This thesis proposes an extension of David Lewis's causal influence account of causation, providing a method to calculate the `degrees of causal influence.' By providing a quantitative approach to causal influence, I find that that the influence approach can assess statements that involve causal redundancies, allowing the assessor to attribute primary causal responsibility to the contending cause with a higher net influence value. The causal influence calculation also addresses criticisms towards Lewis's influence account, namely those involving `inert zones' of influence, the use of the term `might,' trumping versus symmetric overdetermination, and Lewis's clause requiring stepwise influence. This thesis also compares the results of causal influence in multiple toy cases including Two Rocks, both the asymmetric and symmetric variants, demonstrating that causal influence overcomes many of the core issues in Lewis's initial counterfactual account of causation. Using the asymmetric Two Rocks variant, this thesis also provides a detailed example of how to use the calculation and a discussion of the calculation's limitations. The main drawbacks of the quantitative method for causal influence seems to be the effort that it requires and issues in finding measurable qualities to compare the similarity/difference between possible worlds. Using the Two Rocks case, however, the causal influence calculation reaches the same conclusions as what Lewis suggests. A primary remaining issue is applying the calculation to instances of causation by omission, however this seems to only be a problem in using the equations rather than a problem within the idea of causal influence itself. Also, there may still be issues in justifying comparative overall similarity. However, this is an issue that both the counterfactual and influence accounts face.

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.

Four-dimensionalism is a popular philosophical view of how we persist through time. However, some philosophers, such as Mark Johnston and Eric Olson, argue that four-dimensionalism has perverse implications on our practical ethics. This is because, if four-dimensionalism is true, then there exist entities called personites. And if personites exist, then many of the ordinary prudential, social, and moral habits we engage in, like present self-sacrifice for future benefit, promising to do something painful in the future, or being held responsible for something the we did in the past, subjects personites to suffering without sufficient compensation, consent, or desert. And this would be immoral according to our common-sense morality. In this paper, I argue that if four-dimensionalism is true, and personites exist, then we are still morally permitted to engage in the above practices. If four-dimensionalism turns out to be true, it has no perverse implications on how we ought to live.