research
FABIAN
DVORAK
research |
stratEst | sandWind
Negotiating Cooperation under Uncertainty: Communication in Noisy,
Indefinitely Repeated Interactions
with Sebastian Fehrler
American Economic Journal: Microeconomics |
paper
|
online
appendix |
replication
package |
cooperation
barcode
Details
Case studies of cartels and recent theory suggest that
communication is a key factor for cooperation under imperfect
monitoring, where actions can only be observed with noise. We conduct a
laboratory experiment to study how communication affects cooperation
under different monitoring structures. Pre-play communication reduces
strategic uncertainty and facilitates very high cooperation rates at the
beginning of an interaction. Under perfect monitoring, this is
sufficient to reach a high and stable cooperation rate. However,
repeated communication is important to maintain a high level of
cooperation under imperfect monitoring, where players face additional
uncertainty about the history of play.
Reference: Dvorak, F., Fehrler, S. (2024). Negotiating cooperation under
uncertainty: communication in noisy, indefinitely repeated interactions.
American Economic Journal: Microeconomics, 16(3), 232–258. https://doi.org/10.1257/mic.20210117
stratEst: A Software Package for Strategy Frequency Estimation
Journal of the Economic Science Association |
paper
|
replication package |
package
vignette |
CRAN |
GitHub
Details
stratEst is a software package for strategy frequency estimation
in the freely available statistical computing environment R (R
Development Core Team, 2022). The package aims at minimizing the
start-up costs of running the modern strategy frequency estimation
techniques used in experimental economics. Strategy frequency estimation
(Stahl and Wilson, 1994; Stahl and Wilson, 1995) models the choices of
participants in an economic experiment as a finite-mixture of individual
decision strategies. The parameters of the model describe the associated
behavior of each strategy and its frequency in the data. stratEst
provides a convenient and flexible framework for strategy frequency
estimation, allowing the user to customize, store and reuse sets of
candidate strategies. The package includes useful functions for data
processing and simulation, strategy programming, model estimation,
parameter testing, model checking, and model selection.
Reference: Dvorak, F. (2023). stratEst: a software package for strategy
frequency estimation. Journal of the Economic Science
Association, 9, 337–349. https://doi.org/10.1007/s40881-023-00141-7
Adverse Reactions to the Use of Large Language Models in Social
Interactions
with Regina Stumpf, Sebastian Fehrler and Urs Fischbacher
PNAS Nexus |
paper
|
online
appendix |
replication package |
preregistration
Details
Large language models are poised to reshape the way individuals
communicate and interact. While this form of artificial intelligence has
the potential to efficiently make many human decisions, there is limited
understanding of how individuals will respond to its use in social
interactions. In particular, it remains unclear how individuals interact
with large language models when the interaction has consequences for
other people. Here, we report the results of a large-scale,
pre-registered online experiment (N = 3,552) showing that human players’
fairness, trust, trustworthiness, cooperation, and coordination in
economic two-player games decrease when the decision of the interaction
partner is taken over by ChatGPT. On the contrary, we observe no adverse
reactions when individuals are uncertain whether they are interacting
with a human or a large language model. At the same time, participants
often delegate decisions to the large language model, especially when
the model’s involvement is not disclosed, and individuals have
difficulty distinguishing between decisions made by humans and those
made by artificial intelligence.
Reference: Dvorak, F., Stumpf, R., Fehrler, S., and Fischacher, U.
(2025). Adverse Reactions to the Use of Large Language Models in Social
Interactions. PNAS Nexus, pgaf112, https://doi.org/10.1093/pnasnexus/pgaf112
Similarity and Consistency in Algorithm-Guided Exploration
with Ludwig Danwitz, Yongping Bao, Lars Hornuf, Hsuan Yu Lin,
Sebastian Fehrler and Bettina von Helversen
Journal of Behavioral Decision Making |
paper
|
online
appendix |
replication package |
pregistration
Details
Algorithmic advice has the potential to significantly improve
human decision-making, especially in dynamic and complex tasks that
require a balance between exploration and exploitation. This study
examines conditions under which individuals are willing to accept advice
from algorithms in such scenarios, focusing on the interaction between
participants’ exploration preferences and those of the advising
algorithm. In an online experiment, we designed reinforcement learning
algorithms to prioritize either exploration or exploitation and observed
participants’ decision-making behavior, modeled using a cognitive
framework analogous to the algorithm. Contrary to expectations,
participants did not show a preference for algorithms that matched their
own exploration tendencies. In particular, participants were more likely
to follow the advice of exploitative, consistent algorithms, possibly
interpreting consistency as an indicator of competence. Although the
participants benefited from the advice of the exploratory algorithm,
their reluctance to follow it, regardless of whether the recommendation
had been ignored previously or not, highlights a potential challenge in
promoting effective collaboration between humans and algorithms.
