Aided and unaided decision making: Improving intuitive judgement

Intuitive judgement forms the basis of decision making both by experts, in professional settings, and by people in everyday life. Psychologists have studied the rationality of intuitive judgements. In this paper three approaches to decision making will be discussed: unqualified rationalism, qualified rationalism and irrationalism. The first approach holds that man is essentially rational, the second that serious cognitive biases exist, and the third that thinking is strongly influenced by non-cognitive sources of distortion, i.e. emotions and motives. Evidence on judgement is reviewed and found to support the last two approaches. Various ways of improving judgements, as suggested by the three basic viewpoints, are then presented.     

The implementation of forecasting models

The use of forecasting models can help managers make better decisions, a fact that motivates this study. Findings from research on the implementation of operations research/management science are generalized to include forecasting models. The similarity between forecasting and other models allows conclusions to be drawn about managing forecasting model implementation: these include better management support, closer links to management performance, improved user–preparer relationships, more goal congruence, minimized perception of change and an appropriate configuration of the forecasting system to user needs, style, resources and environment.     

Dynamic mechanistic explanation: computational modeling of circadian rhythms as an exemplar for cognitive science

We consider computational modeling in two fields: chronobiology and cognitive science. In circadian rhythm models, variables generally correspond to properties of parts and operations of the responsible mechanism. A computational model of this complex mechanism is grounded in empirical discoveries and contributes a more refined understanding of the dynamics of its behavior. In cognitive science, on the other hand, computational modelers typically advance de novo proposals for mechanisms to account for behavior. They offer indirect evidence that a proposed mechanism is adequate to produce particular behavioral data, but typically there is no direct empirical evidence for the hypothesized parts and operations. Models in these two fields differ in the extent of their empirical grounding, but they share the goal of achieving dynamic mechanistic explanation. That is, they augment a proposed mechanistic explanation with a computational model that enables exploration of the mechanism’s dynamics. Using exemplars from circadian rhythm research, we extract six specific contributions provided by computational models. We then examine cognitive science models to determine how well they make the same types of contributions. We suggest that the modeling approach used in circadian research may prove useful in cognitive science as researchers develop procedures for experimentally decomposing cognitive mechanisms into parts and operations and begin to understand their nonlinear interactions.     

The many faces of unification and pluralism in economics: The case of Paul Samuelson's Foundations of Economic Analysis

The history of modern economics abounds with pleas for more pluralism as well as pleas for more unification. These seem to be contradictory goals, suggesting that pluralism and unification are mutually exclusive, or at least that they involve trade-offs with more of one necessarily being traded off against less of the other. This paper will use the example of Paul Samuelson's Foundations of Economic Analysis (1947) to argue that the relationship between pluralism and unification is often more complex than this simple dichotomy suggests. In particular, Samuelson's Foundations is invariably presented as a key text in the unification of modern economics during the middle of the twentieth century; and in many ways that is entirely correct. But Samuelson's unification was not at the theoretical (causal and explanatory) level, but rather at the purely mathematical derivational level. Although this fact is recognized in the literature on Samuelson, what seems to be less recognized is that for Samuelson, much of the motivation for this unification was pluralist in spirit: not to narrow scientific economics into one single theory, but rather to allow for more than one theory to co-exist under a single unified derivational technique. This hidden pluralism will be discussed in detail. The paper concludes with a discussion of the implications for more recent developments in economics.     

Tourism forecasting: A review of empirical research

Many publications on tourism forecasting have appeared during the past twenty years. The purpose of this article is to organize and summarize that scattered literature. General conclusions are also drawn from the studies to help those wishing to develop tourism forecasts of their own. The forecasting techniques discussed include time series models, econometric causal models, the gravity model and expert-opinion techniques. The major conclusions are that time series models are the simplest and least costly (and therefore most appropriate for practitioners); the gravity model is best suited to handle international tourism flows (and will be most useful to governments and tourism agencies); and expert-opinion methods are useful when data are unavailable. Further research is needed on the use of economic indicators in tourism forecasting, on the development of attractivity and emissiveness indexes for use in gravity and econometric models and on empirical comparisons among the different methods.     

