A Test of ‘Utilitarianism for Animals, Kantianism for People’

Many people believe that animals possess moral status, but human beings possess higher moral status than animals. To provide a theoretical basis for this view, Robert Nozick proposed Utilitarianism for Animals, Kantianism for People. The present manuscript evaluates Nozick’s proposal by identifying the tradeoffs in welfare that it permits in medical research with animals and assessing whether those tradeoffs are indeed permissible. This analysis suggests that at least some deontological side constraints apply to the treatment of sentient animals, hence, Utilitarianism for Animals, Kantianism for People is mistaken. Because Nozick’s proposal represents a prominent attempt to provide a theoretical basis for the common belief that human beings possess higher moral status than animals, this conclusion is noteworthy in its own right. Moreover, by granting equal moral weight to the interests of animals, but reserving deontological side constraints for human beings, Utilitarianism for Animals, Kantianism for People offers one of the more plausible bases for the claim that there are degrees of moral status among those who matter morally. The manuscript thus ends by considering the implications of the present analysis for the possibility that moral status comes in degrees.

Measuring Voluntary Responses in Healthcare Utilization During and After COVID-19 Pandemic: Evidence from Taiwan

Healthcare has been one of the most affected sectors during the coronavirus disease 2019 (COVID-19) pandemic. The utilization of related services for non-COVID-19 diseases fell dramatically following the point at which the virus broke out; however, little is known about whether this observed decline in healthcare use was due to voluntary behaviors or enforced measures. This paper quantifies the spontaneous change in healthcare utilization during and after the pandemic. We utilize a county-by-week-level dataset from Taiwan's National Health Insurance (NHI) record, covering the entire Taiwanese population, and a difference-in-differences design. Our results indicate that even if there were no human mobility restrictions or supply-side constraints, people voluntarily reduced their demand for healthcare, due to fears of contagion, or COVID-related precautionary behaviors. We find that the number of outpatient visits (inpatient admissions) decreased by 21% (11%) during the pandemic period (February to May 2020). Furthermore, the demand response of healthcare for infectious diseases (e.g., flu) was much greater and more persistent than for non-infectious diseases, thereby suggesting that the substantial decline in accessing healthcare was induced by positive public health externality of prevention measures for COVID-19. Finally, we find that the demand for healthcare services did not get back to the pre-pandemic baseline, even when there were no local coronavirus cases for 253 consecutive days (mid-April to December 2020) in Taiwan.

Routing two stacking cranes with predetermined container sequences

The scheduling of gantry cranes with respect to mutual interference has received considerable attention in recent years. We consider a subproblem which arises when each crane has a sequence of tasks to be assigned. The problem is concerned with resolving the interference between two cranes by determining which crane avoids the other in order to let it complete its next task first. We provide a fairly general problem framework accounting for different crane systems and various side constraints. We assume a cost function for each task that determines the cost of completing the task at a specific point in time. We then distinguish between the objectives to minimize both the total cost and the maximum cost among tasks. A general dynamic programming framework is provided which allows us to solve all problem versions in pseudo-polynomial time. Furthermore, we show that while the general problem aiming for minimum total cost is binary NP-hard, the general problem aiming for minimum maximum cost can be solved in polynomial time. Finally, we address two important special cases of the former, and we show that they can be solved in polynomial time as well.

Malaysia

Malaysia entered the pandemic from a robust economic position but has nonetheless been significantly affected. A synchronous fiscal, monetary and financial policy response has helped cushion the economic impact. As a result, after a deep recession in 2020, and assuming the pandemic is brought under control in Malaysia and globally, growth would rebound to 6.5 percent in 2021 as supply side constraints are lifted and domestic and external demand recover. Large downside risks will remain.

Effect on Regional integration on Inter-regional Trade in Africa

Regional integration offers participating countries advantages including the enlargement of the economic space, increased efficiency and the development of interregional trade. In the African regional communities, these assets are slow to emerge because of insufficient progress in the implementation of protocols, lack of economic diversification and various supply-side constraints. Moreover, Africa’s ability to participate fully in world trade is currently hampered by weaknesses in private investment inflows, commodity dependence, non-existent or poorly developed infrastructure, and small regional markets

Courses timetabling based on hill climbing algorithm

In addition to its monotonous nature and excessive time requirements, the manual school timetable scheduling often leads to more than one class being assigned to the same instructor, or more than one instructor being assigned to the same classroom during the same slot time, or even leads to exercise in intentional partialities in favor of a particular group of instructors. In this paper, an automated school timetable scheduling is presented to help overcome the traditional conflicts inherent in the manual scheduling approach. In this approach, hill climbing algorithms have been modified to transact hard and soft constraints. Soft constraints are not easy to be satisfied typically, but hard constraints are obligated. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first 5 seconds and by 50% from the second iteration while the optimal solution will be achieved after 15 iterations ensuring that more than 50% of scientific courses will take place in the early slots time while more than 50% of non-scientific courses will take place during the later time's slots.

Why No True Reliabilist Should Endorse Reliabilism

Critics have recently argued that reliabilists face trade-off problems, forcing them to condone intuitively unjustified beliefs when they generate lots of true belief further downstream. What these critics overlook is that reliabilism entails that there are side-constraints on belief-formation, on account of which there are some things you should not believe, even if doing so would have very good epistemic consequences. However, we argue that by embracing side-constraints the reliabilist faces a dilemma: she can either hold on to reliabilism, and with it aforementioned side-constraints, but then needs to explain why we should allow the pursuit of justification to get in the way of the acquisition of true belief; or she can deny that there are side-constraints – and in effect give up on reliabilism. We'll suggest that anyone moved by the considerations that likely attract people to reliabilism in the first place – the idea the true belief is good, and as such should be promoted – should go for the second horn, and instead pursue a form of epistemic utilitarianism.

Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation

One of the major challenges in the coordination of large, open, collaborative, and commercial vehicle fleets is dynamic task allocation. Self-concerned individually rational vehicle drivers have both local and global objectives, which require coordination using some fair and efficient task allocation method. In this paper, we review the literature on scalable and dynamic task allocation focusing on deterministic and dynamic two-dimensional linear assignment problems. We focus on multiagent system representation of open vehicle fleets where dynamically appearing vehicles are represented by software agents that should be allocated to a set of dynamically appearing tasks. We give a comparison and critical analysis of recent research results focusing on centralized, distributed, and decentralized solution approaches. Moreover, we propose mathematical models for dynamic versions of the following assignment problems well known in combinatorial optimization: the assignment problem, bottleneck assignment problem, fair matching problem, dynamic minimum deviation assignment problem, Σk-assignment problem, the semiassignment problem, the assignment problem with side constraints, and the assignment problem while recognizing agent qualification; all while considering the main aspect of open vehicle fleets: random arrival of tasks and vehicles (agents) that may become available after assisting previous tasks or by participating in the fleet at times based on individual interest.