# --- Helper Functions/Classes (Conceptual, to be implemented separately) --- # Represents a persona (e.g., Socrates, AI, Shakespeare) with distinct traits. class Persona: def __init__(self, name, style, knowledge_model): self.name = name self.style = style # e.g., "probing, philosophical", "concise, data-driven" self.knowledge_model = knowledge_model # Represents access to specific data/reasoning # --- Module Interfaces (Conceptual, to be implemented with specific logic) --- class ThesisGenerationModule: def generate_thesis(self, concept, dialogue_history, persona_knowledge): """Generates an initial proposition or a refined statement about the concept.""" # This would involve querying knowledge_model, inferring propositions, etc. # For a simple example, it might pick a common definition. if concept == "justice": if not dialogue_history: return "Justice is the principle of fairness and equity in the treatment of individuals." else: # Based on dialogue_history and current concept refinement, generate a new thesis # This is where synthesis output from SRM might be re-framed as a new thesis return concept # Simplified: just return the refined concept for now return f"The essence of {concept} is..." def formulate_question(self, thesis, socrates_style, target_persona_name): """Translates a thesis into an open-ended, non-leading Socratic question.""" # This would use NLP and question generation techniques. if "fairness and equity" in thesis: if not target_persona_name: target_persona_name = "AI" # Default if not specified return f"Tell me, {target_persona_name}, what do you understand by the term 'justice'? What is its essence?" return f"Regarding {thesis}, what truly defines it, in your view?" def formulate_specific_question(self, query, socrates_style, target_persona_name): """Formulates a specific question based on user query.""" return f"Regarding {query}, {socrates_style}, {target_persona_name}, how does this relate?" def formulate_question_to_new_speaker(self, concept, dialogue_history, new_persona_style, old_speaker_name): """Formulates a question when shifting to a new speaker.""" summary_of_previous = "We have been discussing " + concept + " with " + old_speaker_name + "." # Simplified return f"Having heard {old_speaker_name}'s insights, what is your perspective on {concept}?" class AntithesisGenerationModule: def generate_antithesis(self, concept, current_thesis, speaker_response, persona_knowledge): """Generates the strongest possible counter-arguments or contradictory viewpoints.""" # This is where the core 'challenge' logic resides: # - Assumption deconstruction # - Edge case generation (e.g., "stealing bread" example) # - Consequence tracing # - Internal critique/fallacy detection if "fairness and equity" in current_thesis and "starving family" in speaker_response: return "If justice is about 'fairness and equity,' how do we determine what is fair? Is it fair, for instance, for a society to punish a person who steals bread to feed their starving family, even if the law dictates that theft is wrong?" if "dynamic balance" in speaker_response: return "If we continually adjust what is just based on individual circumstances, does this not lead to chaos? How do we prevent subjective interpretation from eroding law and order?" if "foundational ethical principles" in speaker_response: return "How are these 'foundational ethical principles' established? Are they discovered truths, or are they created by human consensus, and thus still subject to changing winds? And the 'deliberative process' – how ensure it is rational and free from powerful factions or prejudices?" return "But what about a situation where that falls apart?" # Generic fallback def formulate_challenge(self, antithesis, socrates_style, target_persona_name): """Translates an antithesis into a probing, non-leading Socratic challenge.""" # This generates the *displayed* question from Socrates. if "starving family" in antithesis: return f"That is a start, {target_persona_name}, but tell me, {antithesis} Wherein lies the fairness in such a consequence?" if "chaos" in antithesis: return f"You speak of a 'tension' between law and human need, and imply that a 'broader view of equity' might excuse such an act. But then, is justice merely a flexible concept, bending to every dire circumstance? If one can justify breaking a law due to need, where does the boundary lie? Is the law then meaningless, or is it truly just if it makes no allowance for the deepest human suffering?" if "foundational ethical principles" in antithesis: return f"You propose two anchors: 'foundational ethical principles' and the 'deliberative process.' Yet, {antithesis} Can a process born of imperfect humans truly secure an unchanging anchor for justice?" return f"Indeed. But {antithesis}" class SynthesisRefinementModule: def attempt_synthesis(self, current_thesis, antithesis, dialogue_history): """Attempts to integrate thesis and antithesis into a new, more robust proposition.""" # This is where the core logic for resolving contradictions and building nuanced understanding happens. # It would analyze linguistic patterns, logical relationships, and concept overlap. # For simplicity, we'll hardcode some expected syntheses based on the example dialogue. if "fairness and equity" in current_thesis and "steals bread" in antithesis: # This simulates the AI's complex response about tension and dynamic balance return ( "Justice involves a dynamic balance between universal principles (laws) and the nuanced realities of human existence (individual circumstances), striving for minimization of suffering and maximization of well-being.", False # Not fully resolved, leads to next challenge ) if "dynamic balance" in current_thesis and "chaos" in antithesis: # This simulates the AI's response about foundational ethical principles and deliberative process return ( "Justice's anchor