When and how to use scenario planning: A heuristic approach with illustration

This paper offers a step-by-step analysis of a heuristic approach to scenario planning, taking a managerial perspective. The scenario method is contrasted in general with more traditional planning techniques, which tend to perform less well when faced with high uncertainty and complexity. An actual case involving a manufacturing company is used to illustrate the main steps of the proposed heuristic. Its essence is to identify relevant trends and uncertainties, and blend them into scenarios that are internally consistent. In addition, the scenarios should bound the range of plausible uncertainties and challenge managerial thinking. Links to decision making are examined next, including administrative policies as well as integrative techniques. At the strategic level, a key-success-factor matrix is proposed for integrating scenarios, competitor analysis and strategic vision. At the operational level, Monte Carlo simulation is suggested and illustrated as one useful technique for combining scenario thinking with formal project evaluation (after appropriate translations). The paper concludes with a general discussion of scenario planning, to place it in a broader perspective.     

A new method of scenario analysis for strategic planning

We first present scenario analysis as a qualitative forecasting technique useful for strategic planning. Then we develop an overview of the two classes of methods for scenario analysis described in the literature. Based on both classes, a new method is developed which especially fits the needs of strategic planning. The method can be divided into three stages: 1. Determination of compatible scenarios, 2. Determination of scenario probabilities, and 3. Determination of main scenarios. An example is given to illustrate the method.     

形象思维模拟的可能途径

无论思维科学、认知科学,还是人工智能,形象思维与抽象思维在其中均居同等重要的地位,而事实上对抽象思维的研究与对形象思维的认识极不平衡,对形象思维过程的研究尚属阙如。本文介绍认知神经科学中功能柱以及心像概念,讨论艺术创作的模拟,并给出基于形象思维的心电图波形识别方式。动机是尝试在认知神经科学初步结论的基础上,探讨形象思维的模拟途径。要实现人工智能,形象思维的模拟、模式和模型是必然要考虑的问题。     

Forecasting Government Bond Yields with Neural Networks Considering Cointegration

This paper discusses techniques that might be helpful in predicting interest rates and tries to evaluate a new hybrid forecasting approach. Results of examining government bond yields in Germany and France reported in this study indicate that a hybrid forecasting approach which combines techniques of cointegration analysis with neural network (NN) forecasting models can produce superior results to the use of NN forecasting models alone. The findings documented in this paper could be a consequence of the fact that examining differenced data under certain conditions will lead to a loss of information and that the inclusion of the error correction term from the cointegration model can help to cope with this problem. The paper also discusses some possibly interesting directions for further research. Copyright © 2015 John Wiley & Sons, Ltd.     

Ψ-epistemic quantum cosmology?

This paper provides a prospectus for a new way of thinking about the wavefunction of the universe: a Ψ-epistemic quantum cosmology. We present a proposal that, if successfully implemented, would resolve the cosmological measurement problem and simultaneously allow us to think sensibly about probability and evolution in quantum cosmology. Our analysis draws upon recent work on the problem of time in quantum gravity and causally symmetric local hidden variable theories. Our conclusion weighs the strengths and weaknesses of the approach and points towards paths for future development.     

A non‐Gaussian generalization of the Airline model for robust seasonal adjustment

In their seminal book Time Series Analysis: Forecasting and Control, Box and Jenkins (1976) introduce the Airline model, which is still routinely used for the modelling of economic seasonal time series. The Airline model is for a differenced time series (in levels and seasons) and constitutes a linear moving average of lagged Gaussian disturbances which depends on two coefficients and a fixed variance. In this paper a novel approach to seasonal adjustment is developed that is based on the Airline model and that accounts for outliers and breaks in time series. For this purpose we consider the canonical representation of the Airline model. It takes the model as a sum of trend, seasonal and irregular (unobserved) components which are uniquely identified as a result of the canonical decomposition. The resulting unobserved components time series model is extended by components that allow for outliers and breaks. When all components depend on Gaussian disturbances, the model can be cast in state space form and the Kalman filter can compute the exact log‐likelihood function. Related filtering and smoothing algorithms can be used to compute minimum mean squared error estimates of the unobserved components. However, the outlier and break components typically rely on heavy‐tailed densities such as the t or the mixture of normals. For this class of non‐Gaussian models, Monte Carlo simulation techniques will be used for estimation, signal extraction and seasonal adjustment. This robust approach to seasonal adjustment allows outliers to be accounted for, while keeping the underlying structures that are currently used to aid reporting of economic time series data. Copyright © 2006 John Wiley & Sons, Ltd.     