Explorative algorithms have the potential to promote behavioral
diversification, but this effect is negated when humans disregard their
advice. In such cases, algorithmic guidance can unintentionally decrease
behavioral diversity by reinforcing established patterns.
Reference: Danwitz et al. (2025). Similarity and Consistency in
Algorithm-Guided Exploration. Journal of Behavioral Decision
Making, 38(5), e70055, https://doi.org/10.1002/bdm.70055
Genetic Modulation of Oxytocin Sensitivity: A Pharmacogenetic
Approach
with Frances Chen, Robert Kumsta, Gregor Domes, Onn Yim, Richard
Ebstein and Markus Heinrichs
Translational Psychiatry |
paper |
online
appendix
Details
Intranasal administration of the neuropeptide oxytocin has been
shown to influence a range of complex social cognitions and social
behaviors, and it holds therapeutic potential for the treatment of
mental disorders characterized by social functioning deficits such as
autism, social phobia and borderline personality disorder. However,
considerable variability exists in individual responses to oxytocin
administration. Here, we undertook a study to investigate the role of
genetic variation in sensitivity to exogenous oxytocin using a
socioemotional task. In a randomized, double-blind, placebo-controlled
experiment with a repeated-measures (crossover) design, we assessed the
performance of 203 men on an emotion recognition task under oxytocin and
placebo. We took a haplotype-based approach to investigate the
association between oxytocin receptor gene variation and oxytocin
sensitivity. We identfied a six-marker haplotype block spanning the
promoter region and intron 3 that was significantly associated with our
measure of oxytocin sensitivity. Specifically, the TTCGGG haplotype
comprising single-nucleotide polymorphisms
rs237917-rs2268498-rs4564970-rs237897-rs2268495-rs53576 is associated
with increased emotion recognition performance under oxytocin versus
placebo, and the CCGAGA haplotype with the opposite pattern. These
results on the genetic modulation of sensitivity to oxytocin document a
significant source of individual differences with implications for
personalized treatment approaches using oxytocin administration.
Reference: Chen, F. S., Kumsta, R., Dvorak, F., Domes, G., Yim, O.-S.,
Ebstein, R. P. & Heinrichs, M. (2015). Genetic modulation of
oxytocin sensitivity: a pharmacogenetic approach. Translational
Psychiatry, 5, e664. https://doi.org/10.1038/tp.2015.163
Tend-and-befriend Toward Peers, Fight-or-flight Toward Stressors: A
Dual Response to Stress
with Laura Oswald, Jeremy Jamieson, Markus Heinrichs and Bastian
Schiller
Psychoneuroendocrinology |
paper |
replication data
Details
As stress levels rise globally, it is critical to understand
whether stress promotes or inhibits prosocial behavior. The literature
to date provides inconclusive evidence on this question. Here, we
investigated whether the effects of stress on prosocial behavior depend
on the interaction partner’s social role. Specifically, 121 young adults
(59 men, 62 women, age range = 18 – 34 years) performed the Trier Social
Stress Test for Groups (TSST-G) before dividing resources among
themselves and either another participant (i.e., a peer) or a TSST-G
jury member (i.e., the stressor). We found that participants behaved
less prosocially toward a stress-inducing TSST-G jury member compared to
a peer across various behavioral measures (i.e., trust, trustworthiness,
sharing, punishment). Overall, our results suggest that individuals
adapt their behavior toward others depending on the social role of their
interaction partner, behaving more prosocially toward potential allies
than toward those who thwart social goals. More broadly, our study
highlights the importance of considering situational variables when
examining the effect of stress on prosocial behavior.
Reference: Oswald, L., Dvorak, F., Jamieson, J. P., Heinrichs, M.,
& Schiller, B. (2026). Tend-and-befriend toward peers,
fight-or-flight toward stressors: a dual response to stress.
