Fate, freedom and contingency

Argument for fatalism attempts to prove that free choice is a logical or conceptual impossibility. The paper argues that the first two premises of the argument are sound: propositions are either true or false and they have their truth-value eternally. But the claim that from the fatalistic premises with the introduction of some innocent further premise dire consequences follow as regards to the possibility of free choice is false. The introduced premise, which establishes the connection between the first two premises (which are about the nature of propositions) and the concept of free choice is not innocent. It creates the impression that the truth of certain propositions can somehow determine the occurrence of certain events. But no proposition can have such an effect since the counterfactuals “If proposition P were true, event E would happen” does not say anything about determination. The argument for fatalism is, however, not a boring sophism. It does reveal something about the nature of propositional representation. It shows that each proposition represents necessarily the fact what it represents, i.e. it shows that propositions have their truth conditions non-contingently. But from this nothing follows as regards to the contingent nature of the facts represented. On the bases of the first two premises of the argument for fatalism we cannot infer to the impossibility of free choice. The argument for fatalism should not be interpreted as an attempt to prove on purely logical or conceptual grounds that we do not have the ability to influence future events by our choices. But it could be used to show something about the nature of propositional representation.     

Support vector regression for warranty claim forecasting

Forecasting the number of warranty claims is vitally important for manufacturers/warranty providers in preparing fiscal plans. In existing literature, a number of techniques such as log-linear Poisson models, Kalman filter, time series models, and artificial neural network models have been developed. Nevertheless, one might find two weaknesses existing in these approaches: (1) they do not consider the fact that warranty claims reported in the recent months might be more important in forecasting future warranty claims than those reported in the earlier months, and (2) they are developed based on repair rates (i.e., the total number of claims divided by the total number of products in service), which can cause information loss through such an arithmetic-mean operation.To overcome the above two weaknesses, this paper introduces two different approaches to forecasting warranty claims: the first is a weighted support vector regression (SVR) model and the second is a weighted SVR-based time series model. These two approaches can be applied to two scenarios: when only claim rate data are available and when original claim data are available. Two case studies are conducted to validate the two modelling approaches. On the basis of model evaluation over six months ahead forecasting, the results show that the proposed models exhibit superior performance compared to that of multilayer perceptrons, radial basis function networks and ordinary support vector regression models.     

On Providing Quality of Service in Grid Computing through Multi-objective Swarm-Based Knowledge Acquisition in Fuzzy Schedulers

Nowadays, Grid computing is increasingly showing a service-oriented tendency and as a result, providing quality of service (QoS) has raised as a relevant issue in such highly dynamic and non-dedicated systems. In this sense, the role of scheduling strategies is critical and new proposals able to deal with the inherent uncertainty of the grid state are needed in a way that QoS can be offered. Fuzzy rule-based schedulers are emerging scheduling schemas in Grid computing based on the efficient management of grid resources imprecise state and expert knowledge application to achieve an efficient workload distribution. Given the diverse and usually conflicting nature of the scheduling optimization objectives in grids considering both users and administrators requirements, these strategies can benefit from multi-objective strategies in their knowledge acquisition process greatly. This work suggests the QoS provision in the grid scheduling level with fuzzy rule-based schedulers through multi-objective knowledge acquisition considering multiple optimization criteria. With this aim, a novel learning strategy for the evolution of fuzzy rules based on swarm intelligence, Knowledge Acquisition with a Swarm Intelligence Approach (KASIA) is adapted to the multi-objective evolution of an expert grid meta-scheduler founded on Pareto general optimization theory and its performance with respect to a well-known genetic strategy is analyzed. In addition, the fuzzy scheduler with multi-objective learning results are compared to those of classical scheduling strategies in Grid computing.     

