18-Week Decision Literacy Curriculum
This curriculum provides a structured introduction to decision literacy for young learners (roughly ages 8–12, with adult guidance as needed). It blends guided instruction with hands-on games and experiments to help students develop confidence in thinking through choices, recognizing cognitive shortcuts, weighing probabilities, and learning from outcomes.
The program progresses from the "hardware" of the brain (probability and biases) to the "software" of strategy (expected value, game theory, and real-world optimization), culminating in a final project where students design, test, and iterate on a personal Decision Protocol.
Lessons are intentionally hands-on, curiosity-driven, and flexible, allowing the instructor to adapt activities based on the learner's engagement and pace.
- Review Curriculum Overview for pacing and teaching assumptions.
- Use Program at a Glance to jump to a specific week quickly.
- Check Advanced Topics for the bonus extension weeks.
- Open How Skills Build Over Time to see how the course connects.
- Save Independent Practice Setup Tips for caregiver logistics.
- Use this page as your roadmap before the course starts or whenever you need to find the right lesson quickly.
- The week-by-week table is the fastest way to jump into a teaching page.
Curriculum Overview
Target Audience
Young learners roughly ages 8–12. Basic reading ability is helpful, but adult guidance is expected. The concepts scale naturally — younger learners engage through games and intuition, while older learners dig into the reasoning and math. Every weekly lesson includes a dedicated 'For Younger Learners (Ages 8–9)' section with concrete guidance on simplifying activities, reducing writing burden, and adapting the session length.
Weekly Structure
Each week contains:
- Two guided sessions (about 30 minutes each)
- One independent practice session (about 30 minutes)
Guided sessions introduce concepts through games, experiments, and conversation. Independent practice sessions reinforce skills through real-world observation, practice, and Decision Journal entries.
The Decision Journal
Beginning Week 1, students maintain a Decision Journal — a running record of their choices, reasoning, predictions, and reflections.
The journal is the single most important artifact. It teaches students to write down their thinking before seeing the result, which prevents hindsight bias and builds genuine self-awareness over time.
Each week includes a specific journal prompt that builds on previous entries. For younger learners, journal entries can be oral (dictated to a facilitator), drawn, or completed using the sentence starters provided in each lesson. The journal should never feel like a writing assignment — it's a thinking tool.
Materials
Most activities use simple, accessible materials:
- Coins, dice (standard six-sided), and playing cards
- Paper, pencils, and a notebook for the Decision Journal
- Occasional use of simple digital tools (online dice roller, basic spreadsheet)
- Small objects for trading games (stickers, erasers, pencils)
- Tokens or counters for simulation games
Final Project
The program culminates in the Optimization Project during Weeks 15–18.
Students identify a recurring friction point in their lives and design a Decision Protocol to address it. They implement the protocol for several weeks, collect data on outcomes, and iterate based on what they learn.
This mirrors the real-world cycle of: Identify → Design → Test → Learn → Improve.
Flexibility & Adaptability
This curriculum is a guide, not a rigid script.
Adjust pacing based on the learner's:
- engagement
- confidence
- curiosity
- age and math comfort
If a concept clicks quickly, explore optional challenges. If a topic needs more time, slow down — the games and discussions are worth lingering on. The ultimate goal is understanding and self-awareness, not coverage.
How to Tell If a Learner Is Progressing
Look for these signs across the curriculum:
- They use the vocabulary unprompted. When a learner says "that's sunk cost" at the dinner table or "what's the expected value?" during a board game, the concepts are sticking.
- Their journal entries get richer. Early entries might be one sentence. By Phase 3, look for entries that mention trade-offs, uncertainty, or multiple options considered.
- They catch their own thinking errors. A learner who says "wait, I think I'm anchoring on that number" is showing real metacognitive growth.
- They ask better questions. Progress shows up as curiosity — "what would happen if…?" and "how likely is that, really?"
If these signals are absent after several weeks, revisit earlier concepts through games rather than re-teaching.
Adapting for Different Ages
The curriculum works across the full 8–12 range, but emphasis shifts:
- Ages 8–9: Lean heavily on the games and physical activities.
- Keep math light — use "more likely / less likely" language before introducing numbers.
- Expect shorter journal entries and provide sentence starters.
- The guided sessions may need to be shorter (20 minutes) with more movement breaks.
- Each weekly lesson page includes a clearly marked 'For Younger Learners' section with the simplest version of the concept, what to skip or shorten, sentence starters for journal work, and what success looks like for that age group.
