Regret Model Research Articles

Italian survey about intraperitoneal drain use in distal pancreatectomy.

Intraperitoneal prophylactic drain (IPD) use in distal pancreatectomy (DP) is still controversial. A survey was carried out through the Italian community of pancreatic surgeons using institutional emails, Twitter, and Facebook accounts of the Italian Association for the Study of the Pancreas (AISP) and the Italian Association of Hepato-biliary-pancreatic Surgery (AICEP). The survey was structured to learn surgeons' practice in using IPD through questions and one clinical vignette. Respondents were asked to report their regrets for omission and commission regarding the IPD use for the clinical scenario, eliciting a scale between 0 (no regret) and 100 (maximum regret). The threshold model and a multilevel mixed regression were built to identify respondents' attitudes. One hundred six surgeons completed the survey. Sixty-three (59.4%) respondents confirmed using at least 1 drain, while 43 (40.6%) placed 2 IPDs. Only 13 respondents (12.3%) declared a change in IPD strategy in patients at high risk of clinically relevant postoperative pancreatic fistula (CR-POPF), while 9 (9.4%) respondents changed their strategy in low-risk POPF situations. Thirty-five (35.5%) respondents declared they would remove the IPD within the third postoperative day (POD) in the absence of CR-POPF suspicion. The median omission regret, which proved to be the wrong decision, was 80 (50-100, IQR). The median regret due to the commission of IPD, which turned out to be useless, was 2.5 (1-20, IQR). The CR-POPF probability threshold at which drainage omission was the less regrettable choice was 7% (1-35, IQR). The threshold to perceive drain omission as the least regrettable choice was higher in female surgeons (P < 0.001), in surgeons who modulated the strategies based on the risk of CR-POPF, and in high volume centers (p = 0.039). The threshold was lower in surgeons who performed minimally invasive distal pancreatectomy (P < 0.001), adopted a closed system (P < 0.001), placed two IPDs (P < 0.001), or perceived the IPD as important to prevent reintervention (p = 0.047). Drain management after DP remains very heterogeneous among surgeons. The regret model suggested that IPD omission could be performed in low-risk patients (7% of CR-POPF), leading to low regret in the case of the wrong decision, making it an acceptable clinical decision.

Exploring the willingness to pay for high-occupancy toll lanes under conditions of low familiarity

This study evaluates details of a progressive transportation development model, wherein high occupancy vehicle (HOV) lanes are transformed into high-occupancy toll (HOT) lanes. Although the management strategy of HOT lanes has been implemented for many years, the implementation of HOT lanes is limited in many regions, and thus, many people remain unfamiliar with the concept. This study aims to evaluate travelers' willingness to pay for HOT lanes based on a “low application” scenario in Shenzhen, China, wherein some HOV lanes are present and may potentially be transformed into HOT lanes, but not all area residents are aware of their intended purpose. According to the Diffusion of Innovation Theory, we leverage this situation to conduct a survey of perceptions and attitudes toward the HOT lanes and evaluate how these perspectives may translate to HOT lane utilization. We further proposed a hybrid utility and regret model considering the impacts of loss aversion caused by tolls or coordinated carpooling for single occupancy vehicles to explore relationships between travel behavior and surveyed demographic and socio-economic characteristics in the context of HOT lanes in places where they are unfamiliar. A sensitivity analysis is conducted to characterize any apparent changing influences. The results show that the model considering decision regret performs well, especially in the scenario with low tolls. Nearly 80% of travelers are willing to pay tolls for HOT lanes, even with limited prior exposure to the concept. The increased acceptance of HOT lanes can be expected to hurt total toll revenue, which may be triggered by easier access to carpool partners associated with the free pass. The study informs a deeper understanding of travelers’ willingness to pay for HOT lanes and helps policy-makers develop differentiated tolling strategies.

