Nash Social Welfare in Multiagent Resource Allocation

We study different aspects of the multiagent resource allocation problem when the objective is to find an allocation that maximizes Nash social welfare, the product of the utilities of the individual agents. The Nash solution is an important welfare criterion that combines efficiency and fairness considerations. We show that the problem of finding an optimal outcome is NP-hard for a number of different languages for representing agent preferences; we establish new results regarding convergence to Nash-optimal outcomes in a distributed negotiation framework; and we design and test algorithms similar to those applied in combinatorial auctions for computing such an outcome directly

Adaptive Strategies for Dynamic Pricing Agents

Dynamic Pricing (DyP) is a form of Revenue Management in which the price of a (usually) perishable good is changed over time to increase revenue. It is an effective method that has become even more relevant and useful with the emergence of Internet firms and the possibility of readily and frequently updating prices. In this paper a new approach to DyP is presented. We design adaptive dynamic pricing strategies and optimize their parameters with an Evolutionary Algorithm (EA) offline while the strategies can deal with stochastic market dynamics quickly online. We design two adaptive heuristic dynamic pricing strategies in a duopoly where each firm has a finite inventory of a single type of good. We consider two cases, one in which the average of a customer population’s stochastic valuation for each of the goods is constant throughout the selling horizon and one in which the average customer valuation for each good is changed according to a random Brownian motion. We also design an agent-based software framework for simulating various dynamic pricing strategies in agent-based marketplaces with multiple firms in a bounded time horizon. We use an EA to optimize the parameters for each of the pricing strategies in each of the settings and compare the strategies with other strategies from the literature. We also perform sensitivity a analysis and show that the optimized strategies work well even when used in settings with varied demand functions

Adaptive Strategies for Dynamic Pricing Agents

Dynamic Pricing (DyP) is a form of Revenue Management in which the price of a (usually) perishable good is changed over time to increase revenue. It is an effective method that has become even more relevant and useful with the emergence of Internet firms and the possibility of readily and frequently updating prices. In this paper a new approach to DyP is presented. We design adaptive dynamic pricing strategies and optimize their parameters with an Evolutionary Algorithm (EA) offline while the strategies can deal with stochastic market dynamics quickly online. We design two adaptive heuristic dynamic pricing strategies in a duopoly where each firm has a finite inventory of a single type of good. We consider two cases, one in which the average of a customer population’s stochastic valuation for each of the goods is constant throughout the selling horizon and one in which the average customer valuation for each good is changed according to a random Brownian motion. We also design an agent-based software framework for simulating various dynamic pricing strategies in agent-based marketplaces with multiple firms in a bounded time horizon. We use an EA to optimize the parameters for each of the pricing strategies in each of the settings and compare the strategies with other strategies from the literature. We also perform sensitivity a analysis and show that the optimized strategies work well even when used in settings with varied demand functions

Adaptive Strategies for Dynamic Pricing Agents

Dynamic Pricing (DyP) is a form of Revenue Management in which the price of a (usually) perishable good is changed over time to increase revenue. It is an effective method that has become even more relevant and useful with the emergence of Internet firms and the possibility of readily and frequently updating prices. In this paper a new approach to DyP is presented. We design adaptive dynamic pricing strategies and optimize their parameters with an Evolutionary Algorithm (EA) offline while the strategies can deal with stochastic market dynamics quickly online. We design two adaptive heuristic dynamic pricing strategies in a duopoly where each firm has a finite inventory of a single type of good. We consider two cases, one in which the average of a customer population's stochastic valuation for each of the goods is constant throughout the selling horizon and one in which the average customer valuation for each good is changed according to a random Brownian motion. We also design an agent-based software framework for simulating various dynamic pricing strategies in agent based marketplaces with multiple firms in a bounded time horizon. We use an EA to optimize the parameters for each of the pricing strategies in each of the settings and compare the strategies with other strategies from the literature. We also perform sensitivity analysis and show that the optimized strategies work well even when used in settings with varied demand functions

Fast and revenue-oriented protection of radial LV cables with smart battery operation

Low-voltage radial electricity cables will have more and more difficulties to carry the increasing load of novel consumption devices (e.g. electric vehicles) and the expected generated input of decentrally-generated power (e.g. from photovoltaic cells). One solution to avoid replacement is to install a battery at the end of a cable which is expected to be overloaded frequently. The intelligent operation of this battery needs to combine the protection of the cable with optimizing its revenue, in order to be economically viable. This paper formulates the offline optimization problem and proposes two robust heuristic online strategies. We show in computer simulations that these heuristics, which make fast just-in-time responses, reliably deliver good results. Our second heuristic, H2, reaches up to 83% of the approximated theoretical optimum

Global, regional, and national burden of colorectal cancer and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Funding: F Carvalho and E Fernandes acknowledge support from Fundação para a Ciência e a Tecnologia, I.P. (FCT), in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of the Research Unit on Applied Molecular Biosciences UCIBIO and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy i4HB; FCT/MCTES through the project UIDB/50006/2020. J Conde acknowledges the European Research Council Starting Grant (ERC-StG-2019-848325). V M Costa acknowledges the grant SFRH/BHD/110001/2015, received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006.proofepub_ahead_of_prin

The global burden of adolescent and young adult cancer in 2019 : a systematic analysis for the Global Burden of Disease Study 2019

Background In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15-39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15-39 years to define adolescents and young adults. Findings There were 1.19 million (95% UI 1.11-1.28) incident cancer cases and 396 000 (370 000-425 000) deaths due to cancer among people aged 15-39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59.6 [54.5-65.7] per 100 000 person-years) and high-middle SDI countries (53.2 [48.8-57.9] per 100 000 person-years), while the highest age-standardised mortality rates were in low-middle SDI (14.2 [12.9-15.6] per 100 000 person-years) and middle SDI (13.6 [12.6-14.8] per 100 000 person-years) countries. In 2019, adolescent and young adult cancers contributed 23.5 million (21.9-25.2) DALYs to the global burden of disease, of which 2.7% (1.9-3.6) came from YLDs and 97.3% (96.4-98.1) from YLLs. Cancer was the fourth leading cause of death and tenth leading cause of DALYs in adolescents and young adults globally. Interpretation Adolescent and young adult cancers contributed substantially to the overall adolescent and young adult disease burden globally in 2019. These results provide new insights into the distribution and magnitude of the adolescent and young adult cancer burden around the world. With notable differences observed across SDI settings, these estimates can inform global and country-level cancer control efforts. Copyright (C) 2021 The Author(s). Published by Elsevier Ltd.Peer reviewe

Fast and Revenue-oriented Protection of Radial LV Cables with Smart Battery Operation

Increasing demand and generation in the low-voltage part of our electricity grids challenge existing infrastructure. A battery installed at the end of often-overheated cables can protect them from overheating, if it is operated intelligently. In addition, it can be crucial to also optimize the battery's revenue. We propose two robust heuristic online strategies, which reach up to 83% of the approximated theoretical optimum1