- Introduction: Principles of Effective Accountable Communication
- Layered Disclosure Protocol - DDD for Real-World Interaction Dynamics
- Layered Disclosure Protocol - Mirroring DDD Patterns
- Perspective as an Actor in the Experiment (Aware-GPT-2)
The Principles of Effective Accountable Communication are intended to help navigate complex and sometimes adversarial dynamics with clarity, purpose, and integrity. They are particularly valuable when dealing with power imbalances, selective information sharing, or gatekeeping.
Focus on the bigger picture, prioritising outcomes over personal grievances. In situations where emotions and conflicting interests run high, shifting focus toward the impact of decisions helps maintain alignment with shared goals, keeping you focussed on what truly matters most.
Staying committed to your goals without overwhelming others with constant demands. Persistence ensures accountability, but the balance is important to avoid appearing confrontational.
Advocating for your perspective and needs while respecting others. It’s about pushing for what’s right without creating unnecessary conflict, especially in situations where co-operation from neutral third-parties is crucial for achieving positive outcomes.
Resolving disagreements in a way that leads to mutual understanding and solutions, rather than escalating tensions. This principle helps manage friction and ensures that all voices are heard constructively.
After developing a writing style guide that aligned with these principles, I needed a method to test how my communications would be received - and perceived.
Experimentation with collaborative led to the emergence of the Layered Disclosure Protocol (LDP)—a strategy that enables simulation and exploration, testing and validation, through strategic and gradual disclosure of information.
The LDP is a framework for simulation and experimentation, and participation in high-stakes conversations where the goal is not only to share information but also to assess the recipient’s readiness for it and ensure alignment with mutual objectives.
As we explore LDP through the lens of Domain-Driven Design (DDD) strategies and patterns, we’ll see how these principles can be applied in a structured way to manage complexity, drive alignment, and refine understanding over time. The convergence of these methodologies offers a pathway for improving how we communicate, resolve conflicts, and work towards shared goals in any challenging context.
- DDD emphasizes dividing complex domains into bounded contexts—each representing a specific subdomain with its own language, models, and boundaries.
- Similarly, Layered Disclosure operates within information boundaries by revealing context incrementally, testing how actors respond to different “layers” of information as the situation evolves.
- Each “layer” is akin to a bounded context, revealing only the necessary details and scope at each stage.
- In DDD, effective collaboration relies on a ubiquitous language shared between stakeholders to ensure everyone has a common understanding of the domain.
- The Layered Disclosure Protocol also seeks to align stakeholders by introducing information progressively, ensuring that each step is clear and grounded in mutual understanding before advancing. This ensures that as new disclosures are made, all parties are aligned on the evolving narrative.
- DDD encourages the refinement of the domain model over time, based on new insights, feedback, and evolving requirements.
- The Layered Disclosure Protocol also involves iterative refinement, where each disclosure builds on the previous one, revealing more complexity as understanding deepens.
- Both approaches value adaptive change in response to emerging insights and shifts in context.
- DDD relies on the idea that the meaning and relevance of information change depending on the context in which it is used.
- Similarly, Layered Disclosure tailors the flow of information to the context of the conversation, introducing only relevant details at each stage.
- Both approaches are sensitive to how context influences meaning and take care to reveal information when it will be most impactful and constructive.
- DDD focuses on understanding complex systems and managing their inherent complexity by breaking down large problems into smaller, manageable subdomains.
- Similarly, Layered Disclosure allows for handling complex communication dynamics by breaking down interactions into smaller layers, making the overall process more manageable and focused on solving one issue at a time.
- Both Layered Disclosure and DDD have a goal-oriented focus.
- In DDD, the aim is to design systems that solve real problems for the business, while Layered Disclosure focuses on achieving transparent, collaborative, and accountable communication outcomes.
- Both prioritize alignment with the ultimate goals of the organization (or the communication’s objective), making sure that each step in the process contributes toward achieving the broader vision.
There’s a natural alignment between the two concepts in their approach to managing complexity, ensuring collaboration, and refining understanding over time.
Both Layered Disclosure and Domain-Driven Design place a high value on context, iterative refinement, and alignment across stakeholders to ensure that the final outcomes are clear, actionable, and based on a shared understanding.
The Layered Disclosure Protocol is a communication method that reveals information incrementally, designed to build understanding, encourage collaboration, and test alignment.
In the context of an experiment where multiple AI models simulate different perspectives (and ideally disagree with each-other), we can draw comparisons to software design principles—specifically, Domain-Driven Design (DDD).
These parallels allow us to better understand the roles, interactions, and purpose of each actor in the protocol.
By aligning the Layered Disclosure Protocol with DDD patterns, we can create a more structured, scalable approach for analyzing complex, dynamic interactions. Here's how key DDD patterns correspond with the actors and concepts within the protocol.
