Credible Adjustment Research Articles

A three-way decision method based on cumulative prospect theory for the hierarchical diagnosis and treatment system of chronic diseases

With the increase in the number of patients with chronic diseases, the contradiction between medical resources and the demand for medical services is increasingly stressed. The hierarchical medical system becomes an important measure to efficiently allocate medical resources. Given that the two-way referral of patients is a core issue of a hierarchical medical system, this paper takes the upward referral as an example and constructs a three-way decision model for the hierarchical diagnosis and treatment system of chronic diseases, which takes teleconsultation as a compromise. Firstly, considering the credibility of decision-making information, a new representation of the loss function is proposed by combining the subjective evaluations of doctors with the personal preferences of patients. Secondly, the credibility adjustment rules are designed, subject to the constraints of the loss function. Furthermore, taking into account the doctors’ psychological behaviors such as risk attitudes when facing uncertain decisions, a three-way decision model based on the cumulative prospect theory was constructed. The main highlight and contribution of this paper is to put forward a new form of the loss function and the adjustment method of the loss function. A medical decision support system is designed based on the proposed method. Finally, a real case of upward referral of patients with coronary heart disease in First Hospital of Qinhuangdao is given to illustrate the validity of the proposed method, and comparisons are conducted to show the superiority of the method. This study provides methodical support for the hierarchical diagnosis and treatment system in the medical field, and it is conducive to promoting the equalization of basic medical services, which has potential theoretical and practical significance.

Machinery Fault Diagnosis Based on Domain Adaptation to Bridge the Gap Between Simulation and Measured Signals

In intelligent fault diagnosis, the success of artificial intelligence (AI) models is highly dependent on labeled training samples, which may not be obtained in real-world applications. Recently, a finite element method (FEM) simulation-based personalized diagnosis method was developed to overcome the problems of insufficient and incomplete labeled training samples. However, the simulation signals obtained using the FEM and measured signals actually have a certain deviation. To supplement the FEM simulation-based personalized diagnosis method, a fault diagnosis method using domain adaptation (DA) is proposed to bridge the gap between simulation signals and measured signals. First, the FEM is adopted to obtain sufficient and complete simulation samples of all the fault categories as the original fault samples in the source domain. Second, the original simulation fault samples are adjusted using a generative adversarial network (GAN)-based DA network to make them similar to the measured samples through the adversarial training of the refiner and domain discriminator. Last, credible adjustment fault samples and measured fault samples obtained in machinery are applied to a convolutional neural network (CNN) for training and testing to complete the fault classification. The data obtained from rolling element bearing and gear test rigs are utilized to explore the feasibility of the proposed method, and the classification accuracies reach 99.44% and 99.58%, respectively. The comparison investigations using experimental data of gears and bearings indicate that the present method can accurately classify faults in machinery.

Whose Hamlet Mocks the Warm Clown?

Whose Hamlet Mocks the Warm Clown?

Delta Boosting Machine with Application to General Insurance

ABSTRACTIn this article, we introduce Delta Boosting (DB) as a new member of the boosting family. Similar to the popular Gradient Boosting (GB), this new member is presented as a forward stagewise additive model that attempts to reduce the loss at each iteration by sequentially fitting a simple base learner to complement the running predictions. Instead of relying on the negative gradient, as is the case for GB, DB adopts a new measure called delta as the basis. Delta is defined as the loss minimizer at an observation level. We also show that DB is the optimal boosting member for a wide range of loss functions. The optimality is a consequence of DB solving for the split and adjustment simultaneously to maximize loss reduction at each iteration. In addition, we introduce an asymptotic version of DB that works well for all twice-differentiable strictly convex loss functions. This asymptotic behavior does not depend on the number of observations, but rather on a high number of iterations that can be augmented through common regularization techniques. We show that the basis in the asymptotic extension differs from the basis in GB only by a multiple of the second derivative of the log-likelihood. The multiple is considered to be a correction factor, one that corrects the bias toward the observations with high second derivatives in GB. When negative log-likelihood is used as the loss function, this correction can be interpreted as a credibility adjustment for the process variance. Simulation studies and real data application we conducted suggest that DB is a significant improvement over GB. The performance of the asymptotic version is less dramatic, but the improvement is still compelling. Like GB, DB provides a high transparency to users, and we can review the marginal influence of variables through relative importance charts and the partial dependence plots. We can also assess the overall model performance through evaluating the losses, lifts, and double lifts on the holdout sample.

