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Prevention of the occurrence and development of emergencies of a natural and man-made nature is one of the basic fundamental foundations of ensuring the national security of any state. The most important mechanism for preventing emergencies is an effective system of monitoring and forecasting emergencies established at the state level. In the process of functioning such a system, one of the main urgent problems requiring constant attention, continuous research, system analysis, and the search for solutions by scientific methods and methods is to increase the reliability of emergency forecasts. In this format, special attention is currently being paid worldwide to a comprehensive assessment of the adverse consequences of emergency situations, primarily related to the safety of the population, environmental conservation, and environmental safety. From the standpoint of solving this significant scientific and practical problem, the purpose of this work was to develop and justify a more advanced method for calculating the feasibility of forecasts of emergencies with environmental consequences as a tool for a reasonable detailed assessment of the quality, optimality of emergency forecasting processes and the reliability of the forecasts themselves.

Research has long established the existence of a global brand halo that benefits global brands by triggering “global equals better” inferences by consumers. Nevertheless, little is known about the conditions under which this halo may or may not be used or about whether and, if so, how it can situationally fade. Drawing from regret theory, the authors posit that anticipating regret can conditionally both attenuate and accentuate consumers’ use of the global brand halo and develop a serial conditional process model to explain the mechanism underlying regret’s influence. The results of two experimental studies show that anticipated regret affects global brand halo use—and subsequently relative preference for global or local brands—by increasing consumers’ need to justify their purchase decision. Whether and how consumers will use the global brand halo depends on consumers’ product category schema, while the intensity of the halo’s use depends on consumers’ maximization tendency. The findings offer a decision-theory perspective on the competition between global and local brands and empirically based advice on managerial interventions that can influence global or local brand market shares.

Geochemical processes in subsurface reservoirs affected by microbial activity change the material properties of porous media. This is a complex biogeochemical process in subsurface reservoirs that currently contains strong conceptual uncertainty. This means, several modeling approaches describing the biogeochemical process are plausible and modelers face the uncertainty of choosing the most appropriate one. The considered models differ in the underlying hypotheses about the process structure. Once observation data become available, a rigorous Bayesian model selection accompanied by a Bayesian model justifiability analysis could be employed to choose the most appropriate model, i.e. the one that describes the underlying physical processes best in the light of the available data. However, biogeochemical modeling is computationally very demanding because it conceptualizes different phases, biomass dynamics, geochemistry, precipitation and dissolution in porous media. Therefore, the Bayesian framework cannot be based directly on the full computational models as this would require too many expensive model evaluations. To circumvent this problem, we suggest to perform both Bayesian model selection and justifiability analysis after constructing surrogates for the competing biogeochemical models. Here, we will use the arbitrary polynomial chaos expansion. Considering that surrogate representations are only approximations of the analyzed original models, we account for the approximation error in the Bayesian analysis by introducing novel correction factors for the resulting model weights. Thereby, we extend the Bayesian model justifiability analysis and assess model similarities for computationally expensive models. We demonstrate the method on a representative scenario for microbially induced calcite precipitation in a porous medium. Our extension of the justifiability analysis provides a suitable approach for the comparison of computationally demanding models and gives an insight on the necessary amount of data for a reliable model performance.

This research addresses the unexplored postpurchase dynamics of global/local brand choices by investigating the experience of regret in global versus local brand purchases. Drawing on regret theory, the authors demonstrate in four complementary studies that the global/local availability of both chosen and forgone brands influences consumer responses to regrettable purchases and that the direction and magnitude of this influence depend on the consumers' product category schema and global identity. Study 1 shows that regrettable decisions to forgo global for local brands elicit stronger regret, lower satisfaction, and higher brand switching than regrettable purchases of global (vs. local) brands for consumers with a global brand superiority schema for the category; the inverse holds for consumers with a local brand superiority schema. Studies 2 and 3 replicate the effect and show that it is mediated by perceived decision justifiability and moderated by global identity. Study 4 further validates the observed effect using a real brand choice task in a category with a local brand–dominated schema. The findings reveal the postpurchase consequences of global/local brand choices and provide concrete advice for global/local branding strategies.

