The 9-No Problems within the DIKWP Model

Yucong Duan

International Standardization Committee of Networked DIKWP for Artificial Intelligence Evaluation(DIKWP-SC)

World Artificial Consciousness CIC(WAC)

World Conference on Artificial Consciousness(WCAC)

(Email: duanyucong@hotmail.com)

Abstract

Effective communication is pivotal within the Data-Information-Knowledge-Wisdom-Purpose (DIKWP) model, facilitating seamless collaboration between stakeholders, including humans and artificial intelligence (AI) systems. Initially, three critical communication deficiencies—Incompleteness, Inconsistency, and Imprecision—were identified, collectively termed the 3-No Problems. However, to achieve semantic completeness and address a broader spectrum of communication challenges, six additional deficiencies are proposed, culminating in the 9-No Problems framework. This document delineates each of the nine communication deficiencies with precise definitions and illustrative DIKWP-based examples, ensuring comprehensive coverage and enhancing the robustness of human-AI interactions.

The DIKWP model serves as a hierarchical framework that transitions from raw Data to actionable Purpose through successive layers of Information, Knowledge, and Wisdom. Effective communication within this framework is essential for accurate data processing, informed decision-making, and the attainment of desired outcomes. The 3-No Problems—Incompleteness, Inconsistency, and Imprecision—identify fundamental communication deficiencies that can disrupt this process. To encompass a more exhaustive range of communication challenges, six additional deficiencies are introduced, expanding the framework to 9-No Problems.

The expanded framework includes the following nine communication deficiencies:

1. Incompleteness (No-Incomplete)

2. Inconsistency (No-Inconsistent)

3. Imprecision (No-Imprecise)

4. Relevance (No-Relevant)

5. Redundancy (No-Redundant)

6. Timeliness (No-Timely)

7. Accuracy (No-Accurate)

8. Accessibility (No-Accessible)

9. Understandability (No-Understandable)

Each deficiency is defined with mathematical semantics and illustrated through practical DIKWP-based examples.

Definition:The absence of necessary elements within any DIKWP component that impedes full comprehension or effective action.

Mathematical Representation:

IncompletenessX=XA∖XB\text{Incompleteness}_X = \mathbb{X}_A \setminus \mathbb{X}_BIncompletenessX​=XA​∖XB​

Where XA\mathbb{X}_AXA​ and XB\mathbb{X}_BXB​ are the sets of DIKWP components from Stakeholders A and B, respectively.

DIKWP Example:Scenario: Human-AI Collaborative Medical Diagnosis

• Data (D): Missing patient demographic details (e.g., age, gender).

• Impact: The AI system generates an incomplete symptom analysis, leading to partial Information about potential diagnoses.

• Outcome: Dr. Smith receives incomplete Information, resulting in an incomplete Knowledge base that may misguide treatment decisions.

Definition:The presence of conflicting or contradictory elements within or across DIKWP components that cause confusion and misalignment.

Mathematical Representation:

InconsistencyX=XA∩XB∁\text{Inconsistency}_X = \mathbb{X}_A \cap \mathbb{X}_B^{\complement}InconsistencyX​=XA​∩XB∁​

Where XB∁\mathbb{X}_B^{\complement}XB∁​ denotes the complement set of XB\mathbb{X}_BXB​, highlighting contradictions.

DIKWP Example:Scenario: Human-AI Supply Chain Management

• Information (I): Conflicting reports on supplier lead times from different data sources.

• Impact: The AI system processes inconsistent Information, disrupting the Knowledge base for inventory planning.

• Outcome: Ms. Johnson faces inconsistent Knowledge, leading to unreliable Wisdom in making inventory decisions.

Definition:The presence of vague, ambiguous, or non-specific elements within DIKWP components that reduce clarity and effectiveness.

Mathematical Representation:

ImprecisionX={x∈X∣Ambiguity(x)>θ}\text{Imprecision}_X = \{ x \in \mathbb{X} \mid \text{Ambiguity}(x) > \theta \}ImprecisionX​={x∈X∣Ambiguity(x)>θ}

Where $\theta$ is a threshold value defining acceptable levels of ambiguity.

DIKWP Example:Scenario: Human-AI Educational Tutoring

• Knowledge (K): Vague explanations of calculus concepts (e.g., "understand derivatives intuitively" without concrete examples).

• Impact: Students receive unclear Knowledge, hindering comprehension and effective learning.

