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DBUE.txt
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DBUE.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)
```