Overconfidence and Managers’ Responsibility Hoarding

Overconfidence is a well-established behavioral phenomenon that involves an overestimation of own capabilities. We introduce a model, in which managers and agents exert effort in a joint production, after the manager decides on the allocation of the tasks. A rational manager tends to delegate the critical task to the agent more often than given by the efficient task allocation. In contrast, an overconfident manager is more likely to hoard responsibility, i.e. to delegate the critical task less often than a rational manager. In fact, a manager with a sufficiently high ability and a moderate degree of overconfidence increases the total welfare by hoarding responsibility and exerting more effort than a rational manager. Finally, we derive the conditions under which responsibility hoarding can persist in an organization, showing that the bias survives as long as the overconfident manager can rationalize the observed output by underestimating the ability of the agent

Inequity Aversion, Overconfidence, and Group Performance

This thesis theoretically investigates the impact of inequity aversion and overconfidence on group performance. The studies presented concentrate on two main topics: First, we investigate the private provision of public goods when agents are motivated by fairness concerns in terms of inequity aversion. Second, we study the consequences of overly optimistic self-perception for the allocation of tasks and the incentives for cooperation in teams. All studies are common regarding three features: First, they all focus on situations in which economically efficient effort choices and contribution levels are not contracted such that incentives for free-riding behavior may exist. Second, both in the public goods and in the team production settings the group outcome is always fully and equally shared by all group members. Finally, both inequity aversion and overconfidence can help to mitigate the negative impact of the free-rider problem leading to more efficient outcomes, even without having to implement the optimal incentive contracts

Overconfidence and Managers’ Responsibility Hoarding

Overconfidence is a well-established behavioral phenomenon that involves an overestimation of own capabilities. We introduce a model, in which managers and agents exert effort in a joint production, after the manager decides on the allocation of the tasks. A rational manager tends to delegate the critical task to the agent more often than given by the efficient task allocation. In contrast, an overconfident manager is more likely to hoard responsibility, i.e. to delegate the critical task less often than a rational manager. In fact, a manager with a sufficiently high ability and a moderate degree of overconfidence increases the total welfare by hoarding responsibility and exerting more effort than a rational manager. Finally, we derive the conditions under which responsibility hoarding can persist in an organization, showing that the bias survives as long as the overconfident manager can rationalize the observed output by underestimating the ability of the agent.organizational behavior; management performance; bounded rationality; behavioral bias

Inequality, Inequity Aversion, and the Provision of Public Goods

We investigate the effects of inequality in wealth on the incentives to contribute to a public good when agents are inequity averse and may differ in ability. We show that equality may lead to a reduction of public good provision below levels generated by purely selfish agents. But introducing inequality motivates more productive agents to exert higher efforts and help the group to coordinate on equilibria with less free-riding. As a result, less able agents may benefit from initially disadvantageous inequality. Moreover, the more inequity averse the agents, the more inequality should be imposed even by an egalitarian social planner.public goods, inequality, inequity aversion, social welfare, voluntary provision, income distribution, heterogeneity

Odorant Inhibition in Mosquito Olfaction.

How chemical signals are integrated at the peripheral sensory system of insects is still an enigma. Here we show that when coexpressed with Orco in Xenopus oocytes, an odorant receptor from the southern house mosquito, CquiOR32, generated inward (regular) currents when challenged with cyclohexanone and methyl salicylate, whereas eucalyptol and fenchone elicited inhibitory (upward) currents. Responses of CquiOR32-CquiOrco-expressing oocytes to odorants were reduced in a dose-dependent fashion by coapplication of inhibitors. This intrareceptor inhibition was also manifested in vivo in fruit flies expressing the mosquito receptor CquiOR32, as well in neurons on the antennae of the southern house mosquito. Likewise, an orthologue from the yellow fever mosquito, AaegOR71, showed intrareceptor inhibition in the Xenopus oocyte recording system and corresponding inhibition in antennal neurons. Inhibition was also manifested in mosquito behavior. Blood-seeking females were repelled by methyl salicylate, but repellence was significantly reduced when methyl salicylate was coapplied with eucalyptol

Novel composite meshes to evaluate their structural property and in vivo biocompatibility for tissue repair