- Oral responses, drawing, and sorting activities are always acceptable alternatives to writing.
- Ages 10–12: Push the quantitative reasoning further. These learners can handle percentages, simple expected value calculations, and more abstract "what if" scenarios. Encourage longer journal reflections and independent research during the practice sessions.
Every lesson also includes a Quick Mastery Check — a brief formative assessment that helps facilitators gauge whether the core concept landed. These take under two minutes and require no special preparation. Lessons that involve group activities include a solo or small-group fallback so that a parent working with one child, or a small homeschool group, can still complete the lesson meaningfully.
Additionally, every weekly lesson page features:
- Pause and Notice — a short values-and-feelings reflection tied to that week's concept
- Spiral Review — brief connections back to earlier weeks so concepts reinforce over time
See the Introduction page for the full age-adaptation guide.
When Content Is Too Easy or Too Hard
- Too easy: Skip the warm-up games and go straight to the challenge extensions listed at the end of each week. Ask the learner to teach the concept to someone else — teaching is the hardest test of understanding. Introduce the Extension weeks earlier.
- Too hard: Break the week into two weeks. Focus on just one key idea per session instead of covering everything. Use more concrete, physical examples before moving to abstract reasoning. There is no penalty for going slower — a learner who deeply understands 12 weeks of material is better off than one who rushed through 18.
Program at a Glance
Each week below links to a detailed lesson page containing learning objectives, guided sessions, independent activities, preparation notes, and Decision Journal prompts.
Phase 1: Probability & The Physics of Choice
Luck vs. Skill and Uncertainty — Students build the habit of separating process from outcome and begin noticing that every choice means giving up alternatives.
| Week | Theme | Focus Highlights |
|---|---|---|
| Week 1 | 🎲 The Coin Flip Lab | Randomness, uncertainty, and why perfectly good choices can lead to bad results |
| Week 2 | 🔍 Process vs. Outcome | Separating the quality of the decision from the quality of the result |
| Week 3 | 📊 Thinking in Probabilities | Frequency, confidence levels, and learning to say "I'm about 70% sure" |
| Week 4 | 📓 The Decision Journal | Formalizing the journal, meeting hindsight bias, and building the recording habit |
- The learner can explain that a good process doesn't guarantee a good outcome (and vice versa).
- They use their Decision Journal without prompting.
- They're comfortable saying "I don't know" or "I'm about 70% sure" instead of treating everything as certain or impossible.
- They can give an example of a time someone got lucky vs. a time someone made a genuinely good decision.
Phase 2: Debugging the Hardware
Cognitive Biases and Shortcuts — Students learn why loss aversion makes opportunity costs feel invisible and why sunk costs trick us into ignoring what else we could do.
| Week | Theme | Focus Highlights |
|---|---|---|
| Week 5 | 🧠 Meet Your Brain's Shortcuts | Fast vs. slow thinking, anchoring, availability, and representativeness |
| Week 6 | 💔 The Loss Aversion Lab | Why losing feels worse than winning feels good, and how this distorts choices |
| Week 7 | 🪤 The Sunk Cost Trap | Why "I've already started" is a dangerous reason to keep going |
| Week 8 | 🔎 Bias Hunters | Spotting cognitive biases in advertisements, apps, social media, and daily life |
- The learner can name at least three cognitive biases and spot one in a real-world ad or conversation.
- They recognize when loss aversion or sunk cost thinking is affecting their own choices (even if they can't always override it yet).
- They understand that "shortcuts" aren't always bad — they're fast but predictably wrong in specific situations.
- Their journal entries start referencing biases by name when reflecting on past decisions.
Phase 3: Data & Signal Processing
Gathering and Filtering Information — Expected value makes opportunity cost mathematical. Signal vs. noise introduces diminishing returns on information: at some point, more research isn't worth the delay.
| Week | Theme | Focus Highlights |
|---|---|---|
| Week 9 | ⚖️ Expected Value | A simple math tool for weighing risks and rewards across uncertain options |
| Week 10 | 📡 Signal vs. Noise | What information actually matters, and when you have "enough" to decide |
| Week 11 | 🚪 Reversible vs. Irreversible | Not all decisions deserve the same effort — matching speed to stakes |
- The learner can do a rough expected value comparison for a simple bet or choice (e.g., "Option A has a higher expected value because…").
