I investigate the impact of assessment design on student decision making in the context of a second year economics course. I develop a dynamic structural model of student test taking where students are uncertain about their ability, and have biased beliefs about the returns to studying. In the model, students exert costly effort to earn ordinal test grades centered around a target average. Between tests, students update their beliefs about their ability between tests. Using novel high frequency data collected from a competitive large course setting, I estimate the true returns to studying, students' beliefs about returns to studying, and students' capacity to update under a Biased Bayesian framework. Then I combine the three components to simulate counterfactuals using the dynamic model. I find that returns to study effort are relatively concave, that students overestimate their returns to studying, and that students vastly under-react to test grades when updating. Counterfactuals suggest that biased model perceptions contribute to large increases in study effort, implying that the phenomenon exerts positive influence regarding knowledge acquisition. In addition, assessment design counterfactuals suggest that more frequent, and more objective tests are of benefit to students, as they contribute to a swifter reduction in students' biased beliefs about their own skills.

  I propose a dynamic discrete choice model of campaign contributions in a two-candidate race. Traditionally, the contributors' problem has been set up as a one-shot simultaneous action game, but in reality, contributors regularly update on others contributors' donation levels, and also receive new information on candidates' win probabilities. So donors can update contribution patterns mid-election. In addition, earlier contributions are more effective, as they give campaigns greater degrees of freedom with regards to how those campaign funds are spent. These two factors suggest that a static setting is inappropriate. I develop a model to account for dynamic strategies and decreasing contribution efficacy, and consider electoral impact of campaign finance in a setting with policy motivated contributors.

Mainstream economic models assume agents are correctly specified regarding their environment and process information without bias. In many settings, the failure of these assumptions can have a significant impact on learning and choice. To understand these issues better, one must obtain data not only on the actual environment but also on agents' dynamic and potentially noisy perception of the environment. We investigate these issues via first-year students in large mathematics classes who receive test scores throughout the term as signals of their ability and return to effort. These test scores are highly informative, and past test scores correlate highly with future scores. We collect a high-frequency dataset by surveying students in an incentive-compatible manner before and after each test. The dataset is also novel in that we are the first to collect belief data regarding not only students' expected grade but also their belief in the noisiness of the test scores. We find that students overestimate the noisiness of tests by a factor of 2 and update in a biased manner. We conduct a randomized control trial where students are not given additional information but are told about the accuracy of tests. We find students can update their beliefs about the noisiness of the testing correctly, and this leads to improvement in their updated beliefs. This suggests a substantial amount of updating failure due to misperception can be corrected.