We document that traditional energy firms are key innovators in the United States' green patent landscape. These firms produce more, and significantly higher-quality, green innovation. In many green technology spaces, they appear to be influential first movers and to produce ongoing foundational aspects of innovation and commercialization on which other alternative energy producers build. They additionally invest significantly in labor and capital to complement these green innovations. These traditional energy firms, however, receive significantly lower environmental, social, and governance (ESG) scores and fund flows and are not rewarded for incremental green innovation. This behavior is consistent with a competitive response by traditional energy firms to preempt obsolescence of current technology by investing in future replacement technologies.
Motivated by the dynamic interdependence of global economic cycles, this study proposes a dual-market economic condition-based model switching (DMEC-MS) method. The method aims to enhance the predictive ability of economic policy uncertainty (EPU) for stock market volatility by incorporating evolving dual-market economic scenarios. In this method, we introduce a new international-domestic economic condition (IDEC) proxy to measure different dual-market economic scenarios. The core mechanism of the DMEC-MS method is to capture the time-varying relationship between EPU and volatility based on the proposed IDEC. The method employs a model selection procedure to extract specific IDEC scenarios that substantially influence the EPU-volatility relationship in real time, facilitating the switch of prediction models. The empirical analysis demonstrates that the DMEC-MS method offers enhanced forecasting power and economic value.
AI is increasingly used in high-stakes decisions; yet, it can reproduce social bias in its outputs. This study tested an end-user-facing, post-processing intervention that pairs model-agnostic counterfactual explanations with brief inoculation training to surface bias at decision time and counter automation bias. Counterfactual inoculation improved bias recognition in hiring, with the strongest effects observed for socially salient cues such as gender and ethnicity. Confidence gains were smaller and inconsistent. Trust did not rise uniformly, indicating calibrated skepticism rather than indiscriminate distrust. Counterfactual audit flags interrupted reflexive acceptance by showing how small input changes affect rankings, while inoculation primed users to look for bias signals, jointly shifting trust toward appropriate calibration. Practical implications include adding simple "what-if" audit flags that work with any model, allowing override or escalation when needed, and providing brief, hands-on training. Limitations include scenario-dependent effects, self-reported outcomes in a simulated workflow, and heteroscedasticity in a subset of models, which was addressed through Welch correction and bootstrapped Analysis of Covariance. Future research should assess durability over time, incorporate behavioral endpoint tools, extend this research to domains beyond hiring, and adapt explanation complexity to the domain and the user. In summary, an inoculation that explains the prevalence of bias in AI hiring tools, plus counterfactuals that expose bias cues offer a practical, interpretable means to make bias visible when decisions are made in and to move end-user trust in AI tools from blind acceptance to calibrated judgment.
We investigate a class of second-order difference equations featuring operator-valued coefficients with the aim of approaching problems of stationary scattering theory. We focus on various compact perturbations of the discrete Laplacian given in a Hilbert space of bi-infinite square-summable sequences with entries from a fixed Hilbert space. This work includes a detailed spectral analysis of the perturbed Laplacian and the construction and study of the corresponding objects pertaining to scattering theory, including the entries of the scattering matrix.
This paper defends the notion of “embedded leadership” as a viable and appropriate concept of leadership. It is a model for leadership “particularly relevant in complex and dynamic environments.” I shall argue that in today’s complex global systems in which all of us exist, this definition of leadership is not only viable in such environments but is more suitable and successful in all forms of organizational, economic, and political life in the 21st century.
