Ethical safety of deep brain stimulation: A study on moral decision-making in Parkinson's disease

INTRODUCTION: The possibility that deep brain stimulation (DBS) in Parkinson's disease (PD) alters patients' decisions and actions, even temporarily, raises important clinical, ethical and legal questions. Abnormal moral decision-making can lead to ethical rules violations. Previous experiments demonstrated the subthalamic (STN) activation during moral decision-making. Here we aim to study whether STN DBS can affect moral decision-making in PD patients. METHODS: Eleven patients with PD and bilateral STN DBS implant performed a computerized moral task in ON and OFF stimulation conditions. A control group of PD patients without DBS implant performed the same experimental protocol. All patients underwent motor, cognitive and psychological assessments. RESULTS: STN stimulation was not able to modify neither reaction times nor responses to moral task both when we compared the ON and the OFF state in the same patient (reaction times, p = .416) and when we compared DBS patients with those treated only with the best medical treatment (reaction times: p = .408, responses: p = .776). CONCLUSIONS: Moral judgment is the result of a complex process, requiring cognitive executive functions, problem-solving, anticipations of consequences of an action, conflict processing, emotional evaluation of context and of possible outcomes, and involving different brain areas and neural circuits. Our data show that STN DBS leaves unaffected moral decisions thus implying relevant clinical and ethical implications for DBS consequences on patients' behavior, on decision-making and on judgment ability. In conclusion, the technique can be considered safe on moral behavior

A Benefit-Cost Analysis of the Student Support Services Program

ABSTRACT Title of Document: A BENEFIT-COST ANALYSIS OF THE STUDENT SUPPORT SERVICES PROGRAM Ed M. Pacchetti, Doctor of Philosophy, 2009 Dissertation Directed by: Jennifer K. Rice, Ph.D. This study extends previous research on the Student Support Services program, a federal program that works to ensure college retention and graduation for low-income and first generation students, by examining the benefits and the costs of higher-impact SSS projects. Higher-impact SSS projects are defined as such because the graduation rates of their participants exceed the national graduation rate for other low-income and first generation students who have not participated in the SSS program. Applying a methodology used in other benefit-cost analyses of education programs, this study explores how the benefits over 40 years following participation in higher-impact SSS projects exceed the costs of these projects. This study focuses on benefits and costs to society. The benefit measures utilized in this study include higher income, lower health care costs and lower costs of crime. The cost measures include grant award costs, institutional project contributions, Pell Grant costs and the costs of Stafford Loan subsidies. The findings show that at three discount rates of 3%, 7% and 10%, the benefits of higher-impact SSS projects consistently exceed their costs. In addition, in most estimates of the future value of benefits generated by higher-impact SSS projects, the benefits generated by these projects are significant enough to provide for the grant award costs of all SSS projects at 4-year colleges and universities in project year 2005-2006, the year that is the focus of this study. This study's findings have implications for future research. Because the benefits of higher-impact SSS projects are significant, future research should focus on identifying the components of these projects responsible for success and incorporating these components into less successful projects in an attempt to increase the college graduation rates of all SSS projects. However, this study emphasizes that benefit-cost analysis should be one of many measures used to evaluate SSS projects and determine program success

For a Pluralism of Climate Modelling Strategies

The continued development of General Circulation Models (GCMs) towards increasing resolution and complexity is a predominantly chosen strategy to advance climate science, resulting in channelling of research and funding to meet this aspiration. Yet many other modelling strategies have also been developed and can be used to understand past and present climates, to project future climates and ultimately to support decision-making. We argue that a plurality of climate modelling strategies and an equitable distribution of funding among them would be an improvement on the current predominant strategy for informing policy making. To support our claim, we use a philosophy of science approach to compare increasing resolution and complexity of General Circulation Models with three alternate examples: the use of machine learning techniques, the physical climate storyline approach, and Earth System Models of Intermediate Complexity. We show that each of these strategies prioritises a particular set of methodological aims, among which are empirical agreement, realism, comprehensiveness, diversity of process representations, inclusion of the human dimension, reduction of computational expense, and intelligibility. Thus, each strategy may provide adequate information to support different specific kinds of research and decision questions. We conclude that, because climate decision-making consists of different kinds of questions, many modelling strategies are all potentially useful, and can be used in a complementary way.JJ acknowledges support from the Swiss National Science Foundation (SNF PRIMAproject, grant PR00P1_208469). ET was supported by a UKRI Future Leaders Fellowship (grant number MR/V024426/1). We thank Roland Séférian and Sébastien Dutreuil for helpful comments.Peer ReviewedPostprint (published version

For a Pluralism of Climate Modelling Strategies

The continued development of General Circulation Models (GCMs) towards increasing resolution and complexity is a predominantly chosen strategy to advance climate science, resulting in channelling of research and funding to meet this aspiration. Yet many other modelling strategies have also been developed and can be used to understand past and present climates, to project future climates and ultimately to support decision-making. We argue that a plurality of climate modelling strategies and an equitable distribution of funding among them would be an improvement on the current predominant strategy for informing policy making. To support our claim, we use a philosophy of science approach to compare increasing resolution and complexity of General Circulation Models with three alternate examples: the use of machine learning techniques, the physical climate storyline approach, and Earth System Models of Intermediate Complexity. We show that each of these strategies prioritises a particular set of methodological aims, among which are empirical agreement, realism, comprehensiveness, diversity of process representations, inclusion of the human dimension, reduction of computational expense, and intelligibility. Thus, each strategy may provide adequate information to support different specific kinds of research and decision questions. We conclude that, because climate decision-making consists of different kinds of questions, many modelling strategies are all potentially useful, and can be used in a complementary way

