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1.2K downloads. Trust evidence available

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Feedback Loops

Activate when: user says "we keep overshooting/undershooting", "the cure is causing the disease", "we're stuck in a loop", "why does this keep happening?", "... Skill: Feedback Loops Owner: deciqai Summary: Activate when: user says "we keep overshooting/undershooting", "the cure is causing the disease", "we're stuck in a loop", "why does this keep happening?", "... Tags: latest:1.0.5 Version history: v1.0.5 | 2026-07-16T17:59:42.787Z | user Description tail link + agents machine-readable metadata line (deciqai.com/s/feedback-loops.json) v1.0.4 | 2026-07-09T11:17:30.887Z | us

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 11, 2026

Version

1.0.5

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.5release · observed Jul 16, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a4mqcnk515kvaca5ze55d0x88pfpx:feedback-loops

Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-deciqai-feedback-loops/snapshot"

Documentation

CLAWHUB

143,438 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: feedback-loops
description: >
Activate when: user says "we keep overshooting/undershooting", "the cure is causing the disease",
"we're stuck in a loop", "why does this keep happening?", "the system keeps fighting back",
"bullwhip effect", "death spiral", "growth flywheel"; system shows oscillation or sudden collapse;
user is planning an intervention in an org/market/supply chain and wants to predict how it will respond.
Do NOT activate when: the decision is a one-shot linear choice with no feedback to future decisions,
or an exogenous shock so large it dominates all internal dynamics is the obvious explanation.
More: deciqai.com/c/feedback-loops
---

# Feedback Loops

## Overview

A system has a **feedback loop** when its output circles back as input to the next cycle. **Reinforcing loops** amplify (compound interest, viral growth, bank runs, death spirals). **Balancing loops** self-correct (thermostats, price discovery, immune response). The critical complication is **delay**: when delay is long relative to response time, even well-designed balancing loops produce oscillation and overshoot — and operators systematically mismanage the system (Sterman 1989: supply-line underweight = 0.34 on a 0–1 scale).

Composes with: `second-order-thinking` · `s-curve-technology-adoption` · `prisoners-dilemma` · `probabilistic-thinking`

## When to Use

Apply when: system shows non-linear surprise (collapse, oscillation, death spiral, growth flywheel); you are intervening in a complex system and success depends on how it responds; trends are not extrapolating well; bullwhip or oscillation in any quantity that should be steady; a capex/AI-adoption flywheel is compounding and you need to know when the balancing limits (power, supply, cost, AI-native competition) will bite and whether it will overshoot.

**When NOT to use:** one-shot linear decision with no feedback; insufficient data to map loops (hand-waving without structure); decision too time-bounded for delays to matter; exogenous shock dominates internal dynamics.

## Coaching Novices (Adaptive Front Door)

- **Engine mode:** concrete case → run The Process directly.
- **Coach mode:** unfamiliar or no concrete case → guide, don't lecture.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. One-line what-it-is: when a system's output circles back as input, you have a feedback loop — it self-amplifies (reinforcing) or self-corrects (balancing), and delays make behavior far worse than expected.
2. Check fit against When to Use / When NOT to use. If it's a one-shot linear decision, redirect.
3. Elicit their real case: a specific behavior or dynamic they face right now — not a hypothetical.
> **[WAIT — do not advance until user responds]**
4. Run The Process one step at a time with their input — map the loop, classify it, locate the delay.
> **[WAIT — do not advance until user responds]**
5. Close by naming the leverage

