Perspectives previously used a fixed set of 8 philosophical personas for all debates. This created a mismatch between the analytical lens and the decision being made. Evaluating a feature prioritisation decision through Sociopath's strategic weakness lens produced interesting debate, but not necessarily useful data.
Two new features address this limitation. Persona Sets provide specialised analytical frameworks tailored to different decision types. Prediction Mode shifts focus from prescriptive recommendations to verifiable forecasts. Both features integrate fully with the Interrogation of Blind Proposals Protocol (IBPP), maintaining the structured challenge-response-verdict system that generates high-quality debate data.
Persona Sets
Decision-making requires cognitive diversity matched to the decision type. Strategic business decisions benefit from stakeholder analysis across competing interests. Product development decisions need perspectives from different roles in the development and usage cycle. Philosophical dilemmas require examination through distinct value systems.
The system now supports multiple persona sets, each optimised for a specific category of decision. Sets are selected at the start of each debate based on the question being asked.
Available Persona Sets
Philosophical (Default): Represents different cognitive frameworks and value systems.
This set works best for ethical dilemmas, personal decisions with moral dimensions, and questions exploring how different value systems interpret the same situation.
Product-Focused: Represents stakeholders in product development.
This set addresses feature prioritisation, UX decisions, technical trade-offs, and product roadmap planning. The perspectives map directly to conversations product teams already have, but structured through systematic interrogation rather than ad-hoc discussion.
Business-Focused: Represents competing organisational interests.
This set supports strategic decisions, resource allocation, stakeholder management, and organisational change. The analysis reveals which stakeholder groups benefit from a decision and which bear its costs.
Predictive (Specialised): Represents distinct forecasting methodologies. More on this below.
How Sets Change Analysis Quality
The philosophical set examines a feature request through abstract principles. The Empath asks how it affects users emotionally. The Idealist questions whether it aligns with product values. The Sociopath identifies ways users might exploit it.
The product-focused set examines the same request through practical concerns. User describes friction in specific workflows. Maintainer calculates operational burden. Architect evaluates system integrity impact. Market Sceptic demands adoption evidence.
The difference produces meaningfully different analysis outputs. Philosophical personas generate interesting ethical deliberation but limited implementation guidance. Product personas generate concrete technical and UX considerations directly relevant to shipping decisions.
Interestingly, when a query is framed a different way to the Philosophical personas (e.g., using the new Prediction Mode - see below), they each use their inherent "world views" to attempt to solve the queries posed in line with their characters. They essentially already have views on how things will work because they have theories on how the world actually works. I also noticed notably higher tension in identical debates conducted using the Philosophical personas than I did with the new persona sets.
Use Cases by Set Type
Philosophical Set:
Should we allow AI-generated content in our platform?
How do we balance privacy and convenience?
What ethical principles should guide content moderation?
Should we accept funding from controversial sources?
Product-Focused Set:
Should we add real-time collaboration to the editor?
How do we prioritise these five feature requests?
Do we rebuild the legacy architecture or patch it?
Should we ship this feature in beta or wait?
Business-Focused Set:
Should we expand into this market segment?
How do we allocate the marketing budget?
Do we pursue acquisition or organic growth?
Should we change our pricing model?
Prediction Mode
Analysis Mode generates recommendations about what should happen. The system evaluates options, debates their merits, and produces a recommended resolution. This works well for prescriptive questions where success depends on implementation quality rather than external factors.
Prediction Mode generates forecasts about what will actually happen. The system estimates probabilities, identifies key uncertainties, and produces confidence-weighted predictions. This works for questions with verifiable outcomes where accuracy can be measured against reality.
The distinction matters because verification mechanisms differ fundamentally. Analysis Mode recommendations can only be evaluated subjectively through user feedback. Prediction Mode forecasts can be evaluated objectively by checking what happened.
Benefits of Verifiable Outcomes
Objective measurement creates feedback loops. When predictions resolve, the system can calculate which personas forecasted accurately and which reasoning patterns led to correct forecasts. This data enables refinement that subjective evaluation cannot provide.
The feedback mechanism addresses a core limitation of analytical systems. Without external signal, there's no way to know if the debate process generates genuinely useful insights or plausible-sounding nonsense. Prediction accuracy provides that signal.
The Predictive Persona Set
Prediction Mode uses a specialised persona set optimised for forecasting rather than moral deliberation. These personas represent distinct epistemic approaches to understanding future events.
Base Rate Analyst examines historical patterns and reference class forecasting. What happened in similar situations and what does statistical evidence suggest?
Insider evaluates qualitative signals and expert judgment. What do people close to the situation observe and what information isn't captured in public data?
Contrarian identifies consensus errors and unpriced risks. What is the crowd missing?
Systems Thinker maps causal chains and feedback loops. What causes what and how do different forces interact?
Trend Analyst tracks momentum and directional movement. What's accelerating or decelerating?
Scenario Planner considers multiple possible futures. What are the discrete scenarios and what triggers each one?
Implementer assesses execution capacity and operational constraints. Can the actors involved actually deliver this? What practical obstacles exist?
Market Reader interprets signals from prediction markets and aggregated forecasts. What does money on the line suggest? How have similar predictions resolved?
{image: Predictive set interface showing 8 forecasting personas}
The personas approach predictions through methodologically distinct frameworks.
Use Cases for Prediction Mode
Political and Electoral Outcomes:
Will Candidate X win the election?
Will this legislation pass by the target date?
Will the approval rating exceed 50% by year end?
Market and Business Predictions:
Will Company X reach $1B valuation within 18 months?
Will this product category grow more than 20% this year?
Will the merger be approved by regulators?
Technology and Product Forecasts:
Will this feature achieve 40% adoption within 6 months?
Will competitors launch a similar product by Q3?
Will this technical approach become industry standard?
Social and Cultural Trends:
Will this regulation be implemented as currently drafted?
Will public opinion shift on this issue by next year?
Will this controversy still be discussed in 6 months?
Integration with Verification Systems
The system supports integration with external prediction platforms. Forecasts can be tracked against Polymarket resolutions, election results, product launches, and other verifiable events. This creates measurable accuracy data that feeds back into persona refinement.
IBPP Support for Both Features
Both Persona Sets and Prediction Mode operate through the Interrogation of Blind Proposals Protocol. Each persona generates an independent initial position without seeing other perspectives. Three challengers interrogate each position through structured challenge-response-verdict cycles. Tension metrics determine synthesis debate length. The full IBPP structure applies regardless of persona set or operational mode.
This means debates in Prediction Mode maintain the same analytical rigour as Analysis Mode debates. Personas defend their probability estimates through the same challenge system that tests ethical arguments. The only difference is the content being debated, not the debate structure.
What's Next
The core infrastructure now supports multiple analytical lenses and operational modes. The system adapts to the decision being made rather than forcing all questions through a single philosophical framework.
I'll be working on integrating Polymarket or similar data to begin testing the predictions to get feedback, enabling the improvement of Predictions and Personas.
I'll now be working on context management to allow the personas to analyse or predict more detailed scenarios with additional context. For example, it may already know all about your product or business, making it easier to start new debates for your specific circumstance.
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