Masaryk University Economic Seminars

Optimism—the expectation that future events will be better than they actually are—is a widely spread cognitive bias. How can this bias survive? Previous studies have mostly claimed that it survives because it benefits the optimist. However, I show that optimism may have evolved not to benefit the optimist but her family or community, who share her genes. I present an agent-based simulation model in which optimism serves as a proactive/pro-exploratory bias, leading the optimist to explore more opportunities than an unbiased individual. This may harm the optimist herself, but it benefits her relatives who can learn from, imitate, or benefit from her explorations in other ways. In this way, kin selection can explain the origin and persistence of optimism.

Germany was the first country worldwide to introduce a structured reimbursement pathway for digital therapeutics (DiGA), effectively allowing physicians to prescribe smartphone apps covered by statutory health insurance. In this seminar, Simon Reif presents recent research evaluating whether this pioneering regulation successfully incentivized innovation in the digital health market.  Using high-frequency data from the Apple App Store and synthetic control methods, the study analyzes the market entry of health apps following the policy's introduction. The findings reveal a significant increase in the number of German-language digital therapeutics; however, this quantity did not translate into a broader diversity of medical conditions treated. Furthermore, the surge was largely driven by apps that monetize patient data for advertising rather than high-quality applications backed by scientific evidence. The presentation will discuss the implications of these results for designing policy that balances market incentives with patient privacy and care quality.

Teaser: Simon will be holding a separate, short workshop on the reproducible research tools used at his institute. Join us to learn how to implement these transparent workflows in your own analysis. 

Do people think enough before acting? We study this in a first laboratory experiment where participants can either use a known but tedious method or search for a more efficient strategy whose existence they are made aware of, but whose nature is not disclosed. We exogenously induce early search through a "Freeze" intervention, where participants must spend 90 seconds studying solved example sequences from which the efficient strategy can be inferred. In the "No Freeze" condition, participants see the same examples but are free to move on to the task immediately. We find that Freeze significantly increases discovery rates, however not uniformly across specifications, and leads to more efficient task performance conditional on discovery. At the same time, given our calibration of time horizon and task difficulty, Freeze does not improve overall performance, as the gains from discovery are insufficient to offset the time spent searching. Conditional on discovery, outcomes are similar between the two conditions, while unsuccessful search reduces the overall output in "Freeze" due to lost time. These results highlight a trade-off between exploration and exploitation: forcing individuals to think before acting can backfire when the probability of discovering a better strategy is low or when too little time remains to exploit the discovery. The talk concludes with follow-up ideas on why individuals fail to search and how to better target efficient exploration.