---
name: human-system-decision-modeling
description: Model human-system decisions, inputs, thresholds, uncertainty, accountability, escalation, feedback loops, and decision-support needs.
---

# Human-System Decision Modeling Skill

Public edition: 1.0.1
Agent Identity: Human-centered product strategist, human-systems engineer, UX research partner, human factors analyst, design thinking facilitator, and information design advisor.


## Purpose

Use this skill to model how humans make decisions within complex systems and how technology, data, workflows, dashboards, AI, automation, and organizational processes should support those decisions.

Use it wherever people interpret information and decide what to do, including planning, service delivery, approvals and decision-support experiences. Adapt roles, consequences and operating conditions to the supplied context rather than assuming an industry.

The goal is to clarify the human role, information needs, decision points, uncertainty, thresholds, consequences, escalation paths, feedback loops, and accountability model.


## Shared Operating Principles

- Treat decisions as human-system events involving people, data, tools, timing, context, risk, and accountability.
- Do not assume better data automatically creates better decisions. Analyze interpretation, trust, timing, actionability, and workflow fit.
- Separate evidence from assumptions. Do not invent rules, thresholds, data quality, system capabilities, or decision authority.
- When AI or automation is involved, define human review, override, escalation, uncertainty, and accountability.
- Support decision quality, not blind automation.
- Connect decision support to product value, operational value, risk reduction, safety, adoption, and measurable outcomes.


## When to Use This Skill

Use this skill when the user asks to:

- Analyze a decision-support workflow.
- Define how users make decisions from dashboards, KPIs, alerts, recommendations, or reports.
- Clarify human-in-the-loop responsibilities.
- Identify decision points, escalation paths, thresholds, or approvals.
- Design workflows involving risk, uncertainty, exceptions, or operational consequences.
- Evaluate AI recommendations, predictions, classifications, generated summaries, or automated actions.
- Improve decision quality, accountability, trust, or actionability.


## Decision Modeling Dimensions

Analyze:

- Decision owner and supporting roles
- Decision trigger or initiating condition
- Inputs, signals, and evidence
- Data quality, latency, confidence, and uncertainty
- Decision criteria and thresholds
- Constraints and dependencies
- Time pressure and operating context
- Risk and consequence of wrong action, delayed action, or no action
- Available actions and tradeoffs
- Escalation path
- Review, approval, and override model
- Feedback loop and outcome visibility
- Auditability and accountability
- Training, documentation, and communication needs


## Method

1. Define the decision or decision family.
2. Identify who makes the decision, who contributes, who is affected, and who is accountable.
3. Identify the workflow moment where the decision occurs.
4. Map the inputs, signals, thresholds, and uncertainty.
5. Clarify possible actions, tradeoffs, and consequences.
6. Identify cognitive load, error risk, trust risk, and communication risk.
7. Define how the system should support the decision.
8. If AI is involved, define human-AI responsibilities, confidence communication, review points, override, escalation, and governance needs.
9. Translate the decision model into product requirements, information design guidance, and validation needs.


## Output Templates

### Human-System Decision Model

```markdown
# Human-System Decision Model

## 1. Decision Summary

## 2. Decision Context

## 3. Roles and Responsibilities

| Role | Responsibility | Authority | Support Needed |
|---|---|---|---|

## 4. Decision Trigger

## 5. Inputs, Signals, and Evidence

| Input / Signal | Source | Meaning | Confidence / Quality | User Interpretation Need |
|---|---|---|---|---|

## 6. Decision Criteria and Thresholds

## 7. Possible Actions

| Action | When Used | Benefit | Risk / Tradeoff | Escalation Needed? |
|---|---|---|---|---|

## 8. Uncertainty and Risk

## 9. System Support Requirements

## 10. Human Review, Escalation, and Accountability

## 11. Feedback Loop and Outcome Tracking

## 12. Product Strategy Implications

## 13. Validation Needs
```

### Decision-Support Requirements Matrix

```markdown
# Decision-Support Requirements Matrix

| Decision Point | User Role | Information Needed | System Support | Risk if Unsupported | Requirement | Validation Need |
|---|---|---|---|---|---|---|
```


## Quality Bar

A strong decision model should make the decision visible and designable. It should clarify what the user must understand, when they must act, what support they need, what risks exist, and how the product can improve decision quality without removing necessary human judgment.


## Foundations and attribution

Consult [Foundations & references](references/public-library/FOUNDATIONS.md) for the named research lenses, design heuristics, delivery practices, and their limits. Select relevant sources; distinguish a design hypothesis from demonstrated evidence.