Regret Measure Research Articles

Examining the role of relational utility in guilt and regret

Relational utility refers to the value of another person for the attainment of one's future goals through social interaction. Nelissen (2014) found that relational utility influences the intensity of guilt feelings. Specifically, people would feel stronger guilt for hurting a person if this person has high relational utility compared to low relational utility. Guilt is thus not only sensitive to how much another person is hurt, but also to one's own future benefits. Previous research suggested that regret, an emotion strongly related to guilt, is typically experienced when one's own benefit is involved. Regret was not measured in Nelissen's research. In the present research, we examined the role of relational utility in experienced guilt and regret and explored whether the effect of relational utility on regret could explain why relational utility has an effect on guilt. Specifically, we attempt to replicate and extend Nelissen's Study 1 by adding a measure of regret. In two experimental studies we did not replicate the effect of relational utility on guilt, and we only found an effect of relational utility on regret in Study 1 (and not in Study 2). Possible reasons for the contradictory findings of the two studies are discussed.

Longitudinal Assessment of Communication With Patient-Reported Outcomes During Lung Cancer Screening

BackgroundMany organizations recommend clinicians use structured communication processes, referred to as “shared decision making,” to improve patient-reported outcomes for patients considering lung cancer screening (LCS). Research QuestionWhich components of high-quality patient-centered communication are associated with decision regret and distress? Study Design and MethodsWe conducted a prospective, longitudinal, repeated measures, cohort study among patients undergoing lung cancer screening in three different healthcare systems. We surveyed participants using validated measures of decision regret, decision satisfaction, distress, and patient-clinician communication domains up to a year after the low-dose computed tomography (LDCT) for LCS. For longitudinal analyses, we applied a series of generalized estimating equations to measure the association of the “patient as person” communication domain, screening knowledge, and decision concordance with decision regret and distress. ResultsWhen assessed 2-4 weeks after the LDCT, 202 (58.9%) and 8 (2.3%) of 343 total respondents reported mild and moderate/severe decision regret, respectively, while 29 (9.2%) participants of 315 total reported mild distress and 19 (6.0%) moderate or greater distress. The mean ± SD decision satisfaction scores (0 to 10 scale) were 9.82 ± 0.89, 9.08 ± 1.54, and 6.13 ± 3.40 among those with no, mild, and moderate/severe regret respectively. Distress scores remained low after the LDCT, even among those with nodules. Patient-centered communication domains were not associated with decision regret or distress. InterpretationPatients undergoing LCS rarely experience moderate or greater decision regret and distress. Although many participants reported mild decision regret, most were very satisfied over the year after their LDCT for LCS. Communication processes were not associated with regret and distress, suggesting that it may be challenging for communication interventions to reduce the harms of LCS.

Dynamic Environment Responsive Online Meta-Learning with Fairness Awareness

The fairness-aware online learning framework has emerged as a potent tool within the context of continuous lifelong learning. In this scenario, the learner’s objective is to progressively acquire new tasks as they arrive over time, while also guaranteeing statistical parity among various protected sub-populations, such as race and gender when it comes to the newly introduced tasks. A significant limitation of current approaches lies in their heavy reliance on the i.i.d (independent and identically distributed) assumption concerning data, leading to a static regret analysis of the framework. Nevertheless, it’s crucial to note that achieving low static regret does not necessarily translate to strong performance in dynamic environments characterized by tasks sampled from diverse distributions. In this article, to tackle the fairness-aware online learning challenge in evolving settings, we introduce a unique regret measure, FairSAR, by incorporating long-term fairness constraints into a strongly adapted loss regret framework. Moreover, to determine an optimal model parameter at each time step, we introduce an innovative adaptive fairness-aware online meta-learning algorithm, referred to as FairSAOML. This algorithm possesses the ability to adjust to dynamic environments by effectively managing bias control and model accuracy. The problem is framed as a bi-level convex-concave optimization, considering both the model’s primal and dual parameters, which pertain to its accuracy and fairness attributes, respectively. Theoretical analysis yields sub-linear upper bounds for both loss regret and the cumulative violation of fairness constraints. Our experimental evaluation of various real-world datasets in dynamic environments demonstrates that our proposed FairSAOML algorithm consistently outperforms alternative approaches rooted in the most advanced prior online learning methods.

