Publications

The history of research in finance and economics has been widely impacted by the field of Agent-based Computational Economics (ACE). While at the same time being popular among natural science researchers for its proximity to the successful methods of physics and chemistry for example, the field of ACE has also received critics by a part of the social science community for its lack of empiricism. Yet recent trends have shifted the weights of these general arguments and poten- tially given ACE a whole new range of realism. At the base of these trends are found two present-day major scientific breakthroughs: the steady shift of psychology towards a hard science due to the advances of neuropsychology, and the progress of reinforcement learning due to increasing computational power and big data. We outline here the main lines of a computational research study where each agent would trade by reinforcement learning.

We analyze the effects of limited feedback on beliefs and contributions in a repeated public goods game setting. In a first experiment, we test whether exogenously determined feedback about a good example (i.e., the maximum con- tribution in a period) in contrast to a bad example (i.e., the minimum contribution in a period) induces higher contributions. We find that when the type of feedback is not transparent to the group members, good examples boost cooperation while bad examples hamper it. There is no difference when the type of feedback is transparent. In a second experiment, feedback is endogenously chosen by a group leader. The results show that a large majority of the group leaders count on the positive effect of providing a good example. This is true regardless whether they choose the feedback type to be transparent or non-transparent. Half of the group leaders make the type of feedback transparent. With endogenously chosen feedback about good examples no difference in contributions can be observed among transparent and non-transparent feedback selection. In both experiments feedback shapes subjects’ beliefs. With exogenously chosen feedback, transparent feedback tends to reduce beliefs when good examples are provided as feedback and tends to increase beliefs in when bad examples are provided as feedback compared to the respective non-transparent cases. Our results shed new light on the design of feedback provision in public goods settings.

Dishonest behavior significantly increases the cost of medical care provision. Upcoding of patients is a common form of fraud to attract higher reimbursements. Imposing audit mechanisms including fines to curtail upcoding is widely discussed among health care policy-makers. How audits and fines affect individual health care providers' behavior is empirically not well understood. To provide new evidence on fraudulent behavior in health care, we analyze the effect of a random audit including fines on individuals' honesty by means of a novel controlled behavioral experiment framed in a neonatal care context. Prevalent dishonest behavior declines significantly when audits and fines are introduced. The effect is driven by a reduction in upcoding when being detectable. Yet, upcoding increases when not being detectable as fraudulent. We find evidence that individual characteristics (gender, medical background, and integrity) are related to dishonest behavior. Policy implications are discussed.

Does it pay off for companies to disclose voluntary commitments to their customers? While voluntary commitments to enhance customers’ benefits became prevalent in many markets, systematic evidence on how customers (if at all) reward companies, which disclose such discretionary kindness, is still lacking. We analyze the consequences of endogenous disclosure of discretionary kindness in a novel experiment (N = 636). We model the decision situation in a bilateral reciprocity game with asymmetric information on the vol-untariness of kindness. Experimental data show that endogenously disclosing discretionary kindness significantly triggers rewards from customers and does not backfire. Findings are robust towards variations in costs of information and the level of customers’ benefits. Survey evidence from a vignette study support our behavioral findings.

We experimentally study the causal effect of being an immigrant or previously convicted on the hiring preferences and wage payments of employers. We find evidence for statistical discrimination against immigrants. Criminal offenders suffer from more severe and taste discrimination.

In a monetarily incentivized Dictator Game, we expected Dictators’ empathy toward the Recipients to cause more pro-social allocations. Empathy was experimentally induced via a commonly used perspective taking task. Dictators (N = 474) were instructed to split an endowment of 10€ between themselves and an unknown Recipient. They could split the money 8/2 (8€ for Dictator, 2€ for Recipient) or 5/5 (5€ each). Although the empathy manipulation successfully increased Dictators’ feelings of empathy toward the Recipients, Dictators’ decisions on how to split the money were not affected. We had ample statistical power (above 0.99) to detect a typical social psychology effect (corresponding to r around 0.20). Other possible determinants of generosity in the Dictator Game should be investigated.

With the advent of online subject pools, conducting experiments outside the laboratory has become more popular among the scientific community. Unlike the lab, online and field environments tend to be accompanied by a loss of control. In this article we introduce otree_tools, a concise yet powerful add-on for oTree (Chen et al., 2016). otree_tools provides novel ways by which to measure behaviors that are potentially important in the social sciences, such as attention, multitasking and effort. The software also features a novel method of tracking time through the identification of noise. We demonstrate the utility of otree_tools with the help of experimental evidence. The software substantially increases control of the environment. Moreover, the original metrics can be employed for innovative outcome variables, opening the avenue for new research opportunities.

A new class of intransitive objects – geometrical and mathematical constructions forming intransitive cycles A>B>C>A – are presented. In contrast to the famous intransitive dice, lotteries, etc., they show deterministic (not probabilistic) intransitive relations. The simplest ones visualize intransitivity that can be understood at a qualitative level and does not require quantitative reasoning. They can be used as manipulatives for learning intransitivity. Classification of the types of situations in which the transitivity axiom does and does not work is presented. Four levels of complexity of intransitivity are introduced, from simple combinatorial intransitivity to a "rhizomatic" one. A possible version of the main educational message for students in teaching and learning transitivity-intransitivity is presented.