The dynamics of reason and its elusive object in Kant, Fichte and Schelling

Kant used transcendental reflection to distinguish in judgment what belongs to its form and what to its material. Regarding the form of judgment, Buchdahl’s work highlights the analogies between the different levels of judgment in Kant’s transcendental ontology. He uses the explicit contingency of judgments of the system of nature to illuminate the contingency of judgments of objects in general. In the Critique of pure reason, Kant had left much of the work of judgment to the unconscious imagination. Fichte and Schelling attempted to make conscious and determinate the work of the unconscious imagination, but found themselves unable to avoid a reflexive regress in trying to objectify and provide a foundation for the activity of the self in judgment. Buchdahl also clarifies the role Kant gave to the object in judgment, as the indeterminate ‘thinghood’ remaining once all forms of cognition are abstracted. Fichte represented this objective side of consciousness as the not-I, as the limit of the activity of the I, as an unconscious, alien element within consciousness. Schelling struggled to illuminate this unconscious object in judgment, to provide a construction of nature, without dissolving its positive presence into abstract formulations. In pursuing relentlessly Kant’s critique of judgment, Fichte and Schelling exposed its opaque points and problematized the ambition to build a complete system of philosophy.     

The problem of confirmation in the Everett interpretation

I argue that the Oxford school Everett interpretation is internally incoherent, because we cannot claim that in an Everettian universe the kinds of reasoning we have used to arrive at our beliefs about quantum mechanics would lead us to form true beliefs. I show that in an Everettian context, the experimental evidence that we have available could not provide empirical confirmation for quantum mechanics, and moreover that we would not even be able to establish reference to the theoretical entities of quantum mechanics. I then consider a range of existing Everettian approaches to the probability problem and show that they do not succeed in overcoming this incoherence.     

The methodological defense of realism scrutinized

I revisit an older defense of scientific realism, the methodological defense, a defense developed by both Popper and Feyerabend. The methodological defense of realism concerns the attitude of scientists, not philosophers of science. The methodological defense is as follows: a commitment to realism leads scientists to pursue the truth, which in turn is apt to put them in a better position to get at the truth. In contrast, anti-realists lack the tenacity required to develop a theory to its fullest. As a consequence, they are less likely to get at the truth.My aim is to show that the methodological defense is flawed. I argue that a commitment to realism does not always benefit science, and that there is reason to believe that a research community with both realists and anti-realists in it may be better suited to advancing science. A case study of the Copernican Revolution in astronomy supports this claim.     

The effectiveness of imprecise probability forecasts

In this paper we investigate the feasibility of algorithmically deriving precise probability forecasts from imprecise forecasts. We provide an empirical evaluation of precise probabilities that have been derived from two types of imprecise probability forecasts: probability intervals and probability intervals with second-order probability distributions. The minimum cross-entropy (MCE) principle is applied to the former to derive precise (i.e. additive) probabilities; expectation (EX) is used to derive precise probabilities in the latter case. Probability intervals that were constructed without second-order probabilities tended to be narrower than and contained in those that were amplified by second-order probabilities. Evidence that this narrowness is due to motivational bias is presented. Analysis of forecasters' mean Probability Scores for the derived precise probabilities indicates that it is possible to derive precise forecasts whose external correspondence is as good as directly assessed precise probability forecasts. The forecasts of the EX method, however, are more like the directly assessed precise forecasts than those of the MCE method.     

Forecasting with an econometric model: The issue of judgemental adjustment

The paper outlines the current state of forecasting with an econometric model. After briefly distinguishing econometric techniques from other statistical approaches and arguing the advantages of this approach the paper concentrates on the issue of judgemental adjustments to models for forecasting purposes. Two types of adjustment are distinguished and the conditions under which each is justified are stated. Guidance in the use of adjustment is offered through a review of considerations in an actual forecasting situation.     

The combination of macroeconomic forecasts

The paper examines combined forecasts based on two components: forecasts produced by Chase Econometrics and those produced using the Box-Jenkins ARIMA technique. Six series of quarterly ex ante and simulated ex ante forecasts are used over 37 time periods and ten horizons. The forecasts are combined using seven different methods. The best combined forecasts, judged by average relative root-mean-square error, are superior to the Chase forecasts for three variables and inferior for two, though averaged over all six variables the Chase forecasts are slightly better. A two-step procedure produces forecasts for the last half of the sample which, on average, are slightly better than the Chase forecasts.     

Constitutive relevance in cognitive science: The case of eye movements and cognitive mechanisms

In this paper I assess whether the recently proposed “No De-Coupling” (NDC) theory of constitutive relevance in mechanisms is a useful tool to reconstruct constitutive relevance investigations in scientific practice. The NDC theory has been advanced as a framework theoretically superior to the mutual manipulability (MM) account of constitutive relevance in mechanisms but, in contrast to the MM account, has not yet been applied to detailed case studies. I argue that the NDC account is also applicable to empirical practice and that it fares better than the MM account on both theoretical and empirical grounds. I elaborate these claims in terms of applications of the NDC theory to two case studies of cognitive science research on the role of eye movements in mechanisms for cognitive capacities.     

