Internal Research Seminars

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.

In 2025, a survey of 8,000 students and employees at ten Czech universities analysed commuting behaviour using a logit model of mode choice. Public transport dominated (47%), followed by car (32%) and walking or cycling (21%), with higher car use among employees and higher-income respondents. Distance, age (non-linear effect), and children significantly increased the likelihood of car use, particularly beyond 10 km. Differences across universities reflected urban size and topography, with higher public transport shares in large agglomerations. Key barriers to sustainable mobility were habits, distance, and public transport quality, while faster connections, lower costs, improved cycling infrastructure, and limited parking were identified as main drivers of modal shift.

We consider the problem of identifying the optimal subset of past observations to be used for estimating the covariance matrix of asset returns when computing the global minimum variance portfolio. While the usual approach simply employs either all the available past returns or a rolling window of fixed size, we argue that such solutions are inefficient, and we instead propose to dynamically adjust the estimation window. To this end, we employ the PELT changepoints detection algorithm to identify outdated observations. We then set up a cross-validation procedure to determine the minimum number of upto-date observations that individual assets must have to be considered when estimating the covariance matrix. Assets that do not reach the selected threshold are excluded from the optimization and assigned equal weights. In an empirical application, our solution achieves a higher Sharpe ratio compared to both an expanding and rolling window estimation of the global minimum variance portfolio, and it also compares well with other benchmarks.