The goals of the course are:
– to provide a basic background of Behavioral Operations
– to explore the role of human behavior in the use of algorithms
Behavioral Operations Management focuses on how human behavior affects operations management decision making and, in turn, how operations management decisions affect human behavior. Most operations management models rely on simplistic assumptions when it comes to human behavior. These simplistic assumptions limit the applicability of these models. This is especially the case in quantitative decision models, heuristics, and algorithms. Examples are scheduling algorithms, forecasting models, and inventory models. These algorithms and models usually do not consider behavioral factors that play a role in decision making such as motivation, trust, cognitive limitations, and psychological ownership. This limits usability, usefulness, and acceptance of models in practice, as shown by recent literature on Algorithm Aversion. In the course we analyze these factors and how their effects can be mitigated by incorporating behavioral factors in the design of quantitative models and algorithms
Day 1 – We start with providing an introduction in theoretical foundations of behavioral operations, by describing the theories in which it is anchored (e.g., organizational behavior and behavioral economics). Subsequently, influential behavioral operations papers will be discussed covering different OM fields such forecasting, scheduling, logistics, and supply chain management.
Day 2 – Part 1: Research in behavioral operations uses methods that complement OM methodologies. We will discuss experiments, task analyses, and vignette based surveys.
Part 2: Participants will present their proposal for the assignment. This includes the theoretical background, research question and/or hypotheses, and methodology.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2016). Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them. Management Science, 64(3), 1155-1170.
Gino, F., & Pisano, G. (2008). Toward a theory of behavioral operations. Manufacturing & Service Operations Management, 10(4), 676-691.
Schweitzer, M. E., & Cachon, G. P. (2000). Decision bias in the newsvendor problem with a known demand distribution: Experimental evidence. Management Science, 46(3), 404-420.
Additional relevant scientific articles will be provided at the start of the course.