Scenario planning: An investigation of the construct and its measurement

Scenario‐planning academicians and practitioners have been observing for more than three decades the importance of this method in dealing with environmental uncertainty. However, there has been no valid scale that may help organizational leaders to act in practice. Our review of prior studies identifies some problems related to conceptualization, reliability, and validity of this construct. We address these concerns by developing and validating a measure of scenario planning based on Churchill's paradigm (Journal of Marketing Research, 1979, 16, 64–73). Our data analysis follows from a sample of 133 managers operating in the healthcare field in France. To validate our scale, we used three approaches: first, an exploratory factor analysis; second, an examination of psychometric proprieties of all dimensions; and third, a confirmatory factor analysis. The results of this study indicate that scenario planning is a multidimensional construct composed of three dimensions: information acquisition, knowledge dissemination, and scenario development and strategic choices.     

Mechanistic explanation,cognitive systems demarcation,and extended cognition

Approaches to the Internalism–Externalism controversy in the philosophy of mind often involve both (broadly) metaphysical and explanatory considerations. Whereas originally most emphasis seems to have been placed on metaphysical concerns, recently the explanation angle is getting more attention. Explanatory considerations promise to offer more neutral grounds for cognitive systems demarcation than (broadly) metaphysical ones. However, it has been argued that explanation-based approaches are incapable of determining the plausibility of internalist-based conceptions of cognition vis-à-vis externalist ones. On this perspective, improved metaphysics is the route along which to solve the Internalist–Externalist stalemate. In this paper we challenge this claim. Although we agree that explanation-orientated approaches have indeed so far failed to deliver solid means for cognitive system demarcation, we elaborate a more promising explanation-oriented framework to address this issue. We argue that the mutual manipulability account of constitutive relevance in mechanisms, extended with the criterion of ‘fat-handedness’, is capable of plausibly addressing the cognitive systems demarcation problem, and thus able to decide on the explanatory traction of Internalist vs. Externalist conceptions, on a case-by-case basis. Our analysis also highlights why some other recent mechanistic takes on the problem of cognitive systems demarcation have been unsuccessful. We illustrate our claims with a case on gestures and learning.     

A dynamic linear model approach for disaggregating time series data

An approach is proposed for obtaining estimates of the basic (disaggregated) series, x_i, when only an aggregate series, y_t, of k period non-overlapping sums of x_i's is available. The approach is based on casting the problem in a dynamic linear model form. Then estimates of x_i can be obtained by application of the Kalman filtering techniques. An ad hoc procedure is introduced for deriving a model form for the unobserved basic series from the observed model of the aggregates. An application of this approach to a set of real data is given.     

远程通讯地学处理:遥感,地理信息系统,全球定位系统和远程通讯的集成

在过去的一段时间里,许多技术在各自的领域范围内得到了迅猛的发展。本文中我们致力于讨论这些技术的一体化。我们的目的在于展示一种行之有效的一体化方法,用此法处理环境问题时,综合利用所有工具和技术将比彼此分离的使用它们更有效。本文主要基于近期在遥感国际期刊(International Journal of Remote Sensing)(Xue et a1．2002)发表的一篇论文,以及一些在该论文发表后的由中国科学院遥感应用研究所的薛勇博士领导的研究小组在研究这一课题产生的其他想法。     

What does interdisciplinarity look like in practice: Mapping interdisciplinarity and its limits in the environmental sciences

