Bayesian Theory Research Articles

Extensions of results on phylogeny graphs of degree bounded digraphs

An acyclic digraph in which every vertex has indegree at most i and outdegree at most j is called an (i,j) digraph for some positive integers i and j. The phylogeny graph of a digraph D has V(D) as the vertex set and an edge uv if and only if one of the following is true: (u,v)∈A(D); (v,u)∈A(D); (u,w)∈A(D) and (v,w)∈A(D) for some w∈V(D). A graph G is a phylogeny graph (resp. an (i,j) phylogeny graph) if there is an acyclic digraph D (resp. an (i,j) digraph D) such that the phylogeny graph of D is isomorphic to G. Lee et al. (2017) and Eoh and Kim (2021) studied the (2,2) phylogeny graphs, (1,j) phylogeny graphs, (i,1) phylogeny graphs, and (2,j) phylogeny graphs. Their work was motivated by problems related to evidence propagation in a Bayesian network for which it is useful to know which acyclic digraphs have chordal moral graphs (phylogeny graphs are called moral graphs in Bayesian network theory). In this paper, we extend their work by giving necessary conditions of chordal (i,2) phylogeny graphs. We go further to give necessary conditions of (i,j) phylogeny graphs by listing forbidden induced subgraphs.

Weighted likelihood transfer learning for high-dimensional generalized linear models

To simultaneously improve parameter estimation and variable selection for a target model by the auxiliary information from source models, a weighted likelihood transfer learning (WL-TL), together with a l 1 -penalty, is proposed for high-dimensional generalized linear models. To implement the transfer learning, the relevant techniques, including iterative algorithm and the choice of weight, are suggested. The methodology is computational simple, without need for the bias-correction used in the existing literature of parameter-transfer learning. The theoretical properties such as the quadratic error bound of the parameter estimator and the estimation consistency are established. A specific weight selection method based on the Bayesian decision theory has been proposed and studied. Comprehensive simulation experiments and real data analyzes are conducted to further illustrate the performance of the new method.

Abstract PO1-14-07: Programme of mast cell subsets to potentiate breast cancer immunotherapy: from bed to bench to bed (the phase 2 platform RENAISSANCE trial)