The organization of collective action is extremely important for societies. A main reason is that many of the key problems societies face are public good problems. We present results from a series of laboratory experiments with large groups of up to 100 subjects. Our results demonstrate that large groups, in which the impact of an individual contribution ( MPCR ) is almost negligible, are able to provide a public good in the same way as small groups in which the impact of an individual contribution is much higher. Nevertheless, we find that small variations in MPCR in large groups have a strong effect on contributions. We develop a hypothesis concerning the interplay between MPCR and group size, which is based on the assumption that the salience of the advantages of mutual cooperation plays a decisive role. This hypothesis is successfully tested in a second series of experiments. Since Mancur Olson’s “Logic of collective action” it is a commonly held belief that in large groups the prospects of a successful organization of collective actions are rather bad. Our results, however, suggest that the chance to successfully organize collective action of large groups and to solve important public good problems is much higher than previously thought.

oTree, a popular platform for conducting behavioral experiments, exchanges data only as a participant exits or enters a web page. In many situations, however, information needs to be gathered and delivered instantaneously. This paper demonstrates a way to add real-time interactions to oTree and presents two ready-made apps: a double auction and a gift exchange with a real effort task. Many auction designs, including the double auction, use time constraints and carry out sales as soon as an ask and a bid are compatible. Instantaneous flow of information is thus a core requirement for programming these auctions in the first place. The real effort task measuring the number of correct answers within a time limit, on the other hand, benefits from the extra flexibility and security that Django Channels provides. Furthermore, real effort tasks are a simple starting point for building real-time interaction apps with oTree.

Recent studies have demonstrated that the right dorsolateral prefrontal cortex (rDLPFC) and the right temporoparietal junction (rTPJ) are causally involved in social norm compliance. Here, we tested the hypothesis that a third party's decision to punish norm violations depends on the activity of the entire rDLPFC/rTPJ network. We used transcranial direct current stimulation (tDCS) to independently or jointly modulate rTPJ and rDLPFC activity during the third-party dictator game. We found a significant effect of anodal tDCS of the rTPJ, which decreased the third-party punishment of moderately unfair splits. Joint stimulation of the rTPJ (by anodal tDCS) and rDLPFC (by cathodal tDCS) produced a marginal effect on third-party punishment.

In the commentary to Jens Mammen’s book A New Logical Foundation for Psychology (2017), three issues are discussed. The first one concerns possible interrelations of: (a) others’ irreplaceability and existential irretrievability rigorously proved by Mammen; and (b) morality and attitudes to the others. Lem’s criticism of Heidegger’s existential philosophy, which paradoxically ignores mass homicide, is discussed in the context of topology of being. Different attitudes to the other as irreplaceable and irretrievable (e.g., in case of apprehension and execution of a murderer) are analyzed. The second issue concerns the possibility of true duplicates of the same person. The paradox of copied complexity is introduced. The third issue concerns reductionism (including brain reductionism) and opportunities to deduce various phenomena of development (mental development, actual genesis of creative thinking, etc.) from the new logical foundation for psychology built by Mammen.

The slippery slope framework of tax compliance emphasizes the importance of trust in authorities as a substantial determinant of tax compliance alongside traditional enforcement tools like audits and fines. Using data from an experimental scenario study in 44 nations from five continents (N = 14,509), we find that trust in authorities and power of authorities, as defined in the slippery slope framework, increase tax compliance intentions and mitigate intended tax evasion across societies that differ in economic, sociodemographic, political, and cultural backgrounds. We also show that trust and power foster compliance through different channels: trusted authorities (those perceived as benevolent and enhancing the common good) register the highest voluntary compliance, while powerful authorities (those perceived as effectively controlling evasion) register the highest enforced compliance. In contrast to some previous studies, the results suggest that trust and power are not fully complementary, as indicated by a negative interaction effect. Despite some between-country variations, trust and power are identified as important determinants of tax compliance across all nations. These findings have clear implications for authorities across the globe that need to choose best practices for tax collection.

In this paper we examine the effects of valence in a continuous spatial voting model with two incumbent candidates and a potential entrant. All candidates are rank-motivated. We first consider the case where the low valence incumbent (LVC) and the entrant have zero valence, whereas the valence of the high valence incumbent (HVC) is positive. We show that a sufficiently large valence of HVC guarantees a unique equilibrium, where the two incumbents prevent the entry of the third candidate. We also show that an increase in valence allows HVC to adopt a more centrist policy position, while LVC selects a more extreme position. We also examine the existence of equilibrium for the cases where the LVC has higher or lower valence than the entrant.

This survey analyzes central ideas and the current state of the economic theory of learning in games. In game theory learning can be thought of as both an alternative to equilibria and as a way to better understand the nature of equilibria. Outside of game theory, theory of learning shows economic theory (for example, the classic Cournot oligopoly) in a new light, provides interesting theoretical problems, is nontrivial from econometric perspective, and can be studied with experimental methods. It also links economics with unexpected scientific disciplines: biology, philosophy of rationality and computer science. However, existing surveys are not particularly accessible to beginners and are not accessible at all in Russian. This survey intends to fill these gaps. It can serve both as an introduction and as a short reference. We analyze issues of classification as well as the models themselves. Theoretical descriptions are illustrated with concrete examples. Special attention is devoted to the empirical and experimental work. We also draw conclusions and hypothesize on perspectives of the field and its future role in economic theory.