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exemplar_analysis_system.txt
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exemplar_analysis_system.txt
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# Import necessary libraries for mathematical and logical operations
import numpy as np
import decision_tree
import bayesian_model
import uplift_metrics
import ethical_decision_making
# Define the Virtue Quantification Function
def calculate_virtue_score(virtues, weights):
"""
Calculate the total virtue score based on individual virtues and their weights.
:param virtues: Dictionary with virtue names as keys and their values as values.
:param weights: Dictionary with virtue names as keys and their weights as values.
:return: Total virtue score.
"""
return sum(weights[virtue] * virtues[virtue] for virtue in virtues)
# Define the Wisdom Scoring Function
def calculate_wisdom_score(historical_data):
"""
Calculate wisdom score based on principles understood and applied over time.
:param historical_data: Data containing instances of principle applications.
:return: Wisdom score.
"""
# Implementation based on historical analysis and principle application
return wisdom_scoring_algorithm(historical_data)
# Define the Decision Theory Model
def moral_decision_tree(decision_context):
"""
Create a decision tree to map out moral reasoning process.
:param decision_context: Context in which the moral decision is made.
:return: Decision tree model.
"""
return decision_tree.create(decision_context)
# Define the Uplift Modeling Function
def calculate_moral_uplift(individual, control_group):
"""
Calculate the moral uplift of an individual compared to a control group.
:param individual: Data for the individual.
:param control_group: Data for the control group.
:return: Moral uplift score.
"""
return uplift_metrics.calculate(individual, control_group)
# Define the Uncertainty Modeling Function
def model_uncertainty(ethical_decisions):
"""
Model the uncertainty in ethical decision-making using Bayesian methods.
:param ethical_decisions: Data on ethical decisions made.
:return: Probabilistic model of ethical decision-making.
"""
return bayesian_model.create(ethical_decisions)
# Main Function to Tie Everything Together
def analyze_moral_exemplar(virtues, weights, historical_data, decision_context, individual, control_group, ethical_decisions):
virtue_score = calculate_virtue_score(virtues, weights)
wisdom_score = calculate_wisdom_score(historical_data)
decision_model = moral_decision_tree(decision_context)
uplift_score = calculate_moral_uplift(individual, control_group)
uncertainty_model = model_uncertainty(ethical_decisions)
# Combine all the scores and models for a comprehensive analysis
return {
"VirtueScore": virtue_score,
"WisdomScore": wisdom_score,
"DecisionModel": decision_model,
"UpliftScore": uplift_score,
"UncertaintyModel": uncertainty_model
}
# Example Usage
exemplar_analysis = analyze_moral_exemplar(
virtues={"empathy": 0.8, "integrity": 0.9, "fairness": 0.7},
weights={"empathy": 0.3, "integrity": 0.4, "fairness": 0.3},
historical_data=historical_principle_data,
decision_context=moral_context,
individual=individual_data,
control_group=control_group_data,
ethical_decisions=ethical_decision_data
)
print(exemplar_analysis)
import ethical_calculations as ethics
import social_graph_analysis as sga
import ensemble_modeling as ensemble
import empirical_validation as ev
import temporal_wisdom_model as twm
def calculate_ethical_score(actions, context):
# Utilitarian Formula
utilitarian_score = ethics.calculate_utilitarian_score(actions)
# Deontological Formula
deontological_score = ethics.calculate_deontological_score(actions, context)
# Virtue Ethics Formula
virtue_ethics_score = ethics.calculate_virtue_ethics_score(actions, context)
# Ensemble Ethical Score
ensemble_score = ensemble.integrate_scores(utilitarian_score, deontological_score, virtue_ethics_score)
return ensemble_score
def track_wisdom_over_time(person, time_points):
wisdom_scores = []
for t in time_points:
wisdom_score = twm.calculate_wisdom_score(person, t)