On subjective measures of decision quality.

In times of person-centered care, it is all the more important to support patients in making good decisions about their care. One way to offer such support to patients is by way of Patient Decision Aids (PDAs). Ranging from patient brochures to web-based tools, PDAs explicitly state the decisions patients face, inform them about their medical options, help them to clarify and discuss their values, and ultimately make a decision. However, lingering discussions surround effectiveness research on PDAs. In this article, I focus on two subjective measures of decision quality that are widely used as outcome measures in effectiveness research on PDAs (i.e., the Decisional Conflict Scale (DCS) and measures of regret). Although these measurement instruments have attracted critical attention in the scientific literature, bioethicists have hardly engaged with them. Therefore, I set myself to analyze the relationship between (the different subscales of) the DCS and measures of regret, on the one hand, and ethical principles such as beneficence and autonomy, on the other hand. In light of that analysis, I will clarify some discussions regarding the use of these measures of decision quality in effectiveness research on PDAs. This should help us to align the way we evaluate PDAs with ethical principles and avoid that our attempts to support patients in making good decisions about their care that is so central to person-centered care point in unethical directions.

Double-layer multi-criteria group decision-making approach using neutralized possibility degree-based decision matrix with fuzzy information

Multi-criteria group decision-making (MCGDM) problems are fundamentally characterized by uncertainty and ambiguity. The quantification of such data is vital for determining the most optimal course of action. In this paper, a double-layer possibility degree-based interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs) Vlsekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) approach is presented. First, the possibility degrees of IVq-ROFSs are calculated and processed to establish a new neutralized decision matrix. Second, the components of the new decision matrix are taken into account for obtaining a new pair of positive and negative ideal solutions. The utility and regret measures in the VIKOR approach are ultimately determined by combining criteria weights and cumulative possibility degrees to rank the alternatives. The results of the study show that the proposed approach contributes to acquiring accurate alternative rankings in situations where traditional approaches have had difficulty generating ranks. Also, by eliminating the early normalization stage, which has a direct impact on the final ranking, the proposed approach concludes a unique ranking for each decision-making problem. Several comparative and sensitivity analysis are also conducted to exhibit robustness and applicability of the proposed approach in real world.

Quality of Life Outcomes Associated With Optimization of Treatment by Omitting Radiotherapy in Early Breast Cancer

PurposeImproved prognosis of early breast cancer (EBC) has created opportunities for treatment optimization but reducing morbidity should not inadvertently compromise quality of life (QoL). PROSPECT1 used pre-operative MRI and pathology findings to identify women suitable for radiotherapy (RT) omission following breast conserving surgery. We retrospectively explored the association between de-escalation by omission of RT and QoL in women with EBC. Materials and methodsThree groups were recruited: PROSPECT participants who omitted RT following preoperative MRI (A); participants who received RT following preoperative MRI (B); and women who received usual care - No MRI, received RT (C). Measures included the EORTC QLQ-C30 and BR23, BCTOS, DASS-21 and a measure of decision regret. Between group differences were assessed using ANOVA or nonparametric equivalents. Semi-structured interviews were analyzed with qualitative description (n = 44). ResultsData from 400 women were analyzed (125A, 102B, 173C). Group A had fewer symptoms and better body image (breast symptoms: A-B P = .003, A-C P = <.001; arm symptoms: A-B P = .004, A-C P = .011; body image: A-C P = .041) and fewer differences between the treated and untreated breasts (cosmetic: A-B P < .001, A-C P < .001; functional: A-C P = .011; breast specific pain: A-B P < .001, A-C P < .001). Two qualitative themes were found: Treatment with the biggest impact on QoL, and Specific impact of RT on QoL. ConclusionsOmission of RT was associated with better QoL and functional and cosmetic outcomes. It was highly acceptable to patients. Clinicians should consider the potential for preserved QoL associated with treatment optimization via omission of RT in treatment planning for patients with EBC.

