Regret Model Research Articles

A robust capacity expansion integrating the perspectives of marginal productivity and capacity regret

This study addresses a capacity expansion problem (CEP). A typical CEP model usually focuses on addressing demand fluctuation for cost minimization and assumes a constant marginal productivity, which may overestimate the capacity level, thus leading to an infeasible capacity plan. However, the marginal productivity theory has some merits which can complement the CEP; for example, a production function estimates the production possibility set limiting the production behavior and characterizes the law of diminishing marginal returns (DMR). To integrate the perspective of marginal productivity factors in the CEP model, we propose a two-stage model to solve the CEP. The first stage estimates the production possibility set and finds the directional marginal productivity (DMP) towards marginal profit maximization. The second stage, which addresses demand fluctuation, develops the minimax regret model, balancing capacity shortage and capacity surplus to build a robust capacity plan. The results of a numerical illustration validate the robust decision generated by the proposed model and correct a typical CEP model without considering marginal productivity, where the major factor affecting capacity decisions is the ability to raise/leverage resource for marginal productivity rather than demand variation and cost structure.

A travel benefits evaluation method based on random regret generalized nested logit model

At present, the factors considered in formulating the transportation policy are not complete, which leads to the gap between the actual effect and expectation. In order to better evaluate the impact of transportation policy changes on consumer welfare (i.e. traveler’s choice benefit), and to more effectively capture the similar characteristics among travel schemes, we develop a generalized nested logit model of random regret based on regret theory which is constructed by combining three aspects of data: the departure time, travel mode and route. In case study, the selection probability is interpreted as an approximate probability demand curve, and the Logsum consumer surplus measurement method is used to evaluate the impact of changes between two policies on traveler benefits under this model structure. The result shows that when the price attribute or the non-price attribute of a nest changes, the consumer surplus of the corresponding alternatives within the nest changes in varying degrees in inverse proportion, thus it better captures the similarity between the schemes. The research results provide reference for relevant departments to formulate and evaluate transportation policies.

A decision frame theory for uncertainty with applications to regret and choice acclimatization

An axiomatization of expected utility under uncertainty is extended in several steps to characterize more complicated decision models. Central to each step is a bijective mapping that, applied to the set of prospects, changes the framing of the decision problem. Static models of subjective expected utility and reference dependence result from stretch mappings and translations (i.e., shifts), respectively. Dynamic models, such as regret and aspiration learning, involve groups of models each of which applies a translation by a different reference prospect (aspiration). The resulting regret model accommodates preference cycles in a higher-dimensional decision space without violating transitivity. An equilibrium of such a dynamic model is characterized as the prospect that is a maximal element in reference to itself. Under aspiration learning, asymptotic stability thus ensures that the aspiration (eventually) matches the equilibrium prospect. The concepts of regret and aspiration learning are combined to a model of choice acclimatization for the purpose of equilibrium selection. The valuation function of this equilibrium selection model is then further specified to accommodate cumulative regret that accrues during the acclimatization process.

Multiattribute Regret: Theory and Experimental Study

The aim of this paper is two-fold. The first part is to generalize the simple regret model by Bell (1982) and Loomes and Sugden (1982) to cope with the situation in which decision outcomes are multi-attributed. We propose a model which combines the simple regret model for ex ante preferences and the additive difference representation for ex post preferences. Then we show a necessary and sufficient axiomatization of our model under Savage’s framework. The proposed model is composed of three types of functions. One is a value function for each attribute. The others are attribute-dependent and holistic regret-rejoicing functions which respectively capture trade-offs among value-differences of chosen and forgone outcomes for each attribute and among all attributes. We also discuss constructive procedures and their consistency. The second part is to conduct an experiment to measure two attributes case. Participants were asked to join in a work to carry over heavy bags at some varying distances. The data supports the framing effect, that is, participants tend to have convex value functions in each attribute and indicate regret averse for weights and regret seeking for distances in gain frame. On the other hand, participants exhibit concave value functions for both attributes and regret seeking for weights and regret averse (almost regret neutral) for distances in loss frame.

Minmax regret maximal covering location problems with edge demands

This paper addresses a version of the single-facility Maximal Covering Location Problem on a network where the demand is: (i) distributed along the edges and (ii) uncertain with only a known interval estimation. To deal with this problem, we propose a minmax regret model where the service facility can be located anywhere along the network. This problem is called Minmax Regret Maximal Covering Location Problem with demand distributed along the edges (MMR-EMCLP). Furthermore, we present two polynomial algorithms for finding the location that minimises the maximal regret assuming that the demand realisation is an unknown constant or linear function on each edge. We also include two illustrative examples as well as a computational study for the unknown constant demand case to illustrate the potential and limits of the proposed methodology.

