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Product updates have become prevalent on online digital platforms, given the unique market, product, and technological characteristics that underlie digital platforms. Scholars have begun to explore the efficacy of updates on digital platforms, highlighting the merits of implementing frequent updates. Drawing on behavioral decision theory, this paper analyzes the trade-offs that consumers encounter between the benefits and costs associated with updates and challenges an implicit assumption held in extant studies with respect to the linear efficacy of updates. In the context of mobile platforms, this paper, using a multi-method study, identifies the existence of an inverted U-shaped relationship between product updates and complementor product performance. We also find that the moderating efficacy of platform endorsement is positive in that it serves as an amplifier of whatever a complementor offers, tightly intertwined with the complementor's offerings. Our paper expands the platform literature by investigating successful complementor product strategies that take into account unique platform characteristics with a strong grounding in interdependent owner–complementor dynamics. [ABSTRACT FROM AUTHOR]

Each of us has a story which we can think of as a set of beliefs, hopefully consistent. We make our decisions in view of our beliefs which may be probabilistic, in the general case, but simple yes or no as in this paper. Our beliefs are our envelope just as the shell of a tortoise is its envelope. Decision theory—or single agent game theory tells us when to make the best choice in a game of us against nature. But nature has no desire to further or frustrate our efforts. Nature is mysterious but not malign. Things change when there are other agents involved. Then the best thing for us to do will depend on what they do. And they will think the same. And we predict their actions in terms of what we think their beliefs are. But how do we coordinate with others whose beliefs are different? This paper addresses the issue of working together despite different beliefs. [ABSTRACT FROM AUTHOR]

This paper is a discussion note on Isaacs, Hájek and Hawthorne (2022) , which claims to offer a new motivation for imprecise probabilities, based on the mathematical phenomenon of non-measurability. In this note, I clarify some consequences of that proposal. In particular, I show that if the proposal is applied to a bounded 3 -dimensional space, then one has to reject at least one of the following: • If A is at most as probable as B and B is at most as probable as C ⁠ , then A is at  most as probable as C ⁠. • Let A ∩ C = B ∩ C = ∅ ⁠. A is at most as probable as B if and only if (A ∪ C) is at  most as probable as (B ∪ C) ⁠. But rejecting either statement seems unattractive. [ABSTRACT FROM AUTHOR]

Covariance matrix estimation is a fundamental statistical task in many applications, but the sample covariance matrix is suboptimal when the sample size is comparable to or less than the number of features. Such high‐dimensional settings are common in modern genomics, where covariance matrix estimation is frequently employed as a method for inferring gene networks. To achieve estimation accuracy in these settings, existing methods typically either assume that the population covariance matrix has some particular structure, for example, sparsity, or apply shrinkage to better estimate the population eigenvalues. In this paper, we study a new approach to estimating high‐dimensional covariance matrices. We first frame covariance matrix estimation as a compound decision problem. This motivates defining a class of decision rules and using a nonparametric empirical Bayes g‐modeling approach to estimate the optimal rule in the class. Simulation results and gene network inference in an RNA‐seq experiment in mouse show that our approach is comparable to or can outperform a number of state‐of‐the‐art proposals. [ABSTRACT FROM AUTHOR]

When planning a two‐arm group sequential clinical trial with a binary primary outcome that has severe implications for quality of life (e.g., mortality), investigators may strive to find the design that maximizes in‐trial patient benefit. In such cases, Bayesian response‐adaptive randomization (BRAR) is often considered because it can alter the allocation ratio throughout the trial in favor of the treatment that is currently performing better. Although previous studies have recommended using fixed randomization over BRAR based on patient benefit metrics calculated from the realized trial sample size, these previous comparisons have been limited by failures to hold type I and II error rates constant across designs or consider the impacts on all individuals directly affected by the design choice. In this paper, we propose a metric for comparing designs with the same type I and II error rates that reflects expected outcomes among individuals who would participate in the trial if enrollment is open when they become eligible. We demonstrate how to use the proposed metric to guide the choice of design in the context of two recent trials in persons suffering out of hospital cardiac arrest. Using computer simulation, we demonstrate that various implementations of group sequential BRAR offer modest improvements with respect to the proposed metric relative to conventional group sequential monitoring alone. [ABSTRACT FROM AUTHOR]