Psychoneuroendocrinology, 191, 107941. https://doi.org/10.1080/10615806.2026.2615347
Demand for Low-carbon Energy Technologies in Private Households:
Evidence from Switzerland
with Ivana Logar and Roland Olschewski
R&R @ Energy Economics |
working
paper
Details
Improving energy efficiency in the residential sector and shifting
away from fossil fuel-based heating are crucial for achieving net-zero
energy systems. Here, we present empirically grounded projections of the
demand for low-carbon energy technologies among private households.
Using a nationwide discrete-choice experiment involving 1,676 homeowners
from all regions of Switzerland, we examine how technology demand is
influenced by subsidies and peer effects. We find substantial demand
among homeowners for capital-intensive, decentralized technologies, such
as rooftop photovoltaic systems, envelope retrofits, and heat pumps,
which can be increased further through subsidies. In contrast, subsidies
have little impact on the demand for centralized technologies.
Electricity storage solutions are generally not attractive to homeowners
unless they are combined with other technologies. At the current speed
of technology adoption, peer effects will play a minor role in
stimulating technology demand, even when technologies are heavily
subsidized. This calls for further efforts to remove the barriers to
technology adoption that currently hinder the spread of technologies
through peer influence.
Demand for Carbon-Neutral Products
with Stefano Carattini, Ivana Logar and Begüm Özdemir
Oluk
R&R @ Journal of Environmental Economics and Management |
working
paper
Details
Corporate social responsibility and the private provision of
(global) public goods are of key interest to economists and
policymakers. Over the last few years, many more private companies made
their operations carbon neutral. It is an empirical question how
consumers value carbon-neutral and low-carbon products, which we address
as follows. First, we provide a meta-analysis of the literature. We
analyze consumers’ demand for carbon-neutral and low-carbon products,
based on an overall sample of 29,666 participants. The focus is on
average willingness to pay for carbon reductions as well as on the
characteristics of the underlying literature, which is mainly based on
stated preferences and controlled environments. Second, we leverage
information on prices and product characteristics from one of the
largest online marketplaces, Amazon’s. Using a hedonic approach, we
infer from revealed preferences on consumers’ valuation of carbon-
neutral products. The staggered process of carbon-neutral certification
leads to a series of quasi-natural experiments, which we use for
identification purposes. We find that the literature suggests a positive
willingness to pay for carbon reductions that exceeds most estimates of
the social cost of carbon. However, this finding is not supported by the
hedonic analyses, where we do not find evidence that consumers value
carbon neutrality.
Reducing Micro- and Nanoplastic Pollution in Freshwater in
Switzerland: A Socio-economic Perspective
with Klaus Glenk, Ivana Logar & Jürgen Meyerhoff
R&R @ Ecological Economics
Details
Research on micro- and nanoplastics (MNPs) surged in recent years.
Despite this, important gaps remain. These include limited insights from
the social and behavioral sciences, in particular for environmental
compartments other than marine. This paper contributes to MNP research
through the lenses of economics and focuses on freshwater compartment,
thereby addressing several research gaps. The main novelty of our study
lies in performing the first cost-benefit analysis (CBA) of measures for
MNP pollution reduction, in this case installing filters in the fourth
wastewater treatment step in Switzerland. Moreover, this is the first
study to elicit people’s preferences, willingness to pay for and to
estimate economic benefits of reducing MNP pollution in the context of
freshwater ecosystems. We use a discrete choice experiment method in a
stated preference survey, conducted among a representative sample of the
German-speaking Swiss population. The survey also inquires public
awareness, risk perception, and concern about MNP. To assess the
economic desirability of investing into pollution reduction, the costs
of filter installations as part of the planed sewage treatment plant
upgrades in Switzerland are compared to the economic benefits derived
from the survey. The CBA results show that the investment is
economically justified as a measure for reducing MNP pollution if it
reduces the share of polluted Swiss water bodies by 11 percentage points
or more. However, this finding should be treated with caution, taking
the caveats of our CBA into account.
