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Artificial Intelligence (AI) is revolutionizing micro and macro-aided decision-making processes across various sectors and disciplines worldwide. This paper uses the probability technique to theoretically forecast AI-aided integration program potential to offer accelerated, concrete and individualized integration support for new and 'old' resident immigrants throughout their stay in Finland. The algorithms are base on various taxonomies and topologies for individualized self-paced, life-long situation-specific integration support. This action research article theoretically reviews two high-quality peer-reviewed publications on AI in aging through systematic reviews and meta-analyses, combining results from multiple impact evaluation studies to construct arguments and draw conclusions for the use of AI in accelerating new migrants' integration in Finland. Data is analyzed qualitatively and quantitively to deduce realistic predictions. This article uses grounded theory to test theories that underscore the role and impact of AI in accelerating integration. This article presents the foundation on which future integration programs will be implemented. [ABSTRACT FROM AUTHOR]

Probabilistic Risk Assessment (PRA) has proven to be an invaluable tool for evaluating risks in complex engineered systems. However, there is increasing concern that PRA may not be adequate in situations with little underlying knowledge to support probabilistic representation of uncertainties. As analysts and policy makers turn their attention to deeply uncertain hazards such as climate change, a number of alternatives to traditional PRA have been proposed. This paper systematically compares three diverse approaches for risk analysis under deep uncertainty (qualitative uncertainty factors, probability bounds, and robust decision making) in terms of their representation of uncertain quantities, analytical output, and implications for risk management. A simple example problem is used to highlight differences in the way that each method relates to the traditional risk assessment process and fundamental issues associated with risk assessment and description. We find that the implications for decision making are not necessarily consistent between approaches, and that differences in the representation of uncertain quantities and analytical output suggest contexts in which each method may be most appropriate. Finally, each methodology demonstrates how risk assessment can inform decision making in deeply uncertain contexts, informing more effective responses to risk problems characterized by deep uncertainty. [ABSTRACT FROM AUTHOR]

Animals can shape their timed behaviors based on experienced probabilistic relations in a nearly optimal fashion. On the other hand, it is not clear if they adopt these timed decisions by making computations based on previously learnt task parameters (time intervals, locations, and probabilities) or if they gradually develop their decisions based on trial and error. To address this question, we tested mice in the timed-switching task, which required them to anticipate when (after a short or long delay) and at which of the two delay locations a reward would be presented. The probability of short trials differed between test groups in two experiments. Critically, we first trained mice on relevant task parameters by signaling the active trial with a discriminative stimulus and delivered the corresponding reward after the associated delay without any response requirement (without inducing switching behavior). During the test phase, both options were presented simultaneously to characterize the emergence and temporal characteristics of the switching behavior. Mice exhibited timed-switching behavior starting from the first few test trials, and their performance remained stable throughout testing in the majority of the conditions. Furthermore, as the probability of the short trial increased, mice waited longer before switching from the short to long location (experiment 1). These behavioral adjustments were in directions predicted by reward maximization. These results suggest that rather than gradually adjusting their time-dependent choice behavior, mice abruptly adopted temporal decision strategies by directly integrating their previous knowledge of task parameters into their timed behavior, supporting the model-based representational account of temporal risk assessment. [ABSTRACT FROM AUTHOR]

This paper presents a new fuzzy group decision-making approach by using probabilities and ordered weighted averages (OWA). Thus, we are able to assess the problem considering probabilistic information and the attitudinal character of the decision maker. For doing so, the fuzzy generalized probabilistic OWA (FGPOWA) operator is presented. It is an aggregation operator that unifies the probability and the OWA operator considering the degree of importance that each concept has in the aggregation. It also uses fuzzy numbers that deal with uncertain environments where the available information is imprecise. Moreover, it includes a wide range of particular cases including the fuzzy generalized probabilistic aggregation, the fuzzy POWA operator and the fuzzy GOWA operator. A further generalization to this approach is presented by using quasi-arithmetic means forming the fuzzy quasi-arithmetic POWA (Quasi-FPOWA) operator. An application in fuzzy group decision making is developed by using the multi-person - FGPOWA (MP-FGPOWA) operator. [ABSTRACT FROM AUTHOR]

