Rastislav Rehák

I am a postdoc at the Max Planck Institute for Behavioral Economics (Bonn) in the team of Axel Ockenfels. I obtained my PhD at CERGE-EI (Prague) under the supervision of Filip Matějka.

I study cognitive processes within behavioral economics, using theory (formal modeling) and experiments. My research focuses on judgment and decision-making, information and time use, with a prominent application to moral reasoning and climate-related behavior. I welcome interdisciplinary collaboration, especially with researchers in cognitive and environmental sciences.

Besides research, I love outdoor activities, especially running.

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Contact: rehak@coll.mpg.de

Research

Sequential Sampling Beyond Decisions? A Normative Model of Decision Confidence [2023, revision in progress]

Idea: When is it optimal to base decision confidence on the same evidence as the decision itself and when is it optimal to gather more evidence?

Abstract: We study informational dissociations between decisions and decision confidence. We explore the consequences of a dual-system model: the decision system and confidence system have distinct goals, but share access to a source of noisy and costly information about a decision-relevant variable. The decision system aims to maximize utility while the confidence system monitors the decision system and aims to provide good feedback about the correctness of the decision. In line with existing experimental evidence showing the importance of post-decisional information in confidence formation, we allow the confidence system to accumulate information after the decision. We aim to base the post-decisional stage (used in descriptive models of confidence) in the optimal learning theory. However, we find that it is not always optimal to engage in the second stage, even for a given individual in a given decision environment. In particular, there is scope for post-decisional information acquisition only for relatively fast decisions. Hence, a strict distinction between one-stage and two-stage theories of decision confidence may be misleading because both may manifest themselves under one underlying mechanism in a non-trivial manner.

Willing to Act, Failing to Impact: Psychological and Social Drivers of Voluntary Climate Action [2025]

with Melisa Kurtis and Axel Ockenfels

Idea: Does it matter to people how their sacrifices for the climate are used? Not as much as one might think.

Abstract: Despite widespread concern about climate change, voluntary mitigation efforts often fail to maximize impact. In two online experiments (n = 1,500), we elicit willingness to mitigate (WTM) by allowing subjects to delete actual CO2 allowances and examine how they allocate the WTM between their own and another’s footprint. While 75% contribute a nonzero WTM, allocations are often inefficient, and many avoid freely available footprint information, suggesting limited efficiency concerns. Self-reported motives show that only half prioritize impact, while others cite fairness, personal responsibility, or intuition. Moreover, both WTM and efficiency are malleable by impact-unrelated nudges: a video emphasizing personal responsibility increases both, whereas social image based on the own footprint raises WTM but reduces efficiency. Our results suggest that voluntary climate action is shaped as much by psychological and social factors as by concern for impact.

Disclosure Discrimination: An Experiment Focusing on Communication in the Hiring Process [2025]

with Sona Badalyan and Darya Korlyakova

Idea: People discriminate in the information they share about job candidates of different gender and nationality.

Abstract: We study disclosure discrimination---biases in what applicant information is communicated within hiring teams. In an online experiment with a representative Czech sample, HR assistants selected which details from real workers’ profiles to share with managers, under randomized candidate names. Assistants disclosed less information about foreigners, reflecting lower attention to their CVs, and emphasized women’s marital status and children more often, consistent with broader gender stereotypes. Bias thus emerges not only in hiring decisions but through internal communication, highlighting the role of organizational design in fair hiring.

Bayesian P(h)ooling: Optimal Pooling Structure in Quadratic Bayesian Persuasion [2025]

with Maxim Senkov

Idea: A simple graph procedure for finding what states of the world a sender wants to pool to manipulate a receiver.

Abstract: We study Bayesian persuasion where the sender and receiver incur quadratic losses from deviating from their state-dependent bliss actions. Misalignment is captured by a flexible function mapping the sender’s bliss action to the receiver’s in each state. We focus on the state-pooling structure of the optimal signal---that is, which subset of states is revealed by a signal realization---and show how it is shaped by the form of misalignment. We provide a method that yields a tight, prior-independent upper bound on the optimal pooling structure. On the technical side, we justify the relevance of higher-order pooling---often dismissed as non-generic---by showing that it arises naturally under costly communication. On the applied side, we highlight the role of magnitude misalignment---where the sender and receiver agree on the direction of responses to state changes but disagree on their intensity.

Using Normative Models Pre-Trained on Cross-Sectional Data to Evaluate Intra-Individual Longitudinal Changes in Neuroimaging Data

Barbora Rehák Bučková, Charlotte Fraza, Rastislav Rehák, Marián Kolenič, Christian Beckmann, Filip Španiel, Andre Marquand, Jaroslav Hlinka

eLife (2025) 13:RP95823

Idea: Methodology for evaluation of a person's brain change relative to a population standard.

Abstract: Longitudinal neuroimaging studies offer valuable insight into brain development, ageing, and disease progression over time. However, prevailing analytical approaches rooted in our understanding of population variation are primarily tailored for cross-sectional studies. To fully leverage the potential of longitudinal neuroimaging, we need methodologies that account for the complex interplay between population variation and individual dynamics. We extend the normative modelling framework, which evaluates an individual’s position relative to population standards, to assess an individual’s longitudinal change compared to the population’s standard dynamics. Using normative models pre-trained on over 58,000 individuals, we introduce a quantitative metric termed ‘z-diff’ score, which quantifies a temporal change in individuals compared to a population standard. This approach offers advantages in flexibility in dataset size and ease of implementation. We applied this framework to a longitudinal dataset of 98 patients with early-stage schizophrenia who underwent MRI examinations shortly after diagnosis and 1 year later. Compared to cross-sectional analyses, showing global thinning of grey matter at the first visit, our method revealed a significant normalisation of grey matter thickness in the frontal lobe over time—an effect undetected by traditional longitudinal methods. Overall, our framework presents a flexible and effective methodology for analysing longitudinal neuroimaging data, providing insights into the progression of a disease that would otherwise be missed when using more traditional approaches.

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