Classical models of decision making deal fairly well with uncertainty, where settings are well-structured in terms of goals, alternatives, and consequences. Conversely, the typical ill-structured nature of strategy choices remains a challenge for extant models. Such cases can hardly build on the past, and their novelty makes the prediction of consequences a very difficult and poorly robust task. The weakness of the classical expected utility model in representing such problems has not been adequately solved by recent extensions. In this paper (...) we offer an explanatory coherence model for decision making in ill-structured problems. We model alternatives as sets of concurrent causal explanations of reality that act as justifications for action. According to these premises, choice is based on an evaluation of the internal coherence and the consistency of competing explanations of the available evidence. This model is psychologically grounded on causal inference and builds on the connectionist tradition of explanatory coherence. To illustrate the model, we consider the decision of investing in a new technology and we discuss how changes in the structure of alternatives may impact on the solution. We show how the final choice depends on collecting the relevant evidence, making the suitable hypotheses, and drawing the consistent causal explanations linking the two. (shrink)

Public health communications often attempt to persuade their audience to adopt a particular belief or pursue a particular course of action. To a large extent, the ethical defensibility of persuasion appears to be assumed by public health practitioners; however, a handful of academic treatments have called into question the ethical defensibility of persuasive risk- and health communication. In addition, the widespread use of persuasive tactics in public health communications warrants a close look at their ethical status, irrespective of previous critiques. (...) In this article, we review some ethical objections previously advanced against the use of persuasion in public health communications, and also consider some novel but potentially relevant objections. We conclude that persuasion is ethically problematic in some circumstances and attempt to clarify what these circumstances are. However, whereas persuasion may be ethically problematic in some circumstances, it need not be viewed as intrinsically problematic. (shrink)

Since children are considered incapable ofgiving informed consent to participate inresearch, regulations require that bothparental permission and the assent of thepotential child subject be obtained. Assent andpermission are uniquely bound together, eachserving a different purpose. Parentalpermission protects the child from assumingunreasonable risks. Assent demonstrates respectfor the child and his developing autonomy. Inorder to give meaningful assent, the child mustunderstand that procedures will be performed,voluntarily choose to undergo the procedures,and communicate this choice. Understanding theelements of informed consent has been theparadigm for (...) assessing capacity to give assent.This method leaves the youngest, leastcognitively mature children vulnerable towaiver of assent and forced researchparticipation. Voluntariness can also becompromised by the influence of authorityfigures who can exert undue influence andcoerce children to participate in research. This paper discusses factors that may influencethe decision to give assent/permission,potential parent-child conflict in theassent/permission process and how it isresolved, and potential parental undueinfluence on research participation. Theseissues are illustrated with quotations drawnfrom a larger qualitative study of parentalpermission and child assent (data notpresented). We suggest a developmentalapproach, viewing assent as a continuum rangingfrom mere affirmation in the youngest childrento the equivalent of the informed consentprocess in the mature adolescent. (shrink)

Determining whether a research risk meets or exceeds a regulatory standard of risk acceptability is difficult. Recently a framework called the systematic evaluation of research risks (SERR) has been proposed as a method of comparing research risks with predetermined standards of acceptability. SERR purports to offer a systematic and largely determinate (definite) way to compare risks and say whether a specific research risk falls below or above an acknowledged standard of acceptable risk. Here the authors review some philosophical problems with (...) this framework, which they take to be representative of determinate approaches to risk comparison, and conclude that it should not be used in a stand-alone or determinate fashion. Instead, the authors suggest that a deliberative approach may be a more viable candidate for future development. Such an approach could be informed by methods such as SERR without being rigidly bound to them. (shrink)

The notion that an independent central bank reduces a country’s inflation is a controversial hypothesis. To date, it has not been possible to satisfactorily answer this question because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses. We develop a causal model that summarizes the economic process of inflation. Based on this causal model and recent data, we discuss and identify the assumptions under which the effect of central bank independence on (...) inflation can be identified and estimated. Given these and alternative assumptions, we estimate this effect using modern doubly robust effect estimators, i.e., longitudinal targeted maximum likelihood estimators. The estimation procedure incorporates machine learning algorithms and is tailored to address the challenges associated with complex longitudinal macroeconomic data. We do not find strong support for the hypothesis that having an independent central bank for a long period of time necessarily lowers inflation. Simulation studies evaluate the sensitivity of the proposed methods in complex settings when certain assumptions are violated and highlight the importance of working with appropriate learning algorithms for estimation. (shrink)