Analysis of driver’s choice behavior based on the combined model of utility-regret

In the context of increasing urban traffic trajectory data, based on large-scale urban traffic trajectory data, in the field of intelligent transportation, it has become an urgent need to analyze travelers’ routing behaviors and establish effective routing models to provide travelers with efficient and reasonable driving route recommendations. It is often the case that urban taxi groups has better route choice behavior than other travel group under the cost-benefit constraints. Therefore, providing route recommendations for other travelers according to the route choice habits of urban taxi group is a feasible solution to the above need. This paper starts with the utility maximization model and the stochastic regret minimization model, for the problem that the traveler cannot satisfy the premise of the complete rationality of the utility maximization model when choosing routes and the random regret minimization model ignores the traveler’s sensitivity to the original value of the alternative attribute, and improve the calculation formula of the regret value in the random regret minimization model. A route choice model under the utility-regret combination rule is proposed based on the combination of the utility model and the improved regret model, and then, based on the historical trajectory data of taxis in Xi’an, the RP data required by the model is extracted, and the parameters of the model are estimated and verified. The experimental results show that the route choice probability calculated by the model is closer to the route choice probability in the real scene than the simple utility maximization model or the simple random regret minimization model.

A minimax regret model for the leader–follower facility location problem

The leader–follower facility location problem consists of a leader and a follower who are competitors that locate new facilities sequentially. Traditional studies have generally assumed that the leader has partial or full advance information of the follower’s response when making a decision. However, this assumption might be invalid or impracticable in practice. In this paper, we consider that the leader needs to locate a predetermined number of new facilities without knowing anything about the follower’s response. By separating the scenarios in which the follower responds with different numbers of new facilities, a minimax regret model is proposed for the leader to minimise its maximum possible loss. Based on the structural characteristics of the proposed model, a set of solving procedures is provided that transforms the follower’s nonlinear (fraction) programming model into a linear model. In the numerical experiments, the proposed model is compared with two other location models, a deterministic model and a risk model, and the efficiency of the linearisation in decreasing the computation time is verified. The results show that the proposed model is more applicable to the leader when there is no information about the number or probability distribution of the follower’s new facilities.

Multiple resource allocation for precision marketing

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 distance-based algorithm, K-Nearest-Neighbour, and support vector machine to capture customers’ preference towards promotion channel. Additionally, on-line 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 a loan agency that offers loans to small business merchants. Our sample contained 525,919 customers who will be introduced to a new financial product. By simulating different scenarios between resources and demand, we showed an up to 22.42% increase in the number of expected merchants when K-NN was performed with optimal resource allocation strategy. Our results also show that K-NN is the most stable method to perform classification, and that distance-based algorithm has the most efficient adoption with on-line learning.

Intrapersonal heterogeneity in car-sharing decision-making processes by activity-travel contexts: A context-dependent latent class random utility – random regret model

This paper investigates stated preferences for car-sharing in the context of travel mode choice under conditions of uncertain travel times. In particular, the study focuses on heterogeneity in the decision-making process due to different activity-travel contexts such as time pressure, activity duration, and uncertain travel times. It is assumed that the contextual factors influence the decision rules that potential users adopt regarding the use of a shared car for a specific trip purpose. A context-dependent latent class model allowing for the random panel effect is estimated, in which the latent classes respectively represent utility maximization and regret minimization decision rules. The model employs the contextual factors as covariates of class membership probability. A stated choice experiment, in which car-sharing, own car and public transport serve as the choice alternatives, is used to estimate the model. Results indicate that the activity-travel contexts play an important role in accounting for heterogeneity in decision rules.

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-infinity 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.

A minimax regret portfolio model based on the investor’s utility loss

The out-of-sample performance of relative robust portfolio optimization methodologies is still little explored in portfolio literature. In this paper, a new minimax regret portfolio optimization model is presented, where regret is defined as the utility loss for the investor. The real benefits of the proposed methodology were analyzed by comparing in-sample and out-of-sample performances of robust and non-robust portfolios. The results suggest that the proposed relative robust model has more value for risk-taking investors. Furthermore, the proposed relative-robust portfolio outperforms the non-robust portfolios, in many of the time windows under analysis, with the exception of the global minimum variance portfolio. Comparatively to the minimax regret model presented by Xidonas et al. (Eur J Oper Res 262:299–305,2017) and the absolute robust approach developed by Kim et al. (Econ Lett 122:154–158, 2014a), the proposed methodology presents itself as a more consistent approach since it generates portfolios that reveal greater stability concerning in-sample and out-of-sample performances. The developed relative robustness approach stands out as a valuable contribution for the assertion of robust optimization, in particular of relative robust methodologies, within the field of portfolio selection under uncertainty.