Lawrence A. Boland's Model building in economics: its purposes and limitations. Cambridge: Cambridge University Press, 2014, 298 pp.

Review of Lawrence A. Boland's Model building in economics: its purposes and limitations. Cambridge: Cambridge University Press, 2014, 298 pp.The purposes and limitations of all types of models used in economics, past and beyond. This is the rather ambitious topic of Lawrence Boland's new book. The book reviews standard microeconomics, macroeconomics, game theory (including evolutionary), experimental economics, and econometrics (both micro and macro). In its mission statement, the book is declared methodology with a small 'm', to be distinguished with a capitalized Methodology discussed by Philosophers. Methodology discussed by Philosophers deals with lofty issues of 'Realism', 'Testing', and 'Explanation', whereas methodologists are interested in, well, such things as the realisticness of assumptions, the testing of models, and explanation. In the end, the biggest drawback of the book ends up being a seemingly aesthetic aversion to completely imagined capital letters. It is clear that the book discusses the very same issues that have vexed philosophers of science and philosophers of economics for at least the last few decades, and arguably for centuries, yet it refuses to engage these resources. This is a regrettable, but perhaps inevitable, consequence of the limits of any one person trying to cover the whole of economics and the relevant philosophy of economics.Boland begins with a thesis about a paradigm and generational shift in economic modeling around the beginning of the 1980s. Economists having their postgraduate studies in the 1980s or later no longer conceive models as being models of a theory, built in order to provide an instrument with which to empirically test that theory. Instead, model building began to be equated with theorizing itself. This shift is argued for in a concise, yet convincing manner. And this shift does not sit all that well with the Popperian spirit of Boland's Methodology (yes, with a capital 'M'). Although the book does not attempt to systematically build a single argument or an encompassing philosophical position, the overall message emerging from the discussion seems to be a plea for more interest in the realisticness and empirical testing of modeling assumptions.The book first goes through the stylized history of the division between micro and macroeconomics. It dates the beginning of modern microeconomics to Marshall and proceeds from there to discuss the need for, and the plausible form of, microfoundations. Standard criticisms against the use of representative agent constructs and the sterility of general equilibrium theory are also raised. The biggest faults located by Boland in theoretical macro models based on equilibrium assumptions are their lack of dynamics and any idea of adjustment process for prices. There is nothing new in these accusations, but Boland raises the issues with a peculiar twist. It would not, in fact, be enough to endow the model with dynamics by having the endogenous variables be determined by their lagged values and additional exogenous variables. This would be merely a combination of exogenous shocks and a predetermined time path, not truly endogenous dynamics resulting from the actions of free agents. Approvingly quoting Hicks, Boland holds that economics must not only include time, but be in time.The argument here is that the problem is not just in the lack of an empirically credible adjustment process, but in the fact that the conceptions of learning and expectation formation implicit in any proposed sketch of an adjustment process are flawed, because they have always been based on inductive reasoning. And inductive reasoning was demonstrated to be impossible by Hume. This foundational problem of the groundlessness of inductive learning is also raised with respect to agents within game-theoretical models: common knowledge of rationality either assumes a foundationally irrational inductive learning process or simply assumes away the whole 'problem of induction'. …

Fiscal Consolidations and Growth: Does Speed Matter?

Should fiscal consolidations be front-loaded or proceed at a more steady pace, and how does this affect growth? We make an attempt to address this question using a three-step methodology. First, we modify a standard regression of growth on consolidation size to allow speed to affect the multiplier. Second, using the narrative dataset of Devries and others (2011), we construct a new sample of multi-year consolidation episodes for 17 advanced economies over 1978-2009. Third, we develop a novel concept of speed to measure the pace of the consolidation episodes identified in the data. The main empirical finding is that fast episodes have higher multipliers than gradual consolidations. This provides some preliminary support for consolidating at a steady pace, market access and a credible adjustment plan permitting. However, as the sample size is small, identifying mechanisms and testing robustness is difficult, and so our findings should not be interpreted causally.

Fiscal Consolidations and Growth: Does Speed Matter?

Should fiscal consolidations be front-loaded or proceed at a more steady pace, and how does this affect growth? We make an attempt to address this question using a three-step methodology. First, we modify a standard regression of growth on consolidation size to allow speed to affect the multiplier. Second, using the narrative dataset of Devries and others (2011), we construct a new sample of multi-year consolidation episodes for 17 advanced economies over 1978-2009. Third, we develop a novel concept of speed to measure the pace of the consolidation episodes identified in the data. The main empirical finding is that fast episodes have higher multipliers than gradual consolidations. This provides some preliminary support for consolidating at a steady pace, market access and a credible adjustment plan permitting. However, as the sample size is small, identifying mechanisms and testing robustness is difficult, and so our findings should not be interpreted causally.

