with Urs Fischbacher and Katrin Schmelz
We provide systematic insights on strategic conformist - as well as anticonformist - behavior in situations where people are evaluated, i.e., where an individual has to be selected for reward (e.g., promotion) or punishment (e.g., layoffs). To affect the probability of being selected, people may attempt to fit in or stand out in order to affect the chances of being noticed or liked by the evaluator. We investigate such strategic incentives for conformity or anticonformity experimentally in three different domains: facts, taste, and creativity. To distinguish conformity and anticonformity from independence, we introduce a new experimental design that allows us to predict participants’ independent choices based on transitivity. We find that the prospect of punishment increases conformity, while the prospect of reward reduces it. Anticonformity emerges in the prospect of reward, but only under specific circumstances. Similarity-based selection (i.e., homophily) is much more important for the evaluators’ decisions than salience. We also employ a theoretical approach to illustrate strategic key mechanisms of our experimental setting.
The Economic Journal | published paper | online appendix | replication package | video creativity task
Reference: Dvorak, F., Fischbacher, U., and Schmelz, K. (forthcoming). Strategic conformity or anticonformity to avoid punishment and attract reward. The Economic Journal, ueae085. https://doi.org/10.1093/ej/ueae085
with Sebastian Fehrler
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.
American Economic Journal: Microeconomics | published paper | online appendix | replication package | cooperation barcode
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 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.
Journal of the Economic Science Association | published paper | package vignette | CRAN | GitHub
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
with Frances Chen, Robert Kumsta, Gregor Domes, Onn Yim, Richard Ebstein, and Markus Heinrichs
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.
Translational Psychiatry | published paper
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
with Regina Stumpf, Sebastian Fehrler, Urs Fischbacher
Generative artificial intelligence (AI) is poised to reshape the way individuals communicate and interact. While this form of AI has the potential to efficiently make numerous human decisions, there is limited understanding of how individuals respond to its use in social interaction. In particular, it remains unclear how individuals engage with algorithms when the interaction entails consequences for other people. Here, we report the results of a large-scale pre-registered online experiment (N = 3,552) indicating diminished fairness, trust, trustworthiness, cooperation, and coordination by human players in economic twoplayer games, when the decision of the interaction partner is taken over by ChatGPT. On the contrary, we observe no adverse welfare effects when individuals are uncertain about whether they are interacting with a human or generative AI. Therefore, the promotion of AI transparency, often suggested as a solution to mitigate the negative impacts of generative AI on society, shows a detrimental effect on welfare in our study. Concurrently, participants frequently delegate decisions to ChatGPT, particularly when the AI’s involvement is undisclosed, and individuals struggle to discern between AI and human decisions.
submitted | working paper
with Klaus Glenk, Ivana Logar and Jürgen Meyerhoff
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.
submitted | working paper
with Yongpin Bao, Ludwig Danwitz, Sebastian Fehrler, Lars Hornuf, Hsuan Yu Lin and Bettina von Helversen
Algorithm-based decision support systems play an increasingly important role in decisions involving exploration tasks, such as product searches, portfolio choices, and human resource procurement. These tasks often involve a trade-off between exploration and exploitation, which can be highly dependent on individual preferences. In an online experiment, we study whether the willingness of participants to follow the advice of a reinforcement learning algorithm depends on the fit between their own exploration preferences and the algorithm’s advice. We vary the weight that the algorithm places on exploration rather than exploitation, and model the participants’ decision-making processes using a learning model comparable to the algorithm’s. This allows us to measure the degree to which one’s willingness to accept the algorithm’s advice depends on the weight it places on exploration and on the similarity between the exploration tendencies of the algorithm and the participant. We find that the algorithm’s advice affects and improves participants’ choices in all treatments. However, the degree to which participants are willing to follow the advice depends heavily on the algorithm’s exploration tendency. Participants are more likely to follow an algorithm that is more exploitative than they are, possibly interpreting the algorithm’s relative consistency over time as a signal of expertise. Similarity between human choices and the algorithm’s recommendations does not increase humans’ willingness to follow the recommendations. Hence, our results suggest that the consistency of an algorithm’s recommendations over time is key to inducing people to follow algorithmic advice in exploration tasks.
submitted | working paper
with researchers of WP6 of SCENE
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.
work in progress
with Stefano Carattini, Ivana Logar and Begüm Özdemir Oluk
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.
work in progress
with Urs Fischbacher, Katrin Schmelz and Georg Sator
Morality is one of the key features of the human species. It enables us to live in and benefit from social groups and thereby constitutes the foundation of social coexistence and civilization. The starting point of our study is the notion that many important moral choices are not made in social isolation, but in the presence of other people and other people’s moral judgments. This poses the question whether moral judgment and decision-making is prone to social influence. We conduct an online experiment which allows us to identify the effect of social information on people’s publicly stated moral convictions. The experimental design uses a battery of moral trilemmas allowing use to separate conformity, anticonformity and independence in moral judgments without drawing on deceptive methods.
work in progress
Matthew Embrey, Friederike Mengel and Ronald Peeters
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.
work in progress
Social Learning with Intrinsic Preferences
with Urs Fischbacher
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.
working paper