This consideration is critical in our study because judges' decisions can affect individual arrestees. Instead of engaging with this philosophical debate, we examine Dawid's recommendation to use the statistical decision theory for answering the following questions: (1) 'should we present PSA to the judge before she makes her decision, or not? As explained above, to decide on the use of PSA, policy makers need to understand if PSA helps judges achieve their goal of preventing a negative outcome while avoiding unnecessarily harsh decisions. Finally, several commentators asked why our study design did not involve blinding of judges either to the intervention or to the fact of the study. [Extracted from the article]

Many theories of rational action are predicated on the idea that what it is rational to do in a given situation depends, in part, on what it is rational to believe in that situation. In short: they treat epistemic rationality as explanatorily prior to practical rationality. If they are right in doing so, it follows, on pain of explanatory circularity, that epistemic rationality cannot itself be a form of practical rationality. Yet, many epistemologists have defended just such a view of epistemic rationality. According to them, there is no such thing as a distinctively epistemic form of rationality which could be explanatorily prior to practical rationality. Rather, they maintain, there is just one form of rationality—practical rationality—of which epistemic rationality is a species. What to make of this conflict? The aim of this paper is to motivate a view about the relationship between epistemic and practical rationality which resolves the conflict in a way that should be attractive to both sides. The central idea is to ground both epistemic and practical rationality in an independently motivated notion of evidential probability which is itself to be understood in non-normative terms. Doing so, I argue, allows us to unify epistemic and practical rationality in a way that does justice to the idea that what it is rational to do depends, in part, on what it is rational to believe; and to do so in a way that avoids explanatory circularity. [ABSTRACT FROM AUTHOR]

The Cohenian paradox is one of the main themes of judicial probability theory and one of the core topics discussed by the new evidence scholarship. To resolve this paradox, evidence scholars nowadays have proposed various solutions, including legal probabilism, judicial Bayesian decision theory and relative plausibility theory. These three solutions can be classified into two approaches, i.e. the probabilism and the explanationism. Among them, the former includes legal probabilism and judicial Bayesian decision theory, and the latter includes the relative plausibility theory. However, the two approaches have recently begun to converge and become more understandable to each other. For example, Welch (2020, Int. J. Evid. Proof , 24, 351–373) has recently defended and improved the relative plausibility theory by substantially improving it with the help of Bayesian decision theory. In this paper, by contrast, we attempt to defend the probabilistic approach—legal probabilism and Bayesian decision theory on the basis of relative plausibility theory. [ABSTRACT FROM AUTHOR]

Orthodox decision theory is fanatical in the way it treats small probabilities of enormous value, if unbounded utility functions are allowed. Some have suggested a fix, Nicolausian discounting, according to which outcomes with small enough probabilities should be ignored when making decisions. However, there are lotteries involving only small-probability outcomes, none of which should intuitively be ignored. So the Nicolausian discounter needs a procedure for distinguishing the problematic cases of small-probability outcomes from the unproblematic ones. In this paper, I present a dilemma for Nicolausian discounting: the view must be coupled either with a procedure that delivers fanatical verdicts of its own, as bad as those of orthodox decision theory, or with one that entails intransitive cyclic preferences. [ABSTRACT FROM AUTHOR]

Motivation Probabilistic Identification of bacterial essential genes using transposon-directed insertion-site sequencing (TraDIS) data based on Tn5 libraries has received relatively little attention in the literature; most methods are designed for mariner transposon insertions. Analysis of Tn5 transposon-based genomic data is challenging due to the high insertion density and genomic resolution. We present a novel probabilistic Bayesian approach for classifying bacterial essential genes using transposon insertion density derived from transposon insertion sequencing data. We implement a Markov chain Monte Carlo sampling procedure to estimate the posterior probability that any given gene is essential. We implement a Bayesian decision theory approach to selecting essential genes. We assess the effectiveness of our approach via analysis of both simulated data and three previously published Escherichia coli , Salmonella Typhimurium and Staphylococcus aureus datasets. These three bacteria have relatively well characterized essential genes which allows us to test our classification procedure using receiver operating characteristic curves and area under the curves. We compare the classification performance with that of Bio-Tradis, a standard tool for bacterial gene classification. Results Our method is able to classify genes in the three datasets with areas under the curves between 0.967 and 0.983. Our simulated synthetic datasets show that both the number of insertions and the extent to which insertions are tolerated in the distal regions of essential genes are both important in determining classification accuracy. Importantly our method gives the user the option of classifying essential genes based on the user-supplied costs of false discovery and false non-discovery. Availability and implementation An R package that implements the method presented in this paper is available for download from https://github.com/Kevin-walters/insdens. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Newcomb's problem has spawned a debate about which variant of expected utility maximisation (if any) should guide rational choice. In this paper, we provide a new argument against what is probably the most popular variant: causal decision theory (CDT). In particular, we provide two scenarios in which CDT voluntarily loses money. In the first, an agent faces a single choice and following CDT's recommendation yields a loss of money in expectation. The second scenario extends the first to a diachronic Dutch book against CDT. [ABSTRACT FROM AUTHOR]

