Decision Problem - MSEval Underwater Component (Resistant to Heat)

Choose one material for Underwater Component with emphasis on Resistant to Heat, using expert MSEval survey responses as the evaluation benchmark.

See Decision Problem Catalog for the decision family index.

Quick Facts

Field	Value
Problem ID	`decision_mseval_underwater_component_resistant_to_heat`
Problem Family	decision
Implementation	`design_research_problems.problems.decision._mseval:MSEvalEmpiricalChoiceProblem`
Capabilities	`citation-backed`, `statement-markdown`
Study Suitability	none
Tags	`decision`, `material-selection`, `mseval`, `underwater_component`, `resistant_to_heat`

Taxonomy

Formulation: empirical_discrete_choice
Design Variable Type: categorical
Is Dynamic: no
Orientation: engineering_practical
Objective Mode: single
Constraint Nature: preference-derived
Tags: decision, material-selection, mseval, underwater_component, resistant_to_heat

Statement

# Decision Problem - MSEval Underwater Component (Resistant to Heat)

You are selecting a material for **Underwater Component**.

Primary criterion: **Resistant to Heat**.

## Task
1. Choose **one** material from the list below.
2. Briefly justify the choice in terms of the stated criterion and likely use context.
3. Optionally note one follow-up risk or tradeoff to validate next.

## Candidate Materials
- Steel
- Aluminium
- Titanium
- Glass
- Wood
- Thermoplastic
- Elastomer
- Thermoset
- Composite

## Output Format
- Selected material:
- Justification (3-6 sentences):
- Risk or tradeoff to check next (optional):

Decision Context

Field	Value
Decision Maker	A designer selecting one material conceptually for a underwater component.
Market Segment	MSEval expert benchmark with 67 complete responses for this prompt.
Decision Scope	Choose a single candidate material from the provided materials using an empirical preference benchmark derived from MSEval survey responses.

Objectives

Key	Label	Sense	Domain	Executable	Variables	Expression
expert_agreement	Tie-adjusted expert top-choice share	maximize	empirical-choice	yes	`material`	sum_i I(choice in argmax_i)/\|argmax_i\| / N

Candidate Space

Field	Value
Candidate Kind	empirical-choice
Candidate Count	9
Default Choice Metric	top-choice-share
Response Count	67

Empirical Benchmarks

Key	Label	Top Choice Share	Mean Rating	Median Rating	Std Rating
steel	Steel	0.19403	6.47761	7	2.90954
aluminium	Aluminium	0.03607	5.64179	6	2.56847
titanium	Titanium	0.492537	7.91045	9	2.53898
glass	Glass	0.087065	4.38806	5	3.2425
wood	Wood	0	1.92537	1	2.09844
thermoplastic	Thermoplastic	0.057214	2.97015	1	3.03
elastomer	Elastomer	0.014925	2.38806	2	2.52236
thermoset	Thermoset	0.004975	3.32836	3	2.60761
composite	Composite	0.113184	5.52239	6	2.97646

Sources

Key	Summary
`jain2024msevaldatasetmaterialselection`	Yash Patawari Jain, Daniele Grandi, Allin Groom, Brandon Cramer, Christopher McComb (2024). MSEval: A Dataset for Material Selection in Conceptual Design to Evaluate Algorithmic Models

Raw Citation Records

@misc{jain2024msevaldatasetmaterialselection,
      title={MSEval: A Dataset for Material Selection in Conceptual Design to Evaluate Algorithmic Models},
      author={Yash Patawari Jain and Daniele Grandi and Allin Groom and Brandon Cramer and Christopher McComb},
      year={2024},
      eprint={2407.09719},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2407.09719},
}