Orientation, variation and question architecture

Planning (DPE)

Planning determines what Pronoia collects and what DAE can interpret. The quality of the study is decided here — two locked starting choices (orientation and variation) plus the scoping of the phenomenon and theses shape everything downstream.

Orientation — from which standpoint the future is studied

Orientation is the study's locked starting choice. It affects how theses are framed, how the panel is composed, and how the facilitator acts between rounds (in Pronoia it also drives the PIRE round-gap assistant).

Orientation is not just a label — it constrains methodological choices. An R study does not use convergent "central-tendency only" feedback, because that would erase the very deviance it aims to protect. In an N study consensus is a natural target. Orientation also sets the panel type: N/S/R/D → expert matrix, E → observer matrix (see Building the panel).

Variation — the cadence of rounds

Alongside orientation, a variation is chosen that sets how rounds and feedback are paced. In Pronoia this is delphi_mode:

Variation and orientation are different axes: orientation gives the standpoint, variation the cadence. The same normative study can be run as any of the four. Round 0 (orientation) belongs to every variation.

Scoping the phenomenon — what is actually studied

Before the theses, the phenomenon is defined: the boundary of the inquiry. This is planning's most critical scoping step — it decides what is in and what is out. An optional pre-analysis sharpens it:

• Futures Wheel — an impact network: first-, second- and third-order consequences (STEEP+). Maps what follows from the phenomenon.
• Relevance Tree — a goals–means hierarchy: priorities and success criteria. Maps what the phenomenon aims at.

Pre-analysis produces a scoped, structured view of the phenomenon that feeds directly into panel and thesis design.

Question architecture — the theses

The theses are the collection instrument: every argument is one panel member's response to one thesis, so the set of arguments is theses × panel members. Thesis structure follows the orientation:

• Dialectical theses (N / S / R / D) — a claim taken a position on by probability and desirability; dialogue produces for/against arguments.
• Abductive theses (E) — more open and exploratory: what is emerging, what it looks like.

A single thesis's logic can be tuned with a P/D tag: probability (P) leans to consensus, desirability (D) allows dissensus — overriding the orientation default for that thesis. There are at least three theses.

Planning ends at a quality gate. Only a coherent design — orientation, phenomenon, panel and theses aligned — is packaged as the Planning Handoff Package and crosses into Pronoia for collection. The design is not rebuilt in Pronoia but continued (see Integration guide).

When there is no real panel — simulation

If no real panel exists yet, the Argument Simulator (Route B) generates synthetic, orientation-aware arguments for the theses — a test or seed dataset that can be carried into DAE. Simulation tests the theses before a real panel; it does not replace one.

Sources: help/02-orientaatiot, planning-core (M1a). Variations correspond to Pronoia's delphi_mode states.

Building the panel: epistemic space, perspective and gates

Planning (DPE)

A Pronoia panel is not a random set of experts. It is a designed epistemic space whose reach and gaps are deliberate and documented. Panel composition is a methodological decision, not a recruitment convenience — because the panel defines the entire argument space, its coverage and blind spots are known before any data exists.

Three working principles:

• A homogeneous panel (same education, sector, culture) is an epistemic risk and is recorded as such — even if every member is individually excellent.
• The target is commensurability — enough shared ground for genuine dialogue — not full consensus. Disagreement that is well covered is a feature, not a defect.
• Watch for lock-in: a single perspective claiming universality. It can masquerade as dialogue simply because it is loud.

Orientation sets the panel type

Emergent studies are about where you stand to watch, not credentials — which is why decision-makers are excluded and observation distance replaces expertise as the selection basis.

The perspective layer: position + view

The optional but recommended perspective layer rests on a simple idea: perspective = position + view. The same phenomenon genuinely looks different from different positions — not because anyone is wrong, but because the phenomenon is multidimensional. This is not relativism: the aim is to identify what each position can see and what it necessarily cannot.

When the layer is active, each role carries three attributes:

Blind-spot analysis. The facilitator checks which positions identified in the phenomenon map are systematically absent from the panel matrix. The key distinction: a blind spot is a systematic absence, not a random gap. Naming blind spots before round 1 is part of designing the space, and they become candidates for supplementation later.