• Outcome: Alex struggles with applying calculus concepts due to imprecise Knowledge, affecting academic performance.

Definition:The extent to which the communicated information is pertinent and applicable to the context or objectives.

Mathematical Representation:

RX=∣Xrelevant∣∣X∣R_X = \frac{|\mathbf{X}_{\text{relevant}}|}{|\mathbf{X}|}RX​=∣X∣∣Xrelevant​∣​

Where Xrelevant⊆X\mathbf{X}_{\text{relevant}} \subseteq \mathbf{X}Xrelevant​⊆X denotes the subset of relevant elements within component $X$.

DIKWP Example:Scenario: Human-AI Financial Forecasting

• Information (I): Inclusion of irrelevant economic indicators not pertinent to stock market trends.

• Impact: Irrelevant Information distracts from critical analysis, diluting the effectiveness of forecasting.

• Outcome: Ms. Johnson's forecasting Knowledge is cluttered with irrelevant data, reducing the precision of investment strategies.

Definition:The presence of unnecessary repetition or duplication of information within DIKWP components.

Mathematical Representation:

ReX=∣Xduplicate∣∣X∣Re_X = \frac{|\mathbf{X}_{\text{duplicate}}|}{|\mathbf{X}|}ReX​=∣X∣∣Xduplicate​∣​

Where Xduplicate⊆X\mathbf{X}_{\text{duplicate}} \subseteq \mathbf{X}Xduplicate​⊆X represents duplicated elements within component $X$.

DIKWP Example:Scenario: Human-AI Environmental Monitoring

• Information (I): Duplicate pollutant measurements from multiple sensors.

• Impact: Redundant Information leads to data overload, complicating analysis.

• Outcome: AI systems process excessive redundant data, slowing down the generation of actionable Knowledge for environmental assessments.

Definition:The relevance of information in terms of its currency and availability when needed.

Mathematical Representation:

TX=Age of InformationMaximum Acceptable AgeT_X = \frac{\text{Age of Information}}{\text{Maximum Acceptable Age}}TX​=Maximum Acceptable AgeAge of Information​

Lower TXT_XTX​ values indicate higher timeliness.

DIKWP Example:Scenario: Human-AI Market Analysis

• Information (I): Outdated market data used for trend forecasting.

• Impact: Timeliness deficiency renders Information obsolete, undermining forecasting accuracy.

• Outcome: Ms. Johnson bases investment strategies on outdated Information, leading to poor financial outcomes.

Definition:The correctness and reliability of the information provided within DIKWP components.

Mathematical Representation:

AX=Number of Accurate ElementsTotal ElementsA_X = \frac{\text{Number of Accurate Elements}}{\text{Total Elements}}AX​=Total ElementsNumber of Accurate Elements​

Where accurate elements $\in \mathbf{X}$ are verified against reliable sources or ground truth data.

DIKWP Example:Scenario: Human-AI Medical Diagnosis

• Data (D): Erroneous patient data entries (e.g., incorrect lab results).

• Impact: Inaccurate Data leads to faulty Information processing, compromising the Knowledge base for diagnosis.

• Outcome: Dr. Smith receives inaccurate Knowledge, potentially leading to misdiagnosis and inappropriate treatment plans.

Definition:The ease with which information can be obtained, understood, and utilized by stakeholders.

Mathematical Representation:

AcX=Accessible ElementsTotal ElementsAc_X = \frac{\text{Accessible Elements}}{\text{Total Elements}}AcX​=Total ElementsAccessible Elements​

Where accessible elements $\in \mathbf{X}$ meet predefined accessibility criteria (e.g., format, language, availability).

DIKWP Example:Scenario: Human-AI Educational Tutoring

• Knowledge (K): Complex calculus explanations in technical jargon inaccessible to the student.

• Impact: Accessibility deficiency hinders the student's ability to comprehend and utilize Knowledge effectively.

• Outcome: Alex struggles to engage with the material, impeding learning progress despite having access to relevant Information.

Definition:The clarity and comprehensibility of the information presented within DIKWP components.

Mathematical Representation:

UX=Clear ElementsTotal ElementsU_X = \frac{\text{Clear Elements}}{\text{Total Elements}}UX​=Total ElementsClear Elements​

Where clear elements $\in \mathbf{X}$ are defined by linguistic clarity, absence of ambiguity, and appropriate complexity.

