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Agricultural entrepreneurship fostering is an important part of the EU and US rural policies. Despite the fact that literature points attention to the issue of limited number of new entrants in agriculture, existing policies are mostly focused on support of existing entrepreneurs. Recent research describes such behavioral reasons of limited number of new entrants as low attractiveness and not appealing image of the agricultural sector. This paper considers the issue of attracting new entrepreneurs to agriculture from behavioral decision theory perspective and suggests celebrity branding as a tool of agricultural entrepreneurship fostering. Through the application of multi-criteria decision analysis, the paper proves that celebrity branding has a significant positive effect on attractiveness of entrepreneurship in agriculture and on expected financial and non-financial benefits from entrepreneurial career in the sector. Also, the paper reveals that financial motivation plays the most important role for individuals attracted to urban spheres of entrepreneurship and is the second most important factor (after self-realization) for individuals attracted to the agricultural sphere of entrepreneurship. The paper also demonstrates a debiasing effect of celebrity branding on the perception of entrepreneurial career in agriculture. [ABSTRACT FROM AUTHOR]

Over the second half of the twentieth century, the moral economy of insurance has shifted from solidarity and mutual support to individual responsibility. In this context, the French regime for the protection against natural catastrophes that took shape in the 1980s exemplifies a strong and almost anachronical political will to foster solidarity at the national level, thus questioning the moral economy of responsibility. This paper offers a textual analysis of the official debates that led to the launch of the regime. It shows how the representatives chose to separate compensation, financed by equal individual participation, from state prevention. This contrasts with other schemes worldwide that rely on rational decision theory to situate the responsibility for prevention at the individual level. In this alternative, risk-based premiums play a theoretically crucial role in risk signals. In practice, however, they lead to affordability issues while failing to govern prevention as theoretically expected. With the climate crisis exacerbating this phenomenon, the examination of the French regime thus allows to fruitfully revive other moral economies of insurance. [ABSTRACT FROM AUTHOR]

Decisions continue to be important to researchers, organizations and societies. However, decision research requires re-orientation to attain the future of data-driven decision making, accommodating such emerging topics and information technologies as big data, analytics, machine learning, and automated decisions. Accordingly, there is a dire need for re-forming decision theories to encompass the new phenomena. This paper proposes a modern data-driven decision theory, DECAS, which extends upon classical decision theory by proposing three main claims: (1) (big) data and analytics (machine) should be considered as separate elements; (2) collaboration between the (human) decision maker and the analytics (machine) can result in a collaborative rationality, extending beyond the classically defined bounded rationality; and (3) meaningful integration of the classical decision making elements with data and analytics can lead to more informed, and possibly better, decisions. This paper elaborates the DECAS theory and clarifies the idea in relation to examples of data-driven decisions. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of decision-making in the context of judgment-based (subjective) deselection using decision theory concepts developed by Daniel Kahneman in collaboration with others. Judgment-based deselection is considered from multiple perspectives using this approach. Situating the deselection context within loss aversion, status quo bias, and the endowment effect (products of prospect theory and behavioral economics) may explain the psychological reluctance accompanying deselection. The role of feedback as a condition for developing intuitive expertise demonstrates the limited potential for developing expertise in subjective deselection. Heuristics and biases employed in decision-making and decision fatigue may account for inconsistencies in deselection decisions. [ABSTRACT FROM AUTHOR]

A limitation of the common measures of diagnostic test performance, such as sensitivity and specificity, is that they do not consider the relative importance of false negative and false positive test results, which are likely to have different clinical consequences. Therefore, the use of classification or prediction measures alone to compare diagnostic tests or biomarkers can be inconclusive for clinicians. Comparing tests on net benefit can be more conclusive because clinical consequences of misdiagnoses are considered. The literature suggested evaluating the binary diagnostic tests based on net benefit, but did not consider diagnostic tests that classify more than two disease states, e.g., stroke subtype (large-artery atherosclerosis, cardioembolism, small-vessel occlusion, stroke of other determined etiology, stroke of undetermined etiology), skin lesion subtype, breast cancer subtypes (benign, mass, calcification, architectural distortion, etc.), METAVIR liver fibrosis state (F0- F4), histopathological classification of cervical intraepithelial neoplasia (CIN), prostate Gleason grade, brain injury (intracranial hemorrhage, mass effect, midline shift, cranial fracture). Other diseases have more than two stages, such as Alzheimer's disease (dementia due to Alzheimer's disease, mild cognitive disability (MCI) due to Alzheimer's disease, and preclinical presymptomatics due to Alzheimer's disease). In diseases with more than two states, the benefits and risks may vary between states. This paper extends the net-benefit approach of evaluating binary diagnostic tests to multi-state clinical conditions to rule-in or rule-out a clinical condition based on adverse consequences of work-up delay (due to false negative test result) and unnecessary workup (due to false positive test result). We demonstrate our approach with numerical examples and real data. [ABSTRACT FROM AUTHOR]

