for the degree of Bachelor of Science with Distinction in Research in May 2018. We would like to thank the Editor Hamid Beladi and two anonymous reviewers for their helpful comments on a previous version of this paper. In addition, the first author would like to thank Dr. Robert Karpman for his help with the Honor's thesis research. Finally, the second author acknowledges funding from the Gosnell endowment at RIT. The usual disclaimer applies.

I explore the decision-making process of a physician in the context of otitis media-commonly known as an ear infection-when resistance to antibiotics is an issue. Otitis media provides a unique context in which to study such decision-making because there is no culture that one can use to definitively prove the presence of a bacterial infection. This creates an atmosphere in which the abuse of antibiotics is possible. I first use a decision tree to characterize the decision-making process and then I conduct numerical analysis using fictitious data to illustrate the working of my model. Next, I show how the dependence of the infection probability, P S , on socioeconomic variables can be used to shed light on a physician's behavior and on the patient-physician relationship. Finally, I discuss how the research presented in this paper might be extended in the future.

We analyze the medical decision-making process, first from the perspective of a patient and then from the perspective of a health care provider. Using a decision-tree, we describe the different actions a mother can take to treat her daughter, who she suspects has otitis media-an ear infection. Next, we use comparative statics and numerical analysis to show how altering inputs (magnitude of infection probability, cost terms) can affect the outputs. Finally, we examine how different socioeconomic variables influence the mother's decision making process.With regard to the health care provider, we delineate the setting in which the physician operates and then derive the long run expected cost of providing health care that this physician seeks to minimize. Next, we set up the cost minimization problem and describe the optimal solution implicitly. Finally, we explain why this implicit characterization of the optimal solution is all that is possible analytically.