"""
In the study of Social Opinion Dynamics, it is crucial to consider multiple factors that significantly influence the formation and spread of opinions within networks of agents. The Activity Driven Model addresses this by simulating the evolution of opinions over time, capturing the complex interactions that lead to consensus formation, polarization, and the influence of external entities such as bots.
This model is highly configurable, allowing users to explore a wide range of scenarios and behaviors by adjusting parameters related to agent activity, social influence, homophily, and more. It also incorporates recent considerations, such as the role of bots—agents with fixed opinions—who interact with humans on social media platforms, thereby influencing human-machine interactions and their effects on opinion dynamics.
As the name suggests, the `Activity Driven` Model focuses on the activities of agents, which represent the interactions they perform within a social network.
The key features of this models are :
Temporal Dynamics
-----------------
max_iterations:
Limits the total number of simulation steps.
If set to None, the model runs indefinitely, allowing for long-term analysis of opinion evolution.
dt:
Defines the time step for each iteration, controlling the pace at which the simulation progresses.
Smaller values lead to more granular updates, while larger values speed up the simulation but might miss finer details.
Agent Behavior and Interaction
------------------------------
m:
Determines how many agents an active agent interacts with during each time step.
Influences the rate of opinion spreading; higher values mean more interactions and potentially faster consensus or polarization.
eps:
Sets the minimum activity level for agents, ensuring no agent is completely inactive.
Helps prevent stagnation in the model by keeping all agents engaged at some level.
gamma:
Controls the distribution of agent activity, typically following a power-law where few agents are very active, and many are less active.
Affects how central or peripheral agents influence the overall opinion dynamics.
homophily:
Measures the tendency of agents to interact with others who share similar opinions.
High homophily can lead to echo chambers, while low homophily promotes diverse interactions.
reciprocity:
Determines whether agents are likely to reciprocate interactions, creating more mutual or one-sided connections.
High reciprocity strengthens bidirectional relationships, potentially stabilizing opinion clusters.
K:
Represents the strength of social influence between agents.
A higher K means opinions are more strongly influenced by interactions, which can accelerate consensus or deepen polarization.
Social Context and Controversialness
------------------------------------
alpha:
Controls the degree of controversialness of the issue being simulated.
A higher alpha can lead to more polarized opinions, as agents might have stronger reactions to the issue.
Bots and External Influence
---------------------------
n_bots:
Specifies the number of bots in the simulation, which are fixed in their opinions.
Bots influence the network without being influenced, potentially driving opinion shifts or reinforcing certain views.
bot_m, bot_activity, bot_opinion, bot_homophily:
Define the specific behaviors and characteristics of bots, such as how often they interact or how similar they are to other agents.
These parameters allow bots to mimic or diverge from regular agents, providing a controlled way to study external influence.
Reluctance and Activity Correlation
-----------------------------------
use_reluctances:
Activates the feature where agents have a reluctance to change their opinions.
Adds complexity by simulating resistance to change, affecting how quickly or slowly opinions evolve.
reluctance_mean, reluctance_sigma, reluctance_eps:
Define the distribution of reluctance across agents, determining the average resistance and its variability.
These parameters help model heterogeneous populations where some agents are more resistant to change than others.
covariance_factor:
Introduces a correlation between an agent's activity level and its reluctance, meaning that activity might influence or be influenced by reluctance.
Allows for more realistic scenarios where active agents may be more or less open to changing their opinions, depending on the sign of the covariance.
Example:
---------
>>> from pyseldonlib import Activity_Driven_Model
>>> # Create the Activity Driven Model
>>> model = Activity_Driven_Model(max_iterations=1000, convergence_tol=1e-6)
>>> # Run the simulation
>>> model.run("output_dir")
>>> # Access the network
>>> network = model.get_Network()
>>> # Access the opinions of the agents
>>> opinions = model.agents_opinions()
>>> activity = model.agents_activity()
>>> reluctance = model.agents_reluctance()
Reference
---------
.. bibliography::
:style: plain
Baumann_2020
Baumann_2021
*************
"""
from bindings import seldoncore
import pathlib
from typing import Optional
from ._basemodel import Base_Model
from ._othersettings import Other_Settings
[docs]
class Activity_Driven_Model(Base_Model):
"""
Activity Driven Model base class for Simulation.
Parameters
-----------
max_iterations : int, default=None
The maximum number of iterations to run the simulation. If None, the simulation runs infinitely.
dt : float, default=0.01
The time step for the simulation.
m : int, default=10
Number of agents contacted, when the agent is active.
eps : float, default=0.01
The minimum activity epsilon.
gamma : float, default=2.1
Exponent of activity power law distribution of activities.
alpha : float default=3.0
Controversialness of the issue, must be greater than 0.
homophily : float, default=0.5
The extent to which similar agents interact with similar other.
Example: If 0.0, agents pick their interaction partners at random.
If 1.0, agents interact only with agents of the same opinion.
reciprocity : float, default=0.5
The extent to which agents reciprocate interactions.
Example: If 0.0, agents do not reciprocate interactions.
If 1.0, agents reciprocate all interactions.
K : float, default=3.0
Social interaction strength.
mean_activities : bool, default=False
Whether use the mean value of the powerlaw distribution for the activities of all agents.
mean_weights : bool, default=False
Whether use the meanfield approximation of the network edges, by default is False.
n_bots : int, default=0
Number of bots in the simulation.
