Please Read Our Three-Guide Introduction First!

Before starting this Intermediate Guide, it is critical to have read our introduction to the three guides, titled Reality-Aligned Thinking and Metacognition: Introduction to the Basic, Intermediate, or Advanced Guides. Skipping it is allowed, just as ignoring the instructions before assembling furniture. The furniture may still stand, but it will probably stare at you with one crooked leg and missing critical parts.

We have also provided pre- and post-completion self-scoring tests for the Intermediate Guide so you can see how you did after completing this guide. These tests will include essential information from the introduction to the three guides.

 Click here for the pretest and click here for the post-test.

Prologue:

This Intermediate guide, along with all three of our guides, is dedicated to the following simple idea. What the world's future needs most right now is not so much another technological breakthrough. It is a widespread and major upgrade in rational thinking, analysis, and decision-making skills that better enable individuals to problem-solve and act on the challenges in their lives, ranging from the simple to the complex.

In case you were in a hurry and didn't read the introduction. The following are critical reminders from the introduction to all three guides:

1. Choose a real current problem.
2. Understand the powerful difference between declarative vs. experiential knowledge.
3. Use AI as an assistant, not an authority.
4. Use the simplest adequate thinking tool.
5. Apply one method at a time.
6. Write results, act carefully, review, repeat.
7. Treat intuition as a signal to test, not a verdict to worship.

Introduction: What the Intermediate Guide Covers and What Each Method Is Best Used For

Best for: harder decisions, uncertain futures, competing explanations, repeated failures, risk reduction, complex personal or organizational problems, institutional problems, strategy, design, incentives, and early systems analysis.

• • Tool selector: Best for matching the problem to the simplest adequate method instead of firing every method cannon at once, which sounds dramatic but usually ruins the carpet.
  • Key assumptions check: Best for finding the hidden beliefs your plan depends on.
  • Devil’s advocacy: Best for stress-testing an idea before reality does it less politely.
  • Premortem: Best for imagining that a plan failed and then working backward to identify likely causes.
  • Indicators and signposts: Best for tracking whether a situation is moving toward or away from your expectations.
  • What-if analysis: Best for exploring how changes in assumptions could change outcomes.
  • Analysis of Competing Hypotheses: Best for comparing multiple explanations and reducing confirmation bias.
  • Forecasting and calibration: Best for making better predictions, tracking accuracy, and learning from prediction errors.
  • Base rates and reference classes: Best for grounding predictions in relevant historical patterns before the imagination starts operating heavy machinery.
  • Brier scoring: Best for measuring forecasting accuracy over time.
  • Causal diagrams: Best for mapping causes, effects, mediators, confounders, and feedback loops.
  • Counterfactual thinking: Best for asking what would likely have happened if one factor had changed.
  • Decision matrices and decision trees: Best for comparing options, trade-offs, uncertainty, and consequences.
  • Sensitivity analysis: Best for identifying which assumptions matter most to the final decision.
  • Value-focused thinking: Best for clarifying what goals, values, and trade-offs should guide the decision.
  • Five Whys and fishbone diagrams: Best for investigating root causes rather than repeatedly treating symptoms and calling it leadership.
  • PDSA/PDCA cycles: Best for testing small improvements, learning quickly, and revising action.
  • After-action review: Best for learning from what actually happened after a decision or project.
  • FMEA, fault-tree analysis, and bow-tie analysis: Best for identifying failure modes, risk pathways, prevention controls, and recovery controls.
  • Resilience thinking: Best for preparing systems to absorb shocks and continue functioning.
  • Design thinking: Best for understanding real user needs, reframing problems, prototyping solutions, and testing them before building a monument to the wrong answer.
  • Jan De Visch-style creative attunement, beginner/intermediate version: Best for approaching unclear, emotionally tangled, early-stage, or symbolically loaded problems before forcing them into rigid analysis too soon.
  • Cynefin sensemaking: Best for distinguishing simple, complicated, complex, chaotic, and disorderly problem contexts.
  • Horizon scanning: Best for identifying emerging trends, weak signals, and future risks.
  • Scenario planning: Best for exploring multiple plausible futures when prediction alone is too narrow.
  • Backcasting: Best for starting with a desired future and working backward to identify necessary steps.
  • Game theory basics: Best for analyzing strategic interaction, incentives, cooperation, competition, free-riding, and coordination failure.
  • Commons and institutional analysis: Best for analyzing shared-resource problems, governance failures, enforcement, legitimacy, monitoring, capture, and who benefits or pays.
  • Kegan-lite subject-object awareness: Best for noticing when your identity, role, loyalty, fear, or worldview may be shaping what you can and cannot see.
  • Systems thinking: Best for understanding interacting parts, feedback loops, delays, boundaries, incentives, emergent behavior, and unintended consequences.
  • Complex adaptive systems: Best for analyzing systems with many interacting agents that learn, adapt, self-organize, and produce surprising outcomes.
  • DSRP: distinctions, systems, relationships, perspectives: Best for improving how you organize information and build better mental models.
  • Meadows system traps and leverage points: Best for identifying recurring system failures and places where change may have an unusually high impact.
  • Safe-to-fail tests and learning loops: Best for experimenting in complex systems without betting the village grain supply on a theory someone made during a conference lunch.

