Hyperbolic Discounting

---
name: hyperbolic-discounting
description: "Activate when: user says 'I'll start tomorrow / next week / next month' repeatedly; someone intends to save / exercise / quit but never follows through; a product's free trial or subscription is creating unexpected lock-in; someone wants to design a commitment device or pre-commitment contract; a team keeps deferring long-horizon work for short-term fires.
  Do NOT activate when: the changed plan reflects genuine new information (rational update, not bias); the time preference shift is due to real uncertainty about future receipt (bird-in-hand is reasonable). More: deciqai.com/s/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`](../loss-aversion-prospect-theory/SKILL.md), [`regret-minimization`](../regret-minimization/SKILL.md), [`compound-interest`](../compound-interest/SKILL.md), and [`okr-goal-setting`](../okr-goal-setting/SKILL.md).

## 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]**
4. Run The Process one step at a time — map asymmetry, estimate β, choose device.
> **[WAIT — do not advance until user responds]**
5. 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: <behavior>
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](examples/david-laibson-golden-eggs-1997.md)*

*→ 2026 lens: [The AI Demo-Dopamine Trap — Chasing the Launch Over the Moat (2023–2026)](examples/ai-demo-dopamine-vs-durable-moats-2023-2026.md)*

## Pack: Present-Bias Patterns

| Domain | Manifestation | Commitment device |
|---|---|---|
| Retirement saving | "I'll save more next year" | Auto-enrollment + SMarT escalation |
| Exercise | Gym membership + no attendance | Pre-paid trainer; class with no-show fee |
| Work focus | Open social media every 10 min | Internet blocker; phone in different room |
| Procrastination | Months of "I'll start tomorrow" | Pre-paid deposit forfeited; public deadline |

## 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](references/sources.md)*

## Common Rationalizations

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

| Fake move | Reality |
|---|---|
| [D] "I just need more willpower" | Willpower is unreliable; the gap is structural, not characterological. |
| [D] "I'll definitely start tomorrow" | The number of times you've said this is the strongest evidence you won't. |
| [D] "I work better under pressure" | Usually post-hoc rationalization. Test it: prep earlier and see if quality drops. |
| [D] "I have other priorities" | True priorities show up in behavior. If it never shows up in action, it's aspiration. |
| [D] "I'll save when I make more money" | Present-bias scales with income; higher earners save at the same rate. |
| [D] "Future-me will handle it" | Future-me has the same bias structure. The present version is already not handling it. |
| [D] "I tried before, didn't work" | Without commitment device, that's expected. What structural support was missing? |
| *→ Add [O] entries here after each real use — paste the actual failure pattern* | *What went wrong and why* |

## 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

---

*Part of **deciqAI Knowledge Skills** — 237 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/hyperbolic-discounting** · Built by deciqAI · github.com/deciqAI · Contributions welcome.*

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