Absolutely, Phil — continuing with Batch 3 of the General Mental Models Pack, covering models 21–30. These are clean Markdown files, ready to drop into:
Mental Models (General)/
This batch moves into cognitive science, learning theory, and reasoning patterns — the stuff that makes LLMs (and humans) sharper.
Let’s get into it.
📄 21 — Cognitive Load Theory
Definition
Human working memory has limited capacity; overload reduces performance and clarity.
When to Use
• UI/UX design • Learning and teaching • Documentation • Workflow design • Decision‑making under pressure
How It Improves Reasoning
It forces you to simplify, structure, and sequence information so the brain can process it efficiently.
Steps
- Identify sources of cognitive load.
- Reduce extraneous load (noise, clutter, irrelevant details).
- Optimize intrinsic load (complexity of the task).
- Add scaffolding to support learning or decision‑making.
Example
A dashboard with 40 KPIs overwhelms users; grouping them into 5 categories reduces load.
Prompts
• “Reduce cognitive load in this workflow.” • “Simplify this explanation without losing meaning.”
📄 22 — Chunking
Definition
Grouping information into meaningful units to improve memory and processing.
When to Use
• Learning • System design • Documentation • Planning • Communication
How It Improves Reasoning
It increases working‑memory efficiency and helps structure complex information.
Steps
- Identify natural groupings.
- Create chunks.
- Build hierarchies or sequences.
- Use labels that capture the essence of each chunk.
Example
Breaking a 20‑step process into 4 phases makes it easier to understand and execute.
Prompts
• “Chunk this information into meaningful groups.” • “Create a hierarchical structure for this content.”
📄 23 — Mental Simulation
Definition
Imagining a scenario step‑by‑step to predict outcomes and identify issues.
When to Use
• Planning • Debugging • Risk analysis • Strategy • Complex decisions
How It Improves Reasoning
It reveals hidden steps, dependencies, and failure points before they occur.
Steps
- Define the scenario.
- Walk through each step mentally.
- Identify points of uncertainty or risk.
- Adjust the plan accordingly.
Example
Simulating a product launch reveals bottlenecks in support and onboarding.
Prompts
• “Simulate this scenario step‑by‑step.” • “Identify failure points through mental simulation.”
📄 24 — Fermi Estimation
Definition
Making quick, approximate calculations using rough assumptions to reach a reasonable estimate.
When to Use
• Early planning • Ambiguous data • Quick decision‑making • Sanity checks
How It Improves Reasoning
It prevents analysis paralysis and helps you reason with incomplete information.
Steps
- Break the problem into components.
- Estimate each component.
- Multiply or combine estimates.
- Check if the result is reasonable.
Example
Estimating how many piano tuners are in a city by breaking the problem into smaller assumptions.
Prompts
• “Perform a Fermi estimate for this question.” • “Give a rough calculation using reasonable assumptions.”
📄 25 — Heuristics vs Algorithms
Definition
Heuristics are fast, intuitive rules of thumb; algorithms are step‑by‑step procedures.
When to Use
• Heuristics: speed, low stakes, incomplete data • Algorithms: precision, high stakes, repeatability
How It Improves Reasoning
It helps you choose the right approach for the situation.
Steps
- Identify stakes and complexity.
- Choose heuristic or algorithmic reasoning.
- Apply the chosen method.
- Validate or refine.
Example
Using a heuristic to choose a restaurant; using an algorithm to calculate taxes.
Prompts
• “Should this be solved with a heuristic or an algorithm?” • “Convert this heuristic into a repeatable algorithm.”
📄 26 — Satisficing
Definition
Choosing the first option that meets the minimum acceptable criteria instead of optimizing endlessly.
When to Use
• Time‑limited decisions • Low‑stakes choices • Avoiding perfectionism • Reducing decision fatigue
How It Improves Reasoning
It prevents wasted effort on over‑optimization.
Steps
- Define minimum acceptable criteria.
- Evaluate options sequentially.
- Choose the first that meets criteria.
- Stop searching.
Example
Picking the first apartment that meets your needs instead of comparing 50.
Prompts
• “Define satisficing criteria for this decision.” • “Identify the first acceptable option.”
📄 27 — Availability Bias
Definition
Overestimating the importance of information that is recent, vivid, or easy to recall.
When to Use
• Risk assessment • Decision‑making • Forecasting • Evaluating evidence
How It Improves Reasoning
It helps you correct for distorted perceptions caused by memorable events.
Steps
- Identify the memorable event influencing judgment.
- Compare with base rates or broader data.
- Adjust perception accordingly.
Example
After hearing about a plane crash, people overestimate the danger of flying.
Prompts
• “Identify availability bias in this reasoning.” • “Compare this perception with base rates.”
📄 28 — Confirmation Bias
Definition
The tendency to seek, interpret, and remember information that confirms existing beliefs.
When to Use
• Research • Analysis • Debates • Diagnostics • Hiring decisions
How It Improves Reasoning
It forces you to consider disconfirming evidence and alternative explanations.
Steps
- Identify your current belief.
- Seek disconfirming evidence.
- Evaluate both sides objectively.
- Update your belief if needed.
Example
A manager only noticing data that supports their preferred strategy.
Prompts
• “Identify confirmation bias in this argument.” • “Provide disconfirming evidence.”
📄 29 — Anchoring
Definition
The tendency to rely too heavily on the first piece of information encountered.
When to Use
• Negotiations • Pricing • Forecasting • Planning • Estimation
How It Improves Reasoning
It helps you adjust away from arbitrary or misleading anchors.
Steps
- Identify the anchor.
- Evaluate whether it is relevant.
- Re‑estimate independently.
- Compare and adjust.
Example
A first price offer sets expectations even if it’s unreasonable.
Prompts
• “Identify anchors affecting this decision.” • “Re‑estimate without using the initial anchor.”
📄 30 — Loss Aversion
Definition
People feel losses more strongly than equivalent gains.
When to Use
• Negotiations • Behavioral design • Investment decisions • Risk analysis
How It Improves Reasoning
It reveals emotional distortions that can lead to overly conservative or irrational choices.
Steps
- Identify potential losses and gains.
- Compare emotional vs rational weighting.
- Adjust decision to reflect true value.
Example
People prefer avoiding a 100, even though the values are equal.
Prompts
• “Identify loss aversion in this decision.” • “Reframe this choice to reduce loss‑focused bias.”