Decision Problem - MSEval Underwater Component (Lightweight)#

Choose one material for Underwater Component with emphasis on Lightweight, 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_lightweight`
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`, `lightweight`

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, lightweight

Statement#

# Decision Problem - MSEval Underwater Component (Lightweight)

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

Primary criterion: **Lightweight**.

## 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	0.03068	3.52239	3	2.72118
aluminium	Aluminium	0.160531	6.23881	7	2.73634
titanium	Titanium	0.359038	7.29851	8	2.51067
glass	Glass	0.013765	2.92537	2	2.56026
wood	Wood	0.015755	2.04478	1	2.19112
thermoplastic	Thermoplastic	0.033665	4.19403	4	2.92968
elastomer	Elastomer	0.006302	3.55224	4	2.79222
thermoset	Thermoset	0.015755	4.19403	5	2.81898
composite	Composite	0.364511	7.46269	8	2.63041

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},
}