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.