def policy_impact_evaluation(model_run_SM, model_run_schelling, IssueInit,
interval_tick):
'''
[Change policy tree/policy instruments -> Change in this function]
This function is used to estimate the impact of the policy instruments and likelihood of impact of the policy families.
The simulations for the different policies are parallelised to gain computational time.
'''
# policy impact evaluation
# initialisation of the vector that will store the KPIs of the mock simulation for each policy instrument
issues = [
0 for l in range(model_run_SM.len_S + model_run_SM.len_PC +
model_run_SM.len_DC)
]
for q in range(model_run_SM.len_S + model_run_SM.len_PC +
model_run_SM.len_DC):
issues[q] = [0 for l in range(len(model_run_SM.policy_instruments))]
# copy of the model in its current state
model_run_schelling_PI_test = copy.deepcopy(model_run_schelling)
# creating the input vector for the parallelised simulation
inputs = []
for j in range(len(model_run_SM.policy_instruments)):
intermediate = []
intermediate.append(model_run_SM.policy_instruments[j])
intermediate.append(interval_tick)
intermediate.append(model_run_schelling_PI_test)
inputs.append(intermediate)
# running the parallel simulation
pool = mp.Pool(8)
results = pool.map(lambda a: model_simulation(a), inputs)
pool.close()
'''
OLD NON-PARALLELISED CODE
# initialisation of the vector that will store the KPIs of the mock simulation for each policy instrument
issues = [0 for l in range(model_run_SM.len_S + model_run_SM.len_PC + model_run_SM.len_DC)]
for q in range(model_run_SM.len_S + model_run_SM.len_PC + model_run_SM.len_DC):
issues[q] = [0 for l in range(len(model_run_SM.policy_instruments))]
# simulating all policy instruments for n ticks to obtain KPIs at the final state
for j in range(len(model_run_SM.policy_instruments)):
# copy of the model in its current state
model_run_schelling_PI_test = copy.deepcopy(model_run_schelling)
# run the simulation with policy introduced and collect data
policy = model_run_SM.policy_instruments[j] # set policy vector for one step
for k in range(interval_tick):
IssueE, type0agents, type1agents = model_run_schelling_PI_test.step(policy)
policy = [None for f in range(len(model_run_SM.policy_instruments[j]))] # reset policy vector after it has been implemented once
# mapping the outcomes to a [0,1] interval
IssueE = issue_mapping(IssueE, type0agents, type1agents)
# store the final state of the belief (last simulation)
for p in range(model_run_SM.len_S + model_run_SM.len_PC + model_run_SM.len_DC):
issues[p][j] = IssueE[p]
'''
type0agents = results[0][1]
type1agents = results[0][2]
for i in range(len(results)):
# mapping the outcomes to a [0,1] interval
IssueEn = issue_mapping(results[i][0], type0agents, type1agents)
# store the final state of the belief (last simulation)
for p in range(model_run_SM.len_S + model_run_SM.len_PC +
model_run_SM.len_DC):
issues[p][i] = IssueEn[p]
# change the policy tree accordingly
# transforming initial KPIs to [0,1] interval
IssueInit = issue_mapping(IssueInit, type0agents, type1agents)
# looking at one policy instrument after the other
impact_policy = [[
0 for l in range(model_run_SM.len_S + model_run_SM.len_PC)
] for r in range(len(model_run_SM.policy_instruments))]
for j in range(len(model_run_SM.policy_instruments)):
# calculating the percentage change from no policy to a policy
for q in range(model_run_SM.len_PC + model_run_SM.len_S):
if issues[q][j] != 0:
impact_policy[j][q] = round(
(IssueInit[model_run_SM.len_DC + q] - issues[q][j]) /
issues[q][j], 3)
if issues[q][j] == 0 and IssueInit[model_run_SM.len_DC + q] == 0:
impact_policy[j][q] = 0
if issues[q][j] == 0 and IssueInit[model_run_SM.len_DC + q] != 0:
impact_policy[j][q] = 1
# selecting the agents of the main simulation
for agent in model_run_SM.schedule.agent_buffer(shuffled=True):
if isinstance(agent, TruthAgent):
# updating the policy tree of the truth agent
agent.policytree_truth[
model_run_SM.len_PC +
j] = impact_policy[j][0:model_run_SM.len_S]
# print("Policy instrument: ", j, " - \n", agent.policytree[model_run_SM.len_PC + j])
# considering the policy families
# policy family 1 (instruments: 0, 1, 2, 3, 8, 9, 10)
# policy family 2 (instruments: 4, 5, 6, 7, 8, 9, 10)
likelihood_PF1 = [0 for f in range(model_run_SM.len_PC)]
len_PF1 = 7
likelihood_PF2 = [0 for f in range(model_run_SM.len_PC)]
len_PF2 = 7
# average the absolute value of their impact per
for j in range(len(model_run_SM.policy_instruments)):
# selecting only policy instruments related to policy family 1
if j == 0 or j == 1 or j == 2 or j == 3 or j == 8 or j == 9 or j == 10:
likelihood_PF1[0] += impact_policy[j][model_run_SM.len_S] / len_PF1
likelihood_PF1[1] += impact_policy[j][model_run_SM.len_S +
1] / len_PF1
# selecting only policy instruments related to policy family 2
if j == 4 or j == 5 or j == 6 or j == 7 or j == 8 or j == 9 or j == 10:
likelihood_PF2[0] += impact_policy[j][model_run_SM.len_S] / len_PF2
likelihood_PF2[1] += impact_policy[j][model_run_SM.len_S +
1] / len_PF2
# rounding values
likelihood_PF1[0] = round(likelihood_PF1[0], 3)
likelihood_PF1[1] = round(likelihood_PF1[1], 3)
likelihood_PF2[0] = round(likelihood_PF2[0], 3)
likelihood_PF2[1] = round(likelihood_PF2[1], 3)
for agent in model_run_SM.schedule.agent_buffer(shuffled=True):
if isinstance(agent, TruthAgent):
# updating the policy tree of the truth agent
agent.policytree_truth[0] = likelihood_PF1
agent.policytree_truth[1] = likelihood_PF2
# this is also used as a warmup time
if i == 0:
policy_chosen = [
None for ite in range(len(model_run_SM.policy_instruments[0]))
]
for warmup_time in range(warmup_tick):
IssueInit, type0agents, type1agents = model_run_schelling.step(
policy_chosen)
# policy impact evaluation
policy_impact_evaluation(model_run_SM, model_run_schelling, IssueInit,
interval_tick)
# running the policy emergence model
if i == 0:
KPIs = issue_mapping(IssueInit, type0agents, type1agents)
else:
KPIs = issue_mapping(KPIs, type0agents, type1agents)
policy_chosen = model_run_SM.step(KPIs)
# run of the segregation model for n ticks
for p in range(interval_tick):
KPIs, type0agents, type1agents = model_run_schelling.step(
policy_chosen)
policy_chosen = [
None for ite in range(len(model_run_SM.policy_instruments[0]))
] # reset policy after it has been implemented once
# output of the data
# Schelling model
# printing the data obtained from the Schelling model