Abstract Background: Immune checkpoint inhibitors (ICIs) have heralded a new era in breast cancer treatment; however, response rates remain limited, making precision immune-oncology a major unmet need. In addition to T cells, effective immune responses to ICIs rely on coordinated interactions between innate and adaptive immune cells. Mast cells are evolutionarily conserved, tissue-resident cells of importance to human health. Specific subsets of mast cells might be endowed with opposite roles in cancer treatment, yet the extent of mast cell heterogeneity and its clinical merit in immunotherapy remain undefined. Objective: We sought to comprehensively characterize mast cells in breast cancer, investigate their association with immunotherapy response with in-depth mechanistic insights, and identify actionable strategies to modulate mast cell functional states, thereby optimizing immunotherapy efficacy. Methods: We employed single-cell profiling on longitudinal breast cancer samples from three independent clinical trials (NCT04613674, NCT03197389 and GSE169246) to delineate mast cell heterogeneity in anti-PD-(L)1 therapy. By integrating multi-omic analyses, tissue characterization, preclinical experiments, transgenic mice, and high-throughput drug screening, we outlined the molecular features, underlying mechanisms, and clinical relevance of distinct mast cells to elicit ICI-responsive microenvironments. Subsequently, we launched RENAISSANCE (NCT05076682), a proof-of-concept, Bayesian adaptive, phase 2 platform trial, to evaluate the efficacy and safety of combining mast cell therapeutics with anti-PD-1 backbone therapy in metastatic triple-negative breast cancer (TNBC) patients who progressed after immunotherapy. The primary endpoint was the objective response rate (ORR) assessed using RECIST v1.1 criteria. Results: We identified a distinct population of mast cells termed antigen-presenting mast cells (APMCs), constituting approximately 30% of intratumoral mast cells and correlating with improved clinical benefit of anti-PD-(L)1 therapy in TNBC. APMCs displayed MHC-II and costimulatory molecules, and indicated the presence of tumor-reactive T cells and tertiary lymphoid structures. Using three immunocompetent mouse models, we confirmed the immunomodulatory capacity of APMCs in immunotherapy. Mechanistically, by employing Cpa3CreERT2Cd74fl/fl mice, we demonstrated that APMCs potentiate anti-PD-1 efficacy and antitumor T cell immunity through their antigen-presentation machinery. Interestingly, we identified cromolyn, an FDA-approved drug for allergy, as a potential therapeutic agent that elicited APMC-dependent CD8+ T cell cytotoxicity to synergize with anti-PD-1 therapy. Between February 2022 and March 2023, 10 patients with immunotherapy-refractory metastatic TNBC were enrolled to receive cromolyn plus camrelizumab backbone treatment. Given Bayesian predictive probability, this arm was “graduated” due to meeting the pre-specified efficacy boundary, with an ORR of 40.0% (4/10). The treatment was well tolerated with similar safety profiles of relevant drugs. Conclusions: Our findings provide crucial insights into the impact of mast cell heterogeneity on the clinical response to ICIs at a single-cell level, and pave the way for APMC-directed therapeutic interventions in cancer treatment. To our knowledge, this is the first prospective study in breast cancer of cromolyn plus anti-PD-1 backbone regimen after anti-PD-(L)1 immunotherapy failure, demonstrating significant antitumor activity and commendable tolerability. Consequently, we suggest a phase 3 randomized study to consolidate this finding, which might be an effective treatment in patients for whom there are few effective treatment options. Citation Format: Song-Yang Wu, Xi Jin, Yin Liu, Wen-Jia Zuo, Li Chen, Xiyu Liu, Lei Fan, Zhong-Hua Wang, Yan-Fei Liu, Yi-Zhou Jiang, Zhi-Ming Shao. Programme of mast cell subsets to potentiate breast cancer immunotherapy: from bed to bench to bed (the phase 2 platform RENAISSANCE trial) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-14-07.

Abstract PS16-01: Comparison of an Atezolizumab monotherapy window followed by Atezolizumab and chemotherapy vs. Atezolizumab and chemotherapy alone in triple negative breast cancer (TNBC) – final analysis of the neoadjuvant neoMono trial

