Hyperbolic Discounting

Hyperbolic Discounting

Overview

People discount the near future far more steeply than the distant future, producing dynamically inconsistent preferences: patient choices for next month reverse when next month arrives. Formalized by Laibson's 1997 β-δ model: any future outcome is shrunk by β ≈ 0.7 relative to the present, then discounted exponentially. The fix is structural: commitment devices that bind the future-impatient self — auto-enrollment, forfeits, friction removal, public accountability.

Composes with loss-aversion-prospect-theory, regret-minimization, compound-interest, and okr-goal-setting.

When to Use

• "I'll start tomorrow / next week / next month" has been said multiple times on the same goal
• Savings, investment, or health behaviors are below the person's own stated intent
• Procrastination is the dominant pattern on a recurring task
• Subscriptions, free trials, or "today only" offers are producing unexpected lock-in
• An org fails to execute long-horizon strategy due to short-term firefighting
• A team chases the immediate AI-demo/launch spike over durable moats, evals, and infra — over-discounting long-term reliability amid AI capex, AI valuations, or fast AI adoption pressure

Not when: apparent impatience reflects real new information; discounting is rational due to genuine uncertainty about future receipt; cost of commitment device exceeds benefit.

Coaching Novices (Adaptive Front Door)

• Engine mode: user has a concrete case → 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: if you reliably want X for future-you but choose ~X for present-you, that gap is hyperbolic discounting — the fix is a commitment device, not willpower.
2. Check fit: if the inconsistency reflects new information, this is a rational update, not present bias.
3. Elicit their real case: what does future-you want? What does present-you actually do? How long has the gap persisted?

[WAIT — do not advance until user responds]

1. Run The Process one step at a time — map asymmetry, estimate β, choose device.

[WAIT — do not advance until user responds]

1. Close: name the specific commitment device chosen + first re-check date.

[WAIT — do not advance until user responds]

The Process

Step 1 — Identify the reversal: intended behavior / actual behavior / repetition count / cost of gap.

Step 2 — Map asymmetry: immediate cost of desired vs immediate benefit of undesired vs delayed benefit of desired vs delayed cost of undesired.

Step 3 — Diagnose β: unaided success rate. If < 30%, internal correction is unlikely — install commitment device.

Step 4 — Choose device: auto-enrollment / financial forfeit to disliked cause / paid trainer with no-show fee / internet blocker / cash-only envelopes / public deadline with accountability partner. Criterion: (a) binds at moment of temptation, (b) hard to circumvent, (c) acceptable to present-self when chosen.

Step 5 — Install with defaults: set desired behavior as default (opt-out, not opt-in); add friction to undesired; remove friction from desired; set public accountability.

Step 6 — Re-check: 30/60/90 day evaluation — was the device circumventable? Re-install stronger or accept revealed preference.

Output: Commitment Device Design

# Commitment Device Design: 
Reversal: intended / actual / frequency / cost
Asymmetry: immediate cost of desired | immediate benefit of undesired | delayed benefit | delayed cost
β estimate: unaided success rate → severity
Device: mechanism | how it binds | friction added | default set | accountability | failure cost
Re-check: 30/60/90 day criteria | owner

→ Method in Action: David Laibson's "Golden Eggs and Hyperbolic Discounting," 1997

→ 2026 lens: The AI Demo-Dopamine Trap — Chasing the Launch Over the Moat (2023–2026)

Pack: Present-Bias Patterns

Applying It Well

Willpower is unreliable; commitment devices are reliable — install structural fixes. Defaults are extraordinarily powerful (auto-enrollment produced a 37-pp shift in 401(k) participation). Educating naives to accurately predict their own future impatience is high-leverage even without other intervention. When a product makes desired-by-provider behavior the default and desired-by-consumer behavior friction-laden, name the exploitation.

→ Primary sources: references/sources.md

Common Rationalizations

[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.

Red Flags

• Desired behavior intended > 30 days and not started
• "I'll start" said multiple times on the same goal
• Commitment devices considered and rejected as "extreme" or "unnecessary"
• Person describes themselves as "lazy" when pattern is structural present-bias

Verification

• Preference reversal specifically named (intended X, actual ¬X)
• Asymmetry (immediate vs delayed) mapped
• Unaided success rate estimated
• Specific commitment device chosen and installed
• Defaults set toward desired behavior; friction added to undesired
• Re-check date on calendar

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Part of deciqAI Knowledge Skills — 227 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/c/hyperbolic-discounting · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/hyperbolic-discounting.json