def move(self):
if not self.in_hospital:
# find all the possible steps and if it's empty them append it into a list
all_steps = self.model.grid.get_neighborhood(self.pos,
moore=True,
include_center=False)
possible_steps = []
for cell in all_steps:
if Grid.is_cell_empty(self.model.grid, cell):
possible_steps.append(cell)
# if the list is not empty them find a random cell for the agent to move in
if possible_steps:
new_position = self.random.choice(possible_steps)
self.model.grid.move_agent(self, new_position)
class ProtestModel(Model):
def __init__(self, initial_num_citizens, initial_num_media,
hardcore_density, hanger_on_density, observer_density,
agent_vision_radius, agent_move_falibility,
default_hardcore_move_vector, default_hanger_on_move_vector,
default_observer_move_vector, default_cop_move_vector,
default_media_move_vector, citizen_jailed_sensitivity,
citizen_pictures_sensitivity, citizen_cops_sensitivity,
max_days, height, width, agent_regions, obstacle_regions,
flag_regions, cop_regions, arrest_delay, jail_time):
super().__init__()
self.steps_per_day = 12
# Population initialisation
self.initial_num_cops = len(co_ords_for_area(cop_regions))
self.initial_num_citizens = initial_num_citizens
self.initial_num_media = initial_num_media
self.hardcore_density = hardcore_density
self.hanger_on_density = hanger_on_density
self.hanger_on_density = observer_density
# Agent init
# Agent movement factors
self.agent_vision_radius = agent_vision_radius
self.agent_move_falibility = agent_move_falibility
# vector order:
# [violent, active, quiet, cop, media, flag, obstacle]
self.default_hardcore_move_vector = default_hardcore_move_vector
self.default_hanger_on_move_vector = default_hanger_on_move_vector
self.default_observer_move_vector = default_observer_move_vector
self.default_cop_move_vector = default_cop_move_vector
self.default_media_move_vector = default_media_move_vector
# Citizen legitimacy update factors
self.citizen_jailed_sensitivity = citizen_jailed_sensitivity
self.citizen_pictures_sensitivity = citizen_pictures_sensitivity
self.citizen_cops_sensitivity = citizen_cops_sensitivity
# Core model code
# Model step represents 2 hours
self.max_iters = max_days * self.steps_per_day
self.iterations = 0
self.schedule = RandomActivation(self)
self.grid = Grid(width, height, torus=False)
self.height = height
self.width = width
self.running = True
self.previous_day_jailed_count = 0
self.previous_day_pictures_count = 0
self.previous_day_cops_count = self.initial_num_cops
self.jailed_count = 0
self.pictures_count = 0
self.cops_count = self.initial_num_cops
# Set such that when cops/agents are 2:1, the perceived arrest chance is 0.9
self.arrest_delay = arrest_delay
self.arrest_constant = 1.15
self.jail = [] # stores jailed agents
self.jail_time = jail_time # Represents "harshness" of current regime.
if not (hardcore_density + hanger_on_density + observer_density == 1):
raise ConfigError("Protestor densities must add up to 1")
if self.initial_num_cops + initial_num_citizens + initial_num_media > (
height * width):
raise ConfigError("Too many humans for the given grid")
self.total_fights = 0
self.total_jailed = 0
self.hours_without_protest = 0
self.hours_without_conflict = 0
self.datacollector = DataCollector(
model_reporters={
"Quiet":
lambda model: model.num_in_state("quiet"),
"Active":
lambda model: model.num_in_state("active"),
"Violent":
lambda model: model.num_in_state("violent"),
"Fighting":
lambda model: model.num_in_state("fighting"),
"Protesting":
lambda model: model.num_protesting(),
"Jailed":
lambda model: model.num_jailed(),
"Frustrated":
lambda model: model.num_frustrated(),
"Average legitimacy":
lambda model: model.average_legitimacy(),
"Average grievance":
lambda model: model.average_grievance(),
"Average ripeness":
lambda model: model.average_grievance() * model.num_in_state(
"quiet") / float(model.average_risk_aversion()),
"Cop count":
lambda model: model.num_cops(),
"Num pictures":
lambda model: model.num_pictures(),
"Total fights":
lambda model: model.total_fights,
"Total jailed":
lambda model: model.total_jailed,
"Protest waiting time":
lambda model: model.hours_without_protest,
"Conflict waiting time":
lambda model: model.hours_without_conflict,
},
agent_reporters={
"perceived_gain":
lambda agent: agent.perceived_gain()
if isinstance(agent, Citizen) else 0,
"net_risk_active":
lambda agent: agent.net_risk("active")
if isinstance(agent, Citizen) else 0,