Foreign Exchange Rate Exposure of Companies under Dynamic Regret

This paper analyzes optimal hedge ratios for foreign exchange (FX) rate risk of companies. Our contribution to the literature is twofold: (i) We present a theoretical two-period regret model that allows us to analyze the determinants of the optimal hedge ratio given the outcome of past hedging decisions and future expectations. The model implies that the optimal hedge ratio depends on the past hedge ratio, the past exchange rate return, the expected exchange rate return and the skewness of its distribution, its covariance to the foreign market return, as well as the company's risk and regret aversion. (ii) We test the related model-derived hypotheses on a broad sample of US non-financial companies over the period 1995 to 2015 and find strong evidence for the model's predictions. By adding a dynamic regret approach to the hedging and FX literature we shed further light on the rationale behind selective hedging.

Laboratory experimentation and simulation of discrete direction choices: Investigating hypothetical bias, decision-rule effect and external validity based on aggregate prediction measures

IntroductionLaboratory experiments have recently become popular methods for understanding decision-making behaviour of humans in evacuations. When designed for individual-level data extraction, these experiments can be analysed by econometric models. These models can subsequently be implemented as part of broader computational tools that simulate evacuation processes. In taking this approach, a modeller will face several major questions at both experiment design and estimation/implementation phases: (I) Can the behaviour be instead inferred, with adequate accuracy, from stated choice surveys (i.e. hypothetical bias)? (II) Given that these laboratory experiments are performed in specific geometric layouts, are their modelling outcomes transferable to geometric layouts other than that of their origin (i.e. external validity)? (III) At the modelling phase, how critical is to determine whether to set the decision rule as random regret minimisation as opposed to random utility maximisation (i.e. decision rule)? This study investigates how each of these three problems impact on prediction outcomes when these models are employed to simulate an evacuation system. MethodsUsing three sets of experimental observations of discrete direction choice (one stated-choice dataset and two revealed-choice datasets) and by integrating their associated choice models with the crowd motion simulation tool that we have developed, we examined these questions mainly based on aggregate simulation outputs, including evacuation times, exit utilisations and movement patterns. FindingsOur findings showed that the effect of decision rule specification on the prediction of aggregate measures was less noticeable and of little practical importance compared to the effect of hypothetical bias. Counterpart regret and utility models resulted in very similar simulated movement patterns, evacuation times and exit utilisations. Changing the source of the model estimation, however, made relatively bigger differences in those predictions, although not to drastic levels. In general, however, models obtained from independent experiments showed great degrees of parameter similarity and prediction consistency, while the most noticable difference between them was related to their scales. Despite the scale difference, models estimated from one experiment well replicated independent observations of the other experiment. In other words, models dervied from all three data sources proved almost equally valid for accurately replicating macro-scale observations. ApplicationsThese questions are of practical importance in the design and analysis of laboratory evacuation experiments and in establishing their external validity/transferability and also in prioritising modelling issues. Future directionsThe question of decision rule could be revisited using more recent versions of the random regret model (than the 2010 variant, applied here). The question can also potentially be extended to comparisons between the econometric and machine-learning methods. The question of experimental validity needs further investigation in relation to the aspects of evacuation decision-making other than the direction choice.

INFRASTRUCTURES’ EFFECT ON ECONOMIC GROWTH IN SOUTHEAST SULAWESI

The aim of the research is to analyze the effect of infrastructures on economic growth in Southeast Sulawesi. The data used the secunder data which formed time series-based. The data was obtained by publication and legal documents of Statistic Center Unit and relevant institution. The method of analysis used ordinary least squares with panel data. Thus, the data analysis of this research was regretion model with panel data. To estimate regretion of panel data, the researcher used common effect,fixed effect and random effect method. The findings of this research showed that insfrastructures comprised road, harbor, water and electicity simultaniously and significantly influenced the economic growth in Southeast Sulawesi. Partially, only harbor and electricity significantly affected on the economic growth Southeast Sulawesi.

Markowitz with regret

Providing a framework to integrate regret as an additional decision criterion in Markowitz’s model of portfolio selection, we propose two different views on regret: An investor might feel regret with respect to the ex-post best alternative either in terms of return or in terms of preference value. Under both views, regret can be captured by adjusting the vector of expected returns or alternatively by adjusting the return covariance matrix, retaining the tractability of the Markowitz model. The regret model, however, has very different implications for how asset characteristics affect optimal portfolios. While the impact of the skewness of an asset is strengthened, the impact of the variance shrinks. Moreover, we show for a variety of real portfolios that the effects of regret on optimal portfolio weights and the ex-ante return distribution are large.