Blaise Pascal famously calculated the expected value of a risky lottery in the 1654 correspondence with Pierre de Fermat in the context of the so-called points problem. Pascal solved this problem by backward induction, whereas Fermat—by counting combinations. This paper analyzes a more complex version of the points problem from an anonymous XVth century Italian manuscript stored in the Vatican Apostolic Library. In this manuscript, the problem of points is solved by backward induction in a similar way to Pascal's train of thought. In this light, Pascal's pioneering contribution may be not as novel as it is traditionally believed. [ABSTRACT FROM AUTHOR]

Underscoring the economic significance of the Knightian distinction between risk and uncertainty, Don Katzner forcefully challenges the continued dominance of the expected utility model based on subjective probability in macroeconomic analysis and offers in its place a simple yet elegant model of decision making inspired by the pioneering work of G.L.S. Shackle. In doing so, Katzner lends support to a research program to identify a more coherent and empirically grounded theory of decision making under uncertainty. Our paper makes three contributions to this program. First, we argue that the appropriate choice of a model of individual behavior under uncertainty cannot be adjudicated merely on logical, a priori grounds, but must ultimately be based on consistency with observed behavior. There are many internally consistent models and their external conditions of validity hardly ever rely on inconceivable scenarios. Second, while Katzner's model certainly provides a plausible foundation for understanding choice under uncertainty, we suggest that it is somewhat underdetermined. It requires further theoretical articulation to yield testable predictions which differentiate it from competing approaches. Third, we discuss recent experimental findings that may need to be addressed by any satisfactory theoretical account of decision making under uncertainty. [ABSTRACT FROM AUTHOR]

For practical decisions on common recurring maintenance actions, the information from routine inspections form a decision basis for the bridge manager. It is often difficult to assess whether this information is sufficient for deciding on a repair action, or if more information is needed. For many bridges the information for supporting decisions may be limited, although those bridges cause large yearly maintenance costs for the society. The purpose of the study presented in this paper is to show how two different models for decision making based on Bayesian decision theory, a point-in-time decision model and a sequential updating decision model, can be used to improve the decision-making process for common maintenance decisions. The models use information from routine inspections and incorporates time dependent aspects such as material degradation and time value of money to improve the decision-making process. The focus is on presenting the methodology with a case study of a concrete bridge in Sweden where the edge beams may have to be replaced. Three assessment approaches are considered: (i) no assessment, (ii) desktop evaluation and (iii) measurements. The main finding is that sequential updating decision making will provide a higher benefit than a point-in-time decision, and thus give higher Value of Information. This value becomes even higher when the measurements are selected for the assessment. The results also show that the edge beams should be replaced. The general approach presented can be applicable to many decision scenarios related to maintenance of deteriorating structures. [ABSTRACT FROM AUTHOR]

This paper is concerned with investigating the adequacy of using the p-value as an estimator for the set specified by the null hypothesis in the Exponential distribution. It is shown that the p-value is an admissible estimator in the one-sided test of the location parameter. When the one-sided test of the scale parameter is considered, the p-value is found to be a generalized Bayes estimator with infinite Bayes risk. However, it is very difficult to find an estimator that dominates it. When the parameter space is restricted, the modified p-value is an admissible estimator in the one-sided test of the scale parameter and performs better than the usual p-value. Although the usual p-value is generally inadmissible in the two-sided test, it can be useful as an estimator in this type of test for the scale parameter. [ABSTRACT FROM AUTHOR]