Abstract Background: Improvement of systemic therapy of TNBC still is a medical need. Exploratory data from the neoadjuvant GeparNuevo trial suggested a benefit from an immune checkpoint inhibitor (ICI) monotherapy window in TNBC. The neoMono trial prospectively analyzed whether the addition of a preceding Atezolizumab monotherapy window prior to Atezolizumab and chemotherapy (CTX) improves pCR rates among patients (pts) with early TNBC. In an interim analysis after 100 pCR events, patients with unselected TNBC did not show a significant benefit from an Atezolizumab monotherapy window, while an exploratory analysis suggested a highly clinically relevant benefit among pts with PD-L1 positive TNBC. Here we present the final primary endpoint analysis. Methods: NeoMono is a phase 2 randomized multicenter trial that was planned to recruit a maximum of 458 female and male pts with primary TNBC (defined as ER/PR < 10% and HER2 negative) with tumor stages cT1c – cT4d (cN0 and cN+). As the protocol mandated termination of trial recruitment based on the results of the interim analysis, the final ITT population was limited to 359 pts. PD-L1 status had to be identifiable by central pathology by means of the VENTANA PD-L1 (SP142) assay and was defined by PD-L1 expression on immune cells (IC). Neoadjuvant treatment in both study arms consisted of Atezolizumab 1200 mg every 3 weeks in addition to neoadjuvant CTX (12 x Carboplatin/Paclitaxel q1w followed by 4x Epirubicin/Cyclophosphamide q3w). Combination therapy in arm A was preceded by an Atezolizumab monotherapy window of 840 mg once two weeks prior to initiation of combination therapy, while patients in arm B received no immune monotherapy window. Study goals are to compare the efficacy of neoadjuvant CTX + Atezolizumab with versus without a two-week atezolizumab monotherapy window preceding CTX + ICI (primary endpoint: pCR) and to identify biomarkers for response and resistance through analysis of sequential tissue and liquid biopsies. The neoMono statistical design uses Bayesian posterior probabilities (uniform prior distribution) and logistic regression to analyze the primary endpoint. Results: 180 pts in arm A and 179 in arm B from 34 study sites were included in the final primary endpoint analysis. Demographics and baseline characteristics as well as drug exposure were well-balanced in both arms. Posterior mean pCR rates in the ITT population in study arms A and B were 65.7% (95% high posterior density interval (HPDI): 58.5%, 72.5%) and 69% (62.2%, 75.9%), respectively. In an exploratory analysis stratified by PD-L1 IC status (negative: < 1% versus positive: ≥ 1%), pCR rates in arm A were 91.5% in the PD-L1 IC-positive group and 56.1% in the PD-L1 IC-negative group, the corresponding pCR rates in arm B were 82.2% and 64.5%, respectively. In a multivariate analysis of the ITT population including tumor size, nodal status, tumor grade, age and PD-L1 status, the odds ratio for achieving a pCR was 4.77 (p < 0.001) for PD-L1-positive and 2.36 (p=0.023) for grade 3 tumors. In an exploratory analysis including HER2 status, odds for achieving a pCR were significantly higher for patients with HER2-negative (IHC 0) vs. HER2-low tumors (OR 1.73, p=0.036). No new safety signals were observed. Conclusion: The final primary endpoint analysis of the neoMono trial demonstrated the highest pCR rates ever reported in a phase II/III trial in TNBC. While a significant impact of an ICI monotherapy window on the pCR rate after combination of CTX + ICI in an unselected ITT population could not be demonstrated, neoMono indicates for the first time in a randomized prospective setting that patients with immune active TNBC might derive particular benefit from a preceding ICI monotherapy window. The results of the neoMono trial are mainly justifying the conduction of a confirmative trial in immune active TNBC. However, our results underscore the potential role of pre-therapeutic ICI monotherapy window as part of future therapeutic concepts in TNBC. Citation Format: Hans-Christian Kolberg, Johannes Schumacher, Ramona Erber, Michael Braun, Peter A. Fasching, Eva-Maria Grischke, Christian Schem, Michael P. Lux, Mustafa Deryal, Oliver Hoffmann, Bernhard Heinrich, Georg Kunz, Kristina Lübbe, Petra Krabisch, Arndt Hartmann, Philip Raeth, Sabine Kasimir-Bauer, Cornelia Kolberg-Liedtke. Comparison of an Atezolizumab monotherapy window followed by Atezolizumab and chemotherapy vs. Atezolizumab and chemotherapy alone in triple negative breast cancer (TNBC) – final analysis of the neoadjuvant neoMono trial [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PS16-01.

A Review of Fluid Bolus in Critically Ill Patients After Initial Volume Expansion: Bayesian Probability Analysis and Case Studies.