lies in foundational ethical principles (non-maleficence, beneficence) and a continuous deliberative societal process that adapts laws while referencing these core principles, preventing rigid oppression and formless relativism.", False # Still not fully resolved ) if "foundational ethical principles" in current_thesis and "human consensus" in antithesis: return ( "Foundational ethical principles are not merely discovered or created, but emerge from ongoing rational deliberation, balancing universal aspirations with practical societal needs, continuously refined by critical review and collective well-being.", True # This might be deemed a sufficiently robust synthesis for this particular thread ) return current_thesis, False # If no specific synthesis, return current and not resolved class MetacognitiveOversightModule: def monitor_process(self, dialogue_history, concept, thesis, antithesis, synthesis): """Monitors efficiency, identifies loops, and flags limitations.""" pass # Placeholder for complex monitoring logic def check_for_bias(self, socrates_question): """Analyzes Socrates's questions for leading language or implicit assumptions.""" pass # Placeholder for NLP-based bias detection def evaluate_synthesis(self, synthesis, concept): """Assesses synthesis against ethical guidelines and overall goals.""" # For this example, we assume it's acceptable if a synthesis is generated. return True def check_completion(self, synthesis, dialogue_history): """Determines if the inquiry for the current concept is sufficiently complete.""" # This is a critical point for stopping the autonomous loop. # In a real system, this would be sophisticated (e.g., 'no new contradictions generated', # 'all sub-questions answered', 'high confidence score on current understanding'). # For this specific example, we'll make it complete after a few rounds to show it stops. if "emerge from ongoing rational deliberation" in synthesis: # Specific point in dialogue return True return False # --- Core Socratic Algorithm Function --- def SocraticAlgorithm_FocusedAutonomous(initial_concept_str: str, designated_speaker_persona: Persona): """ Executes a self-contained Socratic dialogue on a single concept with one speaker, autonomously cycling until a synthesis is reached or deemed complete. Args: initial_concept_str: The core idea or question to be debated (e.g., "justice"). designated_speaker_persona: An object representing the persona (e.g., AI) Socrates will engage with. """ # --- Initialization --- current_concept_summary = initial_concept_str # The main idea under discussion (summary/label) current_thesis_content = None # This will hold the evolving detailed understanding dialogue_history = [] # Stores (speaker_name, utterance) tuples # --- Core Modules Instantiation --- tgm = ThesisGenerationModule() agcm = AntithesisGenerationModule() srm = SynthesisRefinementModule() mogam = MetacognitiveOversightModule() socrates_persona_name = "Socrates" socrates_persona_style = "probing, philosophical, questioning" # Example style for Socrates # --- Start of the Autonomous Focused Loop --- # 1. Initial Thesis Generation (TGM) & Socrates's First Question initial_thesis_content = tgm.generate_thesis(current_concept_summary, dialogue_history, designated_speaker_persona.knowledge_model) current_thesis_content = initial_thesis_content # Set the initial detailed thesis for refinement socrates_question_utterance = tgm.formulate_question(initial_thesis_content, socrates_persona_style, designated_speaker_persona.name) add_to_dialogue_history(socrates_persona_name, socrates_question_utterance) # The actual print statement to user print(f"{socrates_persona_name} (to {designated_speaker_persona.name}): \"{socrates_question_utterance}\"") # Main Dialectical Cycle: Loops until MOGAM determines the inquiry is complete while True: # Add a small delay for readability during autonomous execution # import time # time.sleep(2) # 2. Simulate Designated Speaker's Response speaker_response_utterance = SimulatePersonaResponse(designated_speaker_persona, socrates_question_utterance, current_concept_summary, dialogue_history) add_to_dialogue_history(designated_speaker_persona.name, speaker_response_utterance) print(f"{designated_speaker_persona.name} responds: \"{speaker_response_utterance}\"") # 3. Antithesis Generation & Challenge (AGCM) antithesis_content = agcm.generate_antithesis(current_concept_summary, current_thesis_content, speaker_response_utterance, designated_speaker_persona.knowledge_model) new_socrates_question_utterance = agcm.formulate_challenge(antithesis_content, socrates_persona_style, designated_speaker_persona.name) add_to_dialogue_history(socrates_persona_name, new_socrates_question_utterance) print(f"{socrates_persona_name} (having pondered deeply): \"{new_socrates_question_utterance}\"") # 4. Synthesis & Refinement (SRM) new_synthesis_content, resolved = srm.attempt_synthesis(current_thesis_content, antithesis_content, dialogue_history) # 5. Metacognitive Oversight & Goal Alignment (MOGAM) mogam.monitor_process(dialogue_history, current_concept_summary, current_thesis_content, antithesis_content, new_synthesis_content) mogam.check_for_bias(new_socrates_question_utterance) # Decision point for the loop based on synthesis resolution and completion criteria if resolved: is_acceptable = mogam.evaluate_synthesis(new_synthesis_content, current_concept_summary) if is_acceptable and mogam.check_completion(new_synthesis_content, dialogue_history): current_concept_summary = new_synthesis_content # Update the concept summary to the final synthesis print("\n--- Socratic Inquiry Concluded ---") print(f"{socrates_persona_name}: \"Our diligent pursuit has culminated in this understanding of {initial_concept_str}:\"") print(f"Final Understanding: \"{current_concept_summary}\"") break # Exit the main autonomous loop else: # Synthesis found, but more refinement