Optimal team composition for tool‐based problem solving

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.

Evaluation of the Possibility of Performing the Complex of Works Based on the Models of Their Interaction

“…A review of the application of mathematical models has shown their growing relevance in describing nonlinear processes in socio-economic and biological systems [5,[11][12][13][14]. The ideology of soft modeling is actively used in describing economic processes [15,16], in describing decision-making processes [17][18][19][20], in describing the evolution of systems [21,22], in describing the behavior of groups of people [23,24], in describing information transfer processes [25]. Today is the time of interdisciplinary, so each model built on the fundamental tools of modern mathematics and describing the behavior of a complex system, as a rule, has no analogues and can be considered as a pioneer.…”

“…Today is the time of interdisciplinary, so each model built on the fundamental tools of modern mathematics and describing the behavior of a complex system, as a rule, has no analogues and can be considered as a pioneer. For example, in [15], the classical ecological model was transferred to the "consumer-resource" space, and in [18], multi-agent modeling is used for decision-making. The variety of combinations of methods and approaches makes each model unique, so questions of comparing the quality and adequacy of models make sense only in the context of simulated situations.…”

Leveraging collective action and environmental literacy to address complex sustainability challenges

“…This synergy relies on the diversity of a group across various dimensions, bringing power, strength, and insight to a decision-making process (Bear and Woolley 2011 ; Curşeu and Pluut 2013 ; Freeman and Huang 2015 ; Lu et al 2017 ; Bendor and Page 2019 ). Individuals are limited not only to singular knowledge-perspectives and skillsets, but also to their own experiences, which influence their self-affirming viewpoints and tendencies to seek out confirmatory information for existing beliefs (Kahan et al 2011 ).…”

Assessing impacts of social-ecological diversity on resilience in a wetland coupled human and natural system

“…Leslie and McCabe (2013) and found pastoralists in range systems showed response diversity that increased resilience. In addition to response diversity, social diversity in problem-solving approaches can facilitate collective learning and adaptability, fostering resilience (Bendor and Page 2019). However, diversity can hinder adaptability when cultural differences increase inter-group conflicts (Kotschy et al 2015, Page 2015.…”