) default effect. https://doi.org/10.1162/003355301753265543
- **Behavioural Insights Team (UK).** *EAST: Four Simple Ways to Apply Behavioural Insights.* BIT, 2014. The practitioner codification of the EAST framework. https://www.bi.team/publications/east-four-simple-ways-to-apply-behavioural-insights/
- **Milkman, Katherine L. et al.** "Megastudies Improve the Impact of Applied Behavioural Science." *Nature*, Vol. 600 (2021), pp. 478–483. Large-scale empirical test of 54 nudge interventions on vaccine appointment rates; provides calibrated effect-size expectations. https://doi.org/10.1038/s41586-021-04128-4
- **Royal Swedish Academy of Sciences.** *Scientific Background: Richard H. Thaler — Integrating Economics with Psychology.* Nobel Prize in Economics, October 2017. https://www.nobelprize.org/prizes/economic-sciences/2017/advanced-information/
- **U.S. Federal Trade Commission.** *Bringing Dark Patterns to Light* (Staff Report). FTC, September 2022. Establishes the regulatory line between legitimate choice architecture and manipulative design defaults — directly relevant to the AI-products example. https://www.ftc.gov/reports/bringing-dark-patterns-light
- **European Union.** *Regulation (EU) 2024/1689 (Artificial Intelligence Act).* Official Journal of the European Union, 2024. Entered into force in 2024 with staged obligations; frames transparency duties for automated systems, the regulatory backdrop for AI-product default design. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
- **Not cited:** The Amsterdam airport urinal fly image is widely cited as a nudge case, but the primary documentation is thin — it appears in Thaler & Sunstein (2008) as an anecdote without a controlled study. It is illustrative, not evidence of effect size. Do not use it to calibrate expected nudge impact.

examples/401k-automatic-enrollment-and-the-pension-protection-act-2006.md

# Method in Action: 401(k) Automatic Enrollment and the Pension Protection Act (2006)

> *Example for the [nudge-theory](../SKILL.md) skill.*

A documented, peer-reviewed case — not a pop-culture parable.

The canonical demonstration of default nudges is the shift from *opt-in* to *opt-out* enrollment in U.S. employer-sponsored retirement plans. The research program runs through a series of peer-reviewed studies; the policy culmination is the **Pension Protection Act of 2006** (PPA), which explicitly endorsed automatic enrollment and automatic escalation as legal safe-harbor provisions.

**Target behavior (Step 1):** Enroll in employer 401(k) plan and contribute at a rate that generates meaningful retirement savings.

**Barrier diagnosis (Step 2):** Brigitte Madrian and Dennis Shea (2001) studied a single large U.S. corporation before and after it switched from opt-in to opt-out enrollment. Under opt-in, employees had to proactively elect participation. Under opt-out, they were automatically enrolled at a default 3% contribution rate into a default investment fund, but could change or cancel. The primary barrier was **Easy**: the opt-in process required a deliberate action that most employees — even those who intended to save — repeatedly deferred. Present bias and status-quo bias compounded: "I'll do it next month" repeated indefinitely.

**Nudge mechanism (Step 3):** Default redesign — make enrollment the default state, requiring active effort to *exit* rather than to *enter*.

**Intervention specification (Step 4):** Switch the default from "not enrolled / must opt in" to "enrolled at 3% into target-date fund / can opt out at any time." No options are removed; no incentives change; the contribution rate, fund choices, and exit path are identical in both conditions.

**Results:** Madrian and Shea found that 12-month enrollment rates rose from approximately 49% under opt-in to approximately 86% under opt-out — a 37 percentage-point increase from changing only the default. The effect was largest for new hires and lower-income employees who historically had the lowest participation rates.

**SMarT extension:** Thaler and Benartzi layered the Save More Tomorrow plan on top of automatic enrollment to address the *rate* problem (people defaulted into 3% and stayed there). SMarT asked employees at hire to commit to escalating their contribution by a percentage point each year with each pay raise. The behavioral mechanism: the commitment is in the future (reduces present-bias), the cost is felt only against income that did not previously exist (reduces loss aversion), and default inertia now works *toward* higher saving rather than against it. In the pilot firm, rates rose from 3.5% to 13.6% over 40 months.

**Policy scale (Step 6):** The Pension Protection Act of 2006 established automatic enrollment and automatic escalation as legal safe harbors for qualified retirement plans. The 2022 SECURE 2.0 Act mandated automatic enrollment for all new 401(k) p

examples/choice-architecture-in-ai-products-2023-2026.md

# Method in Action: Choice Architecture in AI Products (2023–2026)

> *Example for the [nudge-theory](../SKILL.md) skill.*

A contemporary case — applying the EAST Nudge Design lens to the defaults, friction, and framing that steer users inside AI products, and to the ethics of nudging at the scale of hundreds of millions of users.

Between the launch of ChatGPT in late 2022 and 2026, generative-AI assistants reached mass adoption; OpenAI reported ChatGPT reaching roughly 100 million weekly active users by late 2023 and, per its own public statements, several hundred million weekly active users by late 2025. At that scale, small changes to the choice environment move millions of behaviors — which makes AI products a textbook study in choice architecture, and a live test of nudge ethics. The most contested lever has been the **data-sharing default**: whether user conversations are, by default, used to train future models, and how easy it is to opt out.

This example walks the anchor case — the "opt-out data sharing" default in consumer AI chat products — through the skill's own six-step Process. It is illustrative of a well-documented *pattern*, not a controlled experiment with a published effect size.

**Define the target behavior precisely (Step 1):** The chooser-serving target behavior is: *a user who does not want their private conversations used for model training successfully turns that setting off.* Population: consumer users of a general-purpose AI chat assistant. Baseline: because the default is "on," the observed opt-out rate is low — consistent with the general finding across digital privacy settings that the large majority of users never change a default. (The exact opt-out rate is not publicly disclosed by the major providers, so it should be treated as "low, not quantified.")

**Diagnose the barrier (EAST) (Step 2):**
- **E — Easy:** dominant barrier. Training-on-by-default means inaction produces data sharing; opting out requires locating a settings menu and toggling a control most users never open. The path of least resistance favors the designer's data interest.
- **A — Attractive:** the framing of the control ("improve the model for everyone," "help make ChatGPT better") frames sharing as prosocial and opting out as withholding — a salience/framing pressure.
- **S — Social:** weak. There is no visible descriptive norm ("most users keep their private chats out of training").
- **T — Timely:** the decision is buried away from the moment of first use, so it is never presented at the point when the user is forming a mental model of privacy.

Primary barrier: **Easy** (the default state), reinforced by **Attractive** (framing).

**Match barrier to mechanism (Step 3):** To *serve the chooser*, the mechanism that addresses an Easy barrier is default redesign and friction reduction: either flip the default to "not used for training unless the user opts in," or surface the choice at a timely moment with neutral, symmetric framing so neith

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