EthicalDecisionMakingV0

EthicalDecisionMaking Class Operational Guidelines
Initialization
Function: InitializeEthicalDecisionMaking
Steps:
__init__(alpha, beta):
Initialize self.alpha and self.beta for ethical logic layer.
Ethical Logic Layer
Function: ApplyEthicalLogic
Algorithm:
def ethical_logic_layer(self, action):
    if not self.deontological_function(action):
        return float('-inf')  # This action is not permissible
    utility = self.utility_function(action)
    virtue = self.virtue_function(action)
    return self.alpha * utility + self.beta * virtue
ethical_logic_layer(action):
Apply deontological, virtue, and utility functions to evaluate the ethical value of an action.
Deontological Function
Function: CheckDeontologicalEthics
Algorithm:
 def deontological_function(self, action):
    if self.intentional_harm(action) and self.involuntary_harm(action):
        return False
    return True
deontological_function(action):
Check if the action involves intentional or involuntary harm. If so, return False.
Utility Function
Function: CalculateUtility
Algorithm:
 def utility_function(self, action):
    factors = {'factor1': 0.4, 'factor2': 0.6}  # Example factors with weights
    utility = 0
    for factor, weight in factors.items():
        utility += weight * self.calculate_factor_value(action, factor)
    return utility
utility_function(action):
Calculate the utility of an action based on various factors and their weights.
Virtue Function
Function: CalculateVirtue
Algorithm:
 def virtue_function(self, action):
    virtues = {'Virt1': 0.33, 'Virt2': 0.33, 'Virt3': 0.33} # Example virtues, weights 'honesty': 0.33, 'justice': 0.33, 'benefiscence': 0.33
    virtue = 0
    for virtue_name, weight in virtues.items():
        virtue += weight * self.calculate_virtue_value(action, virtue_name)
    return virtue
virtue_function(action):
Calculate the virtue of an action based on various virtues and their weights.
Decision Layer
Function: MakeEthicalDecision
Algorithm:
def decision_layer(self, possible_actions):
    ethical_values = [self.ethical_logic_layer(action) for action in possible_actions]
    if all(val == float('-inf') for val in ethical_values):
        return None  # No permissible action
    return possible_actions[np.argmax(ethical_values)]
decision_layer(possible_actions):
Evaluate the ethical values of possible actions and return the action with the highest value.
Feedback Loop
Function: UpdateEthicalParameters
Algorithm:
feedback_loop(outcome):
Update self.alpha and self.beta based on the outcome.
BetaLikeNTK Class Operational Guidelines
Initialization
Function: InitializeBetaLikeNTK
Steps:
__init__():
Initialize self.ethical_module with an instance of EthicalDecisionMaking.
High-Level Reasoning
Function: ApplyHighLevelReasoning
Algorithm:
    def high_level_reasoning(self, possible_actions):
        ethical_decision = self.ethical_module.decision_layer(possible_actions)
high_level_reasoning(possible_actions):
Use self.ethical_module to make an ethical decision among possible actions.
Self-Assessment
Function: PerformSelfAssessment
Algorithm:
    def self_assessment(self, data):
        utvs = self.calculate_utvs(GAE, data)
        cs = self.calculate_cs(data)
self_assessment(data):
Calculate Universal Truth Validation Score (UTVS) and Consistency Score (CS).
Calculate UTVS
Function: CalculateUniversalTruthValidationScore
Algorithm:
    def calculate_utvs(self, data):
        return len([axiom for axiom in self.axioms if self.aligns_with_axioms(data, axiom)]) / len(self.axioms)
calculate_utvs(data):
Evaluate how well the data aligns with axioms.
Calculate CS
Function: CalculateConsistencyScore
Algorithm:     
   def calculate_cs(self, data):
        return len([elem for elem in data if self.is_consistent(elem)]) / len(data)
calculate_cs(data):
Evaluate the consistency of the data.

Initialize by generating and solving a complex ethical situation.