Adaptation planning for climate-resilient urban infrastructures

ABSTRACTThis paper analyzes the interdependency across two critical infrastructures of transportation and motor fueling supply chains, and investigates how vulnerability to climatic extremes in a fueling infrastructure hampers the resilience of a transportation system. The proposed model features both a bi-stage mathematical program and an extension to an ‘α-reliable mean-excess’ regret model. The former aspect allows decision makers to optimize the pre-disaster asset prepositioning against the maximum post-disaster system resilience. The latter aspect of the proposed model devalues the impact of ‘low-probability, high-cost’ sub-scenarios upon model results. The model reveals the reliance of post-disaster urban mobility on the interdependent critical infrastructure of motor fueling supply chains. The results also suggest how investment in the fueling infrastructure’s vulnerable elements protects urban mobility while the transportation network is stressed or under attack.

Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process

This paper studies the distance-based congestion pricing in a network considering the day-to-day dynamic traffic flow evolution process. It is well known that, after an implementation or adjustment of a new congestion toll scheme, the network environment will change and traffic flows will be nonequilibrium in the following days; thus it is not suitable to take the equilibrium-based indexes as the objective of the congestion toll. In the context of nonequilibrium state, prior research proposed a mini–max regret model to solve the distance-based congestion pricing problem in a network considering day-to-day dynamics. However, it is computationally demanding due to the calculation of minimal total travel cost for each day among the whole planning horizon. Therefore, in order to overcome the expensive computational burden problem and make the robust toll scheme more practical, we propose a new robust optimization model in this paper. The essence of this model, which is an extension of our prior work, is to optimize the worst condition among the whole planning period and ameliorate severe traffic congestions in some bad days. Firstly, a piecewise linear function is adopted to formulate the nonlinear distance toll, which can be encapsulated to a day-to-day dynamics context. A very clear and concise model named logit-type Markov adaptive learning model is then proposed to depict commuters’ day-to-day route choice behaviors. Finally, a robust optimization model which minimizes the maximum total travel cost among the whole planning horizon is formulated and a modified artificial bee colony algorithm is developed for the robust optimization model.

HUBUNGAN MOTIVASI BELAJAR DAN INTENSITAS BIMBINGAN BELAJAR ORANG TUA DENGAN PRESTASI BELAJAR PKN SISWA KELAS V SD SEGUGUS II KALASAN SLEMAN

This research aimed to measure correlation between (1) learning motivation on civic education’s learning achievement , (2)parental guidance’s intensityof education oncivic education’s learning achievement, (3) learning motivation and parental guidance’s intensityof education oncivic education’s learning achievement. This research was a correlational study type of research. Data were collected using questionnaire instrument and documentation. Data in this study were analyzed using descriptive analysis and then hypothesis tested using Product Moment correlation followed by two predictor’s multiple regretion which were preceded with normality, independence, and linearity tested. The Result of this research indicated (1) There was positive and significant relationship between learning motivation with civic education’s learning achievement and the correlation coefficient value was = 0,213 >0,195, (2) There was positive and significant relationship between parental guidance’s intensityof education oncivic education’s learning achievement and the correlation coefficient value was= 0,202 >0,195, (3) There was significant relationship between learning motivation and parental guidance’s intensityof education oncivic education’s learning achievement, and then the F value was Fregresi = 4,204 > Ftabel 3,09. This was mean that the result of this research can be applied for five grade’s elementary school students in Gugus II Kalasan, Sleman. Determination coefficient value was 0,273, this mean that civic education’s learning achievement can be explained by learning motivation and parental guidance’s intensityof education by 7,48%, while the rest of 92,52% was explained by the other factors that excluded to regretion model.

A Non-Game-Theoretic Approach to Bidding in First-Price and All-Pay Auctions

We propose a novel approach to the modelling of behavior in first-price and all-pay auctions that builds on the use of a heuristic to achieve an Impulse Balance Equilibrium. The resulting individual decision-making model, nIBE, assumes no expected profit-maximization and accommodates any distribution of private values. Its parameter-free variant entails the Symmetric Bayes Nash Equilibrium bidding strategy for risk neutral bidders. Assuming impulse weighting may lead to under- or overbidding and organizes the effect of end-of-round information feedback on behavior. We assess nIBE’s explanatory power with experimental data and find that it usually outperforms the available regret models of bidding in first-price and all-pay auctions.

Regret-CAPM: A Model of Regret and Asset Pricing

Regret-CAPM: A Model of Regret and Asset Pricing