That a standard of medical care must outline services that benefit the patient is relatively uncontroversial. However, one must determine how the practices outlined in a medical standard of care should benefit the patient. I will argue that practices outlined in a standard of medical care must not detract from the patient's well-functioning and that clinicians can refuse to provide services that do. This paper, therefore, will advance the following two claims: (1) a standard of medical care must not cause dysfunction, and (2) if a physician is medically rational to not provide some service which fails to meet the above condition (i.e. fails to be a standard of medical care), then she may refuse to do so. I then apply my thesis to the prescription of puberty blockers to children with gender dysphoria. [ABSTRACT FROM AUTHOR]

In this paper, we propose a class of general pretest estimators for the univariate normal mean. The main mathematical idea of the proposed class is the adaptation of randomized tests, where the randomization probability is related to a shrinkage parameter. Consequently, the proposed class includes many existing estimators, such as the pretest, shrinkage, Bayes, and empirical Bayes estimators as special cases. Furthermore, the proposed class can be easily tuned for users by adjusting significance levels and probability function. We derive theoretical properties of the proposed class, such as the expressions for the distribution function, bias, and MSE. Our expressions for the bias and MSE turn out to be simpler than those previously derived for some existing formulas for special cases. We also conduct simulation studies to examine our theoretical results and demonstrate the application of the proposed class through a real dataset. [ABSTRACT FROM AUTHOR]

First-price sealed-bid auctions have been modeled using a utility function that takes into account bidders' regret in case of winning or losing the auctioned item. However, that modeling does not consider bidders' wealth which is an important determinant of bidders' behavior in these auctions. In this paper, we apply an adversarial risk analysis approach for these auctions and find solutions using non-strategic play and level-k thinking solution concepts assuming n bidders. We define new regret parameters and a modified utility function to incorporate the effect of bidders' wealth on their bidding behavior. In our modeling, we assume that the auctioned item is a normal item and has a reserve price. We give numerical examples to illustrate our methodology for each solution concept. [ABSTRACT FROM AUTHOR]

The management of civil infrastructures in the aftermath of a seismic event is a concern for decision makers, which have to choose quickly among alternative actions with limited knowledge on the actual structural conditions. The availability of real time Structural Health Monitoring (SHM) data on the asset might be particularly useful. However, SHM data are not collected for free, and the cost of the SHM system should be compared with its associated benefit. A powerful tool to estimate such benefit is the Value of Information (VoI) from Bayesian decision theory. This paper provides a methodology to compute the VoI of SHM for seismic emergency management of roadway bridges. This methodology can be used by decision makers before the installation of a seismic SHM system to quantify the cost benefit of doing so and thus optimize the allocation of economic resources. Results show that the VoI is high when the expected costs of the decision alternatives (such as 'keep the bridge open' or 'close the bridge') evaluated without the SHM information are comparable. In this condition, which is highly dependent on the seismic hazard and on emergency management costs at stake, the SHM information provides the maximum support to decision making. [ABSTRACT FROM AUTHOR]

Pascal's wager is one of the most influential arguments in philosophy and decision theory. This paper reformulates Pascal's wager in terms of two-outcome finite payoffs (one before death and one after death). The proposed reformulation accentuates the lexicographic nature of decision maker's preferences. Lexicographic preferences violate the continuity axiom—a standard assumption of many theories of decision making under risk and uncertainty. Since Pascal's wager assumes lexicographic (i.e., discontinuous) preferences, it is appropriate to represent the latter with lexicographic utility and not conventional expected utility theory. [ABSTRACT FROM AUTHOR]

This essay argues that in order to understand how institutions shape political choices and history we should go further toward understanding the interactive relationships between institutions and the cognitive mind. The article explores the significant body of research and literature developing in social and evolutionary psychology, cognitive science and decision theory. This literature has gone beyond the observation humans are not the individual utility maximizers stylized in early institutionalist theorizing. A massive body of experimental and empirical research clearly demonstrates, for example, that a) individuals only rarely have stable and hierarchical preferences, b) are generally quite unlikely to go through the cognitive effort implied in a 'rational' choice decision matrix, c) humans are efficient decision makers, but we have remarkable capacities to post-hoc justify choices and restructure our memories so that we can view our decisions as coherent. In the end the paper argues that public administration scholars need to adopt a non-reductionist approach to studying human motivation. [ABSTRACT FROM AUTHOR]

