Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
Python 3 source code for "Endogenous Epistemic Factionalization" replication/commentary
# Graphing requirements: scipy and matplotlib
import random
from math import factorial, sqrt
ε = 0.01 # size of edge for B
def binomial(p, n, k):
return (
factorial(n) / (factorial(k) * factorial(n - k)) *
p**k * (1 - p)**(n - k)
)
def euclidean_distance(v, w):
return sqrt(sum((v[i] - w[i]) ** 2 for i in range(len(v))))
def b():
return random.random() < 0.5 + ε
def summarize_experiment(results):
return (len([r for r in results if r]), len(results))
class Agent:
def __init__(self, initial_credences, trial_count, mistrust):
self.credences = initial_credences
self.trial_count = trial_count
self.mistrust = mistrust
def experiment(self):
results = [b() for _ in range(self.trial_count)]
return results
def pure_update(self, credence, hits, trials):
raw_posterior_good = binomial(0.5 + ε, trials, hits) * credence
raw_posterior_bad = binomial(0.5 - ε, trials, hits) * (1 - credence)
normalizing_factor = raw_posterior_good + raw_posterior_bad
return raw_posterior_good / normalizing_factor
def discount_factor(self, reporter_credences):
return min(
1, self.mistrust * euclidean_distance(self.credences, reporter_credences)
)
def update(self, question, hits, trials, reporter_credences):
discount = self.discount_factor(reporter_credences)
posterior = self.pure_update(self.credences[question], hits, trials)
self.credences[question] = (
discount * self.credences[question] + (1 - discount) * posterior
)
def simulation(
agent_count, # number of agents
question_count, # numer of questions
round_count, # number of rounds
trial_count, # number of trials per round
mistrust, # mistrust factor
):
agents = [
Agent(
[random.random() for _ in range(question_count)],
trial_count=trial_count,
mistrust=mistrust,
)
for i in range(agent_count)
]
for _ in range(round_count):
for question in range(question_count):
experiments = []
for agent in agents:
if agent.credences[question] >= 0.5:
experiments.append(
(summarize_experiment(agent.experiment()), agent.credences)
)
for agent in agents:
for experiment, reporter_credences in experiments:
hits, trials = experiment
agent.update(
question,
hits,
trials,
reporter_credences,
)
return agents
# graph it!
import matplotlib.pyplot as plot
from matplotlib.colors import ListedColormap
from mpl_toolkits.mplot3d import Axes3D
from sklearn.cluster import KMeans
def plot_beliefs(agents, cluster=True):
beliefs = [agent.credences for agent in agents]
if cluster:
cluster_model = KMeans(n_clusters=8)
cluster_model.fit(beliefs)
graph_kwargs = {
'c': cluster_model.predict(beliefs),
'cmap': ListedColormap(
["red", "orangered", "green", "blue", "purple", "black", "brown", "deepskyblue"]
)
}
else:
graph_kwargs = {}
figure = plot.figure()
axes = Axes3D(figure)
for scale_setter in [axes.set_xlim, axes.set_ylim, axes.set_zlim]:
scale_setter(0, 1)
axes.scatter(
*[[agent.credences[i] for agent in agents] for i in range(3)],
**graph_kwargs
)
plot.show()
if __name__ == "__main__":
agents = simulation(
agent_count=200, round_count=20, question_count=3, trial_count=50, mistrust=2
)
plot_beliefs(agents)
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.