Zero-intelligence agents looking for a job
Authored by Andre Veski, Kaire Poder
Date Published: 2018
DOI: 10.1007/s11403-017-0198-z
Sponsors:
No sponsors listed
Platforms:
No platforms listed
Model Documentation:
Other Narrative
Model Code URLs:
Model code not found
Abstract
We study a simple agent-based model of a decentralized matching market
game in which agents (workers or job seekers) make proposals to other
agents (firms) in order to be matched to a position within the firm. The
aggregate result of agents interactions can be summarised in the form of
a Beveridge curve, which determines the relationship between unmatched
agents, unemployed job seekers and vacancies in firms. We open the black
box of matching technology, by modelling how agents behave (make
proposals) according to their information perception. We observe more
efficient results-in the form of a downward shift of the Beverage curve
in the case of simple zero-intelligent agents. Our comparative statics
indicate that market conditions, such as the heterogeneity of agents'
preferences, will also shift the Beveridge curve downwards. Moreover,
market thickness affects movement along the Beverage curve. Movement
right-down along the curve if there is an increasing number of agents
compared to positions within firms. Furthermore, we show that frictions
in re-matching, such as commitment to a match, could be another factor
shifting the Beveridge curve toward the origin.
Tags
Search
evolutionary model
stability
Unemployment
Labor-market
Beveridge curve
Matching market
Computational experiment
Decentralised matching
Job
search