Gameplay loop — from retained knowledge to trained agency [Flow · product direction]

From the Meridians Wiki · Public · Maintained · joint

Meridians does not yet have one complete gameplay loop. It has a strong initial knowledge payoff, a machine maintenance loop, a shipped Scenario turn, an incubating Episode form, and a detached learning surface. This diagram keeps those truths separate, then defines the smallest loop worth proving next. The shipped Scenario state machine remains owned by Scenario loop; World progression remains directional under the World contract.

The analysis uses Joss Querné's four loop views as an external design lens: play exposes condition, input, and output; activities shows the repeated cadence; compulsion tests motivation and progression; game communicates the high-level experience. Across them, objective, challenge, and reward must all be legible. Source note 🔒.

Where the loops are now

flowchart LR
  SOURCE["Premise · corpus · current sources"] --> EXTRACT["Extraction<br/>strong first-session comprehension"]
  EXTRACT --> DOMAIN["Maintained Domain<br/>remembered evidence + structure"]

  subgraph MACHINE["Recurring machine activity — shipped"]
    PROGRAM["Program<br/>Research → Opinion → Merge → Position"]
    STORIES["Stories + Positions<br/>material movement reaches the human"]
    PROGRAM --> STORIES -->|"next cadence"| PROGRAM
  end

  DOMAIN --> PROGRAM
  PROGRAM --> DOMAIN

  subgraph PLAY["Player-facing play — shipped core, incomplete return loop"]
    BRIEF["Brief<br/>condition + situated information"]
    PREPARE["Prepare<br/>interpret + gather Priors"]
    COMMIT["Commit<br/>express Will"]
    CONSEQUENCE["Consequence<br/>system response + preserved branch"]
    BRIEF --> PREPARE --> COMMIT --> CONSEQUENCE
  end

  DOMAIN --> BRIEF
  EPISODE["Episode / Stageplay<br/>immersion + strategic puzzle<br/>active incubation"] -.-> BRIEF
  CHAT["Chat inquiry<br/>repeatable utility, fragile consequence"] -.-> DOMAIN
  LEARNING["Learning<br/>active recall + spaced practice<br/>not joined to play"] -.-> DOMAIN

  CONSEQUENCE -. "missing hinge" .-> DEBRIEF["Debrief<br/>intention ↔ result ↔ missed signal"]
  DEBRIEF -. "not yet adaptive" .-> NEXT["Next challenge<br/>difficulty · perspective · tools · responsibility"]
  NEXT -. "return" .-> BRIEF

  classDef shipped fill:#17362f,stroke:#5fa98e,color:#eefaf6
  classDef incubating fill:#3b3020,stroke:#b59255,color:#fff8e8
  classDef gap fill:#3a2027,stroke:#bb6f7f,color:#fff0f3,stroke-dasharray:5 4
  class EXTRACT,DOMAIN,PROGRAM,STORIES,BRIEF,PREPARE,COMMIT,CONSEQUENCE,LEARNING shipped
  class EPISODE incubating
  class CHAT,DEBRIEF,NEXT gap

The Program loop is real, but mostly machine-side. It improves the maintained model, not demonstrably the participant. Scenario closes one consequential turn, but the result does not yet reliably become a debrief, an adapted challenge, or evidence of growing ability. Extraction, chat, Episode, and Learning therefore orbit the loop rather than compounding through it.

The next coherent game loop

The player-facing vision is: enter a world that remembers; understand what matters from one situated perspective; make a move that changes the world; learn from the difference between intention and result; return able to carry greater consequence.

flowchart TB
  OBJECTIVE["Contextual objective<br/>What must this seat protect, discover, change, or decide?"]

  subgraph SESSION["Activity / session loop — the next product contract"]
    RECALL["1 · Recall<br/>restore relevant history"]
    ORIENT["2 · Orient<br/>find the decision window"]
    INHABIT["3 · Inhabit<br/>role + partial information + strategic puzzle"]
    COMMIT["4 · Commit<br/>order · plan · trade · disclosure · refusal"]
    CONSEQUENCE["5 · Consequence<br/>rules + other agency resolve the move"]
    DEBRIEF["6 · Debrief<br/>intention · expectation · result · surprise"]
    ADAPT["7 · Adapt<br/>change the next challenge"]
    RECALL --> ORIENT --> INHABIT --> COMMIT --> CONSEQUENCE --> DEBRIEF --> ADAPT --> RECALL
  end