AI support prompts for the Intermediate Guide

(Please note that these AI prompts and many other prompts will be repeated in the Intermediate Guide.)

• • “Help me identify the key assumptions behind this plan. Rank them by importance and uncertainty: [describe plan].”
  • “Run a premortem on this decision. Imagine it failed badly one year from now. What are the most likely reasons it failed?”
  • “Help me compare three or more possible explanations for this situation. Create an Analysis of Competing Hypotheses-style table using evidence for and against each explanation.”
  • “What indicators or signposts should I track to know whether this situation is improving, worsening, or changing direction?”
  • “Help me build a simple decision matrix for these options: [list options]. Include criteria, trade-offs, risks, reversibility, cost, time, and likely consequences.”
  • “Help me create a causal map for this problem. Identify likely causes, effects, feedback loops, delays, incentives, and possible unintended consequences.”
  • “What are the strongest alternative scenarios for how this situation could develop over the next [time period]?”
  • “Help me identify possible system traps, leverage points, stakeholder incentives, free-rider problems, and places where a small change might produce a large effect.”
  • “Suggest one safe-to-fail test I could run before committing to a larger decision or strategy.”

How to choose the right method for harder problems, instead of attacking every problem with the same heroic little hammer.

The Intermediate Reality-Aligned Thinking Guide is for problems that are too hard for basic, clear thinking alone. The Basic Guide helps you define terms, check evidence, spot common reasoning errors, and stop your first reaction from driving the bus while blindfolded. This guide starts when the problem has more moving parts: uncertainty, competing explanations, risk, forecasting, repeated failure, organizational incentives, social complexity, or system behavior.

The goal is simple: help you choose the right thinking tool for the kind of problem you are actually facing. A problem with unclear evidence requires a different tool than one with hidden causes. A risky decision needs a different tool from a recurring failure. A complex adaptive system needs a different tool from a one-time mistake. Using the wrong tool is how people end up hammering soup and then wondering why dinner is on the ceiling.

This guide is built around real use. Each page explains what the method is best for, when not to use it, how to try it quickly, how to apply it carefully, how AI can help, what mistakes to avoid, and how to test whether it actually helped with one of your real problems.

Quick navigation

• • Overview
  • Best used for
  • 5-minute version
  • 30-minute version
  • Vignette
  • Practice
  • AI prompts
  • FAQ
  • Glossary
  • References
  • Next page

Best used for

• • Choosing where to begin when a problem feels hard, tangled, or high-stakes.
  • Deciding which intermediate tool fits which kind of problem.
  • Preventing method overload before the reader has even begun.
  • Helping readers turn real-life problems into workable thinking tasks.

5-minute version

Use this when the problem is pressing, and you need the fastest, most responsible version of the method. Not perfect, but better than sprinting into a decision while waving a flaming assumption.

1. 1. Name the problem in one sentence.
   2. Decide whether the problem is about evidence, causes, prediction, risk, design, institutions, identity, or systems.
   3. Choose the simplest tool that fits the problem.
   4. Write one sentence about what a better outcome would look like.
   5. Use the matching page in this guide instead of pretending every problem is a nail.

30-minute careful version

Use this when the issue matters enough to deserve a slower look. Thirty minutes of structured thinking can prevent thirty months of cleanup, which is apparently a bargain humans keep trying to avoid.