Background Increasingly, network meta-analysis (NMA) of published survival data are based on parametric survival curves as opposed to reported hazard ratios to avoid relying on the proportional hazards assumption. If a Bayesian framework is used for the NMA, rank probabilities associated with the alternative treatments can be obtained, which directly support decision-making. In the context of survival analysis multiple treatment effect measures are available to inform the rank probabilities. Methods A fractional polynomial NMA of overall survival in advanced melanoma was performed as an illustrative example. Rank probabilities were calculated and presented for the following effect measures: 1) median survival; 2) expected survival; 3) mean survival at the follow-up time point of the trial with the shortest follow-up; 4) hazard or hazard ratio over time; 5) cumulative hazard or survival proportions over time; and 6) mean survival at subsequent time points. The advantages and disadvantages of the alternative measures were discussed. Results Since hazard and survival estimates may vary over time for the compared interventions, calculations of rank probabilities for an NMA of survival curves may depend on the effect measure. With methods 1-3 rank probabilities do not vary over time, which are easier to understand and communicate than rank probabilities that vary over time as obtained with methods 4-6. However, rank probabilities based on methods 4-6 provide useful information regarding the relative treatment effects over time. Conclusions Different approaches to summarize results of a NMA of survival curves with rank probabilities have pros and cons. Rank probabilities of treatment effects over time provide a more transparent and informative approach to help guide decision-making than single rank probabilities based on collapsed measures, such as median survival or expected survival. Rank probabilities based on survival proportions are the most intuitive and straightforward to communicate, but alternatives based on the hazard function or mean survival over time may also be useful. [ABSTRACT FROM AUTHOR]

This article draws on an ethnographic research project that examined how New Zealand child protection social workers discussed decision making about reported risks for children. The study involved a combination of detailed interviews and observations of social work practice in social work offices from late 2002 to mid 2003. In this article, I show how the initial responses by social workers in statutory child protective services were filtered through everyday heuristics about risk as they reasoned with harm and danger probabilities. Social workers who participated in this study tended to treat risk as something that needed to be known early on in the case trajectory. By retaining a focus on what was known and recorded and, therefore, could be identified and used as the basis of interventions, social workers effectively excluded the possibility of a challenge from alternate views or positions about risk. Importantly, this approach to risk prevented social workers, their supervisors and colleagues from exploring the ways in which risk was constructed and used. Thus, they did not consider the extent to which social workers decisions, such as those to remove a child exposed families to other types of risk, to the potential harm of placing children in alternative or foster care. [ABSTRACT FROM PUBLISHER]

Uncertainty is highly relevant in environmental policy because on the one hand environmental problems are regarded as complex and require a long-term perspective and on the other hand the knowledge available to practitioners and policy makers alike is often fragmentary and not systemised. One key issue arising from this is the challenge to develop scientific decision support methods that are capable of dealing with uncertainty in a systematic and differentiated way, integrating scientific as well as practical knowledge. This paper introduces a conceptual framework for perceiving and describing uncertainty in environmental decision-making. The conceptual framework consists of a general definition of uncertainty along with five complementary perspectives on the phenomenon, each highlighting one specific aspect of it. By using the conceptual framework, decision-makers are able to reflect on their knowledge base with regard to its completeness and reliability and to gain a broad picture of uncertainty from various standpoints. The theoretical ideas presented here are complemented by two empirical studies looking at how uncertainty is dealt with in the implementation process of the EU Water Framework Directive. The rather abstract differentiations are illustrated by a number of examples in the form of interview statements and excerpts from the directive and official EU documents. [Copyright &y& Elsevier]