A belief rule-based decision support system for clinical risk assessment of cardiac chest pain

This paper describes a prototype clinical decision support system (CDSS) for risk stratification of patients with cardiac chest pain. A newly developed belief rule-based inference methodology-RIMER was employed for developing the prototype. Based on the belief rule-based inference methodology, the prototype CDSS can deal with uncertainties in both clinical domain knowledge and clinical data. Moreover, the prototype can automatically update its knowledge base via a belief rule base (BRB) learning module which can adjust BRB through accumulated historical clinical cases. The domain specific knowledge used to construct the knowledge base of the prototype was learned from real patient data. We simulated a set of 1000 patients in cardiac chest pain to validate the prototype. The belief rule-based prototype CDSS has been found to perform extremely well. Firstly, the system can provide more reliable and informative diagnosis recommendations than manual diagnosis using traditional rules when there are clinical uncertainties. Secondly, the diagnostic performance of the system can be significantly improved after training the BRB through accumulated clinical cases.     

Strategies for detecting fraudulent claims in the automobile insurance industry

Some property and casualty insurers use automated detection systems to help to decide whether or not to investigate claims suspected of fraud. Claim screening systems benefit from the coded experience of previously investigated claims. The embedded detection models typically consist of scoring devices relating fraud indicators to some measure of suspicion of fraud. In practice these scoring models often focus on minimizing the error rate rather than on the cost of (mis)classification. We show that focusing on cost is a profitable approach. We analyse the effects of taking into account information on damages and audit costs early on in the screening process. We discuss several scenarios using real-life data. The findings suggest that with claim amount information available at screening time detection rules can be accommodated to increase expected profits. Our results show the value of cost-sensitive claim fraud screening and provide guidance on how to render this strategy operational.     

Random sums of exchangeable variables and actuarial applications

In this paper we study the accumulated claim in some fixed time period, skipping the classical assumption of mutual independence between the variables involved. Two basic models are considered: Model 1 assumes that any pair of claims are equally correlated which means that the corresponding square-integrable sequence is exchangeable one. Model 2 states that the correlations between the adjacent claims are the same. Recurrence and explicit expressions for the joint probability generating function are derived and the impact of the dependence parameter (correlation coefficient) in both models is examined. The Markov binomial distribution is obtained as a particular case under assumptions of Model 2.     

Optimal Claiming on Vehicle Insurance

Insurance companies charge reduced premiums to motorists who go through a period of one or more years without making a claim. When an accident occurs the insured frequently finds himself with the option of either making a claim and reverting to a higher premium or paying for the repair himself. Most motorists operate an informal system of "no-claim limits", whereby they only claim for repairs which exceed a certain cost.In this paper it is shown how, under reasonable assumptions regarding the rate of accidents and the distribution of repair costs, optimal no-claim limits can be determined. These minimise the long run average cost of premiums and repairs and show significant savings over the case where all claims are made. The effect of an "excess clause", whereby the insured agrees to pay a first amount of any claim is also studied, and rules for determining whether it is profitable to accept such a clause are presented in the form of a graph.     

An analysis of voluntary recycled content claims under demand benefit and uncertain recycled input supply

We focus on the problem of choosing the optimal recycled content claim under stochastic local recycled content availability under two claim types — period specific (when claims have to hold each period) and average (when claims are evaluated across periods). We show conditions under which specific claims are higher than average claims, and explore cases where the optimal claims and profits are aligned to be in the same direction.     

基于风险调整控制图的产品保证索赔监控和预警研究

为了解决传统索赔监控与预警模型存在的未考虑索赔率大小对预警能力的影响、模型对保证数据精度要求高、难以处理索赔率较低的失效模式等问题,基于非参数估计和泊松分布对单位时间内产品保证索赔记录进行了拟合,根据各监控阶段的可用信息量大小构造了分阶段风险调整控制图对保证索赔进行监控和预警,并采用了在指定监控时间内对所有销售产品进行统一监控的监控方式。模型的有效性利用实际算例从误报警率、报警能力和响应速度三个指标进行了分析。     

