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