Over-Confidence, Shame and Investments

The available evidence from numerous studies suggests that overconfidence is a more important phenomenon in North America than in Japan. The pattern is reversed for shame, which appears to play a more important role among Japanese than North Americans. We develop a model that endogenizes these differences and examines their economic consequences. In addition, it yields novel implications for differences in overconfidence and behavior within countries. A crucial tradeoff arises in the model between the benefits of encouraging improvement on existing activities and the benefits of promoting initiative and investment in new activities. Overconfidence and high sensitivity to shame emerge as substitute mechanisms to induce efficient investment decisions, generating a "North American" equilibrium with overconfidence and low sensitivity to shame, and a "Japanese" equilibrium with high sensitivity to shame and no overconfidence. The analysis identifies the costs as well as bene fits of reliance on each mechanism, and welfare implications

Over-Confidence, Shame and Investments

The available evidence from numerous studies suggests that overconfidence is a more important phenomenon in North America than in Japan. The pattern is reversed for shame, which appears to play a more important role among Japanese than North Americans. We develop a model that endogenizes these differences and examines their economic consequences. In addition, it yields novel implications for differences in overconfidence and behavior within countries. A crucial tradeoff arises in the model between the benefits of encouraging improvement on existing activities and the benefits of promoting initiative and investment in new activities. Overconfidence and high sensitivity to shame emerge as substitute mechanisms to induce efficient investment decisions, generating a "North American" equilibrium with overconfidence and low sensitivity to shame, and a "Japanese" equilibrium with high sensitivity to shame and no overconfidence. The analysis identifies the costs as well as bene fits of reliance on each mechanism, and welfare implications

Overconfidence, Stability and Investments

The available evidence from numerous studies suggests that overconfidence varies significantly across countries. We develop a model that endogenizes these differences and examines their economic consequences. A crucial determinant of difierences in overconfidence is the degree of expected stability of the environment, with greater changefulness giving rise to more overconfident beliefs. When stability is endogenized, multiple equilibria can emerge, \dynamism" and overconfidence reinforcing each other in one case, stability and realistic self-assessment in another. Evidence from 38 countries is consistent with this relationship. Finally, our model also sheds some light on differences in overconfidence within countries, as well as exploring the interaction between overconfidence and sensitivity to shame

Information Disclosure and Consumer Awareness

Whether consumers are aware of potentially adverse product effects is key to private and social incentives to disclose information about undesirable product characteristics. In a monopoly model with a mix of aware and unaware consumers, a larger share of unaware consumers makes information disclosure less likely to occur. Since the firm is not interested in releasing information to unaware consumers, a more precise targeting technology that allows the firm to better keep unaware consumers in the dark leads to more disclosure. A regulator may want to intervene in this market and impose mandatory disclosure rules

Worried about Adverse Product Effects? Information Disclosure and Consumer Awareness

Whether consumers are aware of potentially adverse product effects, is key for private and social incentives to disclose information. To obtain a better understanding of this issue we propose a simple monopoly model that highlights the conceptual difference between consumer unawareness and consumer uncertainty. We show that total surplus may be larger in an environment in which consumers are unaware of the potentially adverse effect. We also show that disclosing information whether a particular ingredient is harmful or not increases consumer surplus, but mandatory disclosure of the level of this ingredient may make consumers worse off

Worried about Adverse Product Effects? Information Disclosure and Consumer Awareness

Whether consumers are aware of potentially adverse product effects, is key for private and social incentives to disclose information. To obtain a better understanding of this issue we propose a simple monopoly model that highlights the conceptual difference between consumer unawareness and consumer uncertainty. We show that total surplus may be larger in an environment in which consumers are unaware of the potentially adverse effect. We also show that disclosing information whether a particular ingredient is harmful or not increases consumer surplus, but mandatory disclosure of the level of this ingredient may make consumers worse off

