The Effect of Ballot Characteristics on the Likelihood of Voting Errors

In the United States, people are asked to vote on a myriad of candidates, offices, and ballot questions. The result is lengthy ballots that are time intensive and complicated to fill out. In this paper, we utilize a new analytical technique harnessing ballot scanner data from a statewide midterm election to estimate the effects of ballot complexity on voting errors. We find that increases in ballot length, increases in the number of local ballot questions, and increases in the number of candidates listed for single offices significantly increase the odds of encountering ballot marking and scanning errors. Our findings indicate that ballots’ characteristics can help election administrators make Election Day planning and resource allocation decisions that decrease ballot errors and associated wait times to vote while increasing the reliability of election results and voter confidence in the electoral process.

Discounting Under Severe Weather Threat

The human and economic costs of severe weather damage can be mitigated by appropriate preparation. Despite the benefits, researchers have only begun to examine if known decision-making frameworks apply to severe-weather-related decisions. Using experiments, we found that a hyperbolic discounting function accurately described participant decisions to prepare for, and respond to, severe weather, although only delays of 1 month or longer significantly changed decisions to evacuate, suggesting that severe weather that is not imminent does not affect evacuation decisions. In contrast, the probability that a storm would impact the participant influenced evacuation and resource allocation decisions. To influence people’s evacuation decisions, weather forecasters and community planers should focus on disseminating probabilistic information when focusing on short-term weather threats (e.g., hurricanes); delay information appears to affect people’s evacuation decision only for longer-term threats, which may hold promise for climate-change warnings.

Decentralize Your Dollars: Incorporating Participatory Grant Making into Resource Allocation Decisions

Participatory grant making is a concept that can support decentralized decision making, shared power, and racial equity in Extension programming. The underlying philosophy of participatory grant making is to empower individuals and communities with flexibility and support to make collective decisions about financial resources. In this article, we draw on our experiences and provide concrete examples to demonstrate the power of this model for improving Extension programming and internal procedures. We conclude that the concept could be adapted for an array of financial decision-making settings to help Extension stay relevant and rooted in the communities we serve.

Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation

COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

The Attributional–Counterfactual Theory of Need: Integrating Theories to Predict Need Norm Use

The justice literature has coalesced around the notion that actors (e.g., supervisors) tend to utilize the norm of equity for resource allocation decisions because it is generally considered most fair when employees who contribute more to the organization receive more resources. Yet, actors might sometimes utilize a need norm to allocate resources to those most in need. Studies that have addressed need-based resource allocations have assumed a relatively straightforward conceptualization of need. However, research from related areas suggests that multiple characteristics of the need itself could trigger actors’ use of a need norm to allocate resources. We advance a theoretical framework that outlines various need characteristics that drive actors’ use of a need norm. The framework draws on the processes outlined in attribution theory and integrates those with the content domains addressed in fairness theory. A discussion of the implications for justice, attribution, and fairness theory research follows.