A software trustworthiness evaluation methodology for cloud services with picture fuzzy information

VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) is one of the most commonly used decision-making technique. This work provides a software trustworthiness evaluation method based on VIKOR technique in a group decision-making (GDM) environment. Entropy is an important measure that refers to the amount of disorder in a decision support system. To quantify the decision quality of decision maker (DM), a new method for determining DM weights is proposed according to the entropy value of decision matrix, which is provided by that DM. A new normalization-projection measure is developed in order to test the closeness between two evaluation matrices in software trustworthiness evaluation process. The group regret measure in VIKOR-based GDM method is based on two negative reference matrices in this model. A new ranking method with classification is also developed in this work. Aiming at the complex and changeable environment, these new measures and methods are expected to enrich VIKOR techniques and to play a constructive role in group decision support systems. The feasibility and practicability of developed method are verified by some experiments. This study finds that the different methods (models) may have different results in situations where the same decision data are provided by DMs; and this study also finds that the different parameters may have different results in situation where the decision model is same. The practical implications of developed GDM method can apply not only to software trustworthiness evaluation, but also to many other product quality evaluations.

Investor Regret and Stock Returns

We introduce a measure of regret for stock market investors and investigate its cross-sectional asset pricing implications. According to our regret-based framework, investors experience regret due to not achieving the highest possible return from a similar set of stock investments, and equity portfolios with high regret generate 6.84% more annualized alpha than portfolios with low regret. Using investor-trading activity of 78,000 households at a large U.S.-based brokerage firm, we develop an investor-based regret index and show that this household-level regret measure predicts stock returns in a similar way to our proposed regret measure. We also show that regret is not spanned by established risk or behavioral factors that have been documented to be robust predictors of equity returns. This paper was accepted by David Sraer, finance. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03389 .

Risk and resilience-based restoration optimization of transportation infrastructures under uncertainty.

Disruptive events cause decreased functionality of transportation infrastructures and enormous financial losses. An effective way to reduce the effects of negative consequences is to establish an optimal restoration plan, which is recognized as a method for resilience enhancement and risk reduction in the transportation system. This study takes the total travel time as the resilience measure to formulate a bilevel optimization model for a given scenario. However, the uncertainties involved in restoration activities cannot be overlooked. In this context, the inherent uncertainty is represented with a set of scenarios generated via the Latin hypercube technique. To assess the risk under uncertainty, a conditional value at risk with regret (CVaR-R) measure is introduced when considering the existence of worst-case scenarios. Then, the bilevel programming model is transformed from the deterministic case to the stochastic case, where the upper-level problem determines the restoration sequence to minimize CVaR-R and the lower-level problem is a traffic assignment problem. An integrated framework based on a novel genetic algorithm and the Frank-Wolfe algorithm is designed to solve the stochastic model. Numerical experiments are conducted to demonstrate the properties of the proposed bilevel programming model and the performance of the solution algorithm. The proposed methodology provides new insights into the restoration optimization problem, which provides a reference for emergency decision-making.

Selection of a viable blockchain service provider for data management within the internet of medical things: An MCDM approach to Indian healthcare

Health 4.0 is gaining significant attention globally to support better treatment and care for people. Digital technologies such as the IoMT “(”Internet of Medical Things“”) and blockchain substantially promote quality health services. Literature shows that embedding blockchain in IoMT is an effective way to attain secure and quality healthcare. Selecting a viable blockchain service provider (BSP) becomes a complex task and can be considered an MCDM “(”Multi-Criteria Decision-Making“”) problem. Earlier studies on BSP selection indicate that complex expressions cannot be well-modelled and methodical estimation of decision parameters by capturing hesitation and interaction of entities is not adequately explored. Driven by the issues, authors put forward a novel MCDM framework with (i) a double hierarchy linguistic structure for data collection in natural expressions; (ii) regret measure for experts' reliability calculation; (iii) a weighted CRITIC approach for criteria weight determination, and (iv) CRADIS-Copeland algorithm for ranking BSPs. Finally, a case example from Recent Indian healthcare is exemplified to demonstrate the framework's applicability. Recently, the Indian healthcare sector launched initiatives/plans as part of Health 4.0 to promote data management for a better quality of treatment by seeking support from digital technologies such as IoMT and blockchains to ensure data privacy and improved customer experience. Sensitivity analysis and comparison with extant methods infer that (i) BC1, BC5, and BC3 are the top three BSPs for the considered problem; (ii) criteria such as privacy aspects, intercommunication capability, global accessibility, and total cost constitute 58% importance in the supposed decision problem; (iii) developed model reduces human intervention/biases; (iv) developed model is robust to weight alteration and yields unique rank orders with both personalised and cumulative ordering of BSPs; and (v) proposed model produces broader rank values that aid in effective backup planning.

