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J. Fernando Sánchez
@@ -8,6 +8,31 @@ Welcome to Soil's documentation!
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Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks.
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If you use Soil in your research, do not forget to cite this paper:
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.. code:: bibtex
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@inbook{soil-gsi-conference-2017,
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author = "S{\'a}nchez, Jes{\'u}s M. and Iglesias, Carlos A. and S{\'a}nchez-Rada, J. Fernando",
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booktitle = "Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection",
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doi = "10.1007/978-3-319-59930-4_19",
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editor = "Demazeau Y., Davidsson P., Bajo J., Vale Z.",
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isbn = "978-3-319-59929-8",
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keywords = "soil;social networks;agent based social simulation;python",
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month = "June",
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organization = "PAAMS 2017",
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pages = "234-245",
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publisher = "Springer Verlag",
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series = "LNAI",
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title = "{S}oil: {A}n {A}gent-{B}ased {S}ocial {S}imulator in {P}ython for {M}odelling and {S}imulation of {S}ocial {N}etworks",
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url = "https://link.springer.com/chapter/10.1007/978-3-319-59930-4_19",
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volume = "10349",
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year = "2017",
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}
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.. toctree::
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:maxdepth: 2
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:caption: Learn more about soil:
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112
docs/models.rst
112
docs/models.rst
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Developing new models
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---------------------
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This document describes how to develop a new analysis model.
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What is a model?
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================
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A model defines the behaviour of the agents with a view to assessing their effects on the system as a whole.
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In practice, a model consists of at least two parts:
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* Python module: the actual code that describes the behaviour.
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* Setting up the variables in the Settings JSON file.
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This separation allows us to run the simulation with different agents.
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Models Code
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===========
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All the models are imported to the main file. The initialization look like this:
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.. code:: python
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import settings
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networkStatus = {} # Dict that will contain the status of every agent in the network
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sentimentCorrelationNodeArray = []
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for x in range(0, settings.network_params["number_of_nodes"]):
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sentimentCorrelationNodeArray.append({'id': x})
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# Initialize agent states. Let's assume everyone is normal.
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init_states = [{'id': 0, } for _ in range(settings.network_params["number_of_nodes"])]
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# add keys as as necessary, but "id" must always refer to that state category
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A new model have to inherit the BaseBehaviour class which is in the same module.
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There are two basics methods:
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* __init__
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* step: used to define the behaviour over time.
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Variable Initialization
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=======================
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The different parameters of the model have to be initialize in the Simulation Settings JSON file which will be
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passed as a parameter to the simulation.
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.. code:: json
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{
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"agent": ["SISaModel","ControlModelM2"],
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"neutral_discontent_spon_prob": 0.04,
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"neutral_discontent_infected_prob": 0.04,
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"neutral_content_spon_prob": 0.18,
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"neutral_content_infected_prob": 0.02,
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"discontent_neutral": 0.13,
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"discontent_content": 0.07,
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"variance_d_c": 0.02,
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"content_discontent": 0.009,
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"variance_c_d": 0.003,
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"content_neutral": 0.088,
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"standard_variance": 0.055,
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"prob_neutral_making_denier": 0.035,
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"prob_infect": 0.075,
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"prob_cured_healing_infected": 0.035,
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"prob_cured_vaccinate_neutral": 0.035,
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"prob_vaccinated_healing_infected": 0.035,
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"prob_vaccinated_vaccinate_neutral": 0.035,
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"prob_generate_anti_rumor": 0.035
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}
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In this file you will also define the models you are going to simulate. You can simulate as many models as you want.
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The simulation returns one result for each model, executing each model separately. For the usage, see :doc:`usage`.
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Example Model
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=============
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In this section, we will implement a Sentiment Correlation Model.
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The class would look like this:
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.. code:: python
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from ..BaseBehaviour import *
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from .. import sentimentCorrelationNodeArray
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class SentimentCorrelationModel(BaseBehaviour):
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def __init__(self, environment=None, agent_id=0, state=()):
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super().__init__(environment=environment, agent_id=agent_id, state=state)
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self.outside_effects_prob = environment.environment_params['outside_effects_prob']
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self.anger_prob = environment.environment_params['anger_prob']
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self.joy_prob = environment.environment_params['joy_prob']
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self.sadness_prob = environment.environment_params['sadness_prob']
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self.disgust_prob = environment.environment_params['disgust_prob']
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self.time_awareness = []
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for i in range(4): # In this model we have 4 sentiments
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self.time_awareness.append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
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sentimentCorrelationNodeArray[self.id][self.env.now] = 0
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def step(self, now):
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self.behaviour() # Method which define the behaviour
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super().step(now)
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The variables will be modified by the user, so you have to include them in the Simulation Settings JSON file.
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