Recent functional neuroimaging and lesion studies demonstrate the involvement of the orbitofrontal/ventromedial prefrontal cortex as a key structure in decision making processes. This region seems to be particularly crucial when contingencies between options and consequences are unknown but have to be learned by the use of feedback following previous decisions (decision making under ambiguity). However, little is known about the neural correlates of decision making under risk conditions in which information about probabilities and potential outcomes is given. In the present study, we used functional magnetic resonance imaging to measure blood-oxygenation-level-dependent (BOLD) responses in 12 subjects during a decision making task. This task provided explicit information about probabilities and associated potential incentives. The responses were compared to BOLD signals in a control condition without information about incentives. In contrast to previous decision making studies, we completely removed the outcome phase following a decision to exclude the potential influence of feedback previously received on current decisions. The results indicate that the integration of information about probabilities and incentives leads to activations within the dorsolateral prefrontal cortex, the posterior parietal lobe, the anterior cingulate and the right lingual gyrus. We assume that this pattern of activation is due to the involvement of executive functions, conflict detection mechanisms and arithmetic operations during the deliberation phase of decisional processes that are based on explicit information. [ABSTRACT FROM AUTHOR]

Revealing or visualizing the thinking involved in making clinical decisions is a challenge. A case study is presented with a visual implement for sharing the diagnostic process. This technique adapts the Bayesian approach to the case presentation. Pretest probabilities and likelihood ratios are gathered to obtain post-test probabilities of every question considered in a patient. The technique advocates for the judicious use of diagnostic options based on the best evidence available. Clinical decisions, presentation skills, and critical evaluation of the literature are also important elements considered in this method. [ABSTRACT FROM AUTHOR]

Fundamental principles of precaution are legal maxims that ask for preventive actions, perhaps as contingent interim measures while relevant information about causality and harm remains unavailable, to minimize the societal impact of potentially severe or irreversible outcomes. Such principles do not explain how to make choices or how to identify what is protective when incomplete and inconsistent scientific evidence of causation characterizes the potential hazards. Rather, they entrust lower jurisdictions, such as agencies or authorities, to make current decisions while recognizing that future information can contradict the scientific basis that supported the initial decision. After reviewing and synthesizing national and international legal aspects of precautionary principles, this paper addresses the key question: How can society manage potentially severe, irreversible or serious environmental outcomes when variability, uncertainty, and limited causal knowledge characterize their decisionmaking? A decision–analytic solution is outlined that focuses on risky decisions and accounts for prior states of information and scientific beliefs that can be updated as subsequent information becomes available. As a practical and established approach to causal reasoning and decision-making under risk, inherent to precautionary decision-making, these (Bayesian) methods help deci- sion-makers and stakeholders because they formally account for probabilistic outcomes, new information, and are consistent and replicable. Rational choice of an action from among various alternatives–defined as a choice that makes preferred consequences more likely–requires accounting for costs, benefits and the change in risks associated with each candidate action. Decisions under any form of the pre- cautionary principle reviewed must account for the con- tingent nature of scientific information, creating a link to the decision–analytic principle of expected value of information (VOI), to show the relevance of new informa- tion, relative to the initial (and smaller) set of data on which the decision was based. We exemplify this see- mingly simple situation using risk management of BSE. As an integral aspect of causal analysis under risk, the methods developed in this paper permit the addition of non-linear, hormetic dose–response models to the current set of regulatory defaults such as the linear, non-threshold models. This increase in the number of defaults is an important improvement because most of the variants of the precautionary principle require cost–benefit balan- cing. Specifically, increasing the set of causal defaults accounts for beneficial effects at very low doses. We also show and conclude that quantitative risk assessment dominates qualitative risk assessment, supporting the extension of the set of default causal models. [ABSTRACT FROM AUTHOR]

Cognitive Continuum Theory can be used to explain why the relationship between research and practice is more problematic than is customarily assumed. The various possible sources of evidence for clinical decision making and the alternative approaches to such decision making can be located within the main modes of this continuum, each of which embodies a different balance of intuition and analysis. Decision analysis is the only technique that provides the analytical depth necessary to arrive at a decision that systematically identifies, structures and integrates all the relevant evidence on clinical parameters and patient preferences, within whatever mode that evidence is generated. The inappropriateness of using research criteria in action evaluations is pointed out. In order to illustrate the application of the Cognitive Continuum Theory and decision analysis to growth hormone (GH) therapy, and to stimulate discussion of this area, a primitive clinical decision analysis and prototype ‘clinical guidance tree’ for GH is presented here. [ABSTRACT FROM AUTHOR]