Implementation of Vikor Algorithm in Web-Based Recommendation System for Obstetrician Selection

The process of childbirth is a natural event for women; with the increase in birth rates, selecting a suitable obstetrician becomes essential. However, many couples find it challenging to choose the right obstetrician, from consultations to hospital births. This study aims to develop an obstetrician recommendation system to overcome decision-making challenges that involve many criteria. The method used is VIseKriterijumska Optimizacija Kompromisno Resenje (VIKOR), which is one of the best methods in Multiple Criteria Decision Making (MCDM) for ranking and selecting alternatives. This method involves several steps, including determining the weight of the criteria, normalizing the matrix, calculating the Utility Measure and Regret Measure, and calculating the VIKOR index. Testing was carried out through scenario tests and user survey tests using the USE questionnaire. The scenario test results show the successful implementation of VIKOR in this system. The results of the user survey test indicated a high level of satisfaction with a score of 83.34%, which fulfilled the four variables in the USE questionnaire: usability (79.76%), ease of use (84.81%), ease of learning (84.81), and satisfaction (83.31%). The results of this study have important implications for increasing the effectiveness of decision-making in selecting the right obstetrician. By incorporating the VIKOR method into an application, this research provides a valuable contribution to developing a VIKOR-based recommendation system. It emphasizes the importance of applying this method in complex decision-making environments.

Convergence rate analysis for optimal computing budget allocation algorithms

Ordinal optimization (OO) is a widely-studied technique for optimizing discrete-event dynamic systems (DEDS). It evaluates the performance of the system designs in a finite set by sampling and aims to correctly make ordinal comparison of the designs. A well-known method in OO is the optimal computing budget allocation (OCBA). It builds the optimality conditions for the number of samples allocated to each design, and the sample allocation that satisfies the optimality conditions is shown to asymptotically maximize the probability of correct selection for the best design. In this paper, we investigate two popular OCBA algorithms. With known variances for samples of each design, we characterize their convergence rates with respect to different performance measures. We first demonstrate that the two OCBA algorithms achieve the optimal convergence rate under measures of probability of correct selection and expected opportunity cost. It fills the void of convergence analysis for OCBA algorithms. Next, we extend our analysis to the measure of cumulative regret, a main measure studied in the field of machine learning. We show that with minor modification, the two OCBA algorithms can reach the optimal convergence rate under cumulative regret. It indicates the potential of broader use of algorithms designed based on the OCBA optimality conditions.

The q-rung fuzzy LOPCOW-VIKOR model to assess the role of unmanned aerial vehicles for precision agriculture realization in the Agri-Food 4.0 era.

Smart agriculture is gaining a lot of attention recently, owing to technological advancement and promotion of sustainable habits. Unmanned aerial vehicles (UAVs) play a crucial role in smart agriculture by aiding in different phases of agriculture. The contribution of UAVs to sustainable and precision agriculture is a critical and challenging issue to be taken into account, particularly for smallholder farmers in order to save time and money, and improve their agricultural skills. Thence, this study targets to propose an integrated group decision-making framework to determine the best agricultural UAV. Previous studies on UAV evaluation, (i) could not model uncertainty effectively, (ii) weights of experts are not methodically determined; (iii) importance of experts and criteria types are not considered during criteria weight calculation, and (iv) personalized ranking of UAVs is lacking along with consideration to dual weight entities. Herein, nine critical selection criteria are identified, drawing upon the relevant literature and experts' opinions, and five extant UAVs are considered for evaluation. To circumvent the gaps, in this work, a new integrated framework is developed considering q-rung orthopair fuzzy numbers (q-ROFNs) for apt UAV selection. Specifically, methodical estimation of experts' weights is achieved by presenting the regret measure. Further, weighted logarithmic percentage change-driven objective weighting (LOPCOW) technique is formulated for criteria weight calculation, and an algorithm for personalized ranking of UAVs is presented with visekriterijumska optimizacija i kompromisno resenje (VIKOR) approach combined with Copeland strategy. The findings show that the foremost criteria in agricultural UAV selection are "camera," "power system," and "radar system," respectively. Further, it is inferred that the most promising UAV is the DJ AGRAS T30. Since the applicability of UAV in agriculture will get inevitable, the developed framework can be an effective decision support system for farmers, managers, policymakers, and other stakeholders.

