Designing Efficient Human-Algorithm Collaboration

This book aims at improving the understanding of how decision makers work with algorithmic support systems to leverage the strengths of both humans and algorithms.

Companies across all industries increasingly use algorithms to assist managers in decision making, problem solving, and data analysis. Business leaders have no doubt that human-algorithm collaboration bears a high potential to increase productivity and performance in many areas. However, companies face challenges to create a collaborative environment that exploits the potential of advice-giving algorithms. To address this challenge, it is essential to know how the design of algorithms affects how humans use their advice for managerial tasks. This book aims at improving the understanding of how decision makers work with algorithmic support systems to leverage the strengths of both humans and algorithms. Motivated by challenges from research and practice, this thesis demonstrates how algorithm transparency, the design of feedback, and the communication of algorithmic certainty affect the use of algorithmic advice for managerial tasks. The results reveal critical connections between the design of algorithms and the use of their advice, and enable us to derive important implications for management practice.

Autorentext
Cedric Lehmann holds a Master of Science in Supply Chain Management and a Bachelor of Science in Business Administration from the University of Cologne. He visited the National University of Singapore and the University of Texas at Dallas. During his Ph.D. studies at the University of Cologne in Germany, his research aimed at improving our understanding of how humans and algorithms can work together to leverage both human and algorithmic strengths.