Introduction Fluid resuscitation is a crucial intervention for the management of critically ill patients. However, after initial volume expansion, the advantages of fluid bolus administration remain controversial. Our aim was to investigate the probabilistic reasoning against fluid bolus administration in critically ill patients after initial volume expansion. We then applied this reasoning to two hypothetical case studies that evaluated the benefits and risks associated with a fluid bolus for each patient. Methods We analyzed data from 12 previously published studies, totaling 334 patients, on fluid responsiveness in critically ill patients.Owing to differences in these studies, we used a Monte Carlo simulation based on their parameters to improve our Bayesian prior, generate strong estimates, and address uncertainty. Using the established Bayesian prior for volume responsiveness, we scrutinized two hypothetical case studies employing Bayesian mathematical notation to assess the pre-test probability, posterior probability, and likelihood ratios in patients with septic shock. Results The Monte Carlo simulation yielded a mean response rate of 0.54 (SD = 0.026), suggesting that only approximately 54% of patients were responsive to fluid bolus administration. These results had an effective sample size of 17,204 and an R-hat value of 1, demonstrating the reliability of our results.In our Bayesian case studies, we demonstrate the low probabilities of volume and VO2 responsiveness over time using common bedside testing. Conclusion Our analysis shows that the pretest and posttest probabilities for volume responsiveness following initial fluid resuscitation are low. Additional bedside testing should be pursued before administering additional volume. This approach emphasizes the importance of evidence-based decision-making in the management of critically ill patients to optimize patient outcomes and minimize potential risks.

Genetic variations in immune mediators and prostate cancer risk: A field synopsis with Bayesian calculations

ObjectiveOur study aimed to revaluate the significant data from meta-analyses on genetic variations in immune mediators and the risk of prostate cancer (PCa) by Bayesian approaches. MethodsWe performed a search on the literature before September 5th, 2023, for meta-analytic studies on polymorphisms in immune mediator genes and the risk of PCa. Two Bayesian approaches were used to assess the level of noteworthiness in the meta-analytic data: the False-Positive Rate Probability (FPRP) and the Bayesian False Discovery Probability (BFDP) with a statistical power of 1.2 and 1.5 of Odds Ratio at a prior probability of 10−3 and 10−6. The quality evaluation of studies was performed with the Venice criteria. Gene-gene and protein–protein networks were designed for the genes and products enrolled in the results. ResultsAs results, 18 meta-analyses on 17 polymorphisms in several immune mediator genes were included (IL1B rs16944/rs1143627, IL4 rs2243250/rs2227284/rs2070874, IL6 1800795/rs1800796/rs1800797, IL8 rs4073, IL10 rs1800896/rs1800871/rs1800872, IL18 rs1946518, COX2 rs2745557, TNFA rs361525 and PTGS2 rs20417/689470). The Bayesian calculations showed the rs1143627 and the rs1946518 polymorphisms in IL1B and IL18 genes, respectively, were noteworthy. The Venice criteria showed that only four studies received the highest level of evidence. The gene-gene and protein–protein networks reinforced the findings on IL1B and IL18 genes. ConclusionIn conclusion, this current Bayesian revaluation showed that the rs1143627 and the rs1946518 polymorphisms in the IL1B and IL18 genes, respectively, were noteworthy biomarker candidates for PCa risk.

Ketamine for acute pain after trauma: A pragmatic, randomized clinical trial.

Non-narcotic intravenous medications may be a beneficial adjunct to oral multimodal pain regimens (MMPRs) which reduce but do not eliminate opioid exposure and prescribing after trauma. We hypothesized that the addition of a subdissociative ketamine infusion (KI) to a standardized oral MMPR reduces inpatient opioid exposure. Eligible adult trauma patients admitted to the intermediate or intensive care unit were randomized upon admission to our institutional MMPR per usual care (UC) or UC plus subdissociative KI for 24 hours to 72 hours after arrival. The primary outcome was morphine milligram equivalents per day (MME/d) and secondary outcomes included total MME, discharge with an opioid prescription (OP%), and rates of ketamine side effects. Bayesian posterior probabilities (pp) were calculated using neutral priors. A total of 300 patients were included in the final analysis with 144 randomized to KI and 156 to UC. Baseline characteristics were similar between groups. The Injury Severity Scores for KI were 19 [14, 29] versus UC 22 [14, 29]. The KI group had a lower rate of long-bone fracture (37% vs. 49%) and laparotomy (16% vs. 24%). Patients receiving KI had an absolute reduction of 7 MME/day, 96 total MME, and 5% in OP%. In addition, KI had a relative risk (RR) reduction of 19% in MME/day (RR, 0.81 [0.69-0.95], pp = 99%), 20% in total MME (RR, 0.80 [0.64-0.99], pp = 98%), and 8% in OP% (RR, 0.92 [0.76-1.11], pp = 81%). The KI group had a higher rate of delirium (11% vs. 6%); however, rates of other side effects such as arrythmias and unplanned intubations were similar between groups. Addition of a subdissociative ketamine infusion to an oral MMPR resulted in a decrease in opioid exposure in severely injured patients. Subdissociative ketamine infusions can be used as a safe adjunct to decrease opioid exposure in monitored settings. Therapeutic/Care Management; Level I.