This paper argues that one problem that besets black-box AI is that it lacks algorithmic justifiability. We argue that the norm of shared decision making in medical care presupposes that treatment decisions ought to be justifiable to the patient. Medical decisions are justifiable to the patient only if they are compatible with the patient’s values and preferences and the patient is able to see that this is so. Patient-directed justifiability is threatened by black-box AIs because the lack of rationale provided for the decision makes it difficult for patients to ascertain whether there is adequate fit between the decision and the patient’s values. This paper argues that achieving algorithmic transparency does not help patients bridge the gap between their medical decisions and values. We introduce a hypothetical model we call Justifiable AI to illustrate this argument. Justifiable AI aims at modelling normative and evaluative considerations in an explicit way so as to provide a stepping stone for patient and physician to jointly decide on a course of treatment. If our argument succeeds, we should prefer these justifiable models over alternatives if the former are available and aim to develop said models if not.

In ten studies (N = 9187), I systematically investigated the direction and size of seven helping effects (the identifiable-victim effect, proportion dominance effect, ingroup effect, existence effect, innocence effect, age effect and gender effect). All effects were tested in three decision modes (separate evaluation, joint evaluation and forced choice), and in their weak form (equal efficiency), or strong form (unequal efficiency). Participants read about one, or two, medical help projects and rated the attractiveness of and allocated resources to the project/projects, or choose which project to implement. The results show that the included help-situation attributes vary in their: (1) Evaluability – e.g., rescue proportion is the easiest to evaluate in separate evaluation. (2) Justifiability – e.g., people prefer to save fewer lives now rather than more lives in the future, but not fewer identified lives rather than more statistical lives. (3) Prominence – e.g., people express a preference to help females, but only when forced to choose.

Although it is reasonable and valuable to seek explanations for decisions made by artificial intelligence (AI), it is simply not possible with black-box AI algorithms. However, these algorithms can produce highly beneficial and efficient outputs that could be extremely useful to patients, treating teams, hospitals, and funding bodies. This poses a dilemma: is black-box AI justifiable to use in healthcare? This article analyses the normative reasons that can defend and justify the use of black-box AI in healthcare; this analysis includes, but does not give lexical priority to, explainability. This is pertinent given the current prohibitions of black-box AI in healthcare, such as in Australia. This article defines justifiability as decisions based on robust reasons and thus identifies reasons that can justify the use of black-box AI in healthcare. These include the algorithms’ explainability and accuracy, the seriousness of the decision's consequences, any relevant bias, the context of the decision, and the level of human intervention. We argue that whilst each of these separate considerations is important, only accuracy and reliability are necessary, and to be sufficient, it is likely that some further reasons arising from the nature and context of the decision will be required.

This article reviews approaches based on artificial intelligence (AI), which contributes to the security of cyber environments. We examine existing techniques using several indicators: explainability, performance and robustness. These indicators have been chosen based on their importance for user acceptance and interpretability of the approach. Indeed, the AI field is vast and is divided into several sub-domains. The two most well-known sub-domains are symbolic AI (representation of knowledge, rules and operations based on symbols) and numeric AI (calculations and algorithms using numeric information, focusing on the result, not the representation of knowledge). While most approaches investigated come from numeric AI, we conclude on the need for hybrid AI systems, combining the advantages of both AI sub-fields while maximising the protection provided against cyberattacks.

We consider a decision maker who ranks actions according to the smooth ambiguity criterion of Klibanoff, Marinacci, and Mukerji (2005). An action is justifiable if it is a best reply to some belief over probabilistic models. We show that higher ambiguity aversion expands the set of justifiable actions. A similar result holds for risk aversion. Our results follow from a generalization of the duality lemma of Wald (1949) and Pearce (1984).

In ten studies (N = 9187), I systematically investigated the direction and size of seven helping effects (the identifiable-victim effect, proportion dominance effect, ingroup effect, existence effect, innocence effect, age effect and gender effect). All effects were tested in three decision modes (separate evaluation, joint evaluation and forced choice), and in their weak form (equal efficiency), or strong form (unequal efficiency). Participants read about one, or two, medical help projects and rated the attractiveness of and allocated resources to the project/projects, or choose which project to implement. The results show that the included help-situation attributes vary in their: (1) Evaluability – e.g., rescue proportion is the easiest to evaluate in separate evaluation. (2) Justifiability – e.g., people prefer to save fewer lives now rather than more lives in the future, but not fewer identified lives rather than more statistical lives. (3) Prominence – e.g., people express a preference to help females, but only when forced to choose.