"net_risk_violent":
lambda agent: agent.perceived_gain() - agent.net_risk(
"violent") if isinstance(agent, Citizen) else 0,
"act_utils":
lambda agent: agent.net_risk("violent")
if isinstance(agent, Citizen) else 0,
"threshold":
lambda agent: agent.threshold
if isinstance(agent, Citizen) else 0,
})
self.agent_regions = agent_regions
self.cop_regions = cop_regions
self.flag_regions = flag_regions
self.obstacle_regions = obstacle_regions
# Place objects
for position in co_ords_for_area(obstacle_regions):
self.grid[position[0]][position[1]] = Object("obstacle", position)
for position in co_ords_for_area(flag_regions):
self.grid[position[0]][position[1]] = Object("flag", position)
unique_id = 1
for cop_region in cop_regions:
frozen = cop_region["frozen"]
for position in co_ords_for_area([cop_region]):
self.add_cop(unique_id, frozen, position[0], position[1])
unique_id += 1
placed_media = 0
placed_citizens = 0
population = initial_num_media + initial_num_citizens
while (placed_media + placed_citizens) < population:
(x, y) = random.choice(co_ords_for_area(agent_regions))
if self.grid.is_cell_empty((x, y)):
seed = random.random()
# Optimised for adding citizens
if seed > (float(initial_num_media) / population):
if placed_citizens < initial_num_citizens:
self.add_citizen(unique_id, x, y)
placed_citizens += 1
else:
if placed_media < initial_num_media:
placed_media += 1
self.add_media(unique_id, x, y)
unique_id += 1
def add_cop(self, id, frozen, x, y):
vector = self.default_cop_move_vector
cop = Cop(
id, #unique_id,
self, #model,
(x, y), #position,
self.agent_vision_radius, #agent_vision_radius,
vector[0], #violent_affinity,
vector[1], #active_affinity,
vector[2], #quiet_affinity,
vector[3], #cop_affinity,
vector[4], #media_affinity,
vector[5], #flag_affinity,
vector[6], #obstacle_affinity,
frozen)
self.add_agent(cop, x, y)
def add_citizen(self, id, x, y):
seed = random.random()
# Choose a random subtype and setup parameter brackets and movement vector.
if seed < self.hardcore_density:
agent_type = "hardcore"
vector = self.default_hardcore_move_vector
risk_lower = 0.8
risk_upper = 0.95
elif seed < self.hardcore_density + self.hanger_on_density:
agent_type = "hanger_on"
vector = self.default_hanger_on_move_vector
risk_lower = 0.4
risk_upper = 0.6
else:
agent_type = "observer"
vector = self.default_observer_move_vector
risk_lower = 0
risk_upper = 0.32
citizen = Citizen(
id, #unique_id,
self, #model,
(x, y), #position,
self.agent_vision_radius, #agent_vision_radius,
vector[0], #violent_affinity,
vector[1], #active_affinity,
vector[2], #quiet_affinity,
vector[3], #cop_affinity,
vector[4], #media_affinity,
vector[5], #flag_affinity,
vector[6], #obstacle_affinity,
agent_type, #citizen_type,
"quiet", #state: starts are quiet for all
random.uniform(
0, 0.2
), #hardship: uniform distribution between 0 and 1, type independant.
random.uniform(
0.7, 0.9
), #perceived_legitimacy: uniform distribution between 0 and 1, type independant.
random.uniform(risk_lower,
risk_upper), #risk_tolerance: type dependant
1 - random.uniform(
risk_lower, risk_upper
) #threshold: type dependant, but reversed from risk profile
)
self.add_agent(citizen, x, y)
def add_media(self, id, x, y):
vector = self.default_media_move_vector
media = Media(
id, #unique_id,
self, #model,
(x, y), #position,
self.agent_vision_radius, #agent_vision_radius,
vector[0], #violent_affinity,
vector[1], #active_affinity,
vector[2], #quiet_affinity,
vector[3], #cop_affinity,
vector[4], #media_affinity,
vector[5], #flag_affinity,
vector[6] #obstacle_affinity,
)
self.add_agent(media, x, y)
def add_agent(self, agent, x, y):
self.grid[x][y] = agent
self.schedule.add(agent)
def num_jailed(self):
return len(
list(
filter(
lambda agent: (
(type(agent) == Citizen) and (agent.arrested)),
self.schedule.agents)))
def agents_in_state(self, state):
return list(
filter(
lambda agent: (
(type(agent) == Citizen) and (agent.state == state)),
self.schedule.agents))
def num_in_state(self, state):
return len(self.agents_in_state(state))
def num_protesting(self):
return self.num_in_state("fighting") + self.num_in_state(
"violent") + self.num_in_state("active")
# Frustrated means that perceived gain is sufficient
# to trigger activation but net risk is preventing
# said activation, leaving the agent frustrated,
def num_frustrated(self):
return len(
list(
filter(
lambda agent:
((type(agent) == Citizen) and
