The decision-theoretic rough set (DTRS) can be regarded as a type of cost-sensitive learning method that incorporates cost functions. Three-way decisions for an object are made in the DTRS model to ...obtain the minimum Bayesian decision cost, including acceptance, rejection and deferment decisions. However, because the cost matrix in traditional cost-sensitive learning problems does not include the cost for deferment decisions, DTRS cannot be directly used to solve traditional cost-sensitive learning problems. In this paper, we present a multiclass three-way decision-theoretic rough set model that can be directly applied to traditional cost-sensitive learning problems. This model can calculate the corresponding cost for deferment decisions based on the traditional cost matrix. In addition, we propose a multiphase cost-sensitive learning method based on the multiclass three-way decision-theoretic rough set model. The deferment and rejection samples are reduced and eliminated through multiphase iterative learning. Compared with the existing multiclass cost-sensitive learning methods, our proposed method can achieve higher classification accuracy and lower misclassification cost.
•This paper is the first to apply dynamic time-varying efficiency scores in distress prediction.•Malmquist DEA is used to produce dynamic efficiency scores over several periods.•Various efficiency ...scores are compared in discrete time hazard models.•A comparison is made between generic and industry-specific models.
Creditors such as banks frequently use expert systems to support their decisions when issuing loans and credit assessment has been an important area of application of machine learning techniques for decades. In practice, banks are often required to provide the rationale behind their decisions in addition to being able to predict the performance of companies when assessing corporate applicants for loans. One solution is to use Data Envelopment Analysis (DEA) to evaluate multiple decision-making units (DMUs or companies) which are ranked according to the best practice in their industrial sector. A linear programming algorithm is employed to calculate corporate efficiency as a measure to distinguish healthy companies from those in financial distress. This paper extends the cross-sectional DEA models to time-varying Malmquist DEA, since dynamic predictive models allow one to incorporate changes over time. This decision-support system can adjust the efficiency frontier intelligently over time and make robust predictions. Results based on a sample of 742 Chinese listed companies observed over 10 years suggest that Malmquist DEA offers insights into the competitive position of a company in addition to accurate financial distress predictions based on the DEA efficiency measures.
Decision-theoretic rough sets (DTRSs) as a classic model of three-way decisions have been widely applied in the area of risk decision-making. When we confront the complicated and uncertain ...environment, one of challenges is to estimate the loss function of DTRSs. As a new generalization of fuzzy sets, dual hesitant fuzzy sets (DHFSs) can handle uncertain information more flexibly in the process of decision making and give a new measure for the determination of loss functions of DTRSs. To have more interesting results in the context of three-way decisions, we introduce the new hesitant format of DHFSs into DTRSs and explore a new three-way decision model. Firstly, we take into account the loss functions of DTRSs with dual hesitant fuzzy elements (DHFEs) and propose a dual hesitant fuzzy DTRS model. In order to satisfy the preconditions of three-way decisions, we analyze the normalized principle of loss functions under the dual hesitant fuzzy environment. Meanwhile, some properties of the expected losses are carefully investigated. Then, we further design two approaches for deriving three-way decisions with the new DTRS model, i.e., Method 1 and Method 2, which mainly relies on the comparisons among the expected losses. Method 1 is a general method based on the scores and the accuracies of DHFEs. Method 2 is a ranking method of possibility degrees with a stochastic strategy and enriches the comparisons among the expected losses. Finally, the assessment of emergency blood transshipment is used to illustrate and compare these proposed methods.
Behavioral and neuroscientific studies explore two pathways through which internalized social norms promote prosocial behavior. One pathway involves internal control of impulsive selfishness, and the ...other involves emotion-based prosocial preferences that are translated into behavior when they evade cognitive control for pursuing self-interest.Wemeasured 443 participants’ overall prosocial behavior in four economic games. Participants’ predispositions social value orientation (SVO) were more strongly reflected in their overall game behavior when they made decisions quickly than when they spent a longer time. Prosocially (or selfishly) predisposed participants behaved less prosocially (or less selfishly) when they spent more time in decision making, such that their SVO prosociality yielded limited effects in actual behavior in their slow decisions. The increase (or decrease) in slower decision makers was prominent among consistent prosocials (or proselfs) whose strong preference for prosocial (or proself) goals would make it less likely to experience conflict between prosocial and proself goals. The strong effect of RT on behavior in consistent prosocials (or proselfs) suggests that conflict between prosocial and selfish goals alone is not responsible for slow decisions. Specifically, we found that contemplation of the risk of being exploited by others (social risk aversion) was partly responsible for making consistent prosocials (but not consistent proselfs) spend longer time in decision making and behave less prosocially. Conflict between means rather than between goals (immediate versus strategic pursuit of self-interest) was suggested to be responsible for the time-related increase in consistent proselfs’ prosocial behavior. The findings of this study are generally in favor of the intuitive cooperation model of prosocial behavior.