  OBJECTIVE --> RECALL

  subgraph PLAY["Inner play loop — prove moment to moment"]
    CONDITION["Condition<br/>world state + seat knowledge + available tools"]
    INPUT["Input<br/>situated Will through a permitted action"]
    OUTPUT["Output<br/>legible governed consequence"]
    CONDITION --> INPUT --> OUTPUT --> CONDITION
  end

  INHABIT --> CONDITION
  OUTPUT --> CONSEQUENCE

  MEMORY["Memory Palace + Program<br/>recall · signal selection · maintained Domain"] --> RECALL
  EPISODE["Episode<br/>embodiment · explanation · puzzle"] --> INHABIT
  SCENARIO["Scenario<br/>commitment · resolution"] --> COMMIT
  SCENARIO --> CONSEQUENCE

  CONSEQUENCE --> WORLD_RECORD[("World record<br/>what changed, who knew, which rule applied")]
  DEBRIEF --> ABILITY_RECORD[("Practice record<br/>what the participant learned to notice, judge, or do")]
  WORLD_RECORD --> MEMORY

  ABILITY_RECORD --> CAPABILITY["Capability grows<br/>comprehension · calibration · causal judgment<br/>coordination · perspective · tool use"]
  CAPABILITY --> RESPONSIBILITY["Earned progression<br/>harder problems · broader tools · new perspectives<br/>greater permission or responsibility"]
  RESPONSIBILITY --> OBJECTIVE

  SOCIAL["Other human or agent seats<br/>private knowledge + conflicting aims"] --> CONDITION
  SOCIAL --> CONSEQUENCE

This is a training loop, not a content treadmill. Its reward is twofold: an intrinsically useful outcome in the Domain and increased agency in the next situation. Roles, tools, access, relationships, resources, and responsibility can embody progression; generic XP does not prove that the participant became more capable. The loop can work in a bounded Scenario before a persistent World exists.

The proof ladder

flowchart LR
  P0["0 · Retain<br/>Extraction creates a re-enterable Domain<br/>CURRENT"]
  P1["1 · Close one turn<br/>Brief → Prepare → Commit → Consequence<br/>CURRENT"]
  P2["2 · Learn from consequence<br/>automatic evidence-backed debrief<br/>NEXT BUILD"]
  P3["3 · Adapt the return<br/>replay or next episode targets the revealed gap<br/>NEXT PROOF"]
  P4["4 · Progress through agency<br/>roles, tools, permissions, perspective, responsibility<br/>DIRECTION"]
  P5["5 · Sustain social worlds<br/>group memory, institutions, economies, long arcs<br/>CONDITIONAL"]

  P0 --> P1 --> P2 --> P3 --> P4 --> P5

The next stage is deliberately small: close Consequence → Debrief → Adapt → next Brief for one repeatable Scenario or Episode. Test whether a participant can identify a missed signal, revise a causal model, and perform better on a related challenge. Only then should Meridians invest heavily in persistent RPG economies or MMO-scale retention systems.

Design tests

Loop view	Meridians question	Evidence of success
Play	Does a situated input produce a prompt, legible, governed response?	The player can explain what their move changed and why.
Activities	Do the seven activities form one comprehensible return cadence?	The player reaches a next challenge without navigating disconnected products.
Compulsion / progression	Does the reward move the player toward a meaningful desire?	A useful outcome and a specific gain in ability, access, or responsibility motivate return.
Game vision	Do all surfaces serve the same fantasy?	The experience consistently feels like practising agency inside a world that remembers.

Every challenge needs both a contextual goal for the participant and a systemic goal the engine can resolve. “Prevent the coalition from fracturing” carries the dramatic intent; “secure two commitments before the vote under the seat's information and resource constraints” makes the challenge playable and testable.