The question how and why the First World War broke out has filled entire learned libraries, but its no less salient and consequential counterpart—how it got to be so protracted when so many of the illusions that contributed to the outbreak were already shattered by Christmas 1914—has received much less attention, though recent scholarship has begun to close the gap. The aims and anxieties of the respective parties certainly played a prime part on both sides, as did their continued hopes of winning and the complications of coalition warfare. But something more insidious counted for just as much: namely how the horrendous manner in which the war began—grievously disappointing expectations on all sides and producing a staggering five million casualties within only a few months—did not induce, as the standards of textbook rationality might suggest, a readiness to recognise where things were headed and a consequent willingness to change course. Instead of prompting the belligerents to heed the sunk cost principle and cut losses by a timely settlement, the opening months had just the opposite effect, hardening positions that should have been discredited, and leaving the belligerents all the more determined to keep fighting to the bitter end at practically any cost not only to their enemies but also to themselves. How this was possible will be explained, in part, by making connections with prospect and decision theory. [ABSTRACT FROM AUTHOR]

Decision theory requires agents to assign probabilities to states of the world and utilities to the possible outcomes of different actions. When agents commit to having the probabilities and/or utilities in a decision problem defined by objective features of the world, they may find themselves unable to decide which actions maximize expected utility. Decision theory has long recognized that work-around strategies are available in special cases; this is where dominance reasoning, minimax, and maximin play a role. Here we describe a different work around, wherein a rational decision about one decision problem can be reached by 'interpolating' information from another problem that the agent believes has already been rationally solved. [ABSTRACT FROM AUTHOR]

A prominent approach to studying the replication crisis has been to conduct replications of several different scientific findings as part of the same research effort. The reported proportion of findings that these programs determined failed to replicate have become important statistics in the replication crisis. However, these "failure rates" are based on decisions about whether individual studies replicated, which are themselves subject to statistical uncertainty. In this article, we examine how that uncertainty impacts the accuracy of reported failure rates and find that the reported failure rates can be substantially biased and highly variable. Indeed, very high or very low failure rates could arise from chance alone. [ABSTRACT FROM AUTHOR]

China-U.S. trade imbalance is one of the most complex macroeconomic issues. Based on the data from 1992 to 2020, we combine a two-period consumption decision-making theory and use the SVAR model to explore the reason and mechanism of the China-U.S. trade imbalance. We find that the difference in comparative advantages of service trade is an important reason for the China-U.S. trade imbalance, and the difference in consumption rate is an important mechanism. Furthermore, we support these arguments with the stylized facts of China's bilateral trade imbalance with other G20 countries. These findings may be a more reasonable explanation for the long-term existence of the huge China-U.S. trade imbalance. [ABSTRACT FROM AUTHOR]

Linear mixed models are standard models to analyze repeated measures or longitudinal data under the assumption of normality for random components in the model. Although the mixed models are often used in both frequentist and Bayesian inference, their evaluation from robustness perspective has not received as much attention in Bayesian inference as in frequentist. The aim of this study is to evaluate Bayesian tests in mixed models for their robustness to normality. We use a general class of exponential power distributions, EPD, and particularly focus on testing fixed effects in longitudinal models. The EPD class contains both light and heavy tailed distributions, with normality as a special case. Further, we consider a new paradigm of Bayesian testing decision theory where the hypotheses are formulated as a mixture model, with subsequent testing based on the posterior distribution of the mixture weights. It is shown that the EPD class provides a flexible alternative to normality assumption, particularly in the presence of outliers. Real data applications are also demonstrated. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence methods contributes to their wide applications for forecasting various financial risks in recent years. This study introduces a novel explainable case-based reasoning (CBR) approach without a requirement of rich expertise in financial risk. Compared with other black-box algorithms, the explainable CBR system allows a natural economic interpretation of results. Indeed, the empirical results emphasize the interpretability of the CBR system in predicting financial risk, which is essential for both financial companies and their customers. In addition, our results show that the proposed automatic design CBR system has a good prediction performance compared to other artificial intelligence methods, overcoming the main drawback of a standard CBR system of highly depending on prior domain knowledge about the corresponding field. [ABSTRACT FROM AUTHOR]

