Identifying the Reasons for Coordination Failure in a Laboratory Experiment
with Davit Khantadze
We investigate the effect of absence of common knowledge on the outcomes of coordination games in a laboratory experiment.
Using cognitive types, we can explain coordination failure in pure coordination games while differentiating between coordination failure due to first- and higher-order beliefs.
In our experiment, around 76% of the subjects have chosen the payoff-dominant equilibrium strategy despite the absence of common knowledge. However, 9% of the players had first-order beliefs that lead to coordination failure and another 9% exhibited coordination failure due to higher-order beliefs. Furthermore, we compare our results with predictions of commonly used models of higher-order beliefs.
Rational Delay of Effort in Projects with Uncertain Requirements
We analyze a dynamic moral hazard problem in teams with imperfect monitoring in continuous time. In the model, players are working together to achieve a breakthrough in a project while facing a deadline. The effort needed to achieve a breakthrough is unknown but players have a common prior about its distribution. This makes the model very flexible, since the distribution over the required effort for a breakthrough can model different types of projects.
We characterize the equilibrium and the welfare-maximizing effort path for general distributions of this breakthrough effort and show that three effects are at work: free-riding (i.e., working less), an encouragement effect (i.e., working more), similar to Bolton and Harris (1999), and a delay of effort (i.e., working later).
This encouragement effect increases or decreases the amount of work players put into the project depending on the type of uncertainty faced. Furthermore, the delay of effort explains the frequently observed last-minute rush before a deadline as a result of the actions of not only rational but also welfare-maximizing players.
Probabilistic Transitivity in Sports
with Johannes Tiwisina
We seek to find the statistical model that most accurately describes empirically observed results in sports. The idea of a transitive relation concerning the team strengths is implemented by imposing a set of constraints on the outcome probabilities. We theoretically investigate the resulting optimization problem and draw comparisons to similar problems from the existing literature including the linear ordering problem and the isotonic regression problem. Our optimization problem turns out to be very complicated to solve. We propose a branch and bound algorithm for an exact solution and for larger sets of teams a heuristic method for quickly finding a "good" solution. Finally we apply the described methods to panel data from soccer, American football and tennis and also use our framework to compare the performance of empirically applied ranking schemes.