404-410Composite meshes of different types have been prepared and used for tissue repair in pelvic floor disorder. An interlocking texture mesh (inter-mesh) and a membrane coated mesh (electro-mesh) have been used based on their structural property and biocompatibility. The proportion of degradation material in inter-mesh (69.6%) is found extremely higher than that of electro-mesh (3.22%), thus leading to higher product weight (65.50±2.31 g/m2) and thickness (0.500±0.025 mm). After 4 weeks of implantation in animal experiment, inter-mesh with surrounding tissues is observed to have higher breaking strength in tensile behavoir and better flexibility. Tissues on inter-mesh are found to grow faster with larger thickness (0.76±0.033 mm). The surface area loss of inter-mesh (2.49±0.25%) is much less than that of electro-mesh (7.49±0.63 %) within the first 2 weeks of implantation. However, the material’s degradation is accelerated after 2 weeks, leading to a higher shrinkage of 13.12±1.48 %

Visual Privacy Protection Based on Type-I Adversarial Attack

With the development of online artificial intelligence systems, many deep neural networks (DNNs) have been deployed in cloud environments. In practical applications, developers or users need to provide their private data to DNNs, such as faces. However, data transmitted and stored in the cloud is insecure and at risk of privacy leakage. In this work, inspired by Type-I adversarial attack, we propose an adversarial attack-based method to protect visual privacy of data. Specifically, the method encrypts the visual information of private data while maintaining them correctly predicted by DNNs, without modifying the model parameters. The empirical results on face recognition tasks show that the proposed method can deeply hide the visual information in face images and hardly affect the accuracy of the recognition models. In addition, we further extend the method to classification tasks and also achieve state-of-the-art performance

Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model

This paper focuses on the recently popular task of point cloud completion guided by multimodal information. Although existing methods have achieved excellent performance by fusing auxiliary images, there are still some deficiencies, including the poor generalization ability of the model and insufficient fine-grained semantic information for extracted features. In this work, we propose a novel multimodal fusion network for point cloud completion, which can simultaneously fuse visual and textual information to predict the semantic and geometric characteristics of incomplete shapes effectively. Specifically, to overcome the lack of prior information caused by the small-scale dataset, we employ a pre-trained vision-language model that is trained with a large amount of image-text pairs. Therefore, the textual and visual encoders of this large-scale model have stronger generalization ability. Then, we propose a multi-stage feature fusion strategy to fuse the textual and visual features into the backbone network progressively. Meanwhile, to further explore the effectiveness of fine-grained text descriptions for point cloud completion, we also build a text corpus with fine-grained descriptions, which can provide richer geometric details for 3D shapes. The rich text descriptions can be used for training and evaluating our network. Extensive quantitative and qualitative experiments demonstrate the superior performance of our method compared to state-of-the-art point cloud completion networks

Novel composite meshes to evaluate their structural property and in vivo biocompatibility for tissue repair

Composite meshes of different types have been prepared and used for tissue repair in pelvic floor disorder. An interlocking texture mesh (inter-mesh) and a membrane coated mesh (electro-mesh) have been used based on their structural property and biocompatibility. The proportion of degradation material in inter-mesh (69.6%) is found extremely higher than that of electro-mesh (3.22%), thus leading to higher product weight (65.50±2.31 g/m2) and thickness (0.500±0.025 mm). After 4 weeks of implantation in animal experiment, inter-mesh with surrounding tissues is observed to have higher breaking strength in tensile behavoir and better flexibility. Tissues on inter-mesh are found to grow faster with larger thickness (0.76±0.033 mm). The surface area loss of inter-mesh (2.49±0.25%) is much less than that of electro-mesh (7.49±0.63 %) within the first 2 weeks of implantation. However, the material’s degradation is accelerated after 2 weeks, leading to a higher shrinkage of 13.12±1.48 %

Gradient constrained sharpness-aware prompt learning for vision-language models

This paper targets a novel trade-off problem in generalizable prompt learning for vision-language models (VLM), i.e., improving the performance on unseen classes while maintaining the performance on seen classes. Comparing with existing generalizable methods that neglect the seen classes degradation, the setting of this problem is more strict and fits more closely with practical applications. To solve this problem, we start from the optimization perspective, and leverage the relationship between loss landscape geometry and model generalization ability. By analyzing the loss landscapes of the state-of-the-art method and vanilla Sharpness-aware Minimization (SAM) based method, we conclude that the trade-off performance correlates to both loss value and loss sharpness, while each of them is indispensable. However, we find the optimizing gradient of existing methods cannot maintain high relevance to both loss value and loss sharpness during optimization, which severely affects their trade-off performance. To this end, we propose a novel SAM-based method for prompt learning, denoted as Gradient Constrained Sharpness-aware Context Optimization (GCSCoOp), to dynamically constrain the optimizing gradient, thus achieving above two-fold optimization objective simultaneously. Extensive experiments verify the effectiveness of GCSCoOp in the trade-off problem.Comment: 19 pages 11 figure