Model Thinking
summary
Explains how to think using models to understand complex real-world systems, presenting model-based thinking techniques. Covers practical ways to apply modeling approaches for analysis and problem solving, with slides available at the provided presentation link.
AI-written summary · about
chapters (21)
- 0:00Introduction
- 1:10Models in life
- 2:13Successful model examples
- 4:18Why models help
- 6:03Outcome types
- 8:28Models for data analysis
- 9:09Monty Hall paradox
- 11:16Segregation models
- 15:08Societal change models
- 18:16Model dynamics
- 20:00Simple rules complex systems
- 22:10Outcome classes in nature
- 23:15Linear models
- 24:41Causality in models
- 26:06Economic growth models
- 28:53Rule of 72
- 30:19Sports success models
- 33:08Regression to mean
- 35:13Prisoner's dilemma
- 37:22Digital system interactions
- 38:23Q&A
related talks
09mSystems Thinking for Testers
10mModel Based Test Automation
42mHow To Test The Inside of Your Head
39mModel Based Testing Shifting Right into the Real World
44mHow to Start Working with DDD Using BDD
01mRethinking testing and risk within complex systems
09mFoundational Skills for Testers
47mTangible Software Quality