The experience is (not) everything: Sequential outcomes and social decision-making

In multiple pre-registered experiments, we examine the effect of sequences of positive and negative experiences on altruism, trust, trustworthiness, and cooperation. For non-social experiences, we find no effect on subsequent behavior in any of these social domains. However, when experiences are social in nature, we find more cooperation after gains than after losses. For neutral experiences with gains equalizing losses, we find no evidence for a differential effect of experiences irrespective of whether the experience is social or not. Our findings are in line with recent evidence on decision making under risk, showing that the effect of prior experiences depends on task similarity. Beyond that, we extend these findings to various forms of social decision making. Our results suggest that the overall valence of an experience (gain or loss) matters, whereas its dynamic trend (improving or deteriorating) does not

Evolution, Bounded Rationality and Institutions

This dissertation consists of five self-contained research papers that cover theoretical work, simulation-based research, and experimental studies. My research interests are mainly focused on two interrelated areas within economics. One area is concerned with the economic consequences as well as the foundations of boundedly rational behavior. The other area is more specific and concerns the design of institutions and how they can be used to shape behavior and align incentives. The first chapter belongs to both areas, Chapters 2 and 3 cover topics from the former area, whereas the last two chapters contribute to the latter area. Chapter 1 concerns the role of trader matching with regard to the selection of market institutions by boundedly rational traders. Chapter 2 presents results on the stability of the Cournot-Nash and the Walrasian equilibrium under imitative behavior. Chapter 3 presents a model linking response times and iterative thinking and provides experimental evidence regarding the underlying processes of iterative thinking. Chapter 4 investigates the effects of a leniency mechanism on collusive bribery and tax evasion. Chapter 5 asks how the timing of punishment and the timing of the resolution of uncertainty affect deterrence of illicit behavior. In the remainder of this section I present a brief introduction for each chapter and summarize the main findings

Imitation, network size, and efficiency

A number of theoretical results have provided sufficient conditions for the selection of payoff-efficient equilibria in games played on networks when agents imitate successful neighbors and make occasional mistakes (stochastic stability). However, those results only guarantee full convergence in the long-run, which might be too restrictive in reality. Here, we employ a more gradual approach relying on agent-based simulations avoiding the double limit underlying these analytical results. We focus on the circular-city model, for which a sufficient condition on the population size relative to the neighborhood size was identified by Alós-Ferrer & Weidenholzer [(2006) Economics Letters, 93, 163–168]. Using more than 100,000 agent-based simulations, we find that selection of the efficient equilibrium prevails also for a large set of parameters violating the previously identified condition. Interestingly, the extent to which efficiency obtains decreases gradually as one moves away from the boundary of this condition

The reinforcement paradox: monetary incentives and Bayesian updating

We report the results of two pre-registered experiments designed to study the reinforcement paradox: increased incentives often fail to increase and sometimes even decrease performance in Bayesian updating tasks. We argue that, in the presence of win/loss cues, higher incentives have two countervailing effects: increased error rates for reinforcement behavior (win-stay, lose-shift) and increased performance for decisions resulting from Bayesian updating. We find some evidence that incentives increase performance when the win/loss cue is removed whereas when reinforcement is active the effects of incentives are mixed

When are efficient conventions selected in networks?

We study the determinants of convergence to efficient conventions in coordination games played on networks, when agents focus on past performance (imitative play). Previous theoretical results provide an incomplete picture and suggest potentially-complex interactions between the features of dynamics and behavior. We conducted an extensive simulation study (with approximately 1.12 million simulations) varying network size, interaction and information radius, the probability of actual interaction, the probability of mistakes, tie-breaking rules, and the process governing revision opportunities. Our main result is that “more interactions,” be it in the form of larger interaction neighborhoods or of a higher interaction probability, lead to less coordination on efficient conventions. A second observation, confirming previous but partial theoretical results, is that a large network size relative to the size of neighborhoods (a “large world”) facilitates convergence to efficient conventions. Third, a larger information neighborhood helps efficiency because it increases visibility of efficient payoffs across the network. Last, technical details of the dynamic specification as tie-breaking or inertia, while often relevant for specific theoretical results, appear to be of little empirical relevance in the larger space of dynamics