DIKWP Example:Scenario: Human-AI Financial Reporting

• Information (I): Financial reports laden with technical financial terminology.

• Impact: Understandability deficiency impedes stakeholders' ability to interpret and act upon Information.

• Outcome: Ms. Johnson finds it challenging to derive actionable Knowledge from complex reports, affecting investment decisions.

To effectively implement the 9-No Problems framework, organizations must establish mechanisms to identify and detect each communication deficiency within DIKWP components.

Methods:

• Data Audits: Regularly review Data for completeness and accuracy.

• Consistency Checks: Implement algorithms to detect inconsistencies across Information and Knowledge bases.

• Precision Analysis: Utilize natural language processing (NLP) tools to assess the precision of Knowledge explanations.

• Relevance Filtering: Apply relevance scoring systems to ensure Information aligns with objectives.

• Redundancy Elimination: Use data deduplication techniques to remove redundant Information and Knowledge.

• Timeliness Monitoring: Set up real-time data feeds and alerts to maintain timely Information.

• Accuracy Verification: Cross-validate Information against trusted sources to ensure accuracy.

• Accessibility Enhancements: Design Information and Knowledge repositories with accessibility standards in mind.

• Understandability Testing: Conduct usability studies and readability assessments to enhance Understandability.

Once deficiencies are identified, targeted remediation strategies must be employed to address each No Problem.

Strategies:

1. Incompleteness:

• Action: Augment missing Data through additional data collection or integration of external sources.

• Example: Incorporate missing demographic data into medical records.

2. Inconsistency:

• Action: Harmonize conflicting Information through data reconciliation processes.

• Example: Standardize supplier lead time data in supply chain systems.

3. Imprecision:

• Action: Refine Knowledge explanations to eliminate ambiguity.

• Example: Provide concrete examples and definitions in calculus tutoring materials.

4. Relevance:

• Action: Filter Information to ensure only contextually pertinent data is communicated.

• Example: Exclude irrelevant economic indicators in financial reports.

5. Redundancy:

• Action: Remove duplicate Information and Knowledge entries to streamline communication.

• Example: Eliminate repeated pollutant measurements in environmental monitoring.

6. Timeliness:

• Action: Update Information in real-time to maintain relevance.

• Example: Integrate live market data feeds into financial forecasting models.

7. Accuracy:

• Action: Implement validation checks to ensure the correctness of Data and Information.

• Example: Cross-verify patient data with reliable medical records.

8. Accessibility:

• Action: Enhance the accessibility of Information through user-friendly formats and interfaces.

• Example: Translate technical reports into layman's terms for broader accessibility.

9. Understandability:

• Action: Simplify complex Information to enhance comprehensibility.

• Example: Use visual aids and simplified language in financial reports to improve stakeholder understanding.

To maintain the integrity of the 9-No Problems framework, continuous monitoring and iterative improvements are essential.

Approaches:

• Feedback Loops: Gather feedback from stakeholders to identify ongoing deficiencies.

• Automated Monitoring: Deploy AI-driven tools to continuously assess communication quality.

• Regular Reviews: Conduct periodic reviews of DIKWP components to ensure compliance with the framework.

• Training and Education: Educate stakeholders on the importance of addressing communication deficiencies and best practices for remediation.

Scenario: Dr. Smith collaborates with AI-Diagnosis to diagnose a patient.

Scenario: Ms. Johnson collaborates with AI-Forecast to predict stock market trends.

Scenario: Alex interacts with AI-Tutor to learn calculus.

Effective communication within the DIKWP framework necessitates not only the transmission of information but also its quality and applicability. Drawing from Shannon's Information Theory, the introduction of additional No Problems aligns with minimizing uncertainty (entropy) and maximizing the mutual information between communicators.

• Entropy Reduction:Deficiencies like Relevance and Accuracy directly contribute to reducing uncertainty by ensuring that only pertinent and correct information is communicated.

• Mutual Information Enhancement:Enhancements in Understandability and Accessibility increase the mutual information by ensuring that the recipient can effectively decode and utilize the information.

Using set theory, the expanded set of No Problems ensures that all potential communication deficiencies are addressed without overlap, adhering to the Mutually Exclusive and Collectively Exhaustive (MECE) principle.

• Mutual Exclusivity:Each No Problem targets a distinct aspect of communication, preventing overlap and ensuring clarity in identification and remediation.

• Collective Exhaustiveness:The combined set of nine No Problems comprehensively covers all critical dimensions of communication challenges within the DIKWP semantic space.