A fifth generation numerical modelling system in coastal zone

Nowadays, artificial intelligence (AI) technology is gradually integrated into the numerical modelling system to make the system more intelligent and more user-friendly. The characteristics of the fifth generation numerical modelling are connected with AI applications. The expert system technology as a widely applied AI technology is integrated into our modelling system for coastal water processes with traditional numerical computational tools and the data and graphical pre-processing and post-processing techniques. Five kinds of knowledge bases are built in the system to describe the existing expertise knowledge about model parameters, relations between parameters and physical conditions, various possible selections for parameters and rules of inference. The inference engine is designed to be driven by the confidence of correctness, and the rule base is built with the factor of confidence to link the various relations. The decision tree is designed to drive the inference engine to explore the route of selection procedure of modeling. The decision tree depends on the real problem specifications and can be modified during the dialogue between the system and the user. The forward chaining and backward chaining inference techniques are mixed together in the system to help matching the parameters in the model and the possible selections with sufficiently high confidence. The expert system technology is successfully integrated into the system to provide help for model parameter selection or model selection, and to make the numerical model system more accessible for non-expert users.     

Simulation of ECB decisions and forecast of short term Euro rate with an adaptive fuzzy expert system

The elaboration of optimal monetary policy strategies, and the statistical estimation of monetary policy rules followed by European Central Bank (ECB) in the new currency area of the Euro, are difficult to follow with the standard statistical models. For this reason we have developed an adaptive fuzzy expert system in order to mimic the framework on which the monetary policy strategy of the ECB is based. The expert system knowledge base consists of a set of fuzzy and crisp rules located at two different hierarchical levels. The high-level of the system receives some intermediate output values from the low-level and processes this information by means of a set of crisp rules. The low-level prepares these intermediate output values with the use of a fuzzy inference engine applied to economic input variables. The use of an expert system allows for modelling the ECB behaviour with the use of wider scope of knowledge, when compared with more traditional computational techniques. Rules at different hierarchical levels and at different intra-level groups, allow for managing the potentially contradictory structure of the ECB strategy. The system has been tested on the economic and financial time series going from the January 1999 to September 2000. The system’s correct prediction was estimated to overall 70% and, considering the complexity of the task, the results obtained are promising.     

Modeling of claim exceedances over random thresholds for related insurance portfolios

Large claims in an actuarial risk process are of special importance for the actuarial decision making about several issues like pricing of risks, determination of retention treaties and capital requirements for solvency. This paper presents a model about claim occurrences in an insurance portfolio that exceed the largest claim of another portfolio providing the same sort of insurance coverages. Two cases are taken into consideration: independent and identically distributed claims and exchangeable dependent claims in each of the portfolios. Copulas are used to model the dependence situations. Several theorems and examples are presented for the distributional properties and expected values of the critical quantities under concern.     

Modeling of claim exceedances over random thresholds for related insurance portfolios

Large claims in an actuarial risk process are of special importance for the actuarial decision making about several issues like pricing of risks, determination of retention treaties and capital requirements for solvency. This paper presents a model about claim occurrences in an insurance portfolio that exceed the largest claim of another portfolio providing the same sort of insurance coverages. Two cases are taken into consideration: independent and identically distributed claims and exchangeable dependent claims in each of the portfolios. Copulas are used to model the dependence situations. Several theorems and examples are presented for the distributional properties and expected values of the critical quantities under concern.     

Non-Inferential Moral Knowledge

In a series of recent papers, Walter Sinnott-Armstrong has developed a novel argument against moral intuitionism. I suggest a defense on behalf of the intuitionist against Sinnott-Armstrong’s objections. Rather than focus on the main premises of his argument, I instead examine the way in which Sinnott-Armstrong construes the intuitionistic position. I claim that Sinnott-Armstrong’s understanding of intuitionism is mistaken. In particular, I argue that Sinnott-Armstrong mischaracterizes non-inferentiality as it figures in intuitionism. To the extent that Sinnott-Armstrong’s account of intuitionism has been adopted by others uncritically, intuitionists have cause for concern. I develop an alternative, and more accurate, reading of what is non-inferential about intuitionistic moral knowledge. In light of this alternative reading, certain elements of Sinnott-Armstrong’s case against intuitionism are significantly weakened. But perhaps more importantly, this paper helps clarify what circumspect intuitionists mean when they claim that some moral knowledge is non-inferential.     