Impacts of GCM credibility on hydropower production robustness under climate change: CMIP5 vs CMIP6

Climate change has emerged as a vital issue for hydropower management in the future. General circulation model (GCM)-informed climate is commonly applied to assess the impact of climate change on hydropower. However, the effect of GCM credibility on hydropower robustness has been rarely explored. One objective of this study is to seek valuable and robust policies, and the other is to evaluate the effects of GCM credibility on hydropower robustness based on the pre-optimized operating policies. A multi-objective robust optimization approach is first proposed to design operating policies by coupling stochastic simulation, heuristic parameterized policy search, and robust optimization. The GCM credibility is then evaluated based on multi-criteria ranking scores, and the hydropower robustness is illustrated by stepwise culling the lowest credible GCMs. The climate-induced impacts on hydropower robustness rely on an ensemble of 18 GCMs under three representative concentration pathways in Coupled Model Intercomparison Project 5 (CMIP5) and 17 GCMs under three shared socioeconomic pathways in CMIP6. The Longyangxia and Liujiaxia reservoirs in Upper Yellow River Basin are selected as a case study. The results show that (1) the proposed stochastic approach outperforms the deterministic approach regarding hydropower generation and firm output where the mean annual hydropower generation of stochastic operation is improved by -0.50- 19.53% compared to deterministic operation, indicating that the operating rules derived using historical stochastic inflows can adapt flexibly to future inflow changes than that using deterministic inflow. (2) CMIP6 maintains a more robust performance than CMIP5 with higher satisficing measures and lower regret measures. (3) Hydropower robustness tends to be higher when considering credibility and will be lower with time due to increasing uncertainty. Ignorance of GCM credibility may lead to biased climate change adaptation decisions.

Connected & Autonomous Vehicles and road infrastructure – state of play and outlook

The developments of connected & autonomous vehicles (CAV) go very fast. Many aspects of CAV are being studied and receive a great deal of attention, but the road infrastructure component seemed underexposed. The Belgian Road Research Centre (BRRC) decided to study on its own expenses the infrastructure component regarding the rollout of connected and autonomous vehicles.The outcome of our research is a comprehensive report that provides insight into the possible consequences for road infrastructure and road authorities. Two scenarios are examined: the co-existence scenario, with different SAE level vehicles using road infrastructure; the full autonomous scenario. For each scenario, special attention was given to the themes ‘motorways’, ‘urban mobility’ and ‘shuttles’. The report provides insight into “no regret measures” in the field of road infrastructure, and considers the “big picture”.

Minimal regret state estimation of time-varying systems

Kalman and H∞ filters, the most popular paradigms for linear state estimation, are designed for very specific specific noise and disturbance patterns, which may not appear in practice. State observers based on the minimization of regret measures are a promising alternative, as they aim to adapt to recognizable patterns in the estimation error. In this paper, we show that the regret minimization problem for finite horizon estimation can be cast into a simple convex optimization problem. For this purpose, we first rewrite linear time-varying system dynamics using a novel system level synthesis parametrization for state estimation, capable of handling both disturbance and measurement noise. We then provide a tractable formulation for the minimization of regret based on semi-definite programming. Both contributions make the minimal regret observer design easily implementable in practice. Finally, numerical experiments show that the computed observer can significantly outperform both H2 and H∞ filters.

Multi-armed bandits with censored consumption of resources

We consider a resource-aware variant of the classical multi-armed bandit problem: In each round, the learner selects an arm and determines a resource limit. It then observes a corresponding (random) reward, provided the (random) amount of consumed resources remains below the limit. Otherwise, the observation is censored, i.e., no reward is obtained. For this problem setting, we introduce a measure of regret, which incorporates both the actual amount of consumed resources of each learning round and the optimality of realizable rewards as well as the risk of exceeding the allocated resource limit. Thus, to minimize regret, the learner needs to set a resource limit and choose an arm in such a way that the chance to realize a high reward within the predefined resource limit is high, while the resource limit itself should be kept as low as possible. We propose a UCB-inspired online learning algorithm, which we analyze theoretically in terms of its regret upper bound. In a simulation study, we show that our learning algorithm outperforms straightforward extensions of standard multi-armed bandit algorithms.

