For those of you with too much time on your hands...
On this very windy, and very Scottish morning here in Edinburgh, I am trying to work out what the difference is between "local information" and "asymmetric information"? Is there a difference? Or is local information simply a case of asymmetric information? Do agents acting solely on local information create a situation of asymmetric information when they interact? I think the answer to this last question is clearly yes!
A classic case of asymmetric information is Akerloff's lemons in the used car market. It is typically assumed that the dealer has more/better/more accurate information about the used-car than does the buyer and thus we get an asymmetry of information. Is it fair to say that the buyer's information set is a subset of the dealer's information set? I will have to go back and review my Akerloff Adverse Selection notes on this point.
Local information seems to me to be a different beast. With local information, two agents have different information sets. There might be some overlap of information sets or there might not. I think for local information to create a case of asymmetric information, the two information sets must overlap in some non-trivial way.
The image I have in my head is of two hill-walkers wandering around the highlands at night wearing the same brand of head torches (so that the amount of terrain that each can see at any given time is the same...no hill-walker has a technological comparative advantage in info gathering). It is pitch black so the only information they can gather about the terrain is from what is illuminated by the head torch. As they each wander around they are collecting information about the terrain locally because of their limited vision. If they happen to wander over some of the same terrain, then they will have this information in common (that is their information sets will have some overlap...think 2D Venn diagram). However they could even have information sets that are entirely disjoint (maybe one is not really a hill-walker and prefers to faff about in the valley, while the other is running around on the crags). If they were to encounter one another in their wanderings, would this interaction be a case of asymmetric information? I think it depends. If one or both hill-walkers were interested in heading in the direction that the other had already been, then this is clearly a case of asymmetric information. Perhaps they would even decide to trade information (assuming they had a technology that would allow them to do this). However if they were interested in heading off in different directions, then this might not be a case of asymmetric information because neither hill-walker has information about where the other is going (there would also be no reason to trade).
Am I right to think about local and asymmetric information in this way? Am I over complicating things by trying to make some distinction? I think this is relevant to my research because financial institutions clearly use their local information to try and create a situation of asymmetric information that they can exploit for profit and trade (which creates financial inter-linkages or more dense financial networks).
Blog Topics...
3D plotting
(1)
Academic Life
(2)
ACE
(18)
Adaptive Behavior
(2)
Agglomeration
(1)
Aggregation Problems
(1)
Asset Pricing
(1)
Asymmetric Information
(2)
Behavioral Economics
(1)
Breakfast
(4)
Business Cycles
(8)
Business Theory
(4)
China
(1)
Cities
(2)
Clustering
(1)
Collective Intelligence
(1)
Community Structure
(1)
Complex Systems
(42)
Computational Complexity
(1)
Consumption
(1)
Contracting
(1)
Credit constraints
(1)
Credit Cycles
(6)
Daydreaming
(2)
Decision Making
(1)
Deflation
(1)
Diffusion
(2)
Disequilibrium Dynamics
(6)
DSGE
(3)
Dynamic Programming
(6)
Dynamical Systems
(9)
Econometrics
(2)
Economic Growth
(5)
Economic Policy
(5)
Economic Theory
(1)
Education
(4)
Emacs
(1)
Ergodic Theory
(6)
Euro Zone
(1)
Evolutionary Biology
(1)
EVT
(1)
Externalities
(1)
Finance
(29)
Fitness
(6)
Game Theory
(3)
General Equilibrium
(8)
Geopolitics
(1)
GitHub
(1)
Graph of the Day
(11)
Greatest Hits
(1)
Healthcare Economics
(1)
Heterogenous Agent Models
(2)
Heteroskedasticity
(1)
HFT
(1)
Housing Market
(2)
Income Inequality
(2)
Inflation
(2)
Institutions
(2)
Interesting reading material
(2)
IPython
(1)
IS-LM
(1)
Jerusalem
(7)
Keynes
(1)
Kronecker Graphs
(3)
Krussel-Smith
(1)
Labor Economics
(1)
Leverage
(2)
Liquidity
(11)
Logistics
(6)
Lucas Critique
(2)
Machine Learning
(2)
Macroeconomics
(45)
Macroprudential Regulation
(1)
Mathematics
(23)
matplotlib
(10)
Mayavi
(1)
Micro-foundations
(10)
Microeconomic of Banking
(1)
Modeling
(8)
Monetary Policy
(4)
Mountaineering
(9)
MSD
(1)
My Daily Show
(3)
NASA
(1)
Networks
(46)
Non-parametric Estimation
(5)
NumPy
(2)
Old Jaffa
(9)
Online Gaming
(1)
Optimal Growth
(1)
Oxford
(4)
Pakistan
(1)
Pandas
(8)
Penn World Tables
(1)
Physics
(2)
Pigouvian taxes
(1)
Politics
(6)
Power Laws
(10)
Prediction Markets
(1)
Prices
(3)
Prisoner's Dilemma
(2)
Producer Theory
(2)
Python
(29)
Quant
(4)
Quote of the Day
(21)
Ramsey model
(1)
Rational Expectations
(1)
RBC Models
(2)
Research Agenda
(36)
Santa Fe
(6)
SciPy
(1)
Shakshuka
(1)
Shiller
(1)
Social Dynamics
(1)
St. Andrews
(1)
Statistics
(1)
Stocks
(2)
Sugarscape
(2)
Summer Plans
(2)
Systemic Risk
(13)
Teaching
(16)
Theory of the Firm
(4)
Trade
(4)
Travel
(3)
Unemployment
(9)
Value iteration
(2)
Visualizations
(1)
wbdata
(2)
Web 2.0
(1)
Yale
(1)
No comments:
Post a Comment