1. 1. Write the problem as a question.
   2. Classify the problem type: unclear truth, competing explanations, uncertain future, hidden cause, risky decision, repeated failure, weak solution design, institutional conflict, identity capture, or complex system.
   3. Choose one primary tool and one backup tool.
   4. Define what evidence, decision, or action would count as progress.
   5. Set a review date to check whether the tool improved your real-world results.

Vignette: The team that kept using the wrong tool

A small nonprofit keeps having projects run late. One person says the problem is laziness. Another says it is bad communication. A third says everyone just needs to care more, the traditional management spell cast before nothing changes.

Using the tool selector, they realize this is not first a motivation problem. It is a repeated failure with unclear causes and workflow delays. They choose failure analysis first, then systems thinking later. In a single meeting, they discover that every project depends on a single overloaded reviewer. The bottleneck had been dressed up as a character flaw. Very human. Very fixable.

Practice: apply this to one of your three current problems

Write down your three most important current problems. Pick one. Then apply the prompts below. Do not merely admire the tool from a safe distance like a museum visitor staring at a fire extinguisher.

1. 1. List your three biggest current problems.
   2. For each one, write: Is this mostly evidence, cause, prediction, decision, risk, design, institution, self-awareness, or system complexity?
   3. Choose one problem to work on first.
   4. Write which page in this guide you should start with and why.
   5. After using the page, write what changed in your understanding or action.

Common mistakes

• • Starting with the most impressive tool instead of the simplest fitting tool.
  • Calling a problem “complex” because it feels annoying.
  • Skipping the Basic Guide because your ego has a tiny crown.
  • Trying to analyze everything at once instead of selecting one entry point.

AI Prompt Support Module

Use AI as a thinking partner, not as a priest, judge, or magical vending machine for certainty. First write your own answer. Then ask AI to challenge, improve, and stress-test it.

I am working on this problem: [describe it]. Classify it as mainly an evidence problem, causal problem, forecasting problem, risk problem, decision problem, design problem, institutional problem, identity/mental-model problem, or complex systems problem. Explain your classification and suggest the simplest useful method to start with.

Here are my three current problems: [list]. For each one, recommend which intermediate thinking tool I should use first, why that tool fits, what mistake it helps prevent, and what result I should expect if I apply it well.

I think I should use [tool] for this problem: [describe]. Challenge that choice. Tell me where this tool fits, where it may not fit, and what simpler or more appropriate tool I should consider first.

FAQ

Do I need to read the Basic Guide first?

Yes, at least review it. Intermediate methods assume you can define claims, check evidence, notice bias, and separate facts from interpretations. Skipping the basics is like building a second floor on pudding.

Should I read this guide in order?

For training, yes. For urgent problems, use the tool selector and go directly to the page that matches your problem type.

What if my problem fits several categories?

Start with the category that blocks progress the most. If no one agrees on what is true, begin with evidence and competing hypotheses. If the facts are clear but action is risky, begin with decision and risk tools.

Glossary

• • Tool selector: A guide for matching a problem type to the thinking method most likely to help.
  • Problem type: The main difficulty in a problem, such as evidence uncertainty, causal confusion, risk, design failure, institutional conflict, or system complexity.
  • Reality-aligned thinking: Thinking that keeps beliefs, models, decisions, and actions tied to evidence, feedback, and real-world consequences.

References and bibliography

These sources are included so readers can go deeper, examine the intellectual foundations, and avoid treating this guide as if it descended from the clouds on a glowing clipboard.

1. 1. Richards J. Heuer Jr., Psychology of Intelligence Analysis, CIA Center for the Study of Intelligence. CIA PDF.
   2. Philip E. Tetlock and Dan Gardner, Superforecasting: The Art and Science of Prediction. See also Good Judgment Open’s explanation of probabilistic scoring. Good Judgment Open FAQ.
   3. The Cynefin Company, “About the Cynefin Framework.” Cynefin framework overview.
   4. Donella H. Meadows, Leverage Points: Places to Intervene in a System. PDF.

Next Page: Structured Analytic Techniques

The next page teaches the first intermediate upgrade: structured analytic techniques. These methods help you catch bad assumptions, test early conclusions, and invite disciplined disagreement before a weak idea hardens into policy, strategy, or family drama.

If you have ever watched a group confidently agree too quickly and then slowly discover reality was not invited to the meeting, this next page is for you.