Citing this article

A standard form of citation of this article is:

Cointet, Jean-Philippe and Roth, Camille (2007). 'How Realistic Should Knowledge Diffusion Models Be?'. Journal of Artificial Societies and Social Simulation 10(3)5 <http://jasss.soc.surrey.ac.uk/10/3/5.html>.

The following can be copied and pasted into a Bibtex bibliography file, for use with the LaTeX text processor:

@article{cointet2007,
title = {How Realistic Should Knowledge Diffusion Models Be?},
author = {Cointet, Jean-Philippe and Roth, Camille},
journal = {Journal of Artificial Societies and Social Simulation},
ISSN = {1460-7425},
volume = {10},
number = {3},
pages = {5},
year = {2007},
URL = {http://jasss.soc.surrey.ac.uk/10/3/5.html},
keywords = {Agent-Based Simulation, Complex Systems, Empirical Calibration and Validation, Knowledge Diffusion, Model Comparison, Social Networks},
abstract = {Knowledge diffusion models typically involve two main features: an underlying social network topology on one side, and a particular design of interaction rules driving knowledge transmission on the other side. Acknowledging the need for realistic topologies and adoption behaviors backed by empirical measurements, it becomes unclear how accurately existing models render real-world phenomena: if indeed both topology and transmission mechanisms have a key impact on these phenomena, to which extent does the use of more or less stylized assumptions affect modeling results? In order to evaluate various classical topologies and mechanisms, we push the comparison to more empirical benchmarks: real-world network structures and empirically measured mechanisms. Special attention is paid to appraising the discrepancy between diffusion phenomena (i) on some real network topologies vs. various kinds of scale-free networks, and (ii) using an empirically-measured transmission mechanism, compared with canonical appropriate models such as threshold models. We find very sensible differences between the more realistic settings and their traditional stylized counterparts. On the whole, our point is thus also epistemological by insisting that models should be tested against simulation-based empirical benchmarks.},
}

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 - How Realistic Should Knowledge Diffusion Models Be?
AU - Cointet, Jean-Philippe
AU - Roth, Camille
Y1 - 2007/06/30
JO - Journal of Artificial Societies and Social Simulation
SN - 1460-7425
VL - 10
IS - 3
SP - 5
UR - http://jasss.soc.surrey.ac.uk/10/3/5.html
KW - Agent-Based Simulation
KW - Complex Systems
KW - Empirical Calibration and Validation
KW - Knowledge Diffusion
KW - Model Comparison
KW - Social Networks
N2 - Knowledge diffusion models typically involve two main features: an underlying social network topology on one side, and a particular design of interaction rules driving knowledge transmission on the other side. Acknowledging the need for realistic topologies and adoption behaviors backed by empirical measurements, it becomes unclear how accurately existing models render real-world phenomena: if indeed both topology and transmission mechanisms have a key impact on these phenomena, to which extent does the use of more or less stylized assumptions affect modeling results? In order to evaluate various classical topologies and mechanisms, we push the comparison to more empirical benchmarks: real-world network structures and empirically measured mechanisms. Special attention is paid to appraising the discrepancy between diffusion phenomena (i) on some real network topologies vs. various kinds of scale-free networks, and (ii) using an empirically-measured transmission mechanism, compared with canonical appropriate models such as threshold models. We find very sensible differences between the more realistic settings and their traditional stylized counterparts. On the whole, our point is thus also epistemological by insisting that models should be tested against simulation-based empirical benchmarks.
ER -