.. note::
Bots are agents that are not influenced by the opinions of other agents, but they can influence the opinions of other agents. So they have fixed opinions and different parameters, the parameters are specified in the following lists.
bot_m : list[int], default=[]
Value of m for the bots, If not specified, defaults to `m`.
bot_activity : list[float], default=[],
The list of bot activities, If not specified, defaults to 0.
bot_opinion : list[float], default=[]
The fixed opinions of the bots.
bot_homophily : list[float], default=[]
The list of bot homophily, If not specified, defaults to `homophily`.
use_reluctances : int, default=False
Whether use reluctances, by default is False and every agent has a reluctance of 1.
reluctance_mean : float, default=1.0
Mean of distribution before drawing from a truncated normal distribution.
reluctance_sigma : float, default=0.25
Width of normal distribution (before truncating).
reluctance_eps : float, default=0.01
Minimum such that the normal distribution is truncated at this value.
covariance_factor : float, default=0.0
Covariance Factor, defines the correlation between reluctances and activities.
rng_seed : int, default=None
The seed for the random number generator. If not provided, a random seed is picked.
agent_file : str, default=None
The file to read the agents from. If None, the agents are generated randomly.
network_file : str, default=None
The file to read the network from. If None, the network is generated randomly
other_settings : Other_Settings, default=None
The other settings for the simulation. If None, the default settings are used.
Attributes
-----------
Network : Network (Object)
The network generated by the simulation.
Opinion : Float
The opinions of the agents or nodes of the network.
"""
def __init__(
self,
max_iterations: int = None,
dt: float = 0.01,
m: int = 10,
eps: float = 0.01,
gamma: float = 2.1,
alpha: float = 3.0,
homophily: float = 0.5,
reciprocity: float = 0.5,
K: float = 3.0,
mean_activities: bool = False,
mean_weights: bool = False,
n_bots: int = 0,
bot_m: list[int] = [],
bot_activity: list[float] = [],
bot_opinion: list[float] = [],
bot_homophily: list[float] = [],
use_reluctances: int = False,
reluctance_mean: float = 1.0,
reluctance_sigma: float = 0.25,
reluctance_eps: float = 0.01,
covariance_factor: float = 0.0,
rng_seed: Optional[int] = None,
agent_file: Optional[str] = None,
network_file: Optional[str] = None,
other_settings: Other_Settings = None,
):
# Other settings and Simulation Options are already intialised in super
super().__init__()
if other_settings is not None:
self.other_settings = other_settings
self._options.model_string = "ActivityDriven"
self._options.model_settings = seldoncore.ActivityDrivenSettings()
self._options.output_settings = self.other_settings.output_settings
self._options.network_settings = self.other_settings.network_settings
self._options.model = seldoncore.Model.ActivityDrivenModel
self._options.model_settings.max_iterations = max_iterations
self._options.model_settings.dt = dt
self._options.model_settings.m = m
self._options.model_settings.eps = eps
self._options.model_settings.gamma = gamma
self._options.model_settings.alpha = alpha
self._options.model_settings.homophily = homophily
self._options.model_settings.reciprocity = reciprocity
self._options.model_settings.K = K
self._options.model_settings.mean_activities = mean_activities
self._options.model_settings.mean_weights = mean_weights
self._options.model_settings.n_bots = n_bots
self._options.model_settings.bot_m = bot_m
self._options.model_settings.bot_activity = bot_activity
self._options.model_settings.bot_opinion = bot_opinion
self._options.model_settings.bot_homophily = bot_homophily
self._options.model_settings.use_reluctances = use_reluctances
self._options.model_settings.reluctance_mean = reluctance_mean
self._options.model_settings.reluctance_sigma = reluctance_sigma
self._options.model_settings.reluctance_eps = reluctance_eps
self._options.model_settings.covariance_factor = covariance_factor
if rng_seed is not None:
self._options.rng_seed = rng_seed
self._simulation = seldoncore.SimulationActivityAgent(
options=self._options,
cli_agent_file=agent_file,
cli_network_file=network_file,
)
self._network = self._simulation.network
[docs]
def agent_activity(self, index: int = None):
"""
Access the agents activity data from the simulated network.
Parameters
-----------
index : int
The index of the agent to access. The index is 0-based. If not provided, all agents are returned.
"""
if index is None:
result = [agent.data.activity for agent in self._simulation.network.agent]
return result
else:
if index < 0 or index >= self.Network.n_agents():
raise IndexError("Agent index is out of range.")
return self._simulation.network.agent[index].data.activity
[docs]
def set_agent_activity(self, index: int, activity: float):
"""
Set the activity of a specific agent.
Parameters
----------
index : int
The index of the agent whose opinion is to be set.
activity : float
The new activity value for the agent.
"""
if index < 0 or index >= self.Network.n_agents():
raise IndexError("Agent index is out of range.")
self._simulation.network.agent[index].data.activity = activity
[docs]
def agent_reluctance(self, index: int = None):
"""
Access the agents reluctance data from the simulated network.
Parameters
-----------
index : int
The index of the agent to access. The index is 0-based. If not provided, all agents are returned.
"""
if index is None:
result = [agent.data.reluctance for agent in self._simulation.network.agent]
return result
else:
if index < 0 or index >= self.Network.n_agents():
raise IndexError("Agent index is out of range.")
return self._simulation.network.agent[index].data.reluctance
[docs]
def set_agent_reluctance(self, index: int, reluctance: float):
"""
Set the reluctance of a specific agent.
Parameters
----------
index : int
The index of the agent whose opinion is to be set.
reluctance : float
The new reluctance value for the agent.
"""
if index < 0 or index >= self.Network.n_agents():
raise IndexError("Agent index is out of range.")
self._simulation.network.agent[index].data.reluctance = reluctance
