Decision Problem - MSEval Underwater Component (High Strength)

Choose one material for Underwater Component with emphasis on High Strength, 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_high_strength`
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`, `high_strength`

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, high_strength

Statement

# Decision Problem - MSEval Underwater Component (High Strength)

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

Primary criterion: **High Strength**.

## 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	0.206136	6.74627	7	3.09615
aluminium	Aluminium	0.072554	5.89552	6	2.58864
titanium	Titanium	0.453648	8.0597	9	2.59884
glass	Glass	0.001658	2.46269	2	2.29181
wood	Wood	0.001658	1.80597	1	1.95587
thermoplastic	Thermoplastic	0.01335	3.20896	3	2.77727
elastomer	Elastomer	0.004643	2.22388	2	2.4235
thermoset	Thermoset	0.035738	3.83582	4	2.79933
composite	Composite	0.210614	6.67164	7	2.86773

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},
}