think-causal-loop-diagrams

name	think-causal-loop-diagrams
description	Builds a signed causal loop diagram by closing the feedback loops in a situation, labeling each loop reinforcing (R) or balancing (B) with its link polarities, and reading likely dynamics (spiral, goal-seeking, or oscillation) off which loop dominates. Use when a situation feeds back on itself - growth that funds more growth, a fix that recreates its own problem, capacity that relieves then re-attracts demand - and you need to see why it keeps accelerating, stalling, or overshooting. Not for a single accumulation, a one-directional consequence tree, or a genuinely linear chain.
license	Apache-2.0
metadata	{"id":"thinking-framework-skills.causal-loop-diagrams","family":"systems-and-consequences","evidence-tier":"M/P","version":"0.1.0","standard":"0.8"}

Causal Loop Diagrams

People narrate systems as one-directional chains and silently drop the loop-back. "More users, so more revenue" omits "...which funds acquisition, which brings more users" - the cycle that actually drives the behavior. This skill performs one distinct move: close the feedback loops and sign them. Trace each cycle back to its start so it closes, give every link a polarity (does a rise in A raise (+) or lower (-) B), and label the whole loop reinforcing (R) when the signs multiply to net-positive (it amplifies: a vicious or virtuous spiral) or balancing (B) when they multiply to net-negative (it counteracts: goal-seeking, or oscillation when delayed). Then read likely behavior off the structure: which loop dominates, and therefore whether the system spirals, seeks a goal, or oscillates. The output is a signed causal loop diagram framed as a structured argument about dynamics - not a prediction. It corrects a specific, well-evidenced failure (people misperceive feedback); it does not claim to predict the system or to teach systems thinking wholesale.

When to Use

• A variable plausibly feeds back on itself through a cycle (growth funds growth; a fix recreates its problem; relief of a constraint re-attracts the load).
• The puzzle is why does this keep accelerating / stalling / overshooting and undershooting - behavior that a linear story cannot explain.
• You want an inspectable, signed structure (R/B loops with polarities) before reasoning about leverage or intervention.

When NOT to Use

• A single accumulation, no loop (one stock, net flow, no cycle): use think-stocks-and-flows-reasoning. That skill reasons about one quantity from its net flow; it does not close or sign a loop.
• You only need to name that feedback exists as one structural layer among events, patterns, and structure: use think-iceberg-model. It names feedback as a structure item but does not close, sign, or diagram loops.
• Forward, one-directional consequences that fan out and do not loop back: use think-futures-wheel. It is an acyclic consequence tree by construction - no loop, no polarity.
• The structure is genuinely open-loop / linear. If the chain does not actually feed back, forcing a loop manufactures false feedback. Say "no closed loop found - this is a linear chain" and stop; do not invent a loop to fill the diagram.
• Teaching general systems thinking, hunting leverage points, or wholesale systems mapping - out of scope (separate catalog rows). This skill does one move: close and sign loops, then read dominance.

Instructions

When asked to map why a situation keeps accelerating, stalling, or oscillating, follow these steps:

1. List the candidate variables. The quantities and conditions in play (users, marketing spend, capacity, defect rate, morale). Use variables that can rise or fall, not events.
2. Find the loops by closing them. For each chain, follow it forward and check whether it returns to a variable it started from. If it does, you have a closed loop. If it does not, mark that chain open (linear) and set it aside - do not force it closed.
3. Sign each link. For every arrow A -> B, assign a polarity: + if a rise in A pushes B up (and a fall pushes it down, same direction), - if a rise in A pushes B down (opposite direction).
4. Label each loop R or B. Multiply the link signs around the loop. Even number of negatives (net +) = reinforcing (R): it amplifies (spiral). Odd number of negatives (net -) = balancing (B): it counteracts (goal-seeking; oscillation if there is a delay). Name and number each loop (R1, B1, ...).
5. Note delays. Mark any link where the effect arrives late; delays in balancing loops are what turn goal-seeking into oscillation/overshoot.
6. Read the behavior off the structure. State which loop currently dominates and the resulting dynamic: reinforcing dominance = a vicious or virtuous spiral; balancing dominance = goal-seeking; a delayed balancing loop = oscillation. Frame this as an argument ("if R1 dominates, expect..."), explicitly not a prediction.
7. Record the open parts honestly. Note where no loop closed, so the diagram does not overstate how much of the situation is actually feedback-driven.
8. Emit the signed causal loop diagram per references/TEMPLATE.md.

Output Format

Use the template in references/TEMPLATE.md. The deliverable is the signed loop diagram - the R/B loop inventory with link polarities and the behavior read-out framed as an argument - not prose.

Quality Checklist

Before finalizing, verify:

• Every loop is genuinely closed (the chain returns to a variable it started from); open chains are marked linear, not forced into loops.
• Every link has a polarity (+/-), and each loop's R/B label follows from the product of its signs.
• Delays are marked where they exist (they drive oscillation/overshoot).
• The behavior read-out names the dominant loop and the resulting dynamic (spiral / goal-seeking / oscillation).
• The read-out is framed as a structured argument, not a prediction or forecast.
• No loop was manufactured to fill the diagram; the linear parts are recorded honestly.
• No overclaim: it externalizes and signs structure; it does not predict the system or teach systems thinking wholesale (see evidence/dossier.md).
• The output is the signed causal loop diagram artifact, not prose.

Evidence

Tier M/P, transferred-evidence. The strongly evidenced fact is the failure this skill targets: people systematically misperceive feedback and accumulation (Sterman 1989, Management Science; Sweeney & Sterman 2000, System Dynamics Review). That base is shared with think-stocks-and-flows-reasoning and does not by itself prove that drawing a causal loop diagram fixes it. The CLD-specific evidence is moderate and conditional: a 2025 quasi-experimental study (ScienceDirect S2451958825000284) finds a conditional effect, and Schaffernicht (2010, Systems Research and Behavioral Science) documents CLD reliability problems (subjectivity, non-reproducibility) - cited here against inflation. All evidence is human-subject, not AI-agent-validated. The transferable claim is scoped to externalizing loop structure and signing polarity, not to predicting system behavior. No effect size is quoted because none has been verified against the source. Full grading: evidence/dossier.md.

Examples

See references/EXAMPLE.md for a completed signed causal loop diagram.