2025-05-20 # definition = The minimal subset of information from set 🧠B that contains all the necessary information to achieve goal 🫀A - A = Goal of choosing the correct exam answer in demand modeling course which consists of multiple choice (one out of four) and binary choice (one out of two) - B = All course knowledge and concepts - MSS(A,B) = Distilled key rules that capture the exact insight needed for each question # instructive examples If I were to replicate the MSS logic, these three examples provide the most instructive illustrations: ## Example 1: Likelihood Ratio Test (Question 1.4) - **Goal (A)**: Understand why LR test statistics must be non-negative - **Information Set (B)**: Full theory of likelihood functions, nested models, parameter constraints, chi-squared distributions, optimization theory, hypothesis testing frameworks - **MSS(A,B)**: "LR test = 2(LLunrest - LLrest) ≥ 0 always (unrestricted model has larger parameter space)" This example powerfully demonstrates how pages of statistical theory can be reduced to a single insight about search spaces that guarantees the correct answer. ## Example 2: Discrete Mixture Models (Question 3) - **Goal (A)**: Formulate choice probabilities in a latent class context - **Information Set (B)**: Probability theory, mixture distributions, latent class models, likelihood derivations, integration over probability spaces, logit formulations - **MSS(A,B)**: "Discrete mixtures: P = π₁P₁ + π₂P₂ where π₁ + π₂ = 1; likelihood = ∏ P^y over choices" This illustrates how complex mathematical concepts requiring multiple equations can be compressed to a minimal formula that preserves the essential structure. ## Example 3: Data Requirements (Question 2.5) - **Goal (A)**: Identify if given data is sufficient for model estimation - **Information Set (B)**: Data collection principles, revealed preference theory, discrete choice modeling frameworks, identification requirements, attribute measurement - **MSS(A,B)**: "Must have attributes for ALL alternatives (chosen + unchosen) to estimate choice models" This example shows how practical modeling knowledge can be reduced to a single critical requirement, eliminating all peripheral considerations. These three examples span different types of knowledge (statistical, mathematical, and practical) and demonstrate how the MSS approach can extract the decisive insight from each information domain. --- using [comparing practice and final demand modeling exam cld](https://claude.ai/chat/2a1d7a3f-3da0-4237-b3a3-3032d916186f)