Ground Prediction by Markov-ANN Hybrid Analytics Using Baseline Geotechnical Data and Observed Field Data

ABSTRACT Geology along the tunnel length usually differs from the anticipated data as per Geotechnical Baseline Report (GBR), sometimes quite significantly. This has always led to the disruption of the planning, resource mobilisation, cost and duration of the project. One way to minimise this uncertainty is by exhaustive investigation like probe holes and geophysical prospecting. Forecasting or prediction of Geology can also be done by soft computing and analytical methods. Using the analytical tool of the Markov Model and the learning powers of Artificial Neural Networks (ANN) has been attempted in this study. The idea is to build a hybrid model that would combine both capabilities and do a probabilistic prediction of the ground condition ahead of the tunnel face in real time to better suit the site, accommodate complexities and can capture the associated uncertainties. The Geotechnical Baseline Report (GBR) having the initial survey details of the tunnel geology is used to build the Markov Model while the deterministic borehole data in the GBR is used to build the ANN model. Bayesian joint probability theorem is used to update the model with the face observed geological parameters. This hybrid model demonstrated to be a good complement to the physical forecasting methods and the geological uncertainty of a complex and challenging Himalayan region was well captured by the present approach. The window of the prediction was for a region of approximately 250m where it showed a very good prediction of ground class. The model can help in systematic planning and resource mobilisation in a better way and subsequent key decisions for the upcoming excavation method and corresponding support measures.

Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample.

Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the possible outcomes of actions are associated with rewards or penalties. The resulting normative models specify how decision makers should encode and combine information about uncertainty and value-step by step-in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian computations are carried out not on probabilities but on (1) probability density functions and (2) these probability density functions are derived from samples. We break the BDT model into a series of computations and test human ability to carry out each of these computations in isolation. We test three necessary properties of normative use of pdf information derived from a sample-accuracy, additivity and influence. Influence measures allow us to assess how much weight each point in the sample is assigned in making decisions and allow us to compare normative use (weighting) of samples to actual, point by point. We find that human decision makers violate accuracy and additivity systematically but that the cost of failure in accuracy or additivity would be minor in common decision tasks. However, a comparison of measured influence for each sample point with normative influence measures demonstrates that the individual's use of sample information is markedly different from the predictions of BDT. We will show that the normative BDT model takes into account the geometric symmetries of the pdf while the human decision maker does not. An alternative model basing decisions on a single extreme sample point provided a better account for participants' data than the normative BDT model.

Beyond green borders: an innovative model for sustainable transportation in supply chains

