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Autumn 2014


Week 1:

1.     Introduction/overview of class, Introduction to Simulink, continuous signals


Week 2:

1.     Simulink tutorial: Continuous signals, discrete signals, processing

2.     Simulink tutorial: Continuous/discrete dynamics, filters, feedback


Week 3:

1.     Wealth calculator in Simulink.  Modeling extreme poverty, income, expenses, savings

2.     Simulating financial life of the poor, computing performance metrics for finances


Week 4:

1.     Monte Carlo simulation, getting data in and out of Simulink, running Simulink from Matlab, computing data, means and standard deviations

2.     Monte Carlo simulation, plotting, running the program, meaning of outputs


Week 5:

1.     Spending strategy design, modifications, proportional control introduction

2.     Spending strategy design, modeling and simulation of proportional control case, tuning the gain, tracking performance


Week 6:

1.     Derivative control, coding, proportional-derivative control

2.     Integral control, coding, proportional-integral control, PID control/tuning, effect of variations in desired wealth trajectory


Week 7:

1.     Computing money expended, PID auto-tune, effects of getting a raise, not following advice of PID, having a “leaky” bank (e.g., thefts), writing an app that is a personal spending advisor

2.     Distributive justice, expectations for achieving equal wealth across a community, computing the receiving/giving of donations


Week 8:

1.     Topology, prototypical topology, wealth distribution policies (half difference example), frequency of update, discretization, delays, religion/secular views, pick a policy

2.     Wealth distribution policies, computing wealth distribution policy for a small community


Week 9:

1.     Wealth distribution policies, computing wealth distribution policy for a small community, impact of policy on individuals, emergence of cooperation (idea of stability)

2.     Wealth distribution policies, impact of changing generosity parameter, impact of community inequality: rich-poor, poor-rich skew via varying ultimate desired wealth and income levels, corruption (hiding money) effects, effects of thefts of donations


Week 10:

1.     Democracy for adjusting a wealth distribution policy, voting based on greed, majority votes

2.     Democracy for adjusting a wealth distribution policy, computational issues, simulations, effect on equal, poor-rich skewed, and rich-poor skewed communities


Week 11:

1.     Models of economic growth (neoclassical), dynamics, equilibria, stability.

2.     Poverty traps: Nonlinearity, dynamics.  Low-capital trap, savings, trap, demographic trap.


Week 12:

1.      Simulation of nonlinear differential equations.  Technology quality (effects on equilibria and dynamics), poverty traps, provisioning, and growth. Sensitivity analysis (1-d and n-dimensional cases), analytical and computational approaches (with example of effects of parameters on equilibria).


Week 13:

1.     Effects of technology diffusion on poverty dynamics: diffusion models (externally-driven, internal-influence), integrated poverty trap-growth/diffusion models

2.     Technology diffusion coupled with poverty trap models: analysis, simulations, and phase plane analysis.  Discussion on model development (physical vs social sciences), and importance of model validation  


Week 14:

1.     Capital investment model, investment rates, democracy/wealth distribution, and provisioning to break poverty traps: model and simulation analysis, discussion on model validity


Week 15:

1.     Cooperator system for sharing a humanitarian engineering technology.  Resources, consumers, maintenance/replacement costs.  Pricing strategies.  Parameters to adjust cooperation.  Model and simulation analysis.

2.     Modifying the poverty trap model to couple in health and education dynamics.  Wealth-health-education model from country-level information. Comparative analysis. Need for data and system identification to validate models.    


Week 16:

1.     Dynamics of democracy, development, and cultural values: data, differential equation models, phase plane analysis, couplings, human development index component effects