The possibility of predetermined choices raises a challenge for Causal Decision Theory [Ahmed 2014b]. Sandgren and Williamson [2021] have recently proposed a response—Selective Causal Decision Theory—that they hope will avoid Ahmed's counterexamples, maintain (a particular kind of) compatibilism, and endorse universal Two-boxing in Newcomb's Problem—CDT's raison d'être. Their proposal does an admirable job of satisfying the first two desiderata. However, in this reply I raise several worries about whether it can satisfy the third. [ABSTRACT FROM AUTHOR]

In game theory and statistical decision theory, a random (i.e., mixed) decision strategy often outperforms a deterministic strategy in minimax expected loss. As experimental design can be viewed as a game pitting the Statistician against Nature, the use of a random strategy to choose a design will often be beneficial. However, the topic of minimax-efficient random strategies for design selection is mostly unexplored, with consideration limited to Fisherian randomization of the allocation of a predetermined set of treatments to experimental units. Here, for the first time, novel and more flexible random design strategies are shown to have better properties than their deterministic counterparts in linear model estimation and prediction, including stronger bounds on both the expectation and survivor function of the loss distribution. Design strategies are considered for three important statistical problems: (i) parameter estimation in linear potential outcomes models, (ii) point prediction from a correct linear model, and (iii) global prediction from a linear model taking into account an L2-class of possible model discrepancy functions. The new random design strategies proposed for (iii) give a finite bound on the expected loss, a dramatic improvement compared to existing deterministic exact designs for which the expected loss is unbounded. for this article are available online. [ABSTRACT FROM AUTHOR]

Risk management decisions in public health require consideration of a number of complex, often conflicting factors. The aim of this review was to propose a set of 10 fundamental principles to guide risk decision-making. Although each of these principles is sound in its own right, the guidance provided by different principles might lead the decision-maker in different directions. For example, where the precautionary principle advocates for preemptive risk management action under situations of scientific uncertainty and potentially catastrophic consequences, the principle of risk-based decision-making encourages decision-makers to focus on established and modifiable risks, where a return on the investment in risk management is all but guaranteed in the near term. To evaluate the applicability of the 10 principles in practice, one needs to consider 10 diverse risk issues of broad concern and explore which of these principles are most appropriate in different contexts. The 10 principles presented here afford substantive insight into the process of risk management decision-making, although decision-makers will ultimately need to exercise judgment in reaching appropriate risk decisions, accounting for all of the scientific and extra-scientific factors relevant to the risk decision at hand. [ABSTRACT FROM AUTHOR]

This article reprises central aspects of John Boyd's thinking about competition and conflict, including his unwavering reliance on analogies and connections between disciplines as disparate as fighter tactics, decision theory, history, philosophy, and physics. We trace how elements of Boyd's ideas about winning and losing have been incorporated (or neglected) in the doctrines of the U.S. military services. Given the richness of Boyd's ideas there is much we can learn from the view of decision making underlying the Observation-Orientation-Decision-Action (OODA) loop, for which he is best known, as well as the analogical reasoning embodied in slide presentations such as his massive "Patterns of Conflict." [ABSTRACT FROM AUTHOR]

While pragmatic arguments are traditionally seen as supporting decision theory, recent discussions suggest the possibility of pragmatic arguments against this theory. I respond to two such arguments, and clarify what it would take for arguments of this sort to succeed. [ABSTRACT FROM AUTHOR]

John Maynard Keynes's Treatise on Probability (1921) and Frank Knight's Risk, Uncertainty and Profit (1921) independently stressed the distinction between insurable risk and uninsurable fundamental uncertainty (in Knight's terminology), inspiring two literatures that have engaged with each other only intermittently. I explore the relationship between what Keynes wrote about uncertainty in his Treatise and what Knight published the same year, and consider what contributions by Truman Bewley and by Kiyohiko Nishimura and Hiroyuki Ozaki on Knightian uncertainty and Knightian decision theory have to offer for furthering understanding of Keynes on uncertainty, particularly as suggesting a response to Alan Coddington's critique of the risk/uncertainty distinction. [ABSTRACT FROM AUTHOR]