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 |
published
paper |
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
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
Adverse Reactions to the Use of Large Language Models in Social
Interactions
with Regina Stumpf, Sebastian Fehrler, Urs Fischbacher
R&R PNAS Nexus |
working paper
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.
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.
Similarity and Consistency in Algorithm-Guided Exploration
with Yongping Bao, Ludwig Danwitz, Sebastian Fehrler, Lars Hornuf,
Hsuan Yu Lin and Bettina von Helversen
submitted |
working
paper
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
participants also benefited from the advice of exploratory algorithms,
their relative reluctance to follow exploratory advice highlights a
potential challenge in promoting effective human-algorithm
collaboration. Explorative algorithms have the potential to foster
behavioral diversification, but this effect is negated if humans
disregard explorative advice. In such cases, algorithmic guidance may
inadvertently reduce behavioral diversity by reinforcing established
patterns.
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.
Public Preferences for Low-Carbon Energy Systems
with researchers of WP6 of SCENE
work in progress
Details
The future decarbonized energy system is expected to rely
increasingly on electricity. This will require efficient technologies
for heating and energy storage as well as advanced flexibility
mechanisms in the residential sector to reduce the load on the
electricity system. As part of Work Package 6 of SCENE, we conduct two
nationwide discrete choice experiments in Switzerland to identify
regional differences in homeowners’ preferences for the adoption of
low-carbon energy technologies and tenants’ preferences for demand-side
flexibility measures, which will be integrated into energy system
modeling to identify socially acceptable pathways to the low-carbon
energy system of the future.
Demand for Carbon-Neutral Products
with Stefano Carattini, Ivana Logar and Begüm Özdemir
Oluk
work in progress
Details
Corporate social responsibility and the private provision of
(global) public goods are of key interest to economists and policymakers
alike. Increasingly, private companies are making their operations
carbon neutral, often leading their own products to also be certified
accordingly. It is an empirical question how consumers value
carbon-neutral products, which we address as follows. First, we provide
a meta-analysis of the literature analyzing demand for products with
carbon-neutral labels, based on an overall sample of 27,241
participants. In this analysis, the focus is on average willingness to
pay for carbon reductions as well as on the characteristics of the
underlying literature, including the use of stated preferences and
population samples, and their association with willingness to pay.
Second, we leverage information on prices and product characteristics
from one of the largest online marketplaces, Amazon’s, to infer from
revealed preferences on consumers’ valuation of carbon-neutral products,
through a hedonic approach. 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, which is
mainly based on survey studies, suggests a positive willingness to pay
for carbon neutrality of products that exceeds most estimates of the
social cost of carbon. However, this finding is not supported by the
hedonic approach, which is based on market prices, where we do not find
evidence for a positive willingness to pay for carbon neutrality for a
wide range of products sold on Amazon.
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.