Spatiotemporal Scales in Modeling: Identifying Target Systems

My dissertation addresses neglected roles of idealization and abstraction in scientific modeling. Current debates about epistemic issues in modeling presuppose that a model in question uncontroversially represents a particular target system. A standard line of argument is that we can gain knowledge of a target system simply by noting what aspects of the target are veridically represented in the model. But this misses epistemically important aspects of modeling. I examine how scientists identify certain phenomena as target systems in their models. Building on the distinction between data and phenomena introduced by Bogen and Woodward, I analyze how scientists target systems from data and from basic theoretical principles. I show that there are two crucial empirical assumptions that are involved in identifying phenomena. These assumptions concern the conditions under which phenomena can be indexed to a particular length or time scale and the conditions under which one can treat phenomena occurring at different length or time scales as distinct. The role of these assumptions in modeling provides the basis for a new argument that shows how, in many cases, idealizations and abstractions in models are essential for providing knowledge about the world in so far as they isolate relevant components of a phenomenon from irrelevant ones. My analysis of the identification of phenomena also shows that structural uncertainty arises in models when the scale of a phenomenon of interest is not properly identified. This clarification promises to improve the communication of the limitation of current climate models to policy makers

Assessing the quality of state-of-the-art regional climate information: the case of the UK Climate Projections 2018

In this paper, we assess the quality of state-of-the-art regional climate information intended to support climate adaptation decision-making. We use the UK Climate Projections 2018 as an example of such information. Their probabilistic, global, and regional land projections exemplify some of the key methodologies that are at the forefront of constructing regional climate information for decision support in adapting to a changing climate. We assess the quality of the evidence and the methodology used to support their statements about future regional climate along six quality dimensions: transparency; theory; independence, number, and comprehensiveness of evidence; and historical empirical adequacy. The assessment produced two major insights. First, a major issue that taints the quality of UKCP18 is the lack of transparency, which is particularly problematic since the information is directed towards non-expert users who would need to develop technical skills to evaluate the quality and epistemic reliability of this information. Second, the probabilistic projections are of lower quality than the global projections because the former lack both transparency and a theory underpinning the method used to produce quantified uncertainty estimates about future climate. The assessment also shows how different dimensions are satisfied depending on the evidence used, the methodology chosen to analyze the evidence, and the type of statements that are constructed in the different strands of UKCP18. This research highlights the importance of knowledge quality assessment of regional climate information that intends to support climate change adaptation decisions

Assessing the quality of regional climate information

There are now a plethora of data, models, and approaches available to produce regional and local climate information intended to inform adaptation to a changing climate. There is, however, no framework to assess the quality of these data, models, and approaches that takes into account the issues that arise when this information is produced. An evaluation of the quality of regional climate information is a fundamental requirement for its appropriate application in societal decision-making. Here, an analytical framework is constructed for the quality assessment of science-based statements and estimates about future climate. This framework targets statements that project local and regional climate at decadal and longer time scales. After identifying the main issues with evaluating and presenting regional climate information, it is argued that it is helpful to consider the quality of statements about future climate in terms of 1) the type of evidence and 2) the relationship between the evidence and the statement. This distinction not only provides a more targeted framework for quality, but also shows how certain evidential standards can change as a function of the statement under consideration. The key dimensions to assess regional climate information quality are diversity, completeness, theory, adequacy for purpose, and transparency. This framework is exemplified using two research papers that provide regional climate information and the implications of the framework are explored

Perspectives on the quality of climate information for adaptation decision support

We summarise the contributions to the Topical Collection on quality of climate information for adaptation decision support. Based on these contributions, we draw some further lessons for the development of high-quality climate information and services, bridging between a “credibility-first” paradigm (exemplified by top-down information provision from systematic downscaling or impact projections) and a “salience-first” paradigm (exemplified by user-led tailored information products or storylines) by looking to identify their respective strengths and use cases. We emphasise that a more nuanced collective understanding of the dimensions of information quality in climate information and services would be beneficial to users and providers and ultimately support more confident and effective climate adaptation decisions and policy-making

Perspectives on the quality of climate information for adaptation decision support

We summarise the contributions to the Topical Collection on quality of climate information for adaptation decision support. Based on these contributions, we draw some further lessons for the development of high-quality climate information and services, bridging between a “credibility-first” paradigm (exemplified by top-down information provision from systematic downscaling or impact projections) and a “salience-first” paradigm (exemplified by user-led tailored information products or storylines) by looking to identify their respective strengths and use cases. We emphasise that a more nuanced collective understanding of the dimensions of information quality in climate information and services would be beneficial to users and providers and ultimately support more confident and effective climate adaptation decisions and policy-making