Navigating Risk Aversion and Regret

This study investigates the distinctive modeling of regret utility when compared with common utility. I also introduce the interplay between common utility and regret utility. Using this model, I examine the differences in decision making, which encompasses issues such as risk sharing and principal–agent dilemmas. Regret utility is set so that its risk aversion shows common utility’s prudence (i.e., downside risk aversion). This paper reveals, both qualitatively and quantitively and with a concrete model, that regret utility leads to a more balanced and optimal ratio of agent payouts to outputs compared with common utility, meaning when major outputs are kept by principal, there are relatively larger agent payouts, and when major outputs are kept by the agent, there are relatively smaller agent payouts. This means that regret makes a more balanced distribution, and regret utility is more conservative (not biased). In addition, preliminary empirical research was performed in which people were asked risk preference or averseness questions, and their risk averseness was calculated by using the CRRA (Constant Relative Risk Aversion) utility function. The regret condition leads to a more conservative attitude. Furthermore, the regret model can be used in other areas, like in conservative investment portfolio optimization.

Intraperitoneal prophylactic drain after pancreaticoduodenectomy: an Italian survey

Intraperitoneal prophylactic drain (IPD) use in pancreaticoduodenectomy (PD) is still controversial. A survey was designed to investigate surgeons’ use of IPD in PD patients through 23 questions and one clinical vignette. For the clinical scenario, respondents were asked to report their regret of omission and commission regarding the use of IPD elicited on a scale between 0 (no regret) and 100 (maximum regret). The threshold model and a multilevel mixed regression were applied. One hundred three (97.2%) respondents confirmed using at least two IPDs. The median regret due to the omission of IPD was 84 (67–100, IQR). The median regret due to the commission of IPD was 10 (3.5–20, IQR). The CR-POPF probability threshold at which drainage omission was the less regrettable choice was 3% (1–50, IQR). The threshold was lower for those surgeons who performed minimally invasive PD (P = 0.048), adopted late removal (P = 0.002), perceived FRS able to predict the risk (P = 0.006), and IPD able to avoid relaparotomy P = 0.036). Drain management policies after PD remain heterogeneous among surgeons. The regret model suggested that IPD omission could be performed in low-risk patients.

Effects of proactive and reactive health control measures on public transport preferences of passengers – A stated preference study during the COVID-19 pandemic

The outbreak of COVID-19 has caused unprecedented disruptions to public transportation services, passengers' mode choice preferences, and their behavioral pattern. Recent studies have updated the systemic shift from shared travel to private mode due to the perceived higher health risk of shared mobility. However, findings from these studies may not be applicable to public transport-oriented cities with very low rate of private car ownership. In the long term when we arrive at a stabilized ‘New Normal’, the need to promote public transport is likely to be re-emphasized with high priority. This study presents insights into the factors affecting public transport (PT) preferences and potential modal shifts within various modes of PT when proactive and reactive health control measures are implemented. An online stated preference survey was conducted in Hong Kong, where public buses, public light buses, MTR, and taxis are the popular modes of public transport. The regret-based panel mixed multinomial logit model was applied in this study, aiming to capture individual’s regret aversion psychology in the context of the COVID-19 pandemic. The results of our random regret model suggest that travelers generally have lower regrets for the chosen mode when having passenger temperature screening, COVID tracing app, and more frequent disinfection, while seem to be unaware of the in-vehicle air quality. Interaction effects between health control measures and personal characteristics (e.g., age and risk attitudes) were also considered. Trip characteristics, including in-vehicle crowding level, fares, in-vehicle travel time, waiting time at a station, walking time, and trip purpose, were found to influence passengers' choices of travel modes. Policy simulations of possible changes in public transport modal preferences in the presence of various health control measures have also been undertaken. The findings suggest that overall health control measures are appreciated by passengers and do align with promotion of a return to public transport use. However, their effects are quite moderate and should be selectively used for different transport modes. Increasing service frequency can be a promising solution, which reduces crowding and waiting time of passengers using public transport modes, and reduces virus transmission risk. This study contributes to a better understanding of public acceptance and preference toward health control measures in public transport, and call for in-depth cost-benefit analysis in the related fields so that better response can be made in possible future public health crisis.