Quantifying the Effectiveness of Electricity Demand Flexibility
Programs: Evidence from Switzerland
with Ivana Logar and Roland Olschewski
submitted |
working
paper
Details
Demand flexibility programs are a promising solution for balancing
residential electricity demand and renewable electricity supply but
projecting their effectiveness requires an in-depth understanding of how
many and which households participate and under which conditions. We use
data from a nationwide survey of 1,772 tenants from different regions of
Switzerland to predict how effective flexibility programs would be at
shifting daily peaks in domestic electricity demand. Combining an
econometric estimation of households’ compensation expectations in
exchange for electricity demands restrictions with data on their
appliance ownership and usage patterns demonstrates a substantial
potential of flexibility programs for managing residential electricity
demand. For Switzerland as a whole, we estimate that one to four
terawatt-hours of residential electricity demand could be shifted away
from daily peaks over the course of one year at a cost of between 23
million and 2 billion Swiss francs. We evaluate program efficiency and
highlight alternative solutions.
Cognitive Models of Bayesian Anchoring in Discrete Choice
Experiments
with Klaus Glenk, Ivana Logar and Jürgen Meyerhoff
submitted |
working
paper
Details
Discrete choice experiments are an important method to derive
willingness-to-pay estimates for non-market goods. Several studies have
shown that willingness-to-pay estimates derived from discrete choice
experiments can be sensitive to the order of the presented choice tasks
or the size of the presented costs, which raises concerns about the
validity of such estimates. In this paper, we present cognitive models
of Bayesian anchoring that control for choice-task ordering and
cost-vector anomalies in discrete choice experiments. We show that
ordering and cost-vector effects arise if respondents update their
marginal utility of money based on the costs presented during the
experiment and introduce novel models based on Bayesian updating that
correct for anchoring processes at the individual level. Using data from
a discrete choice experiment on micro- and nanoplastic pollution of
freshwater ecosystems in Switzerland, we demonstrate how cognitive
modeling can be used to correct willingness-to-pay estimates and discuss
the implications for welfare analysis and policy design.
Social Learning with Intrinsic Preferences
with Urs Fischbacher
working paper
Details
Despite strong evidence for peer effects, little is known about how
individuals balance intrinsic preferences and social learning in
different choice environments. Using a combination of experiments and
discrete choice modeling, we show that intrinsic preferences and social
learning jointly influence participants’ decisions, but their relative
importance varies across choice tasks and environments. Intrinsic
preferences guide participants’ decisions in a subjective choice task,
while social learning determines participants’ decisions in a task with
an objectively correct solution. A choice environment in which people
expect to be rewarded for their choices reinforces the influence of
intrinsic preferences, whereas an environment in which people expect to
be punished for their choices reinforces conformist social learning. We
use simulations to discuss the implications of these findings for the
polarization of behavior.
Eliciting Strategies in Repeated Games of Strategic Substitutes and
Complements
Matthew Embrey, Friederike Mengel and Ronald Peeters
work in progress
Details
We introduce a novel method to elicit strategies in indefinitely
repeated games and apply it to games of strategic substitutes and
complements. We find that out of 256 possible unit recall machines (and
1024 full strategies) participants could use, only five machines are
used more than 5 percent of the time. Those are ‘static Nash’, ‘myopic
best response”, ’Tit-for-Tat’ and two ‘Nash reversion” strategies. We
compare outcome data with ’hot’ treatments and find that the fact that
we elicit strategies did not affect the path of play. We further compare
the frequencies of the elicited strategies with results of the strategy
frequency estimation method. We also discuss applications to IO
literature and compare insights to previous literature on strategy
elicitation mostly focused on the prisoner’s dilemma.
© Fabian Dvorak. Created with Rmarkdown, knitr, and pandoc.
Social Learning with Intrinsic Preferences
with Urs Fischbacherworking paper
Details
Despite strong evidence for peer effects, little is known about how individuals balance intrinsic preferences and social learning in different choice environments. Using a combination of experiments and discrete choice modeling, we show that intrinsic preferences and social learning jointly influence participants’ decisions, but their relative importance varies across choice tasks and environments. Intrinsic preferences guide participants’ decisions in a subjective choice task, while social learning determines participants’ decisions in a task with an objectively correct solution. A choice environment in which people expect to be rewarded for their choices reinforces the influence of intrinsic preferences, whereas an environment in which people expect to be punished for their choices reinforces conformist social learning. We use simulations to discuss the implications of these findings for the polarization of behavior.