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256 lines
11 KiB
Python
256 lines
11 KiB
Python
import random
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import networkx as nx
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from soil.agents import BaseAgent, FSM, state, default_state
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from scipy.spatial import cKDTree as KDTree
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global betweenness_centrality_global
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global degree_centrality_global
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betweenness_centrality_global = None
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degree_centrality_global = None
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class TerroristSpreadModel(FSM):
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"""
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Settings:
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information_spread_intensity
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terrorist_additional_influence
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min_vulnerability (optional else zero)
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max_vulnerability
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prob_interaction
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"""
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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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global betweenness_centrality_global
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global degree_centrality_global
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if betweenness_centrality_global == None:
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betweenness_centrality_global = nx.betweenness_centrality(self.global_topology)
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if degree_centrality_global == None:
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degree_centrality_global = nx.degree_centrality(self.global_topology)
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self.information_spread_intensity = environment.environment_params['information_spread_intensity']
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self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
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self.prob_interaction = environment.environment_params['prob_interaction']
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if self['id'] == self.civilian.id: # Civilian
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self.initial_belief = random.uniform(0.00, 0.5)
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elif self['id'] == self.terrorist.id: # Terrorist
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self.initial_belief = random.uniform(0.8, 1.00)
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elif self['id'] == self.leader.id: # Leader
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self.initial_belief = 1.00
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else:
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raise Exception('Invalid state id: {}'.format(self['id']))
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if 'min_vulnerability' in environment.environment_params:
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self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
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else :
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self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
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self.mean_belief = self.initial_belief
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self.betweenness_centrality = betweenness_centrality_global[self.id]
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self.degree_centrality = degree_centrality_global[self.id]
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# self.state['radicalism'] = self.mean_belief
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def count_neighboring_agents(self, state_id=None):
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if isinstance(state_id, list):
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return len(self.get_neighboring_agents(state_id))
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else:
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return len(super().get_agents(state_id, limit_neighbors=True))
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def get_neighboring_agents(self, state_id=None):
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if isinstance(state_id, list):
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_list = []
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for i in state_id:
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_list += super().get_agents(i, limit_neighbors=True)
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return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
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else:
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_list = super().get_agents(state_id, limit_neighbors=True)
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return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
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@state
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def civilian(self):
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if self.count_neighboring_agents() > 0:
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neighbours = []
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for neighbour in self.get_neighboring_agents():
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if random.random() < self.prob_interaction:
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neighbours.append(neighbour)
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influence = sum( neighbour.degree_centrality for neighbour in neighbours )
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mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
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self.initial_belief = self.mean_belief
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mean_belief = mean_belief * self.information_spread_intensity + self.initial_belief * ( 1 - self.information_spread_intensity )
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self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
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if self.mean_belief >= 0.8:
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return self.terrorist
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@state
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def leader(self):
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self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
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if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
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for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
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if neighbour.betweenness_centrality > self.betweenness_centrality:
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return self.terrorist
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@state
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def terrorist(self):
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if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
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neighbours = self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id])
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influence = sum( neighbour.degree_centrality for neighbour in neighbours )
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mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
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self.initial_belief = self.mean_belief
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self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
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self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
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if self.count_neighboring_agents(state_id=self.leader.id) == 0 and self.count_neighboring_agents(state_id=self.terrorist.id) > 0:
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max_betweenness_centrality = self
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for neighbour in self.get_neighboring_agents(state_id=self.terrorist.id):
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if neighbour.betweenness_centrality > max_betweenness_centrality.betweenness_centrality:
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max_betweenness_centrality = neighbour
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if max_betweenness_centrality == self:
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return self.leader
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def add_edge(self, G, source, target):
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G.add_edge(source.id, target.id, start=self.env._now)
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def link_search(self, G, node, radius):
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pos = nx.get_node_attributes(G, 'pos')
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nodes, coords = list(zip(*pos.items()))
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kdtree = KDTree(coords) # Cannot provide generator.
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edge_indexes = kdtree.query_pairs(radius, 2)
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_list = [ edge[int(not edge.index(node))] for edge in edge_indexes if node in edge ]
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return [ G.nodes()[index]['agent'] for index in _list ]
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def social_search(self, G, node, steps):
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nodes = list(nx.ego_graph(G, node, radius=steps).nodes())
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nodes.remove(node)
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return [ G.nodes()[index]['agent'] for index in nodes ]
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class TrainingAreaModel(FSM):
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"""
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Settings:
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training_influence
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min_vulnerability
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Requires TerroristSpreadModel.
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"""
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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.training_influence = environment.environment_params['training_influence']
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if 'min_vulnerability' in environment.environment_params:
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self.min_vulnerability = environment.environment_params['min_vulnerability']
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else: self.min_vulnerability = 0
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@default_state
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@state
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def terrorist(self):
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for neighbour in self.get_neighboring_agents():
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if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability > self.min_vulnerability:
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neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )
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class HavenModel(FSM):
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"""
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Settings:
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haven_influence
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min_vulnerability
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max_vulnerability
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Requires TerroristSpreadModel.
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"""
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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.haven_influence = environment.environment_params['haven_influence']
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if 'min_vulnerability' in environment.environment_params:
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self.min_vulnerability = environment.environment_params['min_vulnerability']
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else: self.min_vulnerability = 0
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self.max_vulnerability = environment.environment_params['max_vulnerability']
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@state
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def civilian(self):
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for neighbour_agent in self.get_neighboring_agents():
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if isinstance(neighbour_agent, TerroristSpreadModel) and neighbour_agent['id'] == neighbour_agent.civilian.id:
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for neighbour in self.get_neighboring_agents():
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if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability > self.min_vulnerability:
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neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
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return self.civilian
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return self.terrorist
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@state
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def terrorist(self):
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for neighbour in self.get_neighboring_agents():
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if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability < self.max_vulnerability:
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neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
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return self.terrorist
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class TerroristNetworkModel(TerroristSpreadModel):
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"""
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Settings:
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sphere_influence
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vision_range
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weight_social_distance
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weight_link_distance
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"""
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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.vision_range = environment.environment_params['vision_range']
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self.sphere_influence = environment.environment_params['sphere_influence']
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self.weight_social_distance = environment.environment_params['weight_social_distance']
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self.weight_link_distance = environment.environment_params['weight_link_distance']
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@state
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def terrorist(self):
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self.update_relationships()
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return super().terrorist()
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@state
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def leader(self):
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self.update_relationships()
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return super().leader()
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def update_relationships(self):
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if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
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close_ups = self.link_search(self.global_topology, self.id, self.vision_range)
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step_neighbours = self.social_search(self.global_topology, self.id, self.sphere_influence)
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search = list(set(close_ups).union(step_neighbours))
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neighbours = self.get_neighboring_agents()
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search = [item for item in search if not item in neighbours and isinstance(item, TerroristNetworkModel)]
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for agent in search:
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social_distance = 1 / self.shortest_path_length(self.global_topology, self.id, agent.id)
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spatial_proximity = ( 1 - self.get_distance(self.global_topology, self.id, agent.id) )
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prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
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if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
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self.add_edge(self.global_topology, self, agent)
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break
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def get_distance(self, G, source, target):
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source_x, source_y = nx.get_node_attributes(G, 'pos')[source]
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target_x, target_y = nx.get_node_attributes(G, 'pos')[target]
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dx = abs( source_x - target_x )
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dy = abs( source_y - target_y )
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return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
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def shortest_path_length(self, G, source, target):
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try:
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return nx.shortest_path_length(G, source, target)
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except nx.NetworkXNoPath:
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return float('inf')
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