In this paper we take a close look at current interdisciplinary modeling practices in the environmental sciences, and suggest that closer attention needs to be paid to the nature of scientific practices when investigating and planning interdisciplinarity. While interdisciplinarity is often portrayed as a medium of novel and transformative methodological work, current modeling strategies in the environmental sciences are conservative, avoiding methodological conflict, while confining interdisciplinary interactions to a relatively small set of pre-existing modeling frameworks and strategies (a process we call crystallization). We argue that such practices can be rationalized as responses in part to cognitive constraints which restrict interdisciplinary work. We identify four salient integrative modeling strategies in environmental sciences, and argue that this crystallization, while contradicting somewhat the novel goals many have for interdisciplinarity, makes sense when considered in the light of common disciplinary practices and cognitive constraints. These results provide cause to rethink in more concrete methodological terms what interdisciplinarity amounts to, and what kinds of interdisciplinarity are obtainable in the environmental sciences and elsewhere.     

Revisiting Targeted Factors

This paper proposes new methods for ‘targeting’ factors estimated from a big dataset. We suggest that forecasts of economic variables can be improved by tuning factor estimates: (i) so that they are both more relevant for a specific target variable; and (ii) so that variables with considerable idiosyncratic noise are down‐weighted prior to factor estimation. Existing targeted factor methodologies are limited to estimating the factors with only one of these two objectives in mind. We therefore combine these ideas by providing new weighted principal components analysis (PCA) procedures and a targeted generalized PCA (TGPCA) procedure. These methods offer a flexible combination of both types of targeting that is new to the literature. We illustrate this empirically by forecasting a range of US macroeconomic variables, finding that our combined approach yields important improvements over competing methods, consistently surviving elimination in the model confidence set procedure. Copyright © 2016 John Wiley & Sons, Ltd.     

‘Language,Truth and Reason’ 30 years later

This paper traces the origins of the styles project, originally presented as ‘styles of scientific reasoning’. ‘Styles of scientific thinking & doing’ is a better label; the styles can also be called genres, or, ways of finding out. A. C. Crombie’s template of six fundamentally distinct ones was turned into a philosophical tool, but with a tinge of Paul Feyerabend’s anarchism. Ways of finding out are not defined by necessary and sufficient conditions, but can be recognized as distinct within a sweeping, anthropological, vision of the European sciences. The approach is unabashedly whiggish. The emergence of these styles is part of what Reviel Netz calls cognitive history, and is to be understood in an ecological way. How did a species like ours, on an Earth like this, develop a few quite general strategies for finding out about, and altering, its world? At a more analytical level, the project invokes Bernard Williams’ notion of truthfulness to explicate the idea that these styles are ‘self-authenticating’ and without foundations. The paper concludes with open questions. What role (for example) have these few fundamentally distinct genres of inquiry played in the formation of the anomalous Western idea of Nature apart from Man?     

Comparison of Near Neighbour and Neural Network in Travel Forecasting

In this paper we confirm the existence of nonlinear dynamics in a time series of airport arrivals. We subsequently propose alternative non‐parametric forecasting techniques to be used in a travel forecasting problem, emphasizing the difference between the reconstruction and learning approach. We compare the results achieved in point prediction versus sign prediction. The reconstruction approach offers better results in sign prediction and the learning approach in point prediction. Copyright © 2015 John Wiley & Sons, Ltd.     

认知无线电系统中的多中继分布式波束成形方法

本文考虑认知无线电系统中一对认知源目的节点在一组认知中继节点协助下与一对授权发射机和接收机共存的场景,研究了多个单天线认知中继节点在授权接收机处平均干扰功率门限约束及自身独立的平均发射功率约束下,最大化认知目的节点处信干噪比(SINR)的分布式波束成形,从而开发"空谱空洞"的问题.提出了两种波束成形方案:1)最大化SINR的最优策略;2)基于迫零准则的次优策略.最优策略将分布式的波束成形系数求解问题通过半定松弛转化为准凸的优化问题,从而利用二分法及内点法求解;并证明了求得的最优半定松弛解即为原优化问题的最优解.次优策略直接迫零对授权接收机造成干扰,并将来自授权发射机的干扰信号抑制为零.该方法对应的优化问题没有迭代运算,且约束函数简单,算法复杂度低.最后通过数值仿真分析了中继数、认知节点最大的发射功率和授权接收机的干扰功率门限等因素对两类算法平均传输速率的影响,并且通过对比实验验证了考虑授权发射机干扰信号影响带来的性能增益.     

