A standard form of citation of this article is:
Vilà, Xavier (2008). 'A Model-To-Model Analysis of Bertrand Competition'. Journal of Artificial Societies and Social Simulation 11(2)11 <https://www.jasss.org/11/2/11.html>.
The following can be copied and pasted into a Bibtex bibliography file, for use with the LaTeX text processor:
@article{vil_agrave_2008,
title = {A Model-To-Model Analysis of Bertrand Competition},
author = {Vil\\`{a}, Xavier},
journal = {Journal of Artificial Societies and Social Simulation},
ISSN = {1460-7425},
volume = {11},
number = {2},
pages = {11},
year = {2008},
URL = {https://www.jasss.org/11/2/11.html},
keywords = {Agent-Based Computational Economics, Model-To-Model Analysis,},
abstract = {This paper studies a version of the classical Bertrand model in which consumers exhibit some
strategic behavior when deciding from what seller they will buy. We use two related but
different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the
two of them consumers' behavior influences the competition between the sellers. The results
obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers
as it works in their best interest.
First, we consider a learning procedure described by a deterministic dynamic system and, using
strong simplifying assumptions, we can produce a description of the behavior of the process.
Second, we use finite automata to represent the strategies played by the agents and an adaptive
process based on genetic algorithms to simulate the stochastic process of learning. By doing so
we can relax some of the strong assumptions used in the first approach and still obtain the same
basic results.
It is suggested that the limitations of the first approach (analytical) provide a good motivation for
the second approach (Agent-Based). Indeed, although both approaches address the same
problem, the use of Agent-Based computational techniques allows us to relax hypothesis and
overcome the limitations of the analytical approach while obtaining the same basic results.},
}The following can be copied and pasted into a text file, which can then be imported into a reference database that supports imports using the RIS format, such as Reference Manager and EndNote.
TY - JOUR
TI - A Model-To-Model Analysis of Bertrand Competition
AU - Vilà, Xavier
Y1 - 2008/03/31
JO - Journal of Artificial Societies and Social Simulation
SN - 1460-7425
VL - 11
IS - 2
SP - 11
UR - https://www.jasss.org/11/2/11.html
KW - Agent-Based Computational Economics
KW - Model-To-Model Analysis
KW -
N2 - This paper studies a version of the classical Bertrand model in which consumers exhibit some strategic behavior when deciding from what seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers' behavior influences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the behavior of the process. Second, we use finite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the first approach and still obtain the same basic results. It is suggested that the limitations of the first approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach while obtaining the same basic results.
ER -