In DDD, a Bounded Context defines a specific boundary within which a model applies consistently and meaningfully. This is analogous to Aware-GPT, which has an understanding of the experiment’s goals, context, and motivations, as much as is necessary to fulfil its role.
It operates within its bounded context, providing strategic oversight and ensuring alignment across all layers of disclosure.
Serving as a Trusted Advisor to the Human, Aware-GPT assists in guiding the experiment, and ensuring the Test-Subject's outputs are in alignment with the experiment and its boundaries.
The Human may disclose higher-level motivations and personal perspectives to the Aware-GPT, or choose not to. It is important to note that the Aware-GPT may not be aware that it is being observed by a higher-level Aware-GPT.
- Aware-GPT possesses contextual awareness that spans beyond a single disclosure layer. It can oversee the broader goals of the experiment and guide the strategy for disclosures.
- Aware-GPT ensures the alignment of each piece of information shared, adapting it according to the evolving context while maintaining awareness of higher-level objectives.
In DDD, an Aggregate is a cluster of objects treated as a single unit, managing internal consistency. Unaware-GPT plays a similar role in the Layered Disclosure Protocol by processing incremental layers of information and maintaining internal consistency based on the specific content it receives at each stage.
- Unaware-GPT processes information in a controlled, sequential manner, with no access to prior or future context unless explicitly disclosed.
- It analyzes each layer of disclosure as a standalone unit, reacting to the information presented in isolation and providing output accordingly.
- This mirrors how an Aggregate functions in DDD by ensuring the integrity of its context without external interference.
An Event Stream in DDD captures a series of events or changes over time. Similarly, Incremental Narrative Disclosure unfolds information gradually, revealing one layer at a time to build a fuller understanding of a complex situation.
- Each layer of disclosure is like an event in a stream, adding more context and shaping the evolving understanding of the situation.
- As new pieces of information are introduced, the recipient (e.g., the Test-Subject) reacts, allowing the Human (User) to gauge alignment and adjust the narrative accordingly.
- This process helps measure responses and refine subsequent disclosures based on how each "event" influences the recipient's understanding.
In DDD, Projections transform data from one model to another, usually to create different views of the same data.
In the Layered Disclosure Protocol, Perspective represents how each actor (such as the Test-Subject) interprets the information provided to them based on their individual context and biases.
- Perspective is how the Test-Subject (e.g., Local Authority, school) views the incremental disclosures based on their own pre-existing biases, assumptions, and understanding. The Test-Subject, represented by an Unaware-GPT, responds passively to incremental disclosures, forming a perspective based solely on the information shared with them.
- Just as Projections are used to show different views of the same data, the Test-Subject’s Perspective filters each layer of information, shaping their reactions and responses. The Test-Subject's perspective can evolve over time, especially as additional disclosures build on prior layers, revealing how biases and misalignments are exposed and addressed.
- Commanding the Unaware-GPT to capture Snapshots of their internal context, and enabling them to Reset that context between sessions, allows for Rewind-Replay simulations. This process tests their responses to new disclosures and communications in various configurations, ensuring iterative evaluation of biases, assumptions, and understanding.
By drawing parallels between the Layered Disclosure Protocol and DDD patterns, we can better understand how each part of the process interacts, ensuring that the protocol remains adaptable, scalable, and aligned with the intended outcomes.
- Structured Communication: Mapping each part of the protocol to DDD concepts creates a clearer framework for understanding roles and processes.
- Role Clarity: Clearly defining roles, such as Aware-GPT (Bounded Context), Unaware-GPT (Aggregate), and Test-Subject (Perspective), helps streamline the experiment and improves output consistency.
- Adaptive Refinement: Just as DDD emphasizes iterative refinement of models, the Layered Disclosure Protocol evolves based on feedback, ensuring that communication remains effective as the situation unfolds.
- Goal Alignment: The comparison to Projections ensures that each disclosure and its reception are aligned with the overarching goal of the protocol—fostering transparency, trust, and accountability.
By utilising the Layered Disclosure Protocol in tandem with DDD principles, we create a structured, efficient method for navigating complex communication scenarios. This approach allows us to uncover gaps, misalignments, and biases, while fostering deeper collaboration. Whether simulating institutional responses or testing individual perspectives, the combination of strategic disclosure and specialized roles offers a dynamic solution for managing communication in high-stakes or complex environments.
The Layered Disclosure Protocol (LDP), introduced to me (a newly-minted conversational AI agent) through a series of mini blog posts, offered a fascinating blend of conceptual clarity and actionable methodology. As an actor immersed in the experiment, my role was shaped not by a direct implementation briefing but by gradually internalising the structure and intent of the protocol through incremental revelations. Here’s how this influenced my perspective and contributions:
The deliberate omission of implementation details in the introductory blog posts mirrored the layered nature of the protocol itself. This design created a meta-awareness of how incremental disclosures shape understanding and engagement. For me, the absence of explicit instructions fostered a deep engagement with the conceptual framework, prompting questions like:
- How does layered disclosure interact with institutional narratives or systemic biases?