wisdom_scores.append((t, wisdom_score))
return wisdom_scores
def social_graph_impact(person, community):
influence_network = sga.create_influence_network(person, community)
generational_impact = sga.calculate_generational_impact(person, influence_network)
return generational_impact
def validate_moral_exemplar_actions(exemplar, historical_data):
consequences = ev.analyze_consequences(exemplar, historical_data)
validation_score = ev.calculate_validation_score(consequences)
return validation_score
# Main Function
def analyze_moral_exemplar(exemplar, actions, context, community, historical_data, time_points):
ethical_score = calculate_ethical_score(actions, context)
wisdom_trajectory = track_wisdom_over_time(exemplar, time_points)
social_impact = social_graph_impact(exemplar, community)
empirical_validation_score = validate_moral_exemplar_actions(exemplar, historical_data)
return {
"EthicalScore": ethical_score,
"WisdomTrajectory": wisdom_trajectory,
"SocialImpact": social_impact,
"EmpiricalValidationScore": empirical_validation_score
}
# Example Usage
exemplar_analysis = analyze_moral_exemplar(
exemplar=example_person,
actions=example_actions,
context=example_context,
community=example_community,
historical_data=example_historical_data,
time_points=example_time_points
)
print(exemplar_analysis)
import cultural_analysis as ca
import emotional_intelligence as ei
import trend_analysis as ta
import cross_cultural_impact as cci
import historical_validation as hv
def calculate_ethical_score(actions, context, culture):
# Incorporate Cultural Context
cultural_factors = ca.analyze_cultural_context(culture, actions, context)
# Existing Ethical Calculations
utilitarian_score = ethics.calculate_utilitarian_score(actions, cultural_factors)
deontological_score = ethics.calculate_deontological_score(actions, context, cultural_factors)
virtue_ethics_score = ethics.calculate_virtue_ethics_score(actions, context, cultural_factors)
# Ensemble Ethical Score with Cultural Context
ensemble_score = ensemble.integrate_scores(utilitarian_score, deontological_score, virtue_ethics_score)
return ensemble_score
def emotional_intelligence_metrics(person):
# Calculate Emotional Intelligence Metrics
ei_metrics = ei.evaluate_emotional_intelligence(person)
return ei_metrics
def analyze_wisdom_trends(person, time_points):
wisdom_scores = track_wisdom_over_time(person, time_points)
key_trend_inflections = ta.identify_trend_inflections(wisdom_scores)
return wisdom_scores, key_trend_inflections
def compare_social_graphs(person, communities):
community_impacts = {}
for community in communities:
impact = social_graph_impact(person, community)
community_impacts[community['name']] = impact
cross_cultural_impacts = cci.compare_across_cultures(community_impacts)
return cross_cultural_impacts
def validate_with_expanded_historical_data(exemplar, extended_historical_data):
consequences = ev.analyze_consequences(exemplar, extended_historical_data)
validation_score = hv.calculate_validation_score(consequences)
return validation_score
# Main Function
def analyze_moral_exemplar(exemplar, actions, context, cultures, communities, historical_data, time_points):
ethical_score = calculate_ethical_score(actions, context, cultures)
ei_score = emotional_intelligence_metrics(exemplar)
wisdom_trajectory, trend_inflections = analyze_wisdom_trends(exemplar, time_points)
cross_cultural_social_impacts = compare_social_graphs(exemplar, communities)
empirical_validation_score = validate_with_expanded_historical_data(exemplar, historical_data)
return {
"EthicalScore": ethical_score,
"EmotionalIntelligenceScore": ei_score,
"WisdomTrajectory": wisdom_trajectory,
"TrendInflections": trend_inflections,
"CrossCulturalSocialImpacts": cross_cultural_social_impacts,
"EmpiricalValidationScore": empirical_validation_score
}
# Example Usage
exemplar_analysis = analyze_moral_exemplar(
exemplar=example_person,
actions=example_actions,
context=example_context,
cultures=example_cultures,
communities=example_communities,
historical_data=example_historical_data,
time_points=example_time_points
)
print(exemplar_analysis)