_meta.json

{
  "ownerId": "kn754b8sk22s8c6gjxt02bftbn88q7ye",
  "slug": "feedback-loops",
  "version": "1.0.5",
  "publishedAt": 1784224782787
}

references/sources.md

# Sources — feedback-loops

> *Primary sources for the [feedback-loops](../SKILL.md) skill.*

- Forrester, J. W. (1961). *Industrial Dynamics*. MIT Press. The founding text of system dynamics; introduces the formal apparatus for modeling stocks, flows, and feedback loops in organizational and economic systems. ISBN 978-0915299881.
- Sterman, J. D. (1989). "Modeling Managerial Behavior: Misperceptions of Feedback in a Dynamic Decision Making Experiment." *Management Science*, 35(3), 321–339. The foundational quantitative experimental study of the Beer Distribution Game: it fits an anchoring-and-adjustment ordering rule to subjects' decisions and finds they systematically *underweight the supply line* (in-flight orders), placing the optimal weight near 1.0 but observed weights well below it. Order variability amplifies upstream from retailer to factory (the bullwhip effect), and Sterman attributes the dysfunction to a cognitive misperception of feedback — participants fail to account for delays and the accumulating supply line — rather than to noisy or malicious behavior. https://doi.org/10.1287/mnsc.35.3.321
- Sterman, J. D. (2000). *Business Dynamics: Systems Thinking and Modeling for a Complex World*. Irwin/McGraw-Hill. The comprehensive modern reference; includes detailed treatment of the Beer Game and dozens of other case studies. ISBN 978-0072389159.
- Lee, H. L., Padmanabhan, V., & Whang, S. (1997). "Information Distortion in a Supply Chain: The Bullwhip Effect." *Management Science*, 43(4), 546–558. The canonical analysis of the bullwhip effect in real supply chains and the four operational sources: demand-signal processing, order batching, price fluctuation, and rationing/shortage gaming. https://doi.org/10.1287/mnsc.43.4.546
- Meadows, D. H. (1999). *Leverage Points: Places to Intervene in a System*. Sustainability Institute. The famous list of twelve leverage points, ranked from least to most powerful: parameters, buffers, structures, delays, balancing loops, reinforcing loops, information flows, rules, self-organization, goals, paradigms, transcendence. https://donellameadows.org/archives/leverage-points-places-to-intervene-in-a-system/
- Meadows, D. H. (2008, posthumous). *Thinking in Systems: A Primer*. Edited by D. Wright. Chelsea Green Publishing. The most accessible introduction to systems thinking and feedback-loop analysis. ISBN 978-1603580557.
- Senge, P. M. (1990). *The Fifth Discipline: The Art and Practice of the Learning Organization*. Doubleday. Popularized systems thinking in management; chapter 3 introduces the Beer Distribution Game to a general management audience. ISBN 978-0385517256.
- International Energy Agency (2024). *Electricity 2024: Analysis and forecast to 2026*. IEA, Paris. Widely-cited analysis of surging electricity demand from data centres, AI, and cryptocurrency, and of the multi-year lead times for new generation and grid interconnection — the physical delay underlying the balancing loop on AI compute bu

examples/ai-capex-boom-reinforcing-and-balancing-loops-2024-2026.md

# Method in Action: The AI Capex Boom as a Reinforcing Loop Meeting Its Balancing Limits (2024–2026)

> *Example for the [feedback-loops](../SKILL.md) skill.*

Between 2024 and 2026, the largest US technology companies — Microsoft, Alphabet, Amazon, and Meta, joined by chipmaker Nvidia and a wave of AI-native startups led by OpenAI and Anthropic — poured historic sums into AI data-center capacity. Reported annual capital expenditure at the four "hyperscaler" cloud providers rose from roughly $150 billion in 2023 toward figures widely reported in the several-hundred-billion range for 2025, with further increases guided for 2026. The dominant narrative through most of this period was pure reinforcing-loop optimism: spend more, get better models, unlock more demand, justify spending still more.

This example runs the **Feedback-Loop Diagnosis** on that dynamic. The point is not to predict the exact top of the cycle — the skill explicitly warns against extrapolating a feedback system's recent trend — but to show why a runaway reinforcing loop with long build delays is structurally set up to overshoot, and where the balancing loops that eventually check it actually live.

