Classes





Behavioral Economics I

Topic I: Fairness and Social Preferences
Topic II: Experimental Design
Topic III: Identity
Topic IV: Judgment under Uncertainty (Heuristics and Biases)
Topic V: Behavioral Welfare Economics
Topic VI: Happiness
Topic VII: Other Topics
 
Course Materials


Behavioral Economics III: Topics in Terrorism, Risk Perception, and Crisis Informatics.

Topic I: Identity
Topic II: Urban Warfare and Terrorism
              II.a Terrorism and Influence
              ll.b Radicalization and Motivation
             ll.c  Industrial/Organizational psychology and terrorism
Topic III: Economics and Terrorism
         III.a Economics of Crime: Basic Concepts, Models of criminal behavior
         III.b  On the field: Empirical studies and Experiments
Topic IV: Risk Perception and Terrorism
        IV. a Modeling Risk
        IV.b Modeling Emotion-Based  Decision Making in Risk Environments
Topic VI: Crisis Informatics and Terrorism

Course Materials



Machine Learning applications to Behavioral Economics

  • Regression: linear regression, logistic regression
  • Dimensionality Reduction: Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis
  • Probabilistic Models: Naive Bayes, maximum likelihood estimation, bayesian inference
  • Statistical Learning Theory: VC dimension
  • Kernels: Support Vector Machines (SVMs), kernel tricks, duality
  • Sequential Models and Structural Models: Hidden Markov Model (HMM), Conditional Random Fields (CRFs)
  • Clustering: spectral clustering, hierachical clustering
  • Latent Variable Models: K-means, mixture models, expectation-maximization (EM) algorithms, Latent Dirichlet Allocation (LDA), representation learning
  • Deep Learning: feedforward neural network, restricted Boltzmann machine, autoencoders, recurrent neural network, convolutional neural network
  • Reinforcement Learning: Markov decision processes, Q-learning
  • and others, including advanced topics for machine learning in natural language processing and text analysis

Course Materials



Methods for Network Analysis I


Topic I: Forms of Network Data
Topic II: Ego Networks and Experiments
Topic III: Aggregated Ego Networks
Topic IV: Multi-Mode and Hyper Networks
Topic V: Basic network metrics I
Topic VI: Basic network metrics II
Topic VII: Basic network metrics III
Topic VIII: Social Influence Models
Topic IX: Diffusion Models
Topic X: Longitudinal Network Models
Topic XI: QAP and MR/QAP
Topic XII: Exponential Random Graph Models

Course Materials


Methods for Network Analysis Il
 

Course Materials