A Fair Allocation Algorithm for Predictive Police Patrolling

We address the tradeoff of developing good predictive models for police allocation vs. optimally deploying police officers over a city in a way that does not imply an unfair allocation of resources. We modify the fair allocation algorithm of [1] to tackle a real world problem: crime in the city of Bogota, Colombia. Our approach allows for more sophisticated prediction models and we ´ show that the whole methodology outperforms the current police allocating mechanism in the city. Results show that even with a simple model such as a Kernel Density Estimation of crime, one can have much better prediction than the current police model and, at the same time, mitigate fairness concerns. Although we can not provide general performance guarantees, our results apply to a real life problem and should be seriously considered by policy makers.

Fair allocation of conflicting items

We study fair allocation of indivisible items, where the items are furnished with a set of conflicts, and agents are not permitted to receive conflicting items. This kind of constraint captures, for example, participating in events that overlap in time, or taking on roles in the presence of conflicting interests. We demonstrate, both theoretically and experimentally, that fairness characterizations such as EF1, MMS and MNW still are applicable and useful under item conflicts. Among other existence, non-existence and computability results, we show that a $$1/\Delta $$ 1 / Δ -approximate MMS allocation for n agents may be found in polynomial time when $$n>\Delta >2$$ n > Δ > 2 , for any conflict graph with maximum degree $$\Delta$$ Δ , and that, if $$n > \Delta $$ n > Δ , a 1/3-approximate MMS allocation always exists.

Public Opinion on Priorities Toward Fair Allocation of Ventilators During COVID-19 Pandemic: A Nationwide Survey

Background: The rapidly growing imbalance between supply and demand for ventilators during the COVID-19 pandemic has highlighted the principles for fair allocation of scarce resources. Failing to address public views and concerns on the subject could fuel distrust. The objective of this study was to determine the priorities of the Iranian public toward the fair allocation of ventilators during the COVID-19 pandemic.Methods: This anonymous community-based national study was conducted from May 28 to Aug 20, 2020, in Iran. Data were collected via the Google Forms platform, using an online self-administrative questionnaire. The questionnaire assessed participants' assigned prioritization scores for ventilators based on medical and non-medical criteria. To quantify participants' responses on prioritizing ventilator allocation among sub-groups of patients with COVID-19 who need mechanical ventilation scores ranging from −2, very low priority, to +2, very high priority were assigned to each response.Results: Responses of 2,043 participants, 1,189 women, and 1,012 men, were analyzed. The mean (SD) age was 31.1 (9.5), being 32.1 (9.3) among women, and 29.9 (9.6) among men. Among all participants, 274 (13.4%) were healthcare workers. The median of assigned priority score was zero (equal) for gender, age 41–80, nationality, religion, socioeconomic, high-profile governmental position, high-profile occupation, being celebrities, employment status, smoking status, drug abuse, end-stage status, and obesity. The median assigned priority score was +2 (very high priority) for pregnancy, and having <2 years old children. The median assigned priority score was +1 (high priority) for physicians and nurses of patients with COVID-19, patients with nobel research position, those aged <40 years, those with underlying disease, immunocompromise status, and malignancy. Age>80 was the only factor participants assigned −1 (low priority) to.Conclusions: Participants stated that socioeconomic factors, except for age>80, should not be involved in prioritizing mechanical ventilators at the time of resources scarcity. Front-line physicians and nurses of COVID-19 patients, pregnant mothers, mothers who had children under 2 years old were given high priority.

Global Climate Change—Who Ought to Pay the Bill?

Global climate change is a significant challenge for current and, particularly, future generations. In the public debate about the fair allocation of associated costs commonly the moral claim that the developed countries should burden the costs is expressed. To support this claim, often four moral arguments, based on the theory of justice, are raised: (i) the polluter pays, (ii) the historical responsibility, (iii) the beneficiary pays, and (iv) the ability to pay. The aim of the paper is to assess whether these principles impose a duty on the developed countries and whether a fair allocation of costs would be achieved.

Fair Resource Allocation in a Volatile Marketplace

In settings where a platform must allocate finite supplies of goods to buyers, balancing overall platform revenues with the fairness of the individual allocations to platform participants is paramount to the well-functioning of the platform. This is made even more difficult by the fact that the supply of goods is in practice stochastic and difficult to forecast, such as in the case of online ad allocation, where the platform manages a supply of impressions that varies over time. In this paper, we design a fair allocation scheme that works in the presence of supply uncertainty. Algorithmically, the scheme repeatedly solves for Fisher market equilibria in a model predictive control fashion and is proved to admit constant factor guarantees versus the offline optimal. In addition, the scheme is tested on a sequence of real ad datasets, showing strong empirical performance.

Constraints in fair division

The fair allocation of resources to interested agents is a fundamental problem in society. While the majority of the fair division literature assumes that all allocations are feasible, in practice there are often constraints on the allocation that can be chosen. In this survey, we discuss fairness guarantees for both divisible (cake cutting) and indivisible resources under several common types of constraints, including connectivity, cardinality, matroid, geometric, separation, budget, and conflict constraints. We also outline a number of open questions and directions.