The application of a research project selection method

This paper describes the application of a linear programming model to project selection and resource allocation problems in two industrial R & D laboratories. Mathematical models have not been widely used in practice because of the inherent uncertainty of research. The model discussed here yields information about the outcome of decisions under conditions of uncertainty. The elements of the model are developed, using simple examples, and the form of the complete model outlined. Benefit evaluation is carried out using probabilistic networks and uncertainty in benefits included by utilizing the concept of certainty equivalence. The use of the model in practical situations is discussed, with examples of the information obtained from the solution, and sensitivity studies to investigate the uncertain environment are described. The result is a flexible model, which produces information necessary for planning purposes and which is acceptable to practising R & D Managers.     

Probabilistically constrained models for efficiency and dominance in DEA

This paper proposes a stochastic model for data envelopment analysis (DEA), based on the theory of joint probabilistic constraints, which can be used with general multivariate distribution functions. The key assumption is that the random variables representative of the uncertain data follow a discrete distribution or that a discrete approximation of continuous distribution is available. Under this assumption, mixed integer linear models are formulated to tackle, rather originally, dependencies among DMUs inputs, outputs and inputs–outputs through the theory of joint probabilistic constraints. The features of the model are illustrated through an application for the performance evaluation of screening units.     

A Comparison of Fuzzy Set and Probabilistic Paradigms for Ranking Vague Economic Investment Information Using a Present Worth Criterion

This article introduces an approach for comparing the fuzzy set and probabilistic paradigms for ranking vague economic investment information when a present worth criterion is used. Comparisons are made between the two paradigms by forming a ratio of fuzzy ranking value to probabilistic ranking value for a set of tested economic decision conditions (vague cash flows and interest rates). A multivariable analysis of the comparison ratios indicates the dominance or spread required in the expected present worths of competing alternatives to guarantee consistent decision ranking among the paradigms. Results of the research suggest that probabilistic methods will not rank vague economic investment information the same as fuzzy set methods. As a result, decision makers who use probabilistic methods to model vagueness in human thought (the condition for which fuzzy theory was developed) may obtain rankings inconsistent with those obtained via the fuzzy models. In such cases, if decisions are based on project rankings, then different choices or recommendations are likely.     

Fashion retail forecasting by evolutionary neural networks

Recent literature on nonlinear models has shown that neural networks are versatile tools for forecasting. However, the search for an ideal network structure is a complex task. Evolutionary computation is a promising global search approach for feature and model selection. In this paper, an evolutionary computation approach is proposed in searching for the ideal network structure for a forecasting system. Two years’ apparel sales data are used in the analysis. The optimized neural networks structure for the forecasting of apparel sales is developed. The performances of the models are compared with the basic fully connected neural networks and the traditional forecasting models. We find that the proposed algorithms are useful for fashion retail forecasting, and the performance of it is better than the traditional SARIMA model for products with features of low demand uncertainty and weak seasonal trends. It is applicable for fashion retailers to produce short-term retail forecasting for apparels, which share these features.     

DERIVATION OF AN INTERTEMPORAL CORRELATION COEFFICIENT MODEL BASED ON CASH FLOW COMPONENTS AND PROBABILISTIC EVALUATION OF A PROJECT'S NPV

If the first two moments (mean and variance) of the net present value (NPV) are known, various probabilistic information of possible NPV can be derived. However, in general, it is practically impossible to estimate the variance of lengthy investment projects due to difficulties in estimating all the intertemporal correlation coefficients between cash flows of two different periods. In this paper, we derive an estimation model for the intertemporal coefficients based on cash flow components and show how the model, under a certain assumption, can be used for estimating and deriving probabilistic information.     

Assessing the Forecasting Performance of Regime-Switching, ARIMA and GARCH Models of House Prices

While price changes on any particular home are difficult to predict, aggregate home price changes are forecastable. In this context, this paper compares the forecasting performance of three types of univariate time series models: ARIMA, GARCH and regime-switching. The underlying intuition behind regime-switching models is that the series of interest behaves differently depending on the realization of an unobservable regime variable. Regime-switching models are a compelling choice for real estate markets that have historically displayed boom and bust cycles. However, we find that, while regime-switching models can perform better in-sample, simple ARIMA models generally perform better in out-of-sample forecasting.     