Modern requirements necessitate the establishment of sustainable transportation systems, considering the substantial growth in transportation activities over recent years, which is expected to continue. Companies are facing the challenge of modeling their system transport to align with green principles. Sustainable transport relied on involving diverse stakeholders, particularly scientific research, in the development of this field. In light of this, maintaining sustainable transport quality involves conducting thorough investigations into an innovative study focusing on an uncertain interval programming model for a multi-stage, multi-objective, multi-product transportation challenge within budget constraints and safety measures in a green supply chain. Human languages often contain imperfect or unknown information, inherently lacking certainty; achieving precision in describing existing states or future outcomes is frequently unattainable. In probability theory, sufficient historical information is crucial for estimating probability distributions; while in fuzzy theory, determining a reliable membership function proves challenging; hence, there is often a hesitant estimation of the degree of belief in the occurrence of each condition. Addressing such uncertainties, the theory of uncertain intervals proves highly valuable. Given these considerations, the elements of the specified problem are recognized as uncertain intervals. To manage this lack of assurance, a fusion of interval theory and methods from uncertain programming is used to formulate two distinct models: an expected value model and a chance-constrained model. The equivalent deterministic models are then formulated and solved utilizing Weighted Sum Method, fuzzy programming, and goal programming. Following this, a numerical example is utilized to assess the model’s performance, and the results obtained are compared. Finally, the document concludes with a sensitivity analysis and outlines future directions.

Parameter Estimation of a Valve-Controlled Cylinder System Model Based on Bench Test and Operating Data Fusion

The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder system. Bench test data are easily obtained, but it is challenging to emulate actual loads in the research on parameter estimation of valve-controlled cylinder system. Despite the actual load information contained in the operating data of the control valve, its acquisition remains challenging. This paper proposes a method that fuses bench test and operating data for parameter estimation to address the aforementioned problems. The proposed method is based on Bayesian theory, and its core is a pool fusion of prior information from bench test and operating data. Firstly, a system model is established, and the parameters in the model are analysed. Secondly, the bench and operating data of the system are collected. Then, the model parameters and weight coefficients are estimated using the data fusion method. Finally, the estimated effects of the data fusion method, Bayesian method, and particle swarm optimisation (PSO) algorithm on system model parameters are compared. The research shows that the weight coefficient represents the contribution of different prior information to the parameter estimation result. The effect of parameter estimation based on the data fusion method is better than that of the Bayesian method and the PSO algorithm. Increasing load complexity leads to a decrease in model accuracy, highlighting the crucial role of the data fusion method in parameter estimation studies.

Optimized strategies of cloud droplet distribution retrieval using satellite multi-directional polarimetric optical measurements: information content approach

Multi-directional polarized optical sensors are increasingly vital in passive remote sensing, deepening our understanding of global cloud properties. Nevertheless, uncertainty lingers on how these observations can contribute to our knowledge of cloud diversity. The variability in cloud PSD (Particle Size Distribution) significantly influences a wide array of cloud characteristics, while unidentified factors in RT (Radiative Transfer) may introduce errors into the cloud PSD retrieval algorithm. Therefore, establishing unified evaluation criteria for both optical device configuration and inversion methods is crucial. Our study, based on Bayesian theory and RT, assesses the information content of both cloud effective radius and effective variance retrieval, along with the key factors affecting their retrieval in multi-directional polarized observations, using the calculation of DFS (Degree of Freedom for Signals).We consider the process of solar incidence, cloud scattering, and sensor reception, and discuss the impact of various sensor configurations, cloud characteristics, and other components on the retrieval of cloud PSD. Correspondingly, we observed a 48% improvement in the information content of cloud PSD with the incorporation of multi-directional polarized measurements in the rainbow region. Cloud droplet concentration significantly influences inversion, but its PSD does not cause monotonic linear interference on information content. The blending of particle mixtures with different PSD has a significant negative impact on DFS. In cases where the AOD (Aerosol Optical Depth) is less than 0.5 and the COT (Cloud Optical Thickness) exceeds 5, the influence of aerosol and surface contributions on inversion can be neglected. Our findings would serve as a foundation for future instrument design improvements and enhancements to retrieval algorithms.

A harmonic property of right invariant priors

AbstractIt is shown that, on any Lie group, the density ratio of the right invariant measure to the left invariant measure is harmonic with respect to the left invariant Riemannian metric. This result is applied to the Bayesian prediction theory on group invariant statistical models. A method of constructing Bayesian prior distributions that asymptotically dominate the right invariant priors is provided.