The re-emergence of hyphenated history-and-philosophy-of-science and the testing of theories of scientific change

A basic premise of hyphenated history-and-philosophy-of-science is that theories of scientific change have to be based on empirical evidence derived from carefully constructed historical case studies. This paper analyses one such systematic attempt to test philosophical claims, describing its historical context, rationale, execution, and limited impact.     

The causal structure of natural kinds

One primary goal for metaphysical theories of natural kinds is to account for their epistemic fruitfulness. According to cluster theories of natural kinds, this epistemic fruitfulness is grounded in the regular and stable co-occurrence of a broad set of properties. In this paper, I defend the view that such a cluster theory is insufficient to adequately account for the epistemic fruitfulness of kinds. I argue that cluster theories can indeed account for the projectibility of natural kinds, but not for several other epistemic operations that natural kinds support. Natural kinds also play a role in scientific explanations and categorizations. A theory of natural kinds can only account for these additional kind-based epistemic practices if it also analyzes their causal structure.     

模糊测试技术研究

随着人们对软件安全问题关注度的不断提升,漏洞挖掘技术逐渐成为业内热点的研究内容.但传统的漏洞挖掘技术耗时长耗工大,更重要的是不能全面的探测出软件中的漏洞,因此一种简单高效的漏洞挖掘技术,即模糊测试技术,逐渐成为了研究者们关注的重点.本文首先重点介绍了模糊测试技术的相关理论知识及研究进展情况,并对比了传统漏洞挖掘技术与模糊测试技术进而说明模糊测试技术具有传统漏洞挖掘方法无可比拟的优势.之后,研究了目前模糊测试技术在各个领域内的应用情况,并比较了现有模糊测试工具的优缺点.最后列举出模糊测试技术的局限性,并阐述了模糊测试技术未来的发展方向.     

The importance of non-linearities in large forecasting models with stochastic error processes

This paper considers the consequences of the stochastic error process in large non-linear forecasting models. As such models are non-linear, the deterministic forecast is neither the mean nor the mode of the density function of the endogenous variables. Under a specific assumption as to the class of the non-linearity it is shown that the deterministic forecast is actually the vector of marginal medians of the density function. Stochastic simulation techniques are then used to test whether one large forecasting model actually lies within this class.     

The role of probability arguments in the history of science

The paper examines Wesley Salmon’s claim that the primary role of plausibility arguments in the history of science is to impose constraints on the prior probability of hypotheses (in the language of Bayesian confirmation theory). A detailed look at Copernicanism and Darwinism and, more briefly, Rutherford’s discovery of the atomic nucleus reveals a further and arguably more important role of plausibility arguments. It resides in the consideration of likelihoods, which state how likely a given hypothesis makes a given piece of evidence. In each case the likelihoods raise the probability of one of the competing hypotheses and diminish the credibility of its rival, and this may happen either on the basis of ‘old’ or ‘new’ evidence.     

物联网体系结构模型与互连机理

网络实现了信息资源的共享,极大地改变了人们的生产生活方式.物联网将网络从信息空间进一步向物理空间延伸,通过对物理环境的感知与控制,丰富了人类认识世界与改造世界的手段.本文首先提出了包括对象感控层、数据交换层、信息整合层和应用服务层等4个层次的物联网体系结构功能模型,并对体系结构模型及各层实体进行了面向对象的描述.对于物联网系统中最为核心的网元互连问题,我们兼顾考虑强弱网元间的互连互通和网元与物理对象间的互动互操作,提出了支持强弱网元共存的物联网互连模型和基于能力映射与任务迁移的强弱网元互连机制.论文通过原型系统对提出的互连模型和互连机制进行了验证.     

Forecasting US GNP growth: The role of uncertainty

A large number of models have been developed in the literature to analyze and forecast changes in output dynamics. The objective of this paper was to compare the predictive ability of univariate and bivariate models, in terms of forecasting US gross national product (GNP) growth at different forecasting horizons, with the bivariate models containing information on a measure of economic uncertainty. Based on point and density forecast accuracy measures, as well as on equal predictive ability (EPA) and superior predictive ability (SPA) tests, we evaluate the relative forecasting performance of different model specifications over the quarterly period of 1919:Q2 until 2014:Q4. We find that the economic policy uncertainty (EPU) index should improve the accuracy of US GNP growth forecasts in bivariate models. We also find that the EPU exhibits similar forecasting ability to the term spread and outperforms other uncertainty measures such as the volatility index and geopolitical risk in predicting US recessions. While the Markov switching time‐varying parameter vector autoregressive model yields the lowest values for the root mean squared error in most cases, we observe relatively low values for the log predictive density score, when using the Bayesian vector regression model with stochastic volatility. More importantly, our results highlight the importance of uncertainty in forecasting US GNP growth rates.