Forecasting New Product Adoption with Probabilistic Neural Networks

Forecasting the adoption of innovative products is an important managerial task. In this paper we examine the usefulness of a probabilistic neural network (PNN) algorithm for forecasting new product adoption. We compare this approach with one widely accepted forecasting procedure, the binomial logit model, and two other neural network algorithms: a feed‐forward neural network model estimated with backward propagation (NNBP), and a feed‐forward neural network model estimated with a genetic algorithm (NNGA). To test the relative forecasting accuracy of these algorithms, we examine the first‐time adoption of DVD players. Our analysis is based on longitudinal consumer data collected between March 2000 and March 2001. We find that the PNN algorithm significantly outperforms the logit model and the two remaining neural network algorithms.     

Patenting in the shadow of independent discoveries by rivals

This paper studies the decision of whether to apply for a patent in a dynamic model in which firms innovate stochastically and independently. In the model, a firm can choose between patenting and maintaining secrecy to protect a successful innovation. I consider a legal environment characterized by imperfect patent protection and no prior user rights. Thus, patenting grants probabilistic protection, and secrecy is effectively maintained until rivals innovate. I show that (1) firms that innovate early are more inclined to choose secrecy, whereas firms that innovate late have a stronger tendency to patent; (2) the incentives to patent increase with the innovation arrival rate; and (3) an increase in the number of firms may cause patenting to occur earlier or later, depending on the strength of patent protection. The socially optimal level of patent protection, which balances the trade-off between the provision of patenting incentives and the avoidance of deadweight loss caused by a monopoly, is lower with a higher innovation arrival rate or a larger number of firms.     

Moving beyond intuition—Managing allocation decisions in relationship marketing in business-to-business markets

Developing and estimating structural models is becoming a routine practice in marketing. In this study, the possibilities of applying such models in managerial decision making under uncertainty are investigated. In particular the feasibility of exploiting the inherent probabilistic nature of structural models to buttress decision making is demonstrated. The approach is based on making heavy use of standard simulation routines. The model that is under scrutiny describes the relationships between firms' efforts in three areas (the offer, customer relationships, and market positions) on the success of a new product introduction. Special attention is given to the aspect of risk aversion. Accounting for the risk attitude implies different allocation decisions for risk-averse compared to risk-prone managers, in line with common sense.     

Benefits of using hybrid business models within a supply chain

Today's competition between supply chains (SC) requires optimized strategies in order to satisfy customers’ demands. The business models used by the SC members play a big role in this delivery of value to the customer. From here the notion that a mismatch between the intended market and the business model used to address it translates into a poor SC performance. As real-life business environments have become really complex, SC members have been forced to use hybrid business models (that is, the integration of features of two different business models). A review of the literature in the area of supply chain management shows that past research have not paid much attention to this issue. The objective of this paper is to quantitatively evaluate the influence these hybrid business models have on the SC performance. For this purpose, a system dynamics (SD) simulation model is developed and tested under different operational conditions, so conclusions can be derived regarding the benefits of the use of hybrid business models.     

The three faces of corporate renewal: Institution,revolution, and evolution

We examine corporate renewal by taking a structural approach and focusing on the routines and rules that are part of large, established, bureaucratic organizations. We characterize approaches to the management of innovation in terms of three different themes–institutional, revolutional, and evolutional strategies. The first two approaches involve intentional efforts to encourage innovation, either within the current organizational paradigm (institutionalizing innovation) or moving away from it (revolutionary innovation), while the evolutional approach involves less conscious efforts to manage what is viewed as a random, probabilistic process. This paper uses simulation methodology to explore the effectiveness of these strategies on organizational innovation, performance, and resources. The behavior of the