research articles | work in progress | stratEst package

Similarity and Consistency in Algorithm-Guided Exploration

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

Incentives for Conformity and Anticonformity

with Urs Fischbacher and Katrin Schmelz

Conformity and anticonformity are crucial for the stability and dynamism of societies. Thus far, it is unclear whether certain social environments generate incentives for conformity or anticonformity. We theoretically and experimentally show that the evaluation of behavior by peers creates such incentives. In theory, we show that the prospect of punishment creates incentives for conformity, while the prospect of reward can also create incentives for anticonformity. We present data from laboratory experiments that confirm the theoretical predictions across three different choice domains: judgments in the knowledge domain, subjective arts preferences, and decisions in a creativity-related task. To overcome the challenge of disentangling conformity and anticonformity from independence, we introduce a new experimental design in which we infer subjects’ independent preferences using transitivity.

R&R EJ | submited paper | video creativity task

Negotiating Cooperation under Uncertainty: Communication in Noisy, Indefinitely Repeated Interactions

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 present results from a laboratory experiment which show that communication facilitates cooperation by reducing two types of uncertainty. Pre-play communication reduces strategic uncertainty, which boosts cooperation 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.

R&R AEJ Micro | submitted paper

stratEst: a software package for strategy frequency estimation

stratEst is a software package for strategy frequency estimation in the freely available statistical computing environment R (R Core Team 2021). The package aims to minimize the start-up costs of performing the modern strategy frequency estimation techniques used in experimental economics. The strategy frequency estimation method (Dal Bo, Frechette 2011) models participants’ choices in an economic experiment as a finite mixture of individual decision strategies. The parameters of the model describe each strategy’s associated behavior and its frequency in the data. The stratEst package provides a convenient and flexible framework for strategy frequency estimation as it allows the user to customize, store and reuse sets of candidate strategies. The package contains helpful functions for data processing and simulation, strategy programming, model estimation, parameter tests, model checking, and model selection.

R&R JESA | submitted paper | package vignette | latest version on CRAN | development version on GitHub

Preference-biased Social Influence

with Urs Fischbacher

We propose a discrete-choice model that combines intrinsic preferences over choice alternatives with frequency-dependent social influence. The model assumes that the decision-maker has intrinsic preferences over a set of alternatives and observes the choices of a random sample of individuals from a reference group. Based on the observed choices, the decision-maker forms a belief about the frequencies of choices in the reference group and derives utility proportional to the natural logarithm of the expected frequencies. The model allows for variety of different reactions to social influence like conformity, non-conformity, independence or anticonformity and can be extended to accommodate situations in which the decision-maker’s belief about the choice frequencies is biased by intrinsic preferences (motivated beliefs, false-consensus). We study the interplay of intrinsic preferences and social influence in an online experiment in which we measure participants’ intrinsic preferences and provide information about others’ behavior. We find that the model explains the average and individual behavior observed in the experiment well and substantially better than intrinsic preferences or social influence alone.

working paper


Genetic Modulation of Oxytocin Sensitivity: A Pharmacogenetic Approach

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.

© Fabian Dvorak. Created with Rmarkdown, knitr, and pandoc.