A trust model based on fuzzy recommendation for mobile ad-hoc networks

In a mobile ad-hoc network (MANET) environment, intermediate nodes on a communication path are expected to forward packets of other nodes so that the mobile nodes can communicate beyond their wireless transmission range. However, because mobile nodes are usually constrained by limited energy, bandwidth and computational power, a selfish node may be unwilling to spend its resources in forwarding packets which are not of its direct interest, even though it expects other nodes to forward its packets to the destination. Selfish behavior of autonomous network nodes could greatly disrupt network operation and degrade the overall performance of a non-cooperative ad-hoc network. Nodes have to manage the risk involved with the transactions without prior knowledge about each other's reputation. Currently, some methods for trust establishment in MANETs have been proposed based on using cryptographic primitives to address cooperation among nodes attributes and handle selfish behaviors of nodes in MANETs environment. However, these solutions are not always suited to MANETs. The measurement and computation of trust to secure interactions between mobile nodes is crucial for the development of trust mechanisms. The calculation and measurement of trust in unsupervised ad-hoc environment involves complex aspects such as credibility rating for opinions delivered by a node, the honesty of recommendations provided by a mobile node, or the assessment of past experiences with the node one wishes to interact with. The deployment of suitable algorithms and models imitating fuzzy logic can help to solve these problems. In this paper, RFSTrust, a trust model based on fuzzy recommendation similarity, is proposed to quantify and to evaluate the trustworthiness of nodes, which includes five types of fuzzy trust recommendation relationships based on the fuzzy relation theory and a mathematical description for MANETs. Fuzzy logic provides a natural framework to deal with uncertainty and the tolerance of imprecise data inputs for the subjective tasks of trust evaluation, packet forwarding review and credibility adjustment. Theoretical analysis and experimental results show that RFSTrust is still robust under more general conditions where selfish nodes cooperate in an attempt to deliberately subvert the system, end-to-end packet delivery ratio more quickly, and decreases the average energy consumes more effectively. The effect of node rating data's sparsity can be greatly reduced and show the excellent performance on typical data set.

Fuzzy trust evaluation and credibility development in multi-agent systems

E-commerce markets can increase their efficiency through the usage of intelligent agents which negotiate and execute contracts on behalf of their owners. The measurement and computation of trust to secure interactions between autonomous agents is crucial for the success of automated e-commerce markets. Building a knowledge sharing network among peer agents helps to overcome trust-related boundaries in an environment where least human intervention is desired. Nevertheless, a risk management model which allows individual customisation to meet the different security needs of agent-owners is vital. The calculation and measurement of trust in unsupervised virtual communities like multi-agent environments involves complex aspects such as credibility rating for opinions delivered by peer agents, or the assessment of past experiences with the peer node one wishes to interact with. The deployment of suitable algorithms and models imitating human reasoning can help to solve these problems. This paper proposes not only a customisable trust evaluation model based on fuzzy logic but also demonstrates the integration of post-interaction processes like business interaction reviews and credibility adjustment. Fuzzy logic provides a natural framework to deal with uncertainty and the tolerance of imprecise data inputs to fuzzy-based systems makes fuzzy reasoning especially attractive for the subjective tasks of trust evaluation, business-interaction review and credibility adjustment.

Experience Rating with Credibility Adjustment of the Manual Premium

AbstractA common procedure for experience rating is to use Whitney's credibility formula with the manual premium per risk unit estimated by the observed average claim amount per risk unit. As pointed out by Whitney himself, this observed average also needs to be subjected to credibility adjustment. This suggestion is pursued in the paper and an experience rating procedure developed in which there are two stages:(i) revision of the prior expected manual premium in accordance with the observed average claim amount per risk unit;(ii) experience rating of the individual risk classes by the usual methods but using the manual premium obtained in stage (i).Both stages of this procedure involve credibility indices, for which formulae are developed.Conditions under which the “common procedure” referred to above is fairly reliable are found and it is seen, somewhat surprisingly, that the procedure is often more valid than one expects on intuitive grounds. Care is necessary, however, when there are wide differences in size between different risk classes.