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# SAEN-PIGO Employment Prediction This repository provides a reference implementation of the **Stratified Adaptive Employment Network (SAEN)** and the **Policy-Informed Generative Optimization (PIGO)** framework for employment prediction and policy simulation. The code is inspired by the paper *"Deep Learning for College Graduates Employment Prediction: A Computational Approach"* and adapts its main ideas into a practical deep learning project structure. --- ## 1. Overview Modern labor markets are highly dynamic and influenced by: - Individual characteristics (education, age, region, skills) - Employment histories (transitions between employed, unemployed, inactive) - Macroeconomic indicators (unemployment rate, GDP, sector demand) - Policy interventions (subsidies, training programs, incentives) Traditional statistical models often struggle to capture: - Non-linear relationships - Long-term temporal dependencies - Heterogeneity across different groups - Direct effects of policy constraints This project implements a deep learning framework that addresses these issues through: - **SAEN**: A stratified, sequence-based model for employment trajectories - **PIGO**: A policy optimization layer that operates on top of SAEN --- ## 2. Main Components ### 2.1 Stratified Adaptive Employment Network (SAEN) SAEN is designed to model employment dynamics as sequences of states over time: - Each individual belongs (probabilistically) to a **latent stratum** (e.g., age group, education level, region). - Each stratum has its own recurrent unit to capture **group-specific temporal patterns**. - The model incorporates: - **Employment state embeddings** (employed, unemployed, inactive) - **Duration / tenure information** (how long in the current state) - **Individual covariates** (demographics, skills) - **Macroeconomic variables** (e.g., labor market indicators) - **Policy actions** (e.g., training, subsidies) Core ideas: - **Stratum-aware sequence modeling** via group-specific recurrent networks - **Duration and action integration** so the transition probability depends on tenure and interventions - **Macroeconomic attention** to focus on the most relevant historical macro signals - **Residual individual adaptation** to fine-tune group-level behavior for a specific person ### 2.2 Policy-Informed Generative Optimization (PIGO) PIGO is a policy learning and simulation layer that uses SAEN as an environment model. Key ideas: - Treat employment transitions as a **sequential decision problem** - Define a **reward function** that encodes social or organizational objectives, such as: - Higher employment rates - Lower unemployment duration - Fair outcomes across demographic groups - Learn a **stochastic policy** that maps states to policy actions - Integrate: - **Budget or cost constraints** - **Temporal smoothness constraints** (discourage drastic policy changes over time) - **Fairness regularization** across strata The result is a framework that can: 1. Predict employment trajectories under existing conditions 2. Simulate counterfactual outcomes under alternative policies 3. Optimize policies within realistic constraints --- ## 3. Model - `model.py`：Core implementation of SAEN, macroeconomic attention, and PIGO policy network. --- ## 4. Data Representation Although actual datasets are not bundled, the model is designed for generic employment data of the form: - **Sequential records per individual**: - Discrete time steps (e.g., months) - Employment state labels per time step - Tenure in current state - Individual covariates over time (static or slowly varying) - Macroeconomic indicators over time (shared across individuals) - Policy actions applied at each time step The dataset class in this repository is written in a way that you can adapt it to: - Graduate employment datasets - Sector-specific labor datasets - Organizational workforce planning datasets --- ## 5. Training and Evaluation ### 5.1 Training the SAEN model High-level steps: 1. Prepare sequences for each individual. 2. Build train / validation / test splits (preferably chronological). 3. Initialize the SAEN model: - Number of strata - Hidden dimension - Embedding sizes for states, duration, actions - Sizes for individual and macro features 4. Optimize a sequence loss (e.g., negative log-likelihood of next-state prediction). 5. Optionally add: - L1/L2 regularization on parameters - Group sparsity or entropy regularization on strata usage You can integrate early stopping based on validation accuracy, F1 score, or other metrics. ### 5.2 Training the PIGO policy After SAEN is reasonably trained, the policy layer can: - Use SAEN as a **simulator** to roll out trajectories under different policies. - Optimize policy parameters using a policy gradient algorithm: - Estimate returns along sampled trajectories - Include budget constraints via penalties in the loss - Include fairness constraints via penalties on group-level employment gaps - Use a baseline network to reduce gradient variance Evaluation metrics may include: - Overall employment rate at the horizon - Average unemployment duration - Group-level employment parity - Policy cost or budget consumption --- ## 6. Fairness and Ethics Employment prediction and policy simulation can impact real people. This project encourages: - **Fairness-aware training**: - Measure outcomes per stratum (e.g., by region, gender, education level). - Penalize large disparities between groups. - **Transparent use**: - Use the models as decision support, not as the sole decision-maker. - Communicate uncertainty and possible biases in data. - **Ongoing auditing**: - Regularly check performance and fairness metrics. - Update models and policies when data distribution shifts. --- ## 7. Extending the Project Possible directions: - Replace the recurrent backbone with transformer-style sequence encoders. - Add graph-based modules for: - Skill transition graphs - Employer-employee networks - Regional labor mobility graphs - Integrate causal inference techniques to better identify: - True effects of policies - Confounding factors Potential applications: - National or regional labor policy analysis - University graduate employability studies - Organizational workforce planning - Sector-specific labor market analytics --- ## 8. Citation If you use this implementation in a research project or derived work, please cite the original paper on which this implementation is conceptually based, as well as your own project or publication. ---

You are given a 2D integer array intervals where intervals[i] = [starti, endi] represents all the integers from starti to endi inclusively. A containing set is an array nums where each interval from intervals has at least two integers in nums. For example, if intervals = [[1,3], [3,7], [8,9]], then [1,2,4,7,8,9] and [2,3,4,8,9] are containing sets. Return the minimum possible size of a containing set.