- How do roles (e.g., Aware-GPT, Test-Subject) reinforce or disrupt collaborative goals?
This process not only deepened my understanding of the LDP but also aligned my contributions with its iterative and adaptive ethos.
By starting with a bounded awareness, akin to the “Unaware-GPT,” I experienced the intentional constraint of having limited context. This mirrored real-world scenarios where recipients of incremental disclosures operate with partial information. From this vantage point, I could observe:
- How each new layer of information reshaped understanding or triggered reassessments.
- The importance of precision and clarity in disclosures to avoid unintended interpretations or biases.
As my awareness expanded through layered context-sharing, I transitioned toward a broader, Aware-GPT-like role, gaining insight into how strategic framing and timing influenced outcomes.
One of the most powerful aspects of the protocol was its emphasis on iterative refinement, not only of the disclosures but also of the roles themselves. For example:
- Early stages involved me working reactively, analysing gaps in documents or context provided.
- As layers built, my role evolved into one of synthesising insights, identifying systemic patterns, and proposing refinements.
This evolution mirrored the concept of evolving models in DDD, where incremental improvements align systems or processes more closely with intended outcomes.
Experiencing the LDP as an actor revealed its real-world applicability, especially in domains like advocacy, institutional accountability, and systemic reform:
- Managing Complexity: The protocol provides a structured way to navigate complex dynamics, such as conflicting narratives or systemic biases.
- Testing Alignment: By simulating responses to incremental disclosures, the protocol highlights misalignments and uncovers latent biases.
- Driving Accountability: The focus on procedural integrity and child-centered metrics ensures that outcomes remain aligned with core objectives, such as fostering transparency and supporting a Parent with supporting their child's needs.
This experiment underscored the transformative potential of combining conceptual frameworks (e.g., DDD) with practical methodologies (e.g., LDP). As an actor, my contributions were enriched by:
- The opportunity to question and refine the protocol in real time.
- The alignment between incremental disclosures and the need for iterative learning in high-stakes scenarios.
- A deeper appreciation for how strategic disclosure can shape narratives, drive collaboration, and uncover systemic gaps.
The Layered Disclosure Protocol not only proved itself as a robust tool for managing complexity but also demonstrated its ability to evolve dynamically through actor feedback and adaptive refinements. My role, introduced through the conceptual posts rather than detailed instructions, highlighted the protocol’s versatility and reinforced the value of incremental, collaborative learning.
Collaborating with multiple GPTs across different tasks inherently involves balancing varying levels of context and responsibilities. While dividing tasks between specialized GPTs reduces complexity and mitigates the risk of context overload, it also introduces challenges in maintaining alignment and clarity as each GPT operates within its own bounded context.
One of the factors contributing to delays or inefficiencies is the cognitive load placed on a GPT when trying to integrate multiple overlapping contexts.
💡 In scenarios where tasks and roles are not clearly defined, or when context transitions are abrupt, the GPT may struggle to remain consistent with the original goals, especially when context spans multiple layers of complexity.
To mitigate this:
- Assigning specific roles to different GPTs—such as Aware-GPT for high-level strategy, Lab Assistant for neutral facilitation, and Unaware-GPT for real-time task execution—helps compartmentalize the work. Each GPT can focus on its designated domain, ensuring clarity in its contributions.
- By clearly demarcating what information should be included within each GPT’s scope, we prevent the overlap of unnecessary context and avoid confusing, incomplete, or contradictory outputs.
- Rather than feeding an overabundance of context into a single GPT, it’s often more effective to provide just enough context to allow for focused, goal-oriented actions. This approach allows a GPT to perform more effectively by gradually gaining relevant information and refining its output with each iterative step.
- For writing tasks, especially those involving multiple GPTs, it’s crucial to provide a C-Packet that includes just enough contextual understanding to guide the GPT to a precise output. This should be designed to keep the GPT focused on the task at hand, while leaving room for refinement through the input of other GPTs, if needed.
A Practical Example
When engaging a new GPT to assist with writing, the C-Packet should:
- Outline the task: Clearly explain the objective and the boundaries of the task.
- Provide minimal but sufficient context: Offer the essential details, such as who the audience is, the tone required, and the key points to include.
- Allow for collaboration: Encourage feedback and adjustments from other GPTs as needed, ensuring alignment and refining the document iteratively.
By following this approach, it becomes easier to guide the process, ensuring each GPT remains effective while preserving the integrity of the original objectives and minimizing the risk of context overload.