Optimal Team Composition for Tool‐Based Problem Solving

31 Pages Posted: 28 May 2020

See all articles by Jonathan Bendor

Jonathan Bendor

Stanford Graduate School of Business

Scott Page

University of Michigan at Ann Arbor

Date Written: Winter 2019

Abstract

In this paper, we construct a framework for modeling teams of agents who apply techniques or procedures (tools) to solve problems. In our framework, tools differ in their likelihood of solving the problem at hand; agents, who may be of different types, vary in their skill at using tools. We establish baseline hiring rules when a manager can dictate tool choice and then derive results for strategic tool choice by team members. We highlight three main findings: First, that cognitively diverse teams are more likely to solve problems in both settings. Second, that teams consisting of types that master diverse tools have an indirect strategic advantage because tool diversity facilitates coordination. Third, that strategic tool choice creates counterintuitive optimal hiring practices. For example, optimal teams may exclude the highest ability types and can include dominated types. In addition, optimal groups need not increase setwise. Our framework extends to cover teamwork on decomposable problems, to cases where individuals apply multiple tools, and to teams facing a flow or set of problems.

Suggested Citation

Bendor, Jonathan and Page, Scott, Optimal Team Composition for Tool‐Based Problem Solving (Winter 2019). Journal of Economics & Management Strategy, Vol. 28, Issue 4, pp. 734-764, 2019, Available at SSRN: https://ssrn.com/abstract=3607398 or http://dx.doi.org/10.1111/jems.12295

Jonathan Bendor (Contact Author)

Stanford Graduate School of Business ( email )

655 Knight Way
Stanford, CA 94305-5015
United States

Scott Page

University of Michigan at Ann Arbor

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