Cognitive sophistication and deliberation times

Differences in cognitive sophistication and effort are at the root of behavioral heterogeneity in economics. To explain this heterogeneity, behavioral models assume that certain choices indicate higher cognitive effort. A fundamental problem with this approach is that observing a choice does not reveal how the choice is made, and hence choice data is insufficient to establish the link between cognitive effort and behavior. We show that deliberation times provide the missing link, in the form of an individually-measurable correlate of cognitive effort. We present a model of heterogeneous cognitive depth, incorporating stylized facts from the psychophysical literature, which makes predictions on the relation between choices, cognitive effort, incentives, and deliberation times. We confirm the predicted relations experimentally in different kinds of games. However, we also show that imputing cognitive depth from choices alone can lead to erroneous conclusions when the features leading to iterative thinking are not salient

Voting for Compromises: Alternative Voting Methods in Polarized Societies

Democratic societies have been increasingly confronted with extreme, knife-edge election outcomes that affect everybody’s lives and contribute to social instability. Even if political compromises based on social conventions as equity or economic arguments as efficiency are available, polarized societies might fail to select them. We demonstrate that part of the problem might be purely technical and, hence, potentially solvable. We study different voting methods in three experiments (total N = 5, 820), including small, medium-sized, and large electorates, and find that currently-used methods (Plurality Voting and Rank-Order systems) can lead voters to overwhelmingly support egoistic options. In contrast, alternative, more nuanced methods (Approval Voting and Borda Count) reduce the support for egoistic options and favor equity and efficiency, avoiding extreme outcomes. Those methods differ in whether they favor equity or efficiency when the latter benefits a majority. Our evidence suggests that targeted changes in the electoral system could favor socially-desirable compromises and increase social stability

Cognitive sophistication and deliberation times

Differences in cognitive sophistication and effort are at the root of behavioral heterogeneity in economics. To explain this heterogeneity, behavioral models assume that certain choices indicate higher cognitive effort. A fundamental problem with this approach is that observing a choice does not reveal how the choice is made, and hence choice data is insufficient to establish the link between cognitive effort and behavior. We show that deliberation times provide an individually-measurable correlate of cognitive effort. We test a model of heterogeneous cognitive depth, incorporating stylized facts from the psychophysical literature, which makes predictions on the relation between choices, cognitive effort, incentives, and deliberation times. We confirm the predicted relations experimentally in different kinds of games

Effects of institutional history and leniency on collusive corruption and tax evasion

We investigate the effects of an institutional mechanism that incentivizes taxpayers to blow the whistle on collusive corruption and tax compliance. We explore this through a formal leniency program. In our experiment, we nest collusive corruption within a tax evasion framework. We not only study the effect of the presence of such a mechanism on behavior, but also the dynamic effect caused by the introduction and the removal of leniency. We find that in the presence of a leniency mechanism, subjects collude and accept bribes less often while paying more taxes, but there is no increase in bribe offers. Our results show that the introduction of the opportunity to blow the whistle decreases the collusion and bribe acceptance rate, and increases the collected tax yield. It also does not encourage bribe offers. In contrast, the removal of the institutional mechanism does not induce negative effects, suggesting a positive spillover effect of leniency that persists even after the mechanism has been removed

When are efficient conventions selected in networks?

We study the determinants of convergence to efficient conventions in coordination games played on networks, when agents focus on past performance (imitative play). Previous theoretical results provide an incomplete picture and suggest potentially-complex interactions between the features of dynamics and behavior. We conducted an extensive simulation study (with approximately 1.12 million simulations) varying network size, interaction and information radius, the probability of actual interaction, the probability of mistakes, tie-breaking rules, and the process governing revision opportunities. Our main result is that “more interactions,” be it in the form of larger interaction neighborhoods or of a higher interaction probability, lead to less coordination on efficient conventions. A second observation, confirming previous but partial theoretical results, is that a large network size relative to the size of neighborhoods (a “large world”) facilitates convergence to efficient conventions. Third, a larger information neighborhood helps efficiency because it increases visibility of efficient payoffs across the network. Last, technical details of the dynamic specification as tie-breaking or inertia, while often relevant for specific theoretical results, appear to be of little empirical relevance in the larger space of dynamics