This course aims to introduce the use of behavioral and experimental methods that have been increasingly popular in the field of operations management. In particular we use a supply chain scenario to study how human beings make individual and strategic decisions in the face of uncertainty and risk. First, we use the standard newsvendor problem to discuss decisions involving only a single individual. This problem is concerned with the order quantity decision of a retailer that faces probabilistic demand. Then, we consider a simple manufacturer-retailer supply chain where the retailer faces the newsvendor problem, and her problem parameters are determined by the contract that the manufacturer offers. This scenario allows us to study what happens to decisions when two individuals interact strategically with each other . Course discussion is based on results from decision-making experiments with human subjects. In addition to published research papers, we also use data from experiments conducted at Sabanci University.

In this course, we study how human beings make decisions in the face of uncertainty and risk. We investigate individual and strategic decision-making issues using a supply chain context. 

First, we use the standard newsvendor problem to discuss decisions involving only a single individual. This problem is concerned with the order quantity decision of a retailer that faces probabilistic demand. Then, we consider a simple manufacturer-retailer supply chain where the retailer faces the newsvendor problem, and her problem parameters are determined by the “contract” (the wholesale price, etc.) that the manufacturer offers. This scenario allows us to study what happens to decisions when two individuals interact strategically with each other. 
 

To understand human behavior, we study research based on controlled decision-making experiments with human subjects. In addition to published research papers, we also use data from our own experiments conducted here at Sabancı University. In these experiments, student subjects play the roles of manufacturer and retailer, making contract and order quantity decisions for around 40 rounds. Comparing experimental data with theoretical model predictions, we aim to answer questions such as:   

• Do subjects make decisions as predicted by theory? If not, what are the factors that affect their decisions?
• Do subjects use certain decision heuristics in making decisions?
• Can we build statistical models to better predict human behavior?
• Do different subject pools exhibit different behavior?
• Do subjects learn to make better decisions over time? 

We also study the effects of strategic human interaction between the two parties in a supply chain. Relevant questions include:

• Do the subjects behave as predicted by the game-theoretic equilibrium?
• Can the manufacturer anticipate the retailer’s reaction when determining contract parameters?
  Do manufacturers care about fairness when offering the contract? 
  What factors affect the retailer’s contract rejection decision?
  Can we explain subject decisions using personality data? 
   

We consider these questions under different supply chain contracts, including wholesale price, buyback, and revenue sharing contracts. We aim to compare the experimental performance of these contracts with each other and with theoretical predictions. 

These topics have important practical implications for the design of decision-support systems, incentive systems, and supply chain contracts. Understanding human decision-making under risk and strategic interaction is also increasingly important for designing AI-enabled decision-support systems that interact effectively with human managers.

• During lectures, the instructor will ask quick questions (true/false, multiple choice, etc.) using the Top Hat software. The students will need to respond to these quickly.
• The students shall familiarize themselves with the Top Hat software (www.tophat.com) if they have not used it previously. The Top Hat app should be installed on student smartphones / tablets, or it can be accessed via a laptop’s web browser.
• The join code required to enroll in the IE 640 Top Hat page is 341232.