Travel Demand Forecasting: A Fair AI Approach

Artificial Intelligence (AI) and machine learning have been increasingly adopted for travel demand forecasting. The AI-based travel demand forecasting models, though generate accurate predictions, may produce prediction biases and raise fairness issues. Using such biased models for decision-making may lead to transportation policies that exacerbate social inequalities. However, limited studies have been focused on addressing the fairness issues of these models. Therefore, in this study, we propose a novel methodology to develop fairness-aware, highly-accurate travel demand forecasting models. Particularly, the proposed methodology can enhance the fairness of AI models for multiple protected attributes (such as race and income) simultaneously. Specifically, we introduce a new fairness regularization term, which is explicitly designed to measure the correlation between prediction accuracy and multiple protected attributes, into the loss function of the travel demand forecasting model. We conduct two case studies to evaluate the performance of the proposed methodology using real-world ridesourcing-trip data in Chicago, IL and Austin, TX, respectively. Results highlight that our proposed methodology can effectively enhance fairness for multiple protected attributes while preserving prediction accuracy. Additionally, we have compared our methodology with three state-of-the-art methods that adopt the regularization term approach, and the results demonstrate that our approach significantly outperforms them in both preserving prediction accuracy and enhancing fairness. This study can provide transportation professionals with a new tool to achieve fair and accurate travel demand forecasting.Comment: improved the methodology; updated new content

A New Method for Impeller Inlet Design of Supercritical CO2 Centrifugal Compressors in Brayton Cycles

Supercritical Carbon Dioxide (SCO2) is considered as a potential working fluid in next generation power and energy systems. The SCO2\ua0Brayton cycle is advantaged with higher cycle efficiency, smaller compression work, and more compact layout, as compared with traditional cycles. When the inlet total condition of the compressor approaches the critical point of the working fluid, the cycle efficiency is further enhanced. However, the flow acceleration near the impeller inducer causes the fluid to enter two-phase region, which may lead to additional aerodynamic losses and flow instability. In this study, a new impeller inlet design method is proposed to achieve a better balance among the cycle efficiency, compressor compactness, and inducer condensation. This approach couples a concept of the maximum swallowing capacity of real gas and a new principle for condensation design. Firstly, the mass flow function of real gas centrifugal compressors is analytically expressed by non-dimensional parameters. An optimal inlet flow angle is derived to achieve the maximum swallowing capacity under a certain inlet relative Mach number, which leads to the minimum energy loss and a more compact geometry for the compressor. Secondly, a new condensation design principle is developed by proposing a novel concept of the two-zone inlet total condition for SCO2\ua0compressors. In this new principle, the acceptable acceleration margin (AAM) is derived as a criterion to limit the impeller inlet condensation. The present inlet design method is validated in the design and simulation of a low-flow-coefficient compressor stage based on the real gas model. The mechanisms of flow accelerations in the impeller inducer, which form low-pressure regions and further produce condensation, are analyzed and clarified under different operating conditions. It is found that the proposed method is efficient to limit the condensation in the impeller inducer, keep the compactness of the compressor, and maintain a high cycle efficiency

Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning

The growing significance of ridesourcing services in recent years suggests a need to examine the key determinants of ridesourcing demand. However, little is known regarding the nonlinear effects and spatial heterogeneity of ridesourcing demand determinants. This study applies an explainable-machine-learning-based analytical framework to identify the key factors that shape ridesourcing demand and to explore their nonlinear associations across various spatial contexts (airport, downtown, and neighborhood). We use the ridesourcing-trip data in Chicago for empirical analysis. The results reveal that the importance of built environment varies across spatial contexts, and it collectively contributes the largest importance in predicting ridesourcing demand for airport trips. Additionally, the nonlinear effects of built environment on ridesourcing demand show strong spatial variations. Ridesourcing demand is usually most responsive to the built environment changes for downtown trips, followed by neighborhood trips and airport trips. These findings offer transportation professionals nuanced insights for managing ridesourcing services

Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN) for Travel Demand Forecasting During Wildfires

Real-time forecasting of travel demand during wildfire evacuations is crucial for emergency managers and transportation planners to make timely and better-informed decisions. However, few studies focus on accurate travel demand forecasting in large-scale emergency evacuations. Therefore, this study develops and tests a new methodological framework for modeling trip generation in wildfire evacuations by using (a) large-scale GPS data generated by mobile devices and (b) state-of-the-art AI technologies. The proposed methodology aims at forecasting evacuation trips and other types of trips. Based on the travel demand inferred from the GPS data, we develop a new deep learning model, i.e., Situational-Aware Multi-Graph Convolutional Recurrent Network (SA-MGCRN), along with a model updating scheme to achieve real-time forecasting of travel demand during wildfire evacuations. The proposed methodological framework is tested in this study for a real-world case study: the 2019 Kincade Fire in Sonoma County, CA. The results show that SA-MGCRN significantly outperforms all the selected state-of-the-art benchmarks in terms of prediction performance. Our finding suggests that the most important model components of SA-MGCRN are evacuation order/warning information, proximity to fire, and population change, which are consistent with behavioral theories and empirical findings