Robust identical parallel machine scheduling with two-stage time-of-use tariff and not-all-machine option

Time-of-use (TOU) tariff has been implemented in the manufacturing industry to improve energy efficiency by regulating the electricity imbalance between supply and demand. Besides, not-all-machine (NAM) option is another way of energy-saving by using only a subset of all the available machines. This study investigates a robust identical parallel machine scheduling problem with a two-stage TOU tariff and NAM option. Only interval bounds on job processing times are known. The problem is first formulated into a min–max regret model to maximise the robustness. Based on problem properties, both an iterative relaxation-based exact algorithm and a memetic differential evolution-based heuristic are developed to solve the problem. Computational experiments on 240 randomly generated instances with up to 20 jobs are conducted to evaluate the performance of the developed methods. Besides, 900 large-sized randomly generated instances with up to 150 jobs are tested for sensitivity analysis and to identify managerial insights for achieving energy-efficient schedules.

Technical Note—Risk-Averse Regret Minimization in Multistage Stochastic Programs

Regret minimization has gained popularity in a wide range of decision-making problems under uncertainty because of its capacity to identify more opportunistic solutions than worst-case value optimization. Unfortunately, the rigidity of current worst-case regret models and scarcity of tractable solution methods have been serious obstacles in multistage applications. In “Risk-Averse Regret Minimization in Multistage Stochastic Programs,” M. Poursoltani, E. Delage, and A. Georghiou consider a multistage stochastic programming setting with a discrete scenario tree. They introduce the notion of the Δ-regret model, which bridges between the ex ante and ex post regret minimization paradigms that are currently used in the regret minimization literature for single-stage problems. The notion of Δ-regret minimization is investigated for the first time both theoretically and numerically in order to better understand its behavior under a set of popular risk measures.

Analysis of Perception Variance in Regret Choice Modeling Based on GPS Data Considering Building Environment Effects

The purpose of this paper is to deal with the perception variance problem in regret model by scaling perception variance and relaxing the distribution assumption of the random error term, which are corresponding to the attribute and alternative differences, respectively. Specifically, the power coefficient in regret function can capture individual perception variance of specific attributes and latent class structure is used to analyze individual heterogeneity of alternative differences by defining weight function. Accordingly, perceiving behaviors of individuals are analyzed in depth and generating absolute and relative behavior interpretation and asymmetric property. The proposed models are estimated and analyzed using GPS data of taxis in Guangzhou, and bicycle household surveys are collected in Tel Aviv metropolitan area. The results show both significant effects of attributes on drivers’ and cyclists’ route choice behavior and identification of different types (absolute or relative) of perceiving attribute and alternative regret differences of the two travelers by incorporating the ratio of length between chosen route and the shortest route.

Hybrid random utility-random regret model in the presence of preference heterogeneity, modeling drivers’ actions

Despite the importance of drivers’ actions and behaviors, the underlying factors to those actions have not received adequate attention. Understanding the factors contributing to various drivers’ actions before crashes could help policy makers to take appropriate actions to tackle those behaviors before crashes occur. One of the first steps could be to identify contributing factors to drivers’ actions by using a reliable statistical technique. It is reasonable to assume that drivers vary in their decision-making processes. Thus, in this study, in addition to the random utility maximization (RUM), the random regret minimization (RRM), as a psychological representation of the choice-making process, was considered. While most of the past studies, in the context of traffic safety, focused on either the RRM or RUM, both models’ frameworks as hybrid models might be needed to account for the heterogeneity of drivers’ decision-making behaviors. In addition, we accounted for the additional dimensions of preference heterogeneity in the latent class (LC) that the model might not capture. The results showed a significant improvement in the model fit of the mixed hybrid LC model compared with the standard hybrid and simple mixed RRM and RUM models. The emotional conditions of drivers, distraction, environmental conditions, and gender are some of the factors found to impact drivers’ choices. The results suggest that while the majority of attributes are processed according to the RUM, a significant portion of attributes are processed by the RRM. The hybrid model provides a richer understanding regarding factors to drivers’ actions before crashes based on different paradigms.