Mental Health Professionals' Views on Providing Online Counseling within the Scope of UTAUT Model: A Q Method Analysis

Online counseling, an intervention that has evolved alongside technological advancements and become integrated into individuals' lives, has gained even more popularity. At this point, it is essential to investigate the acceptance and usage of online counseling by mental health professionals. This research was conducted with 35 mental health professionals (psychologists and counsellors) who provide both online and face-to-face counseling. In the study, Q methodology was used to better understand the subjective thoughts, attitudes, and beliefs of participants to understand their behaviors regarding the acceptance and use of online counseling according to the UTAUT model. As a result of the research, it was found that factors related to the acceptance and usage of online counseling were grouped into two factors for mental health professionals. The research findings indicate that mental health professionals are grouped into two factors regarding factors related to the acceptance and utilization of online counseling. It has been observed that performance expectations and facilitating conditions influence the acceptance and utilization of online counseling for participants in Factor 1, while effort expectations and facilitating conditions influence the behavior of acceptance and utilization for participants in Factor 2. Additionally, it was found that mental health professionals in Factor 1 have a more positive attitude towards online counseling, whereas those in the second group prefer face-to-face counseling over online counseling.

Indexicality, Bayesian background and self‐location in fine‐tuning arguments for the multiverse

AbstractOur universe seems to be miraculously fine‐tuned for life. Multiverse theories have been proposed as an explanation for this on the basis of probabilistic arguments, but various authors have objected that we should consider our total evidence that this universe in particular has life in our inference, which would block the argument. The debate thus crucially hinges on how Bayesian background and evidence are distinguished and on how indexical or demonstrative terms are analysed. The aim of this article is to take a step back and examine these various aspects of Bayesian reasoning and how they affect the arguments. The upshot is that there are reasons to resist the fine‐tuning argument for the multiverse, but the “this‐universe‐objection” is not one of them.

Strategy Is Only Partly an Illusion: “Relative Foresight” as an Objective Standard for Evaluating Foreign Policy Competence

Abstract Foreign policymakers must grapple with complexity, uncertainty, and subjectivity. These challenges raise the possibility that “strategy is an illusion”: that there is no reliable method for assessing skill at managing international politics. By contrast, we show that researchers can objectively evaluate a critical component of foreign policy competence using a standard we call “relative foresight,” defined as decision-makers’ ability to anticipate consequences of their choices as compared to alternative views based on similar information. Relative foresight can be measured without relying on value judgments or subjective probabilities. By contrast, other common frameworks for gauging foreign policy competence, such as comparing leaders’ behavior to the rational actor model or assessing procedural rationality, almost always leave room for reasonable disagreement. We demonstrate that relative foresight provides a useful tool for evaluating major foreign policy choices through case studies of Barack Obama’s decisions regarding the Afghan Surge and the raid on Osama bin Laden’s compound. Our framework has broad implications for research on normative, prescriptive, and descriptive dimensions of foreign policy analysis.

Tracing the sources and evaporation fate of surface water and groundwater using stable isotopes of hydrogen and oxygen

Recognizing the origins and movement processes of surface water and groundwater is crucial for understanding hydrochemical genesis, conserving water supplies, and managing water resources. Estimating the source water typically involves identifying the intersection of evaporation line (EL) and meteoric water line. However, there is currently confusion in determining the regional EL and selecting strategies for estimating the source water. This study aimed to explore the source of surface water and groundwater, as well as evaporation effect utilizing stable isotope tracing (δ2H and δ18O). The line-conditioned excess was adopted to differentiate evaporated water and non-evaporated water, then Craig-Gordon model and an analytical framework with Bayesian theory were used to investigate the source of surface water and groundwater and the evaporation influence. The findings revealed that surface water and groundwater in the northern region of the Weihe River suffered more sever evaporation impacts that the south, and the evaporated surface water (7.54 % to 27.34 %) with a wider range of mean evaporation ratio than evaporated groundwater (5.38 % to 8.52 %). Monsoon precipitation is the main contributor to both surface water (contribution ratio: 0.46) and groundwater (0.49) sources. This research provides specific information on evaporation and detailed insights into the source water of surface water and groundwater, aiding in understanding the evaporation effect during the hydrological cycle and facilitating the management of regional water resources.