Strategic planning and forecasting

This paper examines the role of forecasting in strategic planning through the use of analytical portfolio models and corporate simulation models. It also outlines a conceptual framework on which it may be possible to develop a theory of strategic planning—namely, microeconomic theory. It, finally, describes a practical application of this theoretical framework, known as the strategy matrix. It concludes by noting the importance of forecasting as an input to the strategy matrix.     

Empirical analysis and forecasting of volatility dynamics in high‐frequency returns with time‐varying components

We study intraday return volatility dynamics using a time‐varying components approach, and the method is applied to analyze IBM intraday returns. Empirical evidence indicates that with three additive components—a time‐varying mean of absolute returns and two cosine components with time‐varying amplitudes—together they capture very well the pronounced periodicity and persistence behaviors exhibited in the empirical autocorrelation pattern of IBM returns. We find that the long‐run volatility persistence is driven predominantly by daily level shifts in mean absolute returns. After adjusting for these intradaily components, the filtered returns behave much like a Gaussian noise, suggesting that the three‐components structure is adequately specified. Furthermore, a new volatility measure (TCV) can be constructed from these components. Results from extensive out‐of‐sample rolling forecast experiments suggest that TCV fares well in predicting future volatility against alternative methods, including GARCH model, realized volatility and realized absolute value. Copyright © 2009 John Wiley & Sons, Ltd.     

Urban bioclimatology

This article deals with the part of urban climatology which is of particular relevance to human beings. Presented first is a summary of all human biometeorologically effective complexes, as well as other factors which are relevant to urban planning and which depend on atmospheric conditions in urban structures in a direct or indirect manner. Later, methods for human biometeorologically significant assessment of thermal and air pollution components of the urban climate are discussed in detail, because these components can be strongly influenced by urban planning. The application of these methods is illustrated by some results of appropriate investigations in urban areas.     

A cautionary note on outlier robust estimation of threshold models

Chan and Cheung (1994) propose a GM approach to outlier robust estimation of threshold models. We show that their estimator can be inconsistent and extremely inefficient even when the model is correctly specified and the disturbances are normally distributed, and outline situations in which the problem is likely to be more severe. Copyright © 2006 John Wiley & Sons, Ltd.     

PARX model for football match predictions

We propose an innovative approach to model and predict the outcome of football matches based on the Poisson autoregression with exogenous covariates (PARX) model recently proposed by Agosto, Cavaliere, Kristensen, and Rahbek (Journal of Empirical Finance, 2016, 38(B), 640–663). We show that this methodology is particularly suited to model the goal distribution of a football team and provides a good forecast performance that can be exploited to develop a profitable betting strategy. This paper improves the strand of literature on Poisson‐based models, by proposing a specification able to capture the main characteristics of goal distribution. The betting strategy is based on the idea that the odds proposed by the market do not reflect the true probability of the match because they may also incorporate the betting volumes or strategic price settings in order to exploit betters' biases. The out‐of‐sample performance of the PARX model is better than the reference approach by Dixon and Coles (Applied Statistics, 1997, 46(2), 265–280). We also evaluate our approach in a simple betting strategy, which is applied to English football Premier League data for the 2013–2014, 2014–2015, and 2015–2016 seasons. The results show that the return from the betting strategy is larger than 30% in most of the cases considered and may even exceed 100% if we consider an alternative strategy based on a predetermined threshold, which makes it possible to exploit the inefficiency of the betting market.