Abstract It’s a well-accepted notion that teams, whose members possess complementary skills, have a higher probability of achieving successful results in science and business. The main task of a leader is to form a compact, high-performing team with such complementary skills, even though no specific formula for this has been provided. The task of selecting `k` members with complementary skills from a pool of `n` candidates is a binomial coefficient complex problem, represented by `C(n,k)`, and the task is one of the most challenging problems in human resource management with uncertainty. Currently, there is no mathematical model or theory capable of quickly selecting a small number of `k` members with `f` complementary skills from a large pool of `n` candidates. This paper proposes a new framework for formulating uncertain members and finding near-optimal solutions in real-time using a pseudorandom inference model with O(1) complexity. A uncertain member i is represented by a vector Si(si1,si2,…,sif) with f skills. Selecting k members among n members with f complementary skills is equivalent to selecting k orthogonal vectors among n vectors. The algorithm suggests that the inclusion of one average or normal member could be beneficial in forming an exceptional team. This framework, along with the theoretical algorithm validated through empirical experiments, holds promising potential for real-world applications across various domains. Highlights • A new framework for creating a great team with complementary skills in real-time. • Complexity of selecting k members among n members is a binomial coefficient:<InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\left(\frac{n}{k}\right)$</EquationSource> <EquationSource Format="MATHML"><math> <mfenced close=")" open="("> <mfrac> <mi>n</mi> <mi>k</mi> </mfrac> </mfenced> </math></EquationSource> </InlineEquation>. • Intelligence in the framework for a great team is inferred by pseudorandom numbers. • A member with f skills of a team can be represented by a vector S(s1,s2,…,sf). • Selecting complementary skill members is equivalent to selecting orthogonal vectors.

Organizing a great team with complementary skills: selecting orthogonal vectors with artificial intelligence

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发表日期：

2025年4月20日

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Yoshiyasu Takefuji

摘要

Abstract

It’s a well-accepted notion that teams, whose members possess complementary skills, have a higher probability of achieving successful results in science and business. The main task of a leader is to form a compact, high-performing team with such complementary skills, even though no specific formula for this has been provided. The task of selecting `k` members with complementary skills from a pool of `n` candidates is a binomial coefficient complex problem, represented by `C(n,k)`, and the task is one of the most challenging problems in human resource management with uncertainty. Currently, there is no mathematical model or theory capable of quickly selecting a small number of `k` members with `f` complementary skills from a large pool of `n` candidates. This paper proposes a new framework for formulating uncertain members and finding near-optimal solutions in real-time using a pseudorandom inference model with O(1) complexity. A uncertain member i is represented by a vector S_i(s_i1,s_i2,…,s_if) with f skills. Selecting k members among n members with f complementary skills is equivalent to selecting k orthogonal vectors among n vectors. The algorithm suggests that the inclusion of one average or normal member could be beneficial in forming an exceptional team. This framework, along with the theoretical algorithm validated through empirical experiments, holds promising potential for real-world applications across various domains.

Highlights

• A new framework for creating a great team with complementary skills in real-time.

• Complexity of selecting k members among n members is a binomial coefficient: $\left(\frac{n}{k}\right)$ $(\frac{n}{k})$ .

• Intelligence in the framework for a great team is inferred by pseudorandom numbers.

• A member with f skills of a team can be represented by a vector S(s₁,s₂,…,s_f).

• Selecting complementary skill members is equivalent to selecting orthogonal vectors.