4,615 research outputs found
Automating decision making to help establish norm-based regulations
Norms have been extensively proposed as coordination mechanisms for both
agent and human societies. Nevertheless, choosing the norms to regulate a
society is by no means straightforward. The reasons are twofold. First, the
norms to choose from may not be independent (i.e, they can be related to each
other). Second, different preference criteria may be applied when choosing the
norms to enact. This paper advances the state of the art by modeling a series
of decision-making problems that regulation authorities confront when choosing
the policies to establish. In order to do so, we first identify three different
norm relationships -namely, generalisation, exclusivity, and substitutability-
and we then consider norm representation power, cost, and associated moral
values as alternative preference criteria. Thereafter, we show that the
decision-making problems faced by policy makers can be encoded as linear
programs, and hence solved with the aid of state-of-the-art solvers
Recommended from our members
A normative approach to multi-agent systems for intelligent buildings
Building Management Systems (BMS) are widely adopted in modern buildings around the world in order to
provide high-quality building services, and reduce the running cost of the building. However, most BMS are
functionality-oriented and do not consider user personalization. The aim of this research is to capture and
represent building management rules using organizational semiotics methods. We implement Semantic
Analysis, which determines semantic units in building management and their relationship patterns of
behaviour, and Norm Analysis, which extracts and specifies the norms that establish how and when these
management actions occur. Finally, we propose a multi-agent framework for norm based building
management. This framework contributes to the design domain of intelligent building management system
by defining a set of behaviour patterns, and the norms that govern the real-time behaviour in a building
Open Science in Software Engineering
Open science describes the movement of making any research artefact available
to the public and includes, but is not limited to, open access, open data, and
open source. While open science is becoming generally accepted as a norm in
other scientific disciplines, in software engineering, we are still struggling
in adapting open science to the particularities of our discipline, rendering
progress in our scientific community cumbersome. In this chapter, we reflect
upon the essentials in open science for software engineering including what
open science is, why we should engage in it, and how we should do it. We
particularly draw from our experiences made as conference chairs implementing
open science initiatives and as researchers actively engaging in open science
to critically discuss challenges and pitfalls, and to address more advanced
topics such as how and under which conditions to share preprints, what
infrastructure and licence model to cover, or how do it within the limitations
of different reviewing models, such as double-blind reviewing. Our hope is to
help establishing a common ground and to contribute to make open science a norm
also in software engineering.Comment: Camera-Ready Version of a Chapter published in the book on
Contemporary Empirical Methods in Software Engineering; fixed layout issue
with side-note
Implementation of the Affordable Care Act in California: A Window of Opportunity for State Policy Makers
Outlines policy goals toward which the state can make significant gains under the 2010 healthcare reform law, including near-universal coverage, delivery system reform, simplifying insurance markets, and prevention and wellness. Considers challenges
Automating FDA Regulation
In the twentieth century, the Food and Drug Administration (“FDA”) rose to prominence as a respected scientific agency. By the middle of the century, it transformed the U.S. medical marketplace from an unregulated haven for dangerous products and false claims to a respected exemplar of public health. More recently, the FDA’s objectivity has increasingly been questioned. Critics argue the agency has become overly political and too accommodating to industry while lowering its standards for safety and efficacy. The FDA’s accelerated pathways for product testing and approval are partly to blame. They require lower-quality evidence, such as surrogate endpoints, and shift the FDA’s focus from premarket clinical trials toward postmarket surveillance, requiring less evidence up front while promising enhanced scrutiny on the back end. To further streamline product testing and approval, the FDA is adopting outputs from computer models, enhanced by artificial intelligence (“AI”), as surrogates for direct evidence of safety and efficacy.
This Article analyzes how the FDA uses computer models and simulations to save resources, reduce costs, infer product safety and efficacy, and make regulatory decisions. To test medical products, the FDA assembles cohorts of virtual humans and conducts digital clinical trials. Using molecular modeling, it simulates how substances interact with cellular targets to predict adverse effects and determine how drugs should be regulated. Though legal scholars have commented on the role of AI as a medical product that is regulated by the FDA, they have largely overlooked the role of AI as a medical product regulator. Modeling and simulation could eventually reduce the exposure of volunteers to risks and help protect the public. However, these technologies lower safety and efficacy standards and may erode public trust in the FDA while undermining its transparency, accountability, objectivity, and legitimacy. Bias in computer models and simulations may prioritize efficiency and speed over other values such as maximizing safety, equity, and public health. By analyzing FDA guidance documents and industry and agency simulation standards, this Article offers recommendations for safer and more equitable automation of FDA regulation
Electronic institutions with normative environments for agent-based E-contracting
Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201
The Federal Reserve's role in retail payments: adapting to a new environment
The U.S. retail payments system is in the midst of a transformation. The shift from paper to electronics, the emergence of new instruments and payments channels, the rise in nonbank participation, the change in risk profiles—all are elements of this new landscape. The Federal Reserve takes as one of its mandates fostering a payments system that is safe, efficient, and accessible. How does the Federal Reserve fulfill this mandate in this new environment? ; Since its beginning, the Federal Reserve has played a crucial role in the U.S. retail payments system. From time to time, that role has been reevaluated The current environment suggests the time may be right for another examination. Other central banks are facing similar issues. ; Weiner reexamines the Federal Reserve’s role in retail payments in light of the evolving payments system. The Federal Reserve will likely continue to play an important role in retail payments. However, given the evolution of the payments system, the role the Federal Reserve plays and the rationale for this role may be different than they have been in the past.
Conceptual Modeling in Law: An Interdisciplinary Research Agenda
The article describes how different approaches from the IS field of conceptual modeling should be transferred to the legal domain to enhance comprehensibility of legal regulations and contracts. It is further described how this in turn would benefit the IS discipline. The findings emphasize the importance of further interdisciplinary research on that topic. A research agenda that synthesizes the presented ideas is proposed based on a framework that structures the research field. Researchers from both disciplines, IS and Law, that are interested in this field should use the research agenda to position their research and to derive new and innovative research questions
- …