More order with less law: on contract enforcement, trust, and crowding

Most contracts, whether between voters and politicians or between house owners and contractors, are incomplete. “More law,” it typically is assumed, increases the likelihood of contract performance by increasing the probability of enforcement and/or the cost of breach. We examine a contractual relationship in which the first mover has to decide whether she wants to enter a contract without knowing whether the second mover will perform. We analyze how contract enforceability affects individual performance for exogenous preferences. Then we apply a dynamic model of preference adaptation and find that economic incentives have a nonmonotonic effect on behavior. Individuals perform a contract when enforcement is strong or weak but not with medium enforcement probabilities: Trustworthiness is “crowded in” with weak and “crowded out” with medium enforcement. In a laboratory experiment we test our model’s implications and find support for the crowding prediction. Our finding is in line with the recent work on the role of contract enforcement and trust in formerly Communist countries

“Though this be madness, yet there is method in’t.” A counterfactual analysis of Richard Wagner’s Tannhäuser

Much like Wagner himself, the eponymous hero of Tannhäuser treads a path of stark contrasts and rapid swings. From Wartburg to the Venusberg and to the Vatican, the gifted bard transforms from self-centered artist to seduced disciple, disillusioned devotee, hopeful lover, self-loathing pilgrim and finally redeemed martyr. He tries everything and everything is trying. These contrasts reach a peak in the opera‟s central episode, the song contest at Wartburg. Tannhäuser has just been welcomed at the court, received Elisabeth‟s favor and affection, and is ready to compete for the contest‟s prize, one as lofty as possibly the princess‟ hand. Instead of securing his reintegration to Wartburg with a brilliant performance, however, he spoils the event with insolent remarks and the exhibitionist disclosure of his Venusberg experience. His behavior offends his peers, scandalizes the court, breaks Elisabeth‟s heart, and brings him to the edge of death. Why would Tannhäuser sacrifice everything for nothing

Learning trust

We examine the effects of different forms of feedback information on the performance of markets that suffer from moral hazard problems due to sequential exchange. As orthodox theory would predict, we find that providing buyers with information about sellers' trading history boosts market performance. More surprisingly, this beneficial effect of incentives for reputation building is considerably enhanced if sellers, too, can observe other sellers' trading history. This suggests that two-sided market transparency is an important ingredient for the design of well-functioning markets that are prone to moral hazard

On beliefs and motives in Richard Wagner's Lohengrin

Once Wagner’s most popular opera, Lohengrin has suffered scholarly neglect in the postwar period. This essay reengages with the work from the novel perspective of game theory analysis. Centering on Elsa’s breach of the Frageverbot, it offers a rigorous epistemological study of the opera’s main characters. Against traditional interpretations of the heroine’s fatal decision, we propose a complex and psychologically more satisfactory account. Elsa asks the forbidden question because she needs to confirm Lohengrin’s belief in her innocence, a belief that Ortrud successfully eroded in Act II. This novel interpretation reveals Elsa as a rational individual, upgrades the dramatic significance of the Act I combat scene, and signals a hermeneutic return to the heart of opera criticism, the drama itself

Half-Periodic Josephson Effect in an s-Wave Superconductor - Normal Metal -d-Wave Superconductor Junction

We predict that the Josephson current in a clean s-wave superconductor-normal metal-d-wave superconductor junction is periodic in superconducting phase difference $\phi$ with period $\pi$ instead of $2\pi$. The frequency of non-stationary Josephson effect is correspondingly $2\omega_J = 4eV.$ The effect is due to coexistence in the normal layer of current carrying Andreev levels with phase differences $\phi$ and $\phi+\pi.$Comment: 4 pages, REVTeX, 3 figure

A note on charitable giving by corporates and aristocrats: Evidence from a field experiment

Multiple sources of funding are becoming increasingly important for charitable organizations. Donations from corporate donors for example account for 25–35% of charitable income for the largest US charities, across charitable sectors. This note presents some tentative first evidence from a natural field experiment to shed light on how different types of potential donors: individuals, corporates and aristocratically titled individuals, respond to the same fundraising drive. Each donor type was randomly assigned to treatments varying in two dimensions: (i) whether information was conveyed about the existence of an anonymous lead donor, and (ii) how individual donations would be matched by the anonymous lead donor. We find that aristocrats are significantly more likely to respond and that corporates give significantly more than individuals. Treatment effects moreover suggest that (proportional) matching is to be avoided for corporate donors

Treatment Learning Causal Transformer for Noisy Image Classification

Current top-notch deep learning (DL) based vision models are primarily based on exploring and exploiting the inherent correlations between training data samples and their associated labels. However, a known practical challenge is their degraded performance against "noisy" data, induced by different circumstances such as spurious correlations, irrelevant contexts, domain shift, and adversarial attacks. In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy by jointly estimating their treatment effects. Motivated from causal variational inference, we propose a transformer-based architecture, Treatment Learning Causal Transformer (TLT), that uses a latent generative model to estimate robust feature representations from current observational input for noise image classification. Depending on the estimated noise level (modeled as a binary treatment factor), TLT assigns the corresponding inference network trained by the designed causal loss for prediction. We also create new noisy image datasets incorporating a wide range of noise factors (e.g., object masking, style transfer, and adversarial perturbation) for performance benchmarking. The superior performance of TLT in noisy image classification is further validated by several refutation evaluation metrics. As a by-product, TLT also improves visual salience methods for perceiving noisy images.Comment: Accepted to IEEE WACV 2023. The first version was finished in May 201