The expanded framework is supported by established Data Quality Dimensions and Cognitive Load Theory, which emphasize the importance of factors like Accuracy, Timeliness, and Understandability in effective communication and data processing.

• Data Quality Dimensions (Wang & Strong, 1996):Highlighting dimensions such as Accuracy, Timeliness, and Accessibility as critical for maintaining high-quality data and information.

• Cognitive Load Theory (Sweller et al., 2011):Emphasizing the need to minimize Redundancy and enhance Understandability to optimize cognitive processing and learning outcomes.

The transition from the initial 3-No Problems to an expanded 9-No Problems framework significantly enhances the semantic completeness within the DIKWP model. By incorporating additional communication deficiencies—Relevance, Redundancy, Timeliness, Accuracy, Accessibility, and Understandability—the framework now comprehensively addresses the multifaceted nature of communication challenges.

Key Takeaways:

1. Semantic Completeness:The 9-No Problems framework ensures that all critical aspects of communication deficiencies are identified and addressed within the DIKWP model.

2. Mathematical Rigor:Each No Problem is defined with precise mathematical semantics, facilitating objective identification and remediation.

3. Practical Applicability:Illustrative DIKWP-based examples demonstrate the real-world relevance and impact of each communication deficiency, highlighting the framework’s utility across diverse scenarios.

4. Enhanced Human-AI Collaboration:By addressing a broader range of communication deficiencies, the framework fosters more effective and meaningful collaborations between humans and AI systems.

Recommendations:

• Adopt the Expanded Framework:Integrate the 9-No Problems into organizational communication protocols to ensure comprehensive coverage of potential deficiencies.

• Develop Mathematical Tools:Create algorithms and metrics based on the formal definitions to detect and quantify these deficiencies in real-time interactions.

• Empirical Validation:Conduct empirical studies across various domains to validate the effectiveness and comprehensiveness of the 9-No Problems framework.

• Continuous Refinement:Regularly update and refine the framework based on emerging communication challenges and advancements in information theory and cognitive science.

Future Directions:

• Advanced Mathematical Modeling:Explore more sophisticated mathematical models, such as Bayesian networks or fuzzy logic, to capture the nuanced interplay between different communication deficiencies.

• AI-Driven Remediation:Develop AI systems capable of autonomously identifying and mitigating these communication deficiencies, enhancing the efficiency and effectiveness of human-AI collaborations.

• Interdisciplinary Integration:Collaborate with fields like linguistics, cognitive psychology, and systems engineering to further refine and expand the framework, ensuring its adaptability to evolving communication landscapes.

By embracing a mathematically rigorous and comprehensive set of communication deficiencies, the DIKWP framework can better facilitate effective and meaningful interactions, ensuring that both human and AI stakeholders achieve their collaborative objectives with clarity, precision, and mutual understanding.

1. Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379–423.

2. Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory. Wiley-Interscience.

3. Fano, R. M. (1961). Transmission of Information: A Statistical Theory of Communication. MIT Press.

4. Wang, R. Y., & Strong, D. M. (1996). Beyond accuracy: What data quality means to data consumers. Journal of Management Information Systems, 12(4), 5-34.

5. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. Springer.

6. ISO/IEC 25012:2008. Software engineering — Software product Quality Requirements and Evaluation (SQuaRE) — Data quality model.

7. Seth, A. K. (2014). A predictive processing theory of sensorimotor contingencies: Explaining the puzzle of perceptual presence and its absence in synesthesia. Cognitive Neuroscience, 5(2), 97–118.

8. Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 5998–6008.

9. Marcus, G., & Davis, E. (2020). GPT-3, Bloviator: OpenAI's language generator has no idea what it's talking about. MIT Technology Review.

10. Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On faithfulness and factuality in abstractive summarization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1906–1919.

The author extends gratitude to Prof. Yucong Duan for his pioneering work on the DIKWP model and foundational theories in information science. Appreciation is also given to colleagues in mathematics, information theory, and cognitive science for their invaluable feedback and insights.

Correspondence and requests for materials should be addressed to [Author's Name and Contact Information].

Keywords: DIKWP Model, 9-No Problems, Communication Deficiencies, Incompleteness, Inconsistency, Imprecision, Relevance, Redundancy, Timeliness, Accuracy, Accessibility, Understandability, Information Theory, Set Theory, Human-AI Interaction, Mathematical Framework