Similarly for (ii): under the assumption of no confounding between HT ht and HT ht , the optimal value HT ht of HT ht will maximise the (estimated) expected utility HT ht (where HT ht now includes PSA, if supplied). Then in problem (ii), for example, one would have to choose HT ht to maximise HT ht . [Extracted from the article]

For evaluating the quality of care provided by hospitals, special interest lies in the identification of performance outliers. The classification of healthcare providers as outliers or non‐outliers is a decision under uncertainty, because the true quality is unknown and can only be inferred from an observed result of a quality indicator. We propose to embed the classification of healthcare providers into a Bayesian decision theoretical framework that enables the derivation of optimal decision rules with respect to the expected decision consequences. We propose paradigmatic utility functions for two typical purposes of hospital profiling: the external reporting of healthcare quality and the initiation of change in care delivery. We make use of funnel plots to illustrate and compare the resulting optimal decision rules and argue that sensitivity and specificity of the resulting decision rules should be analyzed. We then apply the proposed methodology to the area of hip replacement surgeries by analyzing data from 1,277 hospitals in Germany which performed over 180,000 such procedures in 2017. Our setting illustrates that the classification of outliers can be highly dependent upon the underlying utilities. We conclude that analyzing the classification of hospitals as a decision theoretic problem helps to derive transparent and justifiable decision rules. The methodology for classifying quality indicator results is implemented in an R package (iqtigbdt) and is available on GitHub. [ABSTRACT FROM AUTHOR]

I defend counterfactual decision theory, which says that you should evaluate an action in terms of which outcomes would likely obtain were you to perform it. Counterfactual decision theory has traditionally been subsumed under causal decision theory as a particular formulation of the latter. This is a mistake. Counterfactual decision theory is importantly different from, and superior to, causal decision theory, properly so called. Causation and counterfactuals come apart in three kinds of cases. In cases of overdetermination, an action can cause a good outcome without the latter counterfactually depending on the former. In cases of constitution, an action can constitute a good outcome rather than causing it. And in cases of determinism, either the laws or the past counterfactually depend on your action, even though your action cannot cause the laws or the past to be different. In each of these cases, it is counterfactual decision theory which gives the right verdict, and for the right reasons. [ABSTRACT FROM AUTHOR]

In 'The logic of partial supposition' (Analysis vol. 81), Benjamin Eva and Stephan Hartmann investigate partial imaging , a credence-revision method which combines the partiality familiar from Jeffrey Conditioning(The Logic of Decision , 1983) with the formal notion of imaging familiar from Lewis's 'Causal decision theory' (1981). They argue that because partial imaging is non-monotonic, it 'fail[s] to provide a plausible account of the norms of partial subjunctive suppositions'. In this note, I present a notion of partial imaging that does satisfy monotonicity, and discuss some of the applications and ramifications. The account frames conditioning as a form of imaging, and rejects Gärdenfors's principle of linearity in 'Imaging and conditionalization' (1982). [ABSTRACT FROM AUTHOR]

A Bayesian prediction problem for the two-dimensional Wishart model is investigated within the framework of decision theory. The loss function is the Kullback–Leibler divergence. We construct a scale-invariant and permutation-invariant prior distribution that shrinks the correlation coefficient. The prior is the geometric mean of the right invariant prior with respect to permutation of the indices, and is characterized by a uniform distribution for Fisher's |$z$| -transformation of the correlation coefficient. The Bayesian predictive density based on the prior is shown to be minimax. [ABSTRACT FROM AUTHOR]

