decision processes
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2022 ◽  
pp. 84-100
Author(s):  
Samia Hassan Rizk

The advances in biotechnology and computer and data sciences opened the way for innovative approaches to human healthcare. Meanwhile, they created many ethical and regulatory dilemmas such as pervasive global inequalities and security and risk to data privacy. The assessment of health technology is a systematic multidisciplinary process that aims to examine the benefits and risks associated with its use including medical, social, economic, and ethical impacts. It is used to inform policy and optimize decision-making. The advance of technology is creating significant challenges to healthcare regulators who strive to balance patient safety to fostering innovation. The FDA and EMA are modernizing their regulatory approaches to foster innovation in digital technology and improve safety and applicability to patients. On the other hand, data analytic technologies have been introduced into regulatory decision processes.


Author(s):  
Alberto Maria Metelli

AbstractReinforcement Learning (RL) has emerged as an effective approach to address a variety of complex control tasks. In a typical RL problem, an agent interacts with the environment by perceiving observations and performing actions, with the ultimate goal of maximizing the cumulative reward. In the traditional formulation, the environment is assumed to be a fixed entity that cannot be externally controlled. However, there exist several real-world scenarios in which the environment offers the opportunity to configure some of its parameters, with diverse effects on the agent’s learning process. In this contribution, we provide an overview of the main aspects of environment configurability. We start by introducing the formalism of the Configurable Markov Decision Processes (Conf-MDPs) and we illustrate the solutions concepts. Then, we revise the algorithms for solving the learning problem in Conf-MDPs. Finally, we present two applications of Conf-MDPs: policy space identification and control frequency adaptation.


2022 ◽  
pp. 236-262
Author(s):  
Michael D'Rosario ◽  
Carlene D'Rosario

Automated decision support systems with high stake decision processes are frequently controversial. The Online Compliance Intervention (herewith “OCI” or “RoboDebt”) is a system of compliance implemented with the intention to facilitate automatic issuance of statutory debt notices to individuals, taking a receipt of welfare payments and exceeding their entitlement. The system appears to employ rudimentary data scraping and expert systems to determine whether notices should be validly issued. However, many individuals that take receipt of debt notices assert that they were issued in error. The commentary on the system has resulted in a lot of conflation of the system with other system types and caused many to question the role of decision of support systems in public administration given the potentially deleterious impacts of such systems for the most vulnerable. The authors employ a taxonomy of Robotic Process Automation (RPA) issues, to review the OCI and RPA more generally. This paper identifies potential problems of bias, inconsistency, procedural fairness, and overall systematic error. This research also considers a series of RoboDebt specific issues regarding contractor arrangements and the potential impact of the system for Australia's Indigenous population. The authors offer a set of recommendations based on the observed challenges, emphasizing the importance of moderation, independent algorithmic audits, and ongoing reviews. Most notably, this paper emphasizes the need for greater transparency and a broadening of criteria to determine vulnerability that encompasses, temporal, geographic, and technological considerations.


2021 ◽  
Vol 14 (1) ◽  
pp. 271
Author(s):  
Opeyemi Afolabi Femi-Oladunni ◽  
Pablo Ruiz-Palomino ◽  
María Pilar Martínez-Ruiz ◽  
Ana Isabel Muro-Rodríguez

This article offers a semi-systematic literature review on the concept of food values. Specifically, 36 relevant research articles were analyzed. The results underscore the novelty and rapid popularity of this concept in different professional, scientific, and academic fields. Among the findings, the article highlights how the concept of food values has evolved to accommodate the features and behaviors of specific markets. Nonetheless, one can group food values into three distinct clusters. This article expands our understanding on the evolution of food values along different dimensions (various clusters and segments related to geographic regions and social classes). It also identifies several research gaps and translates them into different research proposals.


2021 ◽  
pp. 002224372110735
Author(s):  
Ye Li ◽  
Antonia Krefeld-Schwalb ◽  
Daniel G. Wall ◽  
Eric J. Johnson ◽  
Olivier Toubia ◽  
...  

Researchers and practitioners in marketing, economics, and public policy often use preference elicitation tasks to forecast real-world behaviors. These tasks typically ask a series of similarly-structured questions. The authors posit that every time a respondent answers an additional elicitation question, two things happen: (1) they provide information about some parameter(s) of interest, such as their time preference or the partworth for a product attribute, and (2) the respondent increasingly adapts to the task—i.e., using task-specific decision processes specialized for this task that may or may not apply to other tasks. Importantly, adaptation comes at the cost of potential mismatch between the task-specific decision process and real-world processes that generate the target behaviors, such that asking more questions can reduce external validity. The authors used mouse- and eye-tracking to trace decision processes in time preference measurement and conjoint choice tasks: Respondents increasingly relied on task-specific decision processes as more questions were asked, leading to reduced external validity for both related tasks and real-world behaviors. Importantly, the external validity of measured preferences peaked after as few as seven questions in both types of tasks. When measuring preferences, less can be more.


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