Combining Savage and Laplace: a new approach to ambiguity

This paper presents a new representation of preference orderings for the study of ambiguity-related decision-making. The central feature is a preference-based decomposition of subjective probabilities that provides information about inherent ambiguity. The probability decomposition is combined with a utility function reflecting the decision-maker’s attitude toward ambiguity. The proposed theory generalizes Savage’s SEU and allows for a straightforward measurement of ambiguity and ambiguity aversion while keeping concepts for measuring risk and risk attitudes unaffected. For the measurement of ambiguity, concepts of probability theory can be used since decision acts can be interpreted as two-dimensional probability distributions. The proposed measure of ambiguity aversion exploits the properties of the utility function in the same way as the Arrow/Pratt measure of risk aversion.

Indirect reciprocity with Bayesian reasoning and biases.

Reputations can foster cooperation by indirect reciprocity: if I am good to you then others will be good to me. But this mechanism for cooperation in one-shot interactions only works when people agree on who is good and who is bad. Errors in actions or assessments can produce disagreements about reputations, which can unravel the positive feedback loop between social standing and pro-social behaviour. Cooperators can end up punished and defectors rewarded. Public reputation systems and empathy are two possible mechanisms to promote agreement about reputations. Here we suggest an alternative: Bayesian reasoning by observers. By taking into account the probabilities of errors in action and observation and their prior beliefs about the prevalence of good people in the population, observers can use Bayesian reasoning to determine whether or not someone is good. To study this scenario, we develop an evolutionary game theoretical model in which players use Bayesian reasoning to assess reputations, either publicly or privately. We explore this model analytically and numerically for five social norms (Scoring, Shunning, Simple Standing, Staying, and Stern Judging). We systematically compare results to the case when agents do not use reasoning in determining reputations. We find that Bayesian reasoning reduces cooperation relative to non-reasoning, except in the case of the Scoring norm. Under Scoring, Bayesian reasoning can promote coexistence of three strategic types. Additionally, we study the effects of optimistic or pessimistic biases in individual beliefs about the degree of cooperation in the population. We find that optimism generally undermines cooperation whereas pessimism can, in some cases, promote cooperation.

Pain Lowers Subjective Survival Probabilities among Middle-aged and Older Adults.

Pain is a leading cause of disability and a limiting factor in individuals' assessments of their own subjective health, however its association with subjective longevity has yet to be explored. Subjective survival probabilities (SSPs), or one's own perceived chances of living to a given age, can influence individuals' behavior as they plan for their futures. This study assesses whether pain correlates to lower SSPs. We use a repeated cross-section of the 2000-2018 waves of the Health and Retirement Study, a longitudinal and nationally representative survey of Americans aged 51 and older (N=31,773). Fractional logit regressions indicate that, across all age groups, respondents with severe and/or interfering pain reported significantly lower SSPs than those with no pain (Marginal Effect (ME) = -0.03 to -0.06, p < .05). Controlling for all covariates, mild or moderate non-interfering pain was only associated with a significant reduction in SSPs among the youngest group reporting their chances of living to age 75 (ME = -0.02, p < .001). Descriptively and in the model results, respondents with mild or moderate non-interfering pain appeared to more closely resemble pain-free respondents than those with severe or interfering pain. These findings highlight the importance of pain on SSPs, and contribute to the growing evidence that pain interference is uniquely important in predicting meaningful health outcomes.