We study insurance and portfolio decisions, two opposite risk retention tradeoffs. Using household level data, we identify the first joint determinants (e.g. subjective expectations, risk attitude) ...and frictions (e.g. liquidity constraints, financial literacy) in the literature. We also find key differences between the two decisions. Notably, contrary to economic intuition, risky asset holding and insurance coverage both increase with wealth. We show that this apparent puzzle is driven in part by a specific behavioral pattern (the poor invest too conservatively, while the rich over-insure), and can be explained by two factors: regret avoidance and nonperformance risk.
The incremental learning technology has been widely applied in efficient and effective data mining with big data based on granular computing, rough sets and three-way approaches. In real-life ...applications, the information systems will evolve over time with four levels of variational situations, which can be described by the combination of the variations of attributes, objects, condition attributes values and decision attributes values. Considering updating knowledge with multilevel variations of data, this paper proposes a unified dynamic framework of decision-theoretic rough sets for incrementally updating three-way probabilistic regions, namely, positive region, boundary region and negative region. Through improving the representation of three-way regions based on the well-established Bayesian decision procedure, a novel matrix approach is introduced by the construction of Boolean matrix and specific definition of matrix operation. Subsequently, at the variations of level-1, the fundamental updating propositions, which can induce the corresponding propositions with the variations of level-2, level-3, level-4, respectively, are presented by the matrix updating strategies. Finally, experiments with four incremental algorithms developed for the verification of feasibility and efficiency under multilevel variations of data are conducted by comparison with non-incremental algorithm.
In this paper, we consider two paradigms that are developed to account for uncertainty in optimization models: robust optimization (RO) and joint estimation-optimization (JEO). We examine recent ...developments on efficient and scalable iterative first-order methods for these problems, and show that these iterative methods can be viewed through the lens of online convex optimization (OCO). The standard OCO framework has seen much success for its ability to handle decision-making in dynamic, uncertain, and even adversarial environments. Nevertheless, our applications of interest present further flexibility in OCO via three simple modifications to standard OCO assumptions: we introduce two new concepts of
weighted regret
and
online saddle point problems
and study the possibility of making
lookahead
(anticipatory) decisions. Our analyses demonstrate that these flexibilities introduced into the OCO framework have significant consequences whenever they are applicable. For example, in the strongly convex case, minimizing unweighted regret has a proven optimal bound of
O
(
log
(
T
)
/
T
)
, whereas we show that a bound of
O
(1 /
T
) is possible when we consider weighted regret. Similarly, for the smooth case, considering 1-lookahead decisions results in a
O
(1 /
T
) bound, compared to
O
(
1
/
T
)
in the standard OCO setting. Consequently, these OCO tools are instrumental in exploiting structural properties of functions and results in improved convergence rates for RO and JEO. In certain cases, our results for RO and JEO match the best known or optimal rates in the corresponding problem classes without data uncertainty.
Drawing on three years of field research and extensive theoretical and empirical literature, Democratic Dilemmas chronicles the day-to-day efforts of educators and laypersons working together to ...advance student learning in two California school districts. Julie A. Marsh reveals how power, values, organizational climates, and trust played key roles in these two districts achieving vastly different results. In one district, parents, citizens, teachers, and administrators effectively developed and implemented districtwide improvement strategies; in the other, community and district leaders unsuccessfully attempted to improve systemwide accountability through dialogue. The book highlights the inherent tensions of deliberative democracy, competing notions of representation, limitations of current conceptions of educational accountability, and the foundational importance of trust to democracy and education reform. It further provides a framework for improving community-educator collaboration and lessons for policy and practice.
Product family design (PFD) has been traditionally tackled as a single-level multi-objective optimization problem. This paper reveals a complex type of leader-follower joint optimization (LFJO) ...problems that are widely observed for PFD. Leader-follower decision making is inherent in product family optimization that involves multiple decision makers and encompasses different levels of decision hierarchy, in which many conflicting goals compete to arrive at equilibrium solutions. It is important for PFD to explicitly model such leader-follower decisions in line with a Stackelberg game. Consistent with multiple decision makers across different stages of the PFD process and multiple levels of the PFD decision hierarchy, this paper classifies the leader-follower decisions of PFD using a quartet grid, which serves as a reference model for conceptualization of diverse types of LFJO problems associated with PFD. Coinciding with the bilevel decision structure of game theoretic optimization, each LFJO problem formulation defined from the quartet grid can be quantitatively mapped to a bilevel programming mathematical model to be solved effectively by nested genetic algorithms. A case study of gear reducer PFD is presented to demonstrate the rational and potential of the LFJO quartet grid for dealing with game-theoretic optimization problems underpinning PFD decisions.
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