Signal detection theory (SDT) has been widely used to identify the optimal response of a receiver to a stimulus when it could be generated by more than one signaler type. While SDT assumes that the receiver adopts the optimal response at the outset, in reality, receivers often have to learn how to respond. We, therefore, recast a simple signal detection problem as a multi-armed bandit (MAB) in which inexperienced receivers chose between accepting a signaler (gaining information and an uncertain payoff) and rejecting it (gaining no information but a certain payoff). An exact solution to this exploration–exploitation dilemma can be identified by solving the relevant dynamic programming equation (DPE). However, to evaluate how the problem is solved in practice, we conducted an experiment. Here humans (n = 135) were repeatedly presented with a four readily discriminable signaler types, some of which were on average profitable, and others unprofitable to accept in the long term. We then compared the performance of SDT, DPE, and three candidate exploration–exploitation models (Softmax, Thompson, and Greedy) in explaining the observed sequences of acceptance and rejection. All of the models predicted volunteer behavior well when signalers were clearly profitable or clearly unprofitable to accept. Overall however, the Softmax and Thompson sampling models, which predict the optimal (SDT) response towards signalers with borderline profitability only after extensive learning, explained the responses of volunteers significantly better. By highlighting the relationship between the MAB and SDT models, we encourage others to evaluate how receivers strategically learn about their environments. [ABSTRACT FROM AUTHOR]

(1) Suppose that you care only about speaking the truth, and are confident that some particular deterministic theory is true. If someone asks you whether that theory is true, are you rationally required to answer 'yes'? (2) Suppose that you face a problem in which (as in Newcomb's problem) one of your options – call it 'taking two boxes' – causally dominates your only other option. Are you rationally required to take two boxes? Those of us attracted to causal decision theory are under pressure to answer 'yes' to both questions. However, it has been shown that many existing decision theories are inconsistent with doing so (Ahmed 2014). A simple proof shows that the same goes for an even wider class of theories: all 'suppositional' decision theories. The moral is that causal decision theorists must either answer 'no' to one of the above questions, or else abandon suppositional decision theories. [ABSTRACT FROM AUTHOR]

In 'An argument against causal decision theory', Jack Spencer shows that standard formulations of causal decision theory run afoul of his Guaranteed Principle. In the sequential choice problem he employs to make this case, the transgression stems from an awkward discrepancy between how causalists typically value present vs future acts. This note suggests a version of causal decision theory that avoids this incongruity and so respects the Guaranteed Principle in Spencer's problem. However, this formulation, and hence symmetric appraisal of present and future acts, is also shown to be insufficient to secure causalist satisfaction of the Guaranteed Principle in general. [ABSTRACT FROM AUTHOR]

Formulating accurate and robust classification strategies is a key challenge of developing diagnostic and antibody tests. Methods that do not explicitly account for disease prevalence and uncertainty therein can lead to significant classification errors. We present a novel method that leverages optimal decision theory to address this problem. As a preliminary step, we develop an analysis that uses an assumed prevalence and conditional probability models of diagnostic measurement outcomes to define optimal (in the sense of minimizing rates of false positives and false negatives) classification domains. Critically, we demonstrate how this strategy can be generalized to a setting in which the prevalence is unknown by either (i) defining a third class of hold-out samples that require further testing or (ii) using an adaptive algorithm to estimate prevalence prior to defining classification domains. We also provide examples for a recently published SARS-CoV-2 serology test and discuss how measurement uncertainty (e.g. associated with instrumentation) can be incorporated into the analysis. We find that our new strategy decreases classification error by up to a decade relative to more traditional methods based on confidence intervals. Moreover, it establishes a theoretical foundation for generalizing techniques such as receiver operating characteristics by connecting them to the broader field of optimization. [ABSTRACT FROM AUTHOR]

Although Laird and Ware's methodology does not involve causal inference, Laird mentions how such inference can be added to the analysis in Ryan (2015, section 9 on statistical genetics). Anna Choi and Weng Kee Wong's contribution to the Discussion of 'Assumption-lean inference for generalised linear model parameters' by Vansteelandt and Dukes We congratulate the authors for major advances in the methodology of estimands, in particular assumption-lean reference for the estimands in possibly misspecified generalised linear models. [Extracted from the article]