Optimal Virtual Power Plant Operational Regime Under Reserve Uncertainty

Virtual power plant (VPP) has become an important resource for reserve provision owing to its fast-responding capability. In this paper, an optimal VPP operational regime considering reserve uncertainty is proposed, which includes a novel day-ahead offering strategy and a real-time dispatching model. At the day-ahead stage, the offering strategy gives the VPP’s price-dependent offers in the energy market under multiple uncertainties on market price, renewable generation, and calls of reserve deployment. A hybrid stochastic minimax regret (MMR) model is proposed to facilitate making offering decisions in the electricity market. At the real-time dispatching stage, generation scheduling can be realized based on the MMR criterion in an online fashion. To alleviate the intrinsic conservativeness of the dispatching model, a self-adaptive algorithm is also proposed to instantly modify the confidence bounds. The proposed regime is comprehensively tested through extensive case studies, which demonstrate the effectiveness of our method in obtaining operational decisions that are less conservative.

Regretting your occupation constructively: A qualitative study of career choice and occupational regret

Regret is a universal emotion that can be experienced throughout the course of life, with varying degrees of consequence. However, studies have rarely explored regret as it pertains to one's occupational choice. Through an inductive qualitative study drawing on 51 career-based interviews, we develop a model of occupational regret that reveals how individuals experience and deal with this emotion over time. This model shows that the experience of occupational regret is episodic in nature, involves cycles, and manifests differently dependent upon the path taken through episodes. Five different pathways through regret episodes emerged from our analysis, each leading to a distinct regret profile. These profiles are termed Early Responders, Explorers, Stagnaters, Reconciled Acceptors, and Crystallisers. The findings expand our knowledge of domain specific regret and facilitate the identification of possible strategies to constructively navigate this emotion.

How auctioneers set reserve prices in procurement auctions

We introduce a behavioral model that effectively predicts auctioneers’ reserve choice patterns in English clock auctions across varying reserve price formats (whether reserve prices are set ex-ante or ex-post auction events), number of bidders and the distribution of suppliers’ costs. In a two-parameter model, which we call Subjective Conditional Probability (SCP), auctioneers have subjective judgement of conditional probabilities. We theoretically show that the SCP explains two intuitive, but sub-optimal, reserve price setting patterns. It predicts and provides rationalizations for ex-ante reserve prices that decrease in the number of bidders and ex-post reserve prices that increase in the realized auction price. We conduct two experiments; one with a uniform cost distribution and another with a left-skewed cost distribution. We validate the SCP model internally and externally by comparing it to a wide range of models, including reduced form linear regression, risk aversion, anticipated regret model, and subjective probability judgement via in-sample and out-of-sample predictions. We conclude that the SCP has strong external validity across a variety of procurement environments.

Dynamic Marketing Resource Allocation with Two-Stage Decisions

In the precision marketing of a new product, it is a challenge to allocate limited resources to the target customer groups with different characteristics. We presented a framework using the distance-based algorithm, K-nearest neighbors, and support vector machine to capture customers’ preferences toward promotion channels. Additionally, online learning programming was combined with machine learning strategies to fit a dynamic environment, evaluating its performance through a parsimonious model of minimum regret. A resource optimization model was proposed using classification results as input. In particular, we collected data from an institution that provides financial credit products to capital-constrained small businesses. Our sample contained 525,919 customers who will be introduced to a new product. By simulating different scenarios between resources and demand, we showed an up to 22.42% increase in the number of expected borrowers when KNN was performed with an optimal resource allocation strategy. Our results also show that KNN is the most stable method to perform classification and that the distance-based algorithm has the most efficient adoption with online learning.

