Ian P. McCarthy

Large language models (LLMs) not only mimic human language generally but are also showing the ability to exhibit nuanced, idiosyncratic human behavior. This could lead to the expanded use of LLMs in research and a diminution of the influence of humans. It also has implications for AI alignment and the role of the technology in society.

The meteoric rise in cybercrime in recent years has resulted in renewed efforts to stem the potential negative effects of these nefarious activities. In this context, the role of the chief information security officer (CISO) has become one of strategic importance, safeguarding the integrity of the organization’s digital assets. Given the economic impact of cybercrime, it has become critically important to understand the cybercrime-related issues that organizations face. We sought to identify these issues by conducting a bibliographic analysis of cybercrime research. The results identified the most prolific and impactful authors, journals, and countries of publication, the most influential articles, and trends in the literature on cybercrime. The research suggests that interest in the field is wide-reaching with the growth in publications stemming from diverse academic disciplines and geographies. The identified trends represent critical knowledge areas for the CISO that are likely to continue the expansion of the field.

During the COVID-19 pandemic, there has been considerable research on how regional and country-level forecasting can be used to anticipate required hospital resources. We add to and build on this work by focusing on ward-level forecasting and planning tools for hospital staff during the pandemic. We present an assessment, validation, and deployment of a working prototype forecasting tool used within a modified Traffic Control Bundling (TCB) protocol for resource planning during the pandemic. We compare statistical and machine learning forecasting methods and their accuracy at one of the largest hospitals (Vancouver General Hospital) in Canada against a medium-sized hospital (St. Paul’s Hospital) in Vancouver, Canada through the first three waves of the COVID-19 pandemic in the province of British Columbia. Our results confirm that traditional statistical and machine learning (ML) forecasting methods can provide valuable ward-level forecasting to aid in decision-making for pandemic resource planning. Using point forecasts with upper 95% prediction intervals, such forecasting methods would have provided better accuracy in anticipating required beds on COVID-19 hospital units than ward-level capacity decisions made by hospital staff. We have integrated our methodology into a publicly available online tool that operationalizes ward-level forecasting to aid with capacity planning decisions. Importantly, hospital staff can use this tool to translate forecasts into better patient care, less burnout, and improved planning for all hospital resources during pandemics.

Blockchain technologies, specifically those related to cryptocurrencies and non-fungible tokens (NFTs), have recently garnered significant public interest. Blockchain has been lauded as potentially transformative across various industries, including decentralized finance, supply chain management, healthcare, file storage, and more. However, it has also been the source of much investor angst, evidenced by numerous media headlines reporting instances of fraud and catastrophic asset losses. In this paper, we examine whether the current incarnations of blockchain technologies provide a well-functioning market to its participants. We apply Roth’s market design framework to evaluate whether blockchain marketplaces provide safety, thickness, low congestion, and low repugnance. We find that blockchain applications in their current state do not represent a well-functioning market. We conclude by suggesting additional research avenues and explore whether they can still provide value to organizations even in their suboptimal market state.

Previous studies on voting bias in competitive awards have not fully considered the role of cultural similarity. Using data for the Best FIFA Men’s Player Award, we evaluate the extent of voting bias in this Award using three cultural similarity factors (cultural distance, cultural clusters, and collectivism), six established in-group factors (nationality, club, league, geography, ethnicity, religion, and language) and the impartiality of the voter’s country. Using statistical and econometric methods, we find that voter-player cultural similarity is positively associated with voting bias and find no evidence of impartiality when it comes to cultural or national ties. We also find that media voters are less biased than captain voters and coach voters, and that coaches are less biased than captains.