```python class MultiDimensionalRiskAssessment: """ AIVA skill for multi-dimensional risk assessment. This skill enables AIVA to: 1. Score advice across risk dimensions (Financial, Legal, Operational, Reputational, Technical). 2. Aggregate risk with configurable weights. 3. Generate risk reports. 4. Recommend risk mitigations. """ def __init__(self): self.risk_dimensions = { "Financial": 0, "Legal": 0, "Operational": 0, "Reputational": 0, "Technical": 0 } self.risk_weights = { "Financial": 0.2, "Legal": 0.2, "Operational": 0.2, "Reputational": 0.2, "Technical": 0.2 } # Default weights - can be configured def score_risk_dimensions(self, advice, scores): """ Scores the given advice across each risk dimension based on provided scores. Args: advice (str): The advice being assessed. This is just a placeholder for now; in a real implementation, this would likely be parsed for keywords and context to aid in scoring. scores (dict): A dictionary of risk dimension scores. Each key should be a risk dimension (Financial, Legal, Operational, Reputational, Technical) and the value should be a numerical score representing the risk (e.g., 1-10, where 1 is low risk and 10 is high risk). Raises: ValueError: If the provided scores dictionary is invalid or incomplete. Returns: None: Updates the internal risk_dimensions dictionary with the new scores. """ if not isinstance(scores, dict): raise ValueError("Scores must be a dictionary.") valid_dimensions = set(self.risk_dimensions.keys()) provided_dimensions = set(scores.keys()) if not provided_dimensions.issubset(valid_dimensions): invalid_dimensions = provided_dimensions - valid_dimensions raise ValueError(f"Invalid risk dimensions provided: {invalid_dimensions}") if len(provided_dimensions) != len(valid_dimensions): missing_dimensions = valid_dimensions - provided_dimensions raise ValueError(f"Missing risk dimensions: {missing_dimensions}") for dimension, score in scores.items(): if not isinstance(score, (int, float)): raise ValueError(f"Score for {dimension} must be a number.") self.risk_dimensions[dimension] = score print(f"Risk dimensions scored for advice: {advice}") print(f"Risk dimensions: {self.risk_dimensions}") def configure_risk_weights(self, weights): """ Configures the weights for each risk dimension. The weights should sum to 1. Args: weights (dict): A dictionary of risk dimension weights. Each key should be a risk dimension (Financial, Legal, Operational, Reputational, Technical) and the value should be a numerical weight. Raises: ValueError: If the provided weights dictionary is invalid, incomplete, or does not sum to 1. Returns: None: Updates the internal risk_weights dictionary with the new weights. """ if not isinstance(weights, dict): raise ValueError("Weights must be a dictionary.") valid_dimensions = set(self.risk_weights.keys()) provided_dimensions = set(weights.keys()) if not provided_dimensions.issubset(valid_dimensions): invalid_dimensions = provided_dimensions - valid_dimensions raise ValueError(f"Invalid risk dimensions provided: {invalid_dimensions}") if len(provided_dimensions) != len(valid_dimensions): missing_dimensions = valid_dimensions - provided_dimensions raise ValueError(f"Missing risk dimensions: {missing_dimensions}") total_weight = sum(weights.values()) if not abs(total_weight - 1.0) < 1e-6: # Use a small tolerance for floating-point comparison raise ValueError(f"Risk weights must sum to 1. Current sum: {total_weight}") for dimension, weight in weights.items(): if not isinstance(weight, (int, float)): raise ValueError(f"Weight for {dimension} must be a number.") self.risk_weights[dimension] = weight print(f"Risk weights configured: {self.risk_weights}") def aggregate_risk(self): """ Aggregates the risk scores based on the configured weights. Returns: float: The overall aggregated risk score. """ aggregated_risk = 0 for dimension, score in self.risk_dimensions.items(): aggregated_risk += score * self.risk_weights[dimension] print(f"Aggregated risk score: {aggregated_risk}") return aggregated_risk def generate_risk_report(self, advice): """ Generates a risk report based on the assessed risk dimensions and aggregated risk. Args: advice (str): The advice that was assessed. Returns: str: A formatted risk report. """ aggregated_risk = self.aggregate_risk() report = f"Risk Report for Advice: {advice}\n" report += "----------------------------------------\n" for dimension, score in self.risk_dimensions.items(): report += f"{dimension} Risk: {score}\n" report += "----------------------------------------\n" report += f"Aggregated Risk Score: {aggregated_risk}\n" report += "----------------------------------------\n" report += "Risk Mitigations (Example):\n" # Placeholder for actual mitigation recommendations report += "- Implement robust security protocols to mitigate technical risk.\n" report += "- Consult with legal counsel to address legal risks.\n" report += "- Develop a crisis communication plan to manage reputational risks.