Evaluation on e-marketing exposure practice to minimize the customers’ online shopping purchase regret

As many of recent study showed that e-marketing activities lead into higher marketing performance such as sales, we believe that e-marketing should be evaluated in terms of its negative impact on marketing practices. This study aimed to evaluate e-marketing exposure and its impact on consumer behavior. Although we believed that marketing could lead a better purchase experience and sales, this study proposed that marketing could also lead into regretful experience to customers. In order to evaluate the regret model in online shopping activities, we proposed a behavioral model based on e-marketing exposure. In total, 400 participants were used in this study. Totally, 363 had regretful experience on online market and used in this study indicating 90.75% data used in this study. Data were collected through questionnaires given to online consumer. Data were analyzed through SEM-PLS as we employ 4-point forced likert scale, which lead to non-parametric analysis. As a result, we found that the excessive marketing exposure ultimately led to higher regret level to the customers. It is true that e-marketing exposure led to desirable behavior such as more rational at evaluating price and shaping a good image and perception toward the products; however, it also led to higher chance for impulsive buying behavior. This behavior led to purchasing regret. As we want to avoid regret as many as possible due to sustainable purchase and growth in the future, we must control e-marketing exposure so that it will not lead to regret on customers.

Multi-Attribute Selection Procedures Based on Regret and Rejoice for the Decision-Maker

Feelings influence human beings’ decision-making; therefore, incorporation of feeling factors in decision-making is very important. Regret and rejoice are very important emotional feelings that can have a great impact on decision-making if they are considered together. While regret has received most of the attention in related research, rejoice has been less considered even though it can greatly influence people’s preferences in decision-making. Furthermore, systematically incorporating regret and rejoice in the multi-criteria decision-making (MCDM) modeling frameworks for decision-making has received little research attention. In this paper, we introduce a new multi-attribute selection procedure that incorporates both regret and rejoice to select the best choice. We utilize the positional advantage operator concept to develop regret and rejoice mathematical equations, and prove them. The proposed MCDM procedure that incorporates these two emotional factors offers a decision-maker the flexibility to trade off some benefits in order to gain a state of psychological satisfaction. More specifically, regret and rejoice are presented mathematically to enable the decision-maker to determine the values of regret and rejoice, and then make the decision in which the rejoice value is higher than the regret value. To test the performance of this new procedure, we apply it to three numerical examples proposed in previous works. The results are matched with those obtained by other methods such as the regret model, VIKOR, PROMETHEE I, and PROMETHEE II, thereby proving the efficacy of the new procedure.

Fine-tuned robust optimization: Attaining robustness and targeting ideality

The paper provides a Robust Optimization framework with original concepts and fundamentals that combine ideals from relative regret models and static robust optimization with novel decision-making concepts. The algorithm uses a fine-tuning strategy to attain a robust solution close as possible to an ideal target, while simultaneously guarantees robustness according to a preset trade-off risk. The framework comprises original concepts, a mathematical approach, and an algorithm. Statistical treatment based on the original framework concepts supports short, middle, or long-term decision-making settings. The framework is highly tractable as the algorithm forces the creation of a setting for a robust optimization considering the specified risk. This manuscript provides an in-depth discussion of its philosophy, objective, original concepts, fields of application, as well as statistical and probabilistic fundamentals. The framework can be applied in linear and nonlinear mathematical models if the objective function is monotonic in the domain of the active convex region. In addition, it is needed to follow some axioms stated in the paper. The framework is recommended for discrete and continuous optimization problems, including SDP, LP, NLP, MILP or MINLP. Real-world applications regarding nonlinear models are restrict. Operations research are a great scope of application having a myriad number of linear models. Numerical simulations were done considering a planning of a sales problem, solving several examples to validate the study and to gain a better understanding of the framework. All results have showed high tractability and performance. The paper discusses real-world applications with references in fields such as supply chain planning, design optimization, MPC control theory, and portfolio optimization.