## 1. Name system + variable of interest

**System:** the AI compute build-out — the interconnected market of AI model developers, cloud infrastructure providers, chip suppliers, and the enterprises and consumers buying AI products.

**Variable of interest:** installed AI compute capacity (roughly, GPU-equivalents in service), and the capital-expenditure *rate* funding its growth. The two are not the same thing, which matters at step 7.

## 2. List drivers and outputs

- **Drivers that raise capex:** expected demand for AI products; belief that model quality scales with compute (the "scaling laws" thesis); competitive fear of being left behind; cheap capital and strong balance sheets; falling cost-per-token making new use cases viable.
- **What capex changes in turn:** more installed compute → larger/better-trained models → more capable AI products → (claimed) more end-user demand → more revenue and more investor conviction → still more capex. Capex also drives up demand for chips, electrical power, and data-center real estate.

## 3. Identify loops

- **R1 (the flywheel):** capex → compute → better models → more demand → more revenue/conviction → more capex. This is the loop everyone was pitching.
- **R2 (capital-markets amplifier):** rising AI revenue and rising valuations → cheaper capital and investor pressure to spend → more capex → more revenue expectation. A financial reinforcing loop stacked on top of the physical one.
- **B1 (physical limits):** more capex → more compute demanded → scarce inputs (advanced-packaging capacity, high-bandwidth memory, electrical power, grid interconnects) → input prices/lead-times rise → effective cost of adding capacity rises → capex growth checked.
- **B2 (unit economics / return on capital):** more capex → more depreciation and more capacity

examples/forresters-beer-distribution-game-stermans-1989-measurement.md

# Method in Action: Forrester's Beer Distribution Game & Sterman's 1989 Measurement

> *Example for the [feedback-loops](../SKILL.md) skill.*

The empirical foundation for "operators systematically mismanage dynamic feedback systems" rests on the **Beer Distribution Game**, a simulation developed by **Jay Forrester** and his colleagues at MIT Sloan in the early 1960s, and the quantitative experimental work of **John Sterman**, who in 1989 published the first rigorous measurement of how participants actually behave in it.

Forrester had founded the field of **system dynamics** in the 1950s after moving from electrical engineering and servo-control theory into management. His 1961 book *Industrial Dynamics* argued that the behavior of organizations and economic systems is overwhelmingly driven by **feedback loop structure** — not by external events, not by personalities, not by the quality of individual decisions, but by the way the system's variables circle back to each other through delays and stocks. Most management problems, he claimed, were systems-structure problems disguised as people problems.

To teach this — and to test it — Forrester and his students designed a simulation game. The setup was deliberately minimal:

- A four-tier linear supply chain: **Factory → Distributor → Wholesaler → Retailer → Customer**
- Each tier had only one decision per round: **how many cases of beer to order from the upstream supplier**
- The customer demand was set by the experimenter — typically constant for several rounds, then a single small step-up (say, from 4 cases per round to 8), then constant again at the new level for the rest of the game
- A delivery delay of two rounds between placing an order and receiving the shipment
- Participants could see only their own inventory, backlog, and incoming orders — not the rest of the system
- The objective was simple: minimize total cost (inventory holding cost + backlog penalty)

If players acted "rationally" — recognized the small step-up in demand, adjusted their orders by exactly the same step, and held the new equilibrium — the system would settle smoothly at the new demand level after the two-round shipping delay. Total cost would be modest.

In practice, that almost never happens. What happens instead is the **bullwhip effect**: the small step-up in customer demand produces, over the four tiers, increasingly violent oscillations. The retailer over-orders to compensate for an empty shelf; the wholesaler sees this large order and over-orders to refill; the distributor over-orders even more; the factory cranks up production massively. By the time the factory's expanded output arrives, the higher-tier inventories have refilled, demand has stabilized, and now everyone is sitting on huge oversupply — at which point they all slash orders. The factory then slashes production. Two rounds later, everyone is in stockout. The system oscillates for many rounds before settling, if it settles at all. Total cost is typi

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