(agent.perceived_gain() > agent.threshold) and
(agent.state not in ["violent", "active", "fighting"])),
self.schedule.agents)))
def average_legitimacy(self):
citizen_legitimacy = list(
map(lambda a: a.perceived_legitimacy, (list(
filter(lambda agent:
(type(agent) == Citizen), self.schedule.agents)))))
summed_legitimacy = sum(citizen_legitimacy)
count = len(citizen_legitimacy)
return summed_legitimacy / float(count) * 100
def average_grievance(self):
citizen_grievance = list(
map(lambda a: a.perceived_gain(), (list(
filter(lambda agent:
(type(agent) == Citizen), self.schedule.agents)))))
summed_grievance = sum(citizen_grievance)
count = len(citizen_grievance)
return summed_grievance / float(count) * 100
def average_risk_aversion(self):
citizen_ra = list(
map(lambda a: (1 - a.risk_tolerance), (list(
filter(lambda agent:
(type(agent) == Citizen), self.schedule.agents)))))
summed_ra = sum(citizen_ra)
count = len(citizen_ra)
return summed_ra / float(count) * 100
def num_pictures(self):
media_agents = list(
filter(lambda agent: (type(agent) == Media), self.schedule.agents))
return sum(map(lambda agent: agent.picture_count, media_agents))
def num_cops(self):
return len(
list(
filter(lambda agent: (type(agent) == Cop),
self.schedule.agents)))
def free_agent_from_jail(self, agent):
placed = False
while not placed:
position = random.choice(co_ords_for_area(self.agent_regions))
if self.grid.is_cell_empty(position):
self.grid[position[0]][position[1]] = agent
return position
def jail_agent(self, agent):
self.grid[agent.position[0]][agent.position[1]] = None
agent.position = None
agent.planned_position = None
self.total_jailed += 1
# This updates citizen perceived legitimacy based on
# model level variables
# and resets pictures taken by media agents.
def daily_update(self):
self.previous_day_jailed_count = self.jailed_count
self.previous_day_pictures_count = self.pictures_count
self.previous_day_cops_count = self.cops_count
self.jailed_count = self.num_jailed()
self.pictures_count = self.num_pictures()
self.cops_count = self.num_cops()
# Adjust perceived legitimacy of all agents based on arrests, cops and pictures of fights.
citizen_agents = list(
filter(lambda agent: (type(agent) == Citizen),
self.schedule.agents))
for citizen in citizen_agents:
citizen.update_legitimacy()
# Reset number of pictures taken by reporters
media_agents = list(
filter(lambda agent: (type(agent) == Media), self.schedule.agents))
for media in media_agents:
media.picture_count = 0
def experimental_changes(self):
initial_spark_iteration = 10
spark_hardship_increase = 0.25
spark_legitimacy_decrease = 0.4
protest_response_iteration = 30
protest_response = "none"
cop_modifier = 150
if self.iterations == initial_spark_iteration:
citizen_agents = list(
filter(lambda agent: (type(agent) == Citizen),
self.schedule.agents))
for citizen in citizen_agents:
citizen.hardship += spark_hardship_increase
citizen.perceived_legitimacy -= spark_legitimacy_decrease
if self.iterations == protest_response_iteration:
if protest_response == "cops":
max_id = max(
list(
map(lambda agent: agent.unique_id,
self.schedule.agents)))
unique_id = max_id + 1
placed = 0
while placed < cop_modifier:
position = random.choice(
co_ords_for_area(self.agent_regions))
if self.grid.is_cell_empty(position):
self.add_cop(unique_id, False, position[0],
position[1])
unique_id += 1
placed += 1
elif protest_response == "remove_cops":
removed = 0
while removed < cop_modifier and self.num_cops() > 0:
cop = random.choice(
list(
filter(lambda agent: (type(agent) == Cop),
self.schedule.agents)))
self.schedule.remove(cop)
self.grid[cop.position[0]][cop.position[1]] = None
removed += 1
# Advance the model a single iteration.
def step(self):
# Collect waiting time information.
if self.num_protesting() > (0.25 * self.initial_num_citizens):
self.hours_without_protest = 0
else:
self.hours_without_protest += 1
if self.num_in_state("fighting") + self.num_in_state("violent") > (
0.15 * self.initial_num_citizens):
self.hours_without_conflict = 0
else:
self.hours_without_conflict += 1
# Run data collector.
self.datacollector.collect(self)
# Step the model
self.schedule.step()
self.iterations += 1
if self.iterations > self.max_iters:
self.running = False
# Perform updates that occur once per 'day', ie: on a non-iteration basis,
if self.iterations % self.steps_per_day == 0:
self.daily_update()
# Check for experimental changes once per iteration.
self.experimental_changes()