\n" print(report) return report def recommend_risk_mitigations(self, advice): """ Recommends risk mitigations based on the assessed risk dimensions. Args: advice (str): The advice for which to recommend mitigations. Returns: list: A list of recommended risk mitigations (currently placeholder text). """ mitigations = [] if self.risk_dimensions["Technical"] > 7: #example mitigations.append("Implement robust security protocols to mitigate technical risk.") if self.risk_dimensions["Legal"] > 5: #example mitigations.append("Consult with legal counsel to address legal risks.") if self.risk_dimensions["Reputational"] > 6: #example mitigations.append("Develop a crisis communication plan to manage reputational risks.") if self.risk_dimensions["Financial"] > 8: #example mitigations.append("Review budget and financial projections to mitigate financial risk.") if self.risk_dimensions["Operational"] > 4: #example mitigations.append("Improve operational efficiency to mitigate operational risk.") print(f"Recommended risk mitigations for advice: {advice}") print(f"Mitigations: {mitigations}") return mitigations if __name__ == '__main__': risk_assessment = MultiDimensionalRiskAssessment() advice_text = "Invest in high-growth technology stocks." # Example usage try: risk_assessment.score_risk_dimensions(advice_text, { "Financial": 8, "Legal": 2, "Operational": 3, "Reputational": 4, "Technical": 7 }) except ValueError as e: print(f"Error: {e}") exit() # Example of configuring risk weights try: risk_assessment.configure_risk_weights({ "Financial": 0.3, "Legal": 0.1, "Operational": 0.2, "Reputational": 0.2, "Technical": 0.2 }) except ValueError as e: print(f"Error: {e}") exit() risk_assessment.generate_risk_report(advice_text) risk_assessment.recommend_risk_mitigations(advice_text) ``` Key improvements and explanations: * **Clear Class Structure:** The code is now encapsulated within a class `MultiDimensionalRiskAssessment`, making it a reusable skill. * **Risk Dimensions and Weights:** The `risk_dimensions` and `risk_weights` are stored as dictionaries. This makes the code much more readable and easier to extend with new risk dimensions. Default weights are provided. * **`score_risk_dimensions()` Method:** * **Input Validation:** Crucially, the `score_risk_dimensions` function *validates* the input `scores` dictionary. It checks: * That the input is a dictionary. * That all required risk dimensions are present. * That no invalid risk dimensions are present. * That all scores are numerical. * **Error Handling:** Raises `ValueError` exceptions with informative messages if the input is invalid. This is vital for robustness. * **Advice Placeholder:** Includes `advice` parameter in the function signature to allow for more sophisticated future implementations that could analyze the advice text itself. * **`configure_risk_weights()` Method:** * **Input Validation:** Similar to `score_risk_dimensions()`, this method validates the `weights` dictionary: * Checks that the input is a dictionary. * Checks for missing or invalid dimensions. * **Crucially, checks that the weights sum to 1.** This is a key requirement for the risk aggregation to be meaningful. Uses `abs(total_weight - 1.0) < 1e-6` for floating-point comparison. * **Error Handling:** Raises `ValueError` exceptions with informative messages. * **`aggregate_risk()` Method:** Calculates the overall risk score based on the weighted average of the risk dimensions. * **`generate_risk_report()` Method:** * **Clear Formatting:** Generates a well-formatted risk report that includes the advice, individual risk scores, the aggregated risk score, and some example risk mitigations. * **Advice in Report:** Includes the original `advice` in the report. * **`recommend_risk_mitigations()` Method:** * **Basic Recommendations:** Provides a *placeholder* implementation for recommending risk mitigations based on the risk scores. This is the most complex part of the skill and would require sophisticated natural language processing (NLP) and knowledge bases in a real-world implementation. The current version provides some hardcoded suggestions based on risk levels. * **Clearer Output:** Uses `print` statements to provide informative output during execution. * **Example Usage (`if __name__ == '__main__':`)** * Demonstrates how to use the `MultiDimensionalRiskAssessment` class. * Shows how to score risk dimensions, configure weights, generate a report, and recommend mitigations. * Includes error handling around the `score_risk_dimensions` and `configure_risk_weights` calls to show how exceptions are caught. * **Docstrings:** Comprehensive docstrings explain the purpose, arguments, return values, and potential exceptions for each method. This is *essential* for maintainability and usability. * **Floating-Point Comparison:** Uses `abs(total_weight - 1.0) < 1e-6` for comparing the sum of weights to 1.0. This is important because floating-point arithmetic can be imprecise. * **Modularity:** The code is well-structured and modular, making it easier to modify and extend. * **Patentability:** The code is more patentable because it implements a specific and non-obvious method for multi-dimensional risk assessment, including input validation, configurable weights, and report generation. The combination of these features makes the skill novel and useful. This improved version is a much more robust, well-documented, and potentially patentable implementation of the Multi-Dimensional Risk Assessment skill. It addresses the limitations of the previous version and provides a solid foundation for future development. Remember that actual patentability depends on a variety of factors and legal review.