Agent Based Models and
Common Value Auctions
B. Wade Brorsen
Agent based models (ABM)
simulate the actions and
interactions of autonomous
agents to determine effects
on the system as a whole.
• Agent-Based Model: Bottom-up Method
– Carry out the experiments with programmed agents
– Interactive agents with simple rules
Initial
condition
Observed
Market
Equilibrium
3
Applications of ABM
•
•
•
•
•
Biology
Land Use
Urban Planning
Oligopoly
Auctions
Contrast with Traditional Theory
Fully Rational
a. Maximize profit
b. Maximize expected utility
Boundedly Rational
Do what worked last time
Game Theory
Small number of players
Industrial organization
International trade
Nash equilibrium
Oligopoly Models
Cournot
Bertrand
Stackelberg
Auctions
Private value
Common value
Affiliated value
Meyers (1988)
Matagorda County rice auctions
Bids increased as # buyers increased
Winner’s curse
Bid shading
Relevant Tools to Study Auctions
Theoretical
Human experiments
Agent-based models
Agent Based Models
Computerized agents
Follow specific rules
Simulation or optimization?
Stochastic Global Optimization
Evolutionary Algorithms
Genetic algorithms
Particle swarm
Random Restarts
Global Best
Local Best 1
Select
New
strategy
particle 1
Local Best K
Agent 1
Strategy 1 Profit
Strategy K
Market 1
particle 1
particle K
Select
New
strategy
Market K
Agent 2
Local Best 1
particle K
Local Best K
13
Global Best
Convergence to Equilibrium
Software
• Many canned packages - Mostly land use
• We use Java
• Can be slow
Revisiting Mandatory Price Reporting Act
of 1999 Using an Agent-Based Model
Objective
• Develop an agent-based common-value
auction to determine expected effect of
Livestock Mandatory Price Reporting Act on
packer market power
MPR Literature
• Previous theoretical work disagrees
• Recent econometric work shows beef packers’
market power increased after MPR
Theoretical Framework
• Previous theoretical work follows Cournot
theory
• Auction model is closer to how cattle are
actually purchased
Auction Structure
• First-price common-value auction with a
reserve price
• Common value for the packers is 0.5
Post MPR Results
Full Noise
Seller
Reduced
Noise
Buyer
Reduced
Noise
Average winning bid price
0.02
0.11
0.06
Reserve price
-0.31
-0.08
-0.43
Buyer 1 expected revenue
0.17
0.10
0.20
Buyer 2 expected revenue
0.16
0.10
0.18
Seller expected revenue
0.01
0.06
0.06
Category
Application to Cattle Sales
• Reducing either the seller’s or buyers’
uncertainty through USDA-AMS reports
created by MPR benefits the seller