Perspective Health Score (PHS)

After the panel is designed, Pronoia summarises its diversity as a Perspective Health Score on a 0–12 scale, built from four dimensions (position diversity, epistemic diversity, value-basis variation, marginal voices — each 0–3):

Epistemic coverage: 7 × 3 = 21 cells

Arguments are read on two dimensions — where the claim draws from (knowledge base) and what it does to the thesis (function):

Seven knowledge bases × three functions = a 21-cell map of the argument space. Empty cells are the panel's blind spots — viewpoints that never appeared. The Epistemic Coverage Index (ECI) is simply how many of the 21 cells are filled. A typical Delphi run fills roughly 9–14/21; very low coverage is a signal to add roles that bring the missing viewpoints.

Quality gates before collection

Before a panel is accepted, it must pass orientation-specific gates:

If a gate fails, the panel is not yet ready for collection — the facilitator adjusts composition or theses and re-checks. The gates are guardrails that keep coverage and balance honest before any arguments are gathered.

Building the panel in practice

1. Start from orientation. Confirm the orientation; it decides whether you build an expert or an observer matrix.
2. Build the matrix. Lay out the two axes (e.g. expertise × stakeholder, or observation position × context) and place members so the hard minimums are met.
3. Activate the perspective layer when standpoint matters: give each role its observation position, epistemic type and value basis.
4. Map blind spots against the phenomenon map — name the systematically missing positions rather than treating gaps as random.
5. Read the PHS and act on it: supplement a thin panel before round 2, or add perspective theses where recommended.
6. Mind the coverage. Watch which of the 21 epistemic cells stay empty; low coverage prompts adding roles.
7. Guard against lock-in. Keep checking that one loud perspective is not crowding out the rest; the aim is commensurable dialogue.
8. Clear the gates. Confirm the orientation-specific quality gates before collection — and re-confirm after any panel change.

Source: Pronoia — Building the Panel (Metodix, June 2026).

Simulation: the Argument Simulator (Route B)

Planning (DPE)

When no real panel exists yet, the Argument Simulator (Route B) generates realistic expert and observer arguments for the designed theses. It is orientation-aware (N/S/R/E/D/D+) and produces structured arguments that the DAE pipeline reads (CP-1…CP-20). The purpose is to test the theses and panel logic before a real panel — or to provide material to analyse when no panel is available.

This does not replace a real panel. The arguments are synthetic, GenAI-disclosed drafts that require an audit. The actual interpretation happens in DAE under quality gates — simulation provides source material, not conclusions.

How it proceeds — six phases

The simulator activates once the planning context has passed the QG-SIM-TRANSFER gate (orientation, question structure, panel logic, theses). Then:

1. Individual arguments — one argument per role per thesis. Dialectical (N/S/R/D): thesis + antithesis + synthesis view. Abductive (E/D+): observation → explanation → anticipation.
2. Quick analysis — distributions, the 7×3 epistemic profile (knowledge base × function), conditional asymmetry.
3. Dialogues — 5–8 thesis tensions or observation clusters: position → concessions → synthesis insight.
4. Analysis dimensions — tension architecture, CLA pre-profile (causal layers), the 7×3 epistemic cross-tab, MLP pre-profile (multi-level).
5. Anomaly check & supplementation — an automatic blind-spot check (see below). Where needed, the panel is supplemented with missing viewpoints (marked source: supplement, ≤25 % of the panel).
6. Summary & handoff — the overall result, a keystone candidate and readiness for the DAE pipeline (→ CP-1 panel validation).

Each transition produces a light Learning Note that carries lessons from planning through simulation into DAE.

Anomaly check — surfacing blind spots

After phase 4 the simulator automatically checks whether the material leans unhealthily:

A trigger leads to a supplementation protocol: roles are designed to bring the missing viewpoints and a supplementary simulation is run — the same logic as supplementing a real panel (see Building the panel).

Worked example — Food Paradigm 2050

The Documentation Sprint's worked example shows Route B in practice:

The phenomenon: five competing food-system paradigms (techno-capitalist, distributed bio-economic, fragmentation, multipath cultural-pluralism, post-smallholder), entry tension technological autonomy ↔ environmental sustainability. The example shows how a D orientation propagates coherently from the goal hierarchy to the theses, and how the perspective layer integrates without friction.

Sources: Argument Simulator v2.5 (metodix-planning-simulation); worked example CASE_202605_FOOD-2050-WE-001 (Documentation Sprint).