Cumulative knowledges learning in networks (0.5.0)
We built a model of knowledges diffusion in networks. The particularity is that knowledges to be passed on are cumulative : rely on previous acquisition of specific knowledges of a lower level to be passed from an agent to another. We test for different types of networks, different selection rules for the knowledge to be passed and the possibility to introduce a turnover among agents.
We concentrate on the learning speed and the convergence level of the learning process.
Release Notes
The first things to explore is to vary :
“Forme-du-réseau” : changes the network shape
“niveaux-autorises” : changes how a knowledge is selected to be passed on during an interaction
“parmi-les-derniers?” : the knowledge is selected following the rule defined with “niveaux-autorises” but not among all the knowledges possessed by an agent but among the knowledges she has just learned the previous time step.
“turnover-rate” : changes the probability with which an agent can be replaced during the simulation by a new one who is always a newbie if “repartion-expertise-arrivants” is set to “tous débutants” (“all newbies”)
“Partitionnement?” : creates a partitioned knowledges structure at the initialization
Associated Publications
Cumulative knowledges learning in networks 0.5.0
We built a model of knowledges diffusion in networks. The particularity is that knowledges to be passed on are cumulative : rely on previous acquisition of specific knowledges of a lower level to be passed from an agent to another. We test for different types of networks, different selection rules for the knowledge to be passed and the possibility to introduce a turnover among agents.
We concentrate on the learning speed and the convergence level of the learning process.
Release Notes
The first things to explore is to vary :
“Forme-du-réseau” : changes the network shape
“niveaux-autorises” : changes how a knowledge is selected to be passed on during an interaction
“parmi-les-derniers?” : the knowledge is selected following the rule defined with “niveaux-autorises” but not among all the knowledges possessed by an agent but among the knowledges she has just learned the previous time step.
“turnover-rate” : changes the probability with which an agent can be replaced during the simulation by a new one who is always a newbie if “repartion-expertise-arrivants” is set to “tous débutants” (“all newbies”)
“Partitionnement?” : creates a partitioned knowledges structure at the initialization