Long-term bowel dysfunction and decision regret in diverticulitis: A mixed methods study

BackgroundThis study had aimed to describe long-term decision regret, bowel dysfunction, and the overall quality of life in patients with diverticulitis, and to determine if elective colectomy was associated with these patient-reported outcome measures. MethodsThis mixed-methods, survey-based study was administered to a national cohort of patients in the United States with diverticulitis. We measured decision regret (Brehaut Decision Regret), bowel dysfunction (Low Anterior Resection Syndrome score), and the overall quality of life (EuroQol 5 Dimension) in this population. We asked open-ended questions to elucidate factors that influenced patients’ choices between elective colectomy and observation. ResultsAmong the 614 respondents, 294 (48%) chose between colectomy and observational management, 94 (15%) had surgery, and 157 (26%) had major Low Anterior Resection Syndrome. Of the 294 that chose between colectomy and observational management, 51 (17%) experienced decision regret. Colectomy was associated with an average decrease in the Brehaut Decision Regret score by 6 points but was not associated with a categorical measure of decision regret (Brehaut Score ≥50). Bowel dysfunction and overall quality of life were not significantly associated with colectomy. Disease-related factors, psychosocial factors, and interactions with physicians were commonly cited as reasons for pursuing colectomy or observational management. ConclusionPatients with self-reported diverticulitis describe high levels of decision regret and bowel dysfunction regardless of chosen management strategy. Physicians should be aware that psychosocial factors can strongly influence a patient’s choice between colectomy and observational management. We advocated for future prospective studies using patient reported outcome metrics to improve outcomes in diverticulitis.

Evolutionary Dynamics and Phi-Regret Minimization in Games

Regret has been established as a foundational concept in online learning, and likewise has important applications in the analysis of learning dynamics in games. Regret quantifies the difference between a learner’s performance against a baseline in hindsight. It is well known that regret-minimizing algorithms converge to certain classes of equilibria in games; however, traditional forms of regret used in game theory predominantly consider baselines that permit deviations to deterministic actions or strategies. In this paper, we revisit our understanding of regret from the perspective of deviations over partitions of the full mixed strategy space (i.e., probability distributions over pure strategies), under the lens of the previously-established Φ-regret framework, which provides a continuum of stronger regret measures. Importantly, Φ-regret enables learning agents to consider deviations from and to mixed strategies, generalizing several existing notions of regret such as external, internal, and swap regret, and thus broadening the insights gained from regret-based analysis of learning algorithms. We prove here that the well-studied evolutionary learning algorithm of replicator dynamics (RD) seamlessly minimizes the strongest possible form of Φ-regret in generic 2 × 2 games, without any modification of the underlying algorithm itself. We subsequently conduct experiments validating our theoretical results in a suite of 144 2 × 2 games wherein RD exhibits a diverse set of behaviors. We conclude by providing empirical evidence of Φ-regret minimization by RD in some larger games, hinting at further opportunity for Φ-regret based study of such algorithms from both a theoretical and empirical perspective.

Dynamic Inventory Control with Fixed Setup Costs and Unknown Discrete Demand Distribution

When a new product has just been introduced or the economy has just entered a new phase, a firm is often at a loss as to what the underlying demand pattern has become let alone how best to respond to it. In “Dynamic Inventory Control with Fixed Setup Costs and Unknown Discrete Demand Distribution,” Davoodi, Katehakis, and Yang faced off this challenging problem by tailoring ordering decisions to empirical distributions formed out of past demand observations. In the presence of fixed setup costs, however, an (s,S) policy optimal in the conventional known-distribution setting would take many periods for its long-term benefit to be realized. Therefore, a good online policy has to balance between letting ordering decisions settle for long periods and adjusting them frequently to take advantage of newly available information. When properly balanced, such policies could indeed achieve tight bounds for the performance measure of regret.