Skills DirectorySkills Directory
SkillsLearnSecurityCategoriesDocsCommunityBlog
Sign InSubmit Skill
Skills Directory

Security-tested agent skills for Claude, coding agents, and AI workflows.

Directory

  • Browse Skills
  • All Skills A–Z
  • Claude Skills
  • Claude Code Skills
  • Agent Skills
  • Categories
  • Authors
  • Submit a Skill

Learn

  • Learn Hub
  • Install Claude Skills
  • Write SKILL.md
  • Skills vs MCP
  • Directories Compared

Security

  • Security
  • Methodology
  • Secure Claude Skills
  • Security Badges

Company

  • About
  • Community
  • Blog
  • API Docs
  • Advertise

2026 Skills Directory. All rights reserved.

ProTermsPrivacyRefunds
Back to skills

Jmsc Problem Formulation

ASecurity

Use to formalize the decision problem before model building for a 《管理科学学报》 (Journal of Management Sciences in China) manuscript — identifying the decision maker(s), decision variables, timing / information structure, uncertainty, and the objective, so the problem is well-posed. Use right after fit positioning and before jmsc-model-building.

1,052 stars
0 votes
0 copies
0 views
Added 6/5/2026
ai-agents

Security Analysis

A100/100

Scanned 6/5/2026

Install to Claude Code

$npx -y skills add brycewang-stanford/Awesome-Journal-Skills --skill jmsc-problem-formulation --agent claude-code

Installs into .claude/skills of the current project.

Are you the author of Jmsc Problem Formulation?

Add the live security badge to your README — it updates automatically with every re-scan.

Security grade badge for Jmsc Problem Formulation
[![Security: A — Skills Directory](https://www.skillsdirectory.com/api/skills/brycewang-stanford-jmsc-problem-formulation/badge)](https://www.skillsdirectory.com/skills/brycewang-stanford-jmsc-problem-formulation)

More formats (shields.io, HTML) on the badges page.

Download with Pro
Files
SKILL.md
---
name: jmsc-problem-formulation
description: Use to formalize the decision problem before model building for a 《管理科学学报》 (Journal of Management Sciences in China) manuscript — identifying the decision maker(s), decision variables, timing / information structure, uncertainty, and the objective, so the problem is well-posed. Use right after fit positioning and before jmsc-model-building.
---

# 决策问题形式化(jmsc-problem-formulation)

## 触发时机

- 有一个管理现象,但说不清"谁在做什么决策"
- 模型要素散乱:不知道哪个是决策变量、哪个是参数
- 多主体情境,没理清博弈时序与信息结构
- 不确定问题是"优化"还是"博弈"还是"控制"

## 核心:先把问题问对,再建模

本刊审稿人第一关看的是**问题是否良定义(well-posed)**。建模之前必须答清五个要素,否则后面的模型一定带病。

## 决策问题五要素

1. **决策者**:单主体(优化/控制)还是多主体(博弈/机制设计)?各自目标是否冲突?
2. **决策变量**:决策者能选什么(价格、订货量、契约参数、投资比例…)?连续/离散?静态/动态?
3. **时序与信息**:谁先动谁后动?决策时已知什么、未知什么(完全/不完全信息)?
4. **不确定性**:随机需求/收益的分布是否已知?是随机优化、鲁棒优化还是分布鲁棒?
5. **目标与约束**:优化什么(期望利润/风险调整收益/社会福利)?硬约束/软约束是什么?

## 问题类型识别表

| 特征 | 问题类型 | 典型方法 |
|------|----------|----------|
| 单主体、确定性 | 数学规划 | 凸优化/整数规划 |
| 单主体、随机 | 随机优化/MDP | 动态规划/最优控制 |
| 单主体、分布未知 | 鲁棒/分布鲁棒优化 | min-max |
| 多主体、目标冲突 | 非合作博弈 | 均衡(Nash/SPE) |
| 委托方设计规则 | 机制设计/契约理论 | 激励相容/参与约束 |
| 用实验校准行为 | 行为运作 | 行为模型 + 估计 |

## 自检清单

- [ ] 决策者及其目标已明确,多主体情形目标冲突点清楚
- [ ] 决策变量与参数严格区分(变量是选的,参数是给定的)
- [ ] 时序与信息结构明确(who knows what, when)
- [ ] 不确定性来源与已知/未知边界写清(驱动方法选择)
- [ ] 问题类型已归类,对应方法路线清晰
- [ ] 问题良定义:目标可优化、约束可行域非空

## 反模式

- 决策变量与外生参数混为一谈
- 多主体写成单主体(忽略对手最优反应)
- "不确定"一笔带过,不说分布是否已知(直接决定方法)
- 问题描述就是文献综述,没落到"选什么、优化什么"

## 输出格式

```
【决策者】单 / 多(<主体>,目标冲突点:…)
【决策变量】<变量>(连续/离散,静态/动态)
【时序-信息】<who moves/knows what, when>
【不确定性】无 / 分布已知 / 分布未知(→方法)
【目标-约束】max/min <目标> s.t. <约束>
【问题类型】规划 / 随机优化 / 博弈 / 机制设计 / 行为
【下一步】jmsc-model-building
```

Attribution

brycewang-stanfordbrycewang-stanford
View sourceMore from brycewang-stanford →
SSkills DirectorySkills Directory

Your tool, in front of Claude Code builders.

3 founder slots · $299/mo · GSC-verified traffic · sponsors can never buy grades.

See placements

Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.

Comments (0)

No comments yet. Be the first to comment!

SSkills DirectorySkills Directory

Your tool, in front of Claude Code builders.

3 founder slots · $299/mo · GSC-verified traffic · sponsors can never buy grades.

See placements

Related Skills

Caveman

Ultra-compressed communication mode that cuts output tokens while keeping technical accuracy. Levels: lite, full, ultra and the wenyan variants. Use for /caveman, "caveman mode", "talk like caveman", "be brief" or "less tokens".

1066601 votes

Hyperplan

Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...

686011 votes

Mcp Code Execution

Routes multi-tool workflows through MCP servers for large datasets and pipelines. Use when Bash tool overhead is limiting throughput on data-heavy tasks.

3351 votes

catchup

Recovers the conversation and failed tool calls of a previous Codex, Claude Code, Antigravity, Cline, Copilot CLI, Cursor, DeepSeek Harness, Kimi, OpenCode, Pi Agent, or ZCode session. Use when the user says "catch up", "what did the last session do", "get me up to speed", "I switched agents", asks to recover/summarize a previous session before continuing, or asks to diagnose or report a catchup failure. Do NOT use for the current conversation, git history, or any non-agent log.

651 votes

math-skill

A comprehensive mathematical reasoning skill for AI assistants — handles arithmetic to research-level problems with rigorous step-by-step reasoning, systematic verification, and transparent uncertainty handling

381 votes
View all in ai-agents →