scicom.knowledgespread.model
Classes
A model for knowledge spread. |
Functions
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Get all agents active at time t. |
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Module Contents
- scicom.knowledgespread.model.getActiveAgents(model)
Get all agents active at time t.
- scicom.knowledgespread.model.getNetworkStructure(model)
- class scicom.knowledgespread.model.KnowledgeSpread(num_scientists=100, num_timesteps=20, epiDim=1.0001, epiRange=0.01, oppositionPercent=0.05, loadInitialConditions=False, epiInit='complex', timeInit='saturate', beta=8, slope=5, base=2)
Bases:
mesa.ModelA model for knowledge spread.
Agents have an initial topic vector and are positioned in epistemic space. The number of agents can grow linearly, as a s-curve, or exponentially. Agents initial positions on epistemic space can be diverse (checker board-like), around a central position or in opossing camps.
Agents activation probability is age-dependent. After reaching a personal productivity end, agents are removed from the scheduler.
- Parameters:
num_scientists (int)
num_timesteps (int)
epiDim (float)
epiRange (float)
oppositionPercent (float)
loadInitialConditions (bool)
epiInit (str)
timeInit (str)
beta (int)
slope (int)
base (int)
- numScientists = 100
- numTimesteps = 20
- epiRange = 0.01
- opposPercent = 0.05
- loadInitialConditions = False
- epiInit = 'complex'
- timeInit = 'saturate'
- beta = 8
- slope = 5
- base = 2
- schedule
- space
- socialNetwork
- grid
- datacollector
- running = True
- _setupAgents()
Create initial setup of agents.
- _setupSocialSpace(nEdges=4, density=0.2, densityGrowth=0)
Setup initial social connections.
- step()
Run one simulation step.
- run(n)
Run model for n steps.