Integrated Optimization of Port Rotation Direction and Fleet Deployment for Container Liner Shipping Routes

This paper provides an integrated planning methodology for the optimization of port rotation direction and fleet deployment for container liner shipping routes with consideration of demand uncertainty. We first consider a special case that demand is deterministic. A multicommodity flow network model is developed via minimizing the total network-wide cost. Its decisions are the selection of port rotation direction and fleet deployment and container routings in the shipping network. Afterward, we address the generic case that uncertain demand is considered, which is represented by potentially realizable demand scenarios. We develop a minimax regret model to procure the least maximum regret across all the demand scenarios. The proposed models are applied to an Asia-Europe-Oceania liner shipping network with 46 ports and 12 ship routes. Results could provide the liner company with a comprehensive decision tool to simultaneously determine port rotation direction and fleet deployment when tackling uncertain demand.

Regret in the Newsvendor Model with Demand and Yield Randomness

We study the fundamental stochastic newsvendor model that considers both demand and yield randomness. It is usually difficult in practice to describe precisely the joint demand and yield distribution, although partial statistical information and empirical data about this ambiguous distribution are often accessible. We combat the issue of distributional ambiguity by taking a data‐driven distributionally robust optimization approach to hedge against all distributions that are sufficiently close to a uniform and discrete distribution of empirical data, where closeness is measured by the type‐∞ Wasserstein distance. We adopt the minimax regret decision criterion to assess the optimal order quantity that minimizes the worst‐case regret. Several properties about the minimax regret model, including optimality condition, regret bound, and the worst‐case distribution, are presented. The optimal order quantity can be determined via an efficient golden section search. We extend the analysis to the Hurwicz criterion model, which generalizes the popular albeit pessimistic maximin model (maximizing the worst‐case expected profit) and its (less noticeable) more optimistic counterpart—the maximax model (maximizing the best‐case expected profit). Finally, we present numerical comparisons of our data‐driven minimax regret model with data‐driven models based on the Hurwicz criterion and with a minimax regret model based on partial statistical information on moments.

Robust optimization of resource-constrained assembly line balancing problems with uncertain operation times

PurposeDue to the market trend of low-volume and high-variety, the manufacturing industry is paying close attention to improve the ability to hedge against variability. Therefore, in this paper the assembly line with limited resources is balanced in a robust way that has good performance under all possible scenarios. The proposed model allows decision makers to minimize a posteriori regret of the selected choice and hedge against the high cost caused by variability.Design/methodology/approachA generalized resource-constrained assembly line balancing problem (GRCALBP) with an interval data of task times is modeled and the objective is to find an assignment of tasks and resources to the workstations such that the maximum regret among all the possible scenarios is minimized. To properly solve the problem, the regret evaluation, an exact solution method and an enhanced meta-heuristic algorithm, Whale Optimization Algorithm, are proposed and analyzed. A problem-specific coding scheme and search mechanisms are incorporated.FindingsTheory analysis and computational experiments are conducted to evaluated the proposed methods and their superiority. Satisfactory results show that the constraint generation technique-based exact method can efficiently solve instances of moderate size to optimality, and the performance of WOA is enhanced due to the modified searching strategy.Originality/valueFor the first time a minmax regret model is considered in a resource-constrained assembly line balancing problem. The traditional Whale Optimization Algorithm is modified to overcome the inferior capability and applied in discrete and constrained assembly line balancing problems.

Regret-sensitive equity premium

In a static Lucas tree economy, we propose a model that the representative agent is sensitive to regret, that is, the agent is affected by not only the actual outcome but also value-differences between actual and foregone consequences. Our model generalizes the classical simple regret model pioneered by Bell (1982) and Loomes and Sugden (1982), and makes it possible to derive the equilibrium asset price and to see when the regret effect decreases the price. To verify that our model predicts sufficient decreases of the equilibrium price to explain the empirically high risk premium, we analyze the data of U.S. stock market and GDP growth rates during 1871–2018. The numerical calculation indicates that the estimated equilibrium price is small enough to explain the equity premium puzzle.