Learning about teaching for understanding through the study of tutoring

In this study, we conducted a fine-grained analysis of an expert tutor's (Nancy Mack) tutorial actions as she attempted, successfully, to help students learn fractions with understanding. Our analysis revealed that, as Mack tutored students in two different research studies, she took two types of tutorial actions previously unrecorded in the literature. By analyzing her actions using a methodology involving production rules, we suggest how her content knowledge, pedagogical content knowledge, and her knowledge of her students were interrelated and how they impacted on her instructional decisions and teaching actions. We also provide an example of how using production rules can be useful to discern some of the complexities involved in teaching and tutoring.     

Prospective elementary teachers’ claiming in responses to false generalizations

When faced with a false generalization and a counterexample, what types of claims do prospective K-8 teachers make, and what factors influence the type and prudence of their claims relative to the data, observations, and arguments reported? This article addresses that question. Responses to refutation tasks and cognitive interviews were used to explore claiming. It was found that prospective K-8 teachers’ claiming can be influenced by knowledge of argumentation; knowledge and use of the mathematical practice of exception barring; perceptions of the task; use of natural language; knowledge of, use of, and skill with the mathematics register; and abilities to technically handle data or conceptual insights. A distinction between technical handlings for developing claims and technical handlings for supporting claims was made. It was found that prudent claims can arise from arguer-developed representations that afford conceptual insights, even when searching for support for a different claim.     

A class of mixture of experts models for general insurance: Theoretical developments

In the Property and Casualty (P&C) ratemaking process, it is critical to understand the effect of policyholders’ risk profile to the number and amount of claims, the dependence among various business lines and the claim distributions. To include all the above features, it is essential to develop a regression model which is flexible and theoretically justified. Motivated by the issues above, we propose a class of logit-weighted reduced mixture of experts (LRMoE) models for multivariate claim frequencies or severities distributions. LRMoE is interpretable, as it has two components: Gating functions, which classify policyholders into various latent sub-classes; and Expert functions, which govern the distributional properties of the claims. Also, upon the development of denseness theory in regression setting, we can heuristically interpret the LRMoE as a “fully flexible” model to capture any distributional, dependence and regression structures subject to a denseness condition. Further, the mathematical tractability of the LRMoE is guaranteed since it satisfies various marginalization and moment properties. Finally, we discuss some special choices of expert functions that make the corresponding LRMoE “fully flexible”. In the subsequent paper (Fung et al., 2019b), we will focus on the estimation and application aspects of the LRMoE.     

On the Expected Present Value of Total Dividends in a Risk Model with Potentially Delayed Claims

In this paper, we consider a risk model in which two types of individual claims, main claims and by-claims, are defined. Every by-claim is induced by the main claim randomly and may be delayed for one time period with a certain probability. The dividend policy that certain amount of dividends will be paid as long as the surplus is greater than a constant dividend barrier is also introduced into this delayed claims risk model. By means of the probability generating functions, formulae for the expected present value of total dividend payments prior to ruin are obtained for discrete-type individual claims. Explicit expressions for the corresponding results are derived for K n claim amount distributions. Numerical illustrations are also given.     

Why contextualists cannot know they are right: Self-refuting implications of contextualism

Conversational contextualism in epistemology is characterized by four main theses: 1. the indexicality of knowledge claims thesis; 2. the attributor contextualism thesis; 3. the conversational contextualism thesis, and 4. the main thesis of contextualism according to which a knowledge claim can be true in one context and false in another context in which more stringent standards for knowledge are operant. It is argued that these theses taken together generate problems for contextualism. In particular, it is shown that there is no context in which the contextualist can truthfully claim to know her theory is true. Since these results were obtained only with principles the contextualist cannot give up—like the principle of epistemic closure and the principle that knowledge implies truth—it seems that contextualism is in need of a thoroughgoing revision if it is to become a successful epistemic theory.