Who Are You?

Who Are You?

1. Who are you? (Name, year, major)
2. Computational skills (if any)
• programming, statistical analysis, …
3. What do you want to get from this class?
4. Ask me a question about computational modeling
5. Something interesting about yourself

Getting Started

Getting Started

For Thursday:

• Download and install NetLogo on your computer.
• URL in syllabus and assignment sheet
• Set up Box account
• Details in syllabus and assignment sheet
• https://vanderbilt.box.com
• Make folder for this class with your last name:
• lastname_EES_4760 or lastname_EES_5760
• Share it with me as Editor
• Homework goes in subfolders:
• HW_1, HW_2, …

Agent-Based Modeling

Agent-Based Modeling

• Simulate individuals:
• Autonomous
• Heterogeneous
• Quasi-local
• Bounded rationality
• Simulate environment
• Emphasize simplicity, minimal assumptions
• Emergence: Large-scale phenomena arise from small-scale individual interactions
• Interesting when large-scale is not easily predicted from small-scale

Simple Experiments

• Play with economics
• Simple agents trade with each other
• Confirm 1st welfare theorem:

Trading leads to Pareto equilibrium

• Find conditions for satisfying theorem:
• Not necessary for traders to be completely rational
• How much rationality do you need?
• Equilibration can be slow
• Time-varying preferences can prevent equilibration
• Dynamics of agent-based models connect to nonlinear dynamics and chaos

Economics of Cooperation

Game Theory

• Prisoner’s Dilemma Game:

A \ B B Cooperates B Defects
A Cooperates (3,3) (0,4)
A Defects (4,0) (1,1)
• Nash Equilibrium:
• No matter what player A does, player B is better off defecting
• No matter what player B does, player A is better off defecting
• End result: Both players end up worse off than if they had both cooperated.

Iterated Prisoner’s Dilemma

• R. Axelrod (1981)
• Tournament of algorithms
• Winner: “tit-for-tat”
• Evolutionary Game Theory:
• Basic principles of good strategies:
• Be nice
• Be provocable
• Don’t be too envious
• Don’t be too clever
• Nay & Gilligan (2015)
• Real-world strategies involve randomness, unpredictability

Artificial Anasazi

Example: Artificial Anasazi

Axtell, Dean, Epstein, et al.

Long House Valley (flourished ca. 1800 BCE–1300 CE)

Constructing model

• Paleoclimate:
• Assess different kinds of soil
• Assess tree rings, pollen, etc.
• Reconstruct drought severity index
• Society:
• Archaeology gives #, location of households
• Make assumptions about:
• # people per household,
• Agriculture,
• Devise rules for behavior:
• Marriage, reproduction, migration, …
• Simulate years 800–1300

Comparison

Simulated Historical

Improvements

• Make agents heterogeneous
• Fit parameters to historical data