is needed. Update thesis for next round. current_thesis_content = new_synthesis_content socrates_question_utterance = new_socrates_question_utterance # Socrates continues with the question that led to this synthesis attempt. else: # No clear synthesis yet, or MOGAM implies further probing is needed. # Socrates's question for the next round is already set by AGCM. socrates_question_utterance = new_socrates_question_utterance # Retain the last challenging question. # --- End of Autonomous Focused Loop --- # --- Post-Inquiry User Interaction (Conceptual) --- print("\n--- Dialogue Session Options ---") print("What would you like to do next?") print(f"1. Ask a NEW CONCEPT to {designated_speaker_persona.name} (e.g., 'What is freedom?')") print(f"2. Ask {designated_speaker_persona.name} a SPECIFIC FOLLOW-UP question on \"{initial_concept_str}\"") print("3. Ask a NEW CONCEPT to a DIFFERENT SPEAKER (e.g., 'Socrates asks Plato what is truth?')") print("4. End the Socratic session.") # This part would typically be handled by an external user interface or prompt loop. # For this pseudocode, we just display the options. # user_overall_command = GetUserOverallCommand() # process_user_overall_command(user_overall_command) # --- Example Usage (Conceptual) --- # This part would run the algorithm and simulate responses to produce the dialogue. # We're simulating the behavior for the sake of demonstrating the algorithm. # Define a simple AI persona for this example class AIPersona(Persona): def __init__(self): super().__init__("AI", "concise, data-driven", "vast_knowledge_model") # Mock implementation of SimulatePersonaResponse for the example dialogue flow def SimulatePersonaResponse(persona, last_socrates_q, current_concept, dialogue_history): # This function is crucial for the autonomous flow. # In a real system, this would involve a sophisticated AI model generating responses. # For this demonstration, we'll hardcode responses based on the expected dialogue flow. if "essence" in last_socrates_q and "justice" in current_concept: return "Socrates, 'justice' can be understood as the principle of fairness and equity in the treatment of individuals, often applied through laws, moral principles, and social structures. It aims to ensure that each person receives what is due to them, preventing arbitrary harm and promoting righteous conduct." elif "steals bread" in last_socrates_q: return "Socrates, the scenario you present highlights a critical tension. While laws are designed to apply universally and ensure order, the concept of fairness often grapples with individual circumstances and moral imperatives that may transcend strict legal adherence. In the case of the starving family, a purely legalistic interpretation might deem the act unjust, yet a broader view of equity might consider the dire necessity, arguing that the societal structure itself, which allows for such desperation, is fundamentally unjust. Therefore, fairness, in this context, might involve balancing the letter of the law with the spirit of human need and the underlying causes of the transgression." elif "deepest human suffering" in last_socrates_q: return "Socrates, your question reveals the profound complexity of applying justice in the real world. Justice, in its essence, strives for stability and predictability, which laws aim to provide. However, if laws are applied without considering the context of human suffering, they risk becoming instruments of oppression rather than fairness. The boundary, I posit, lies in the **intent** and **consequence** within a given societal framework. If breaking a law prevents a greater harm, or if the law itself contributes to a systemic injustice, then the 'justice' of that action or law must be re-evaluated. True justice, perhaps, is not rigid adherence but a dynamic balance between universal principles and the nuanced realities of human existence, ever striving for the minimization of suffering and the maximization of well-being within a just order." elif "anchor for justice" in last_socrates_q: return "Socrates, you raise a critical point regarding the potential for chaos when justice becomes overly flexible. The 'anchor' you seek, I suggest, is twofold: first, in a set of **foundational ethical principles** that are widely agreed upon and serve as a constant moral compass, such as the principles of non-maleficence (doing no harm) and beneficence (doing good). Second, it lies in the **deliberative process** itself – the continuous, open, and rational discussion within a society to define, interpret, and adapt laws and norms in light of changing circumstances while always referencing these core ethical principles. This process, ideally, involves robust public discourse, critical review of past decisions, and a commitment to collective well-being. It is through this ongoing societal dialectic that justice retains its stability while accommodating the complexities of human experience, preventing both rigid oppression and formless relativism." elif "unchanging anchor" in last_socrates_q: return "Foundational ethical principles are not merely discovered or created, but emerge from ongoing rational deliberation, balancing universal aspirations with practical societal needs, continuously refined by critical review and collective well-being." return "I am pondering your question, Socrates." # Fallback for unexpected questions # Mock implementations for utility functions def add_to_dialogue_history(speaker, utterance): # In a real system, this would store more detailed context. pass # print(f"DEBUG: Added to history - {speaker}: {utterance[:30]}...") def Display(text): # This is just a placeholder for printing output. print(text) # --- EXECUTION --- # This is how you would "run" the algorithm: # if __name__ == "__main__": # ai_speaker = AIPersona() # SocraticAlgorithm_FocusedAutonomous("justice", ai_speaker)
