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- All Subjects: Actuarial
- All Subjects: Behavioral Economics
- Creators: Economics Program in CLAS
- Creators: Zicarelli, John
- Resource Type: Text
The field of behavioral economics explores the ways in which individuals make choices under uncertainty, in part, by examining the role that risk attitudes play in a person’s efforts to maximize their own utility. This thesis aims to contribute to the body of economic literature regarding risk attitudes by first evaluating the traditional economic method for discerning risk coefficients by examining whether students provide reasonable answers to lottery questions. Second, the answers of reasonable respondents are subject to our economic model using the CRRA utility function in which Python code is used to make predictions of the risk coefficients of respondents via a two-step regression procedure. Lastly, the degree to which the economic model provides a good fit for the lottery answers given by reasonable respondents is discerned. The most notable findings of the study are as follows. College students had extreme difficulty in understanding lottery questions of this sort, with Medical and Life Science majors struggling significantly more than both Business and Engineering majors. Additionally, gender was correlated with estimated risk coefficients, with females being more risk-loving relative to males. Lastly, in regards to the model’s goodness of fit when evaluating potential losses, the expected utility model involving choice under uncertainty was consistent with the behavior of progressives and moderates but inconsistent with the behavior of conservatives.
The objective of this study is to build a model using R and RStudio that automates ratemaking procedures for Company XYZ’s actuaries in their commercial general liability pricing department. The purpose and importance of this objective is to allow actuaries to work more efficiently and effectively by using this model that outputs the results they otherwise would have had to code and calculate on their own. Instead of spending time working towards these results, the actuaries can analyze the findings, strategize accordingly, and communicate with business partners. The model was built from R code that was later transformed to Shiny, a package within RStudio that allows for the build-up of interactive web applications. The final result is a Shiny app that first takes in multiple datasets from Company XYZ’s data warehouse and displays different views of the data in order for actuaries to make selections on development and trend methods. The app outputs the re-created ratemaking exhibits showing the resulting developed and trended loss and premium as well as the experience-based indicated rate level change based on prior selections. The ratemaking process and Shiny app functionality will be detailed in this report.