- They can identify whether a decision is reversible or irreversible and explain why that matters for how much time to spend deciding.
- They understand opportunity cost: choosing one thing means giving up another, and they can name what they're giving up.
- They're getting better at filtering — they can explain why some information matters for a decision and some is just noise.
Phase 4: Game Theory & Social Systems
Decisions in a Network — The commons simulation shows diminishing returns at the group level and makes the opportunity cost of selfishness vivid.
| Week | Theme | Focus Highlights |
|---|---|---|
| Week 12 | 🌊 The Ripple Effect | How your choices affect others and theirs affect you |
| Week 13 | 🤝 The Prisoner's Dilemma | Win-win vs. zero-sum thinking and the power of cooperation |
| Week 14 | 🐟 The Commons Simulation | When individual "best" choices lead to group failure |
- The learner can explain why individual "best" choices sometimes make everyone worse off (tragedy of the commons).
- They can identify a real situation in their life where cooperation helps more than competition.
- They understand that decisions don't happen in isolation — other people are making choices too, and those interact with yours.
- They can spot diminishing returns in a real scenario (e.g., "practicing piano 4 hours isn't twice as useful as 2 hours").
Phase 5: The Optimization Project
Application and Iteration — Students apply every concept (including opportunity cost and diminishing returns) to a real problem, learning that even optimization has diminishing returns.
| Week | Theme | Focus Highlights |
|---|---|---|
| Week 15 | 🔧 Identify Your Friction Point | Choosing a recurring real-life problem and mapping its root causes |
| Week 16 | 📋 Design Your Protocol | Building a systematic decision process with triggers, defaults, and checks |
| Week 17 | 📈 Test and Collect Data | Running the protocol, tracking results, and learning from what happens |
| Week 18 | 🔄 Patch, Present, Reflect | Iterating on the protocol, presenting results, and reflecting on the full journey |
- The learner has designed, tested, and iterated on a personal decision protocol for a real problem in their life.
- Their journal entries show richer reasoning than Week 1 — they reference trade-offs, biases, probabilities, and opportunity costs.
- They can walk someone else through their protocol and explain why each step is there.
- They treat "it didn't work perfectly" as useful data, not failure.
- They can articulate at least one way their thinking about decisions has changed since the start of the course.
Advanced Topics (Optional Extension)
For learners who want to go further, two bonus weeks introduce more advanced decision-making tools. Each extension includes clear readiness guidance — which core weeks should feel solid first, and what type of learner benefits most.
| Week | Theme | Focus Highlights |
|---|---|---|
| Extension 1 | 📐 Bayesian Updating | How new evidence should change your beliefs — and by how much |
| Extension 2 | 🌳 Decision Trees | Mapping complex, multi-step decisions with branching paths and probabilities |
- Extension 1 (Bayesian Updating): Best attempted after the learner is comfortable with confidence levels (Week 3) and expected value (Week 9). Recommended for ages 9+ or advanced younger learners. The learner should be able to assign and update percentage-based confidence ratings before starting.
- Extension 2 (Decision Trees): Best attempted after the learner has completed expected value (Week 9) and reversible vs. irreversible (Week 11). Recommended for ages 10+ or advanced 9-year-olds. The learner should be comfortable with simple multiplication and the concept of branching outcomes.
How Skills Build Over Time
The curriculum is designed as a layered progression where each phase builds directly on the one before it.
Phase 1: Probability & The Physics of Choice teaches students that the world contains genuine randomness and that good process ≠ guaranteed good outcome. This is the essential foundation — without it, students will judge every decision by its result, which makes learning from experience nearly impossible. Phase 1 also introduces opportunity cost implicitly: every coin flip experiment involves choosing one bet over another, and students begin noticing what they give up when they choose.
↓ Why Phase 2 comes next: Once students accept that outcomes are uncertain, the natural question is: "So how do I make good choices if I can't control results?" Phase 2 answers this by showing that the biggest obstacle isn't randomness — it's the brain's own wiring.
Phase 2: Debugging the Hardware reveals the brain's built-in shortcuts — useful most of the time, but predictably unreliable in specific situations. Students learn to recognize anchoring, loss aversion, sunk cost thinking, and other biases. This phase makes opportunity cost explicit through loss aversion and sunk costs: the reason sunk costs trap us is precisely because we fail to see that the real cost of continuing is what else we could do with that time and energy.
↓ Why Phase 3 comes next: Knowing your biases isn't enough — you also need better tools to replace the faulty shortcuts. Phase 3 provides the analytical frameworks that make "think more carefully" into something concrete.
Phase 3: Data & Signal Processing gives students quantitative tools (expected value) and qualitative frameworks (signal vs. noise, reversibility) for making better choices with imperfect information. Expected value calculations are where opportunity cost becomes fully explicit and mathematical — choosing Option A means giving up Option B's expected value. Students also encounter diminishing returns for the first time: more information helps, but each additional piece helps less, and at some point you need to just decide.
↓ Why Phase 4 comes next: Phases 1–3 treat the student as a solo decision-maker. But real decisions happen around other people who are also making choices. Phase 4 shows what changes when decisions interact.
Phase 4: Game Theory & Social Systems extends individual decision-making into social contexts — showing how decisions interact in networks of people. The commons simulation makes diminishing returns visceral: each additional fish taken from the pond yields less, until the pond collapses entirely. Opportunity cost reappears as students discover that cooperating means giving up short-term advantage for long-term shared benefit.
↓ Why Phase 5 comes next: Students now have the complete conceptual toolkit. The final phase asks: "Can you actually use all of this on a real problem in your life?"
Phase 5: The Optimization Project brings everything together in a real-world project where students apply the full toolkit to an actual problem in their lives. The iterative design cycle (identify → design → test → learn → improve) naturally involves every concept from earlier phases: estimating probabilities, watching for biases, calculating trade-offs, considering how their choices affect others, and recognizing diminishing returns on optimization itself.
The Decision Journal runs through all five phases, growing from simple entries in Phase 1 into rich, reflective records by Phase 5. It is the thread that ties the entire curriculum together — and the clearest evidence of a learner's growth.
Independent Practice Setup Tips
Independent practice sessions work best when the learner has clear structure and a supportive environment. These sessions are where concepts move from "something the facilitator explained" to "something I actually use."
General Setup
1. Decision Journal supplies Keep the journal in a consistent, visible location with a pencil. Make it easy to grab and write in. If the journal isn't accessible, the habit won't stick.
2. Visual timer A countdown timer (phone, kitchen timer, or hourglass) helps learners manage the 30-minute session independently. Younger learners (ages 8–9) may do better with 20 minutes.
3. A "Thinking Prompt" card Write the week's key question on an index card and leave it on the table. This gives the learner a clear starting point so they don't spend 10 minutes wondering what to do.
4. Achievement tracker A progress chart showing completed weeks can make growth visible and motivating. Let the learner update it themselves after each session.
5. Weekly show-and-tell After each independent practice session, spend 2–3 minutes letting the learner explain what they discovered or what they wrote in their journal. This brief conversation is surprisingly powerful — it consolidates learning and gives the facilitator a window into the learner's thinking.
Which Weeks Need Extra Support
Not every independent practice session is fully solo. Some weeks work best with a partner or brief adult involvement:
- Weeks 1–4 (Phase 1): Younger learners (8–9) may need help setting up their journal and understanding the prompts. By Week 4, most learners should be able to do the practice session independently.
- Weeks 5–8 (Phase 2): The "bias hunting" activities in Weeks 5 and 8 work better with a partner or family member to discuss findings with. Weeks 6–7 can be done solo.
- Weeks 9–11 (Phase 3): Week 9 (Expected Value) may need brief adult help with the math for younger learners. Weeks 10–11 are good solo sessions.
- Weeks 12–14 (Phase 4): These weeks involve social dynamics, so the practice sessions benefit from a partner — a sibling, parent, or friend to play the game-theory scenarios with. If no second player is available, each lesson includes a solo or small-group fallback.
- Weeks 15–18 (Phase 5): The Optimization Project is inherently independent, but a brief weekly check-in (5 minutes) helps keep the learner on track and thinking clearly about their protocol.
Final Notes
This curriculum is designed to introduce children to decision-making as a skill that can be practiced, studied, and improved — just like reading, math, or sports.
The ideas in this course — probability, cognitive bias, expected value, cooperation, iteration — are the same ideas used by scientists, engineers, investors, and strategists. We've made them accessible and fun for young learners.
By the end of the program, students will have a Decision Journal full of real entries, a tested Decision Protocol they designed themselves, and — most importantly — a fundamentally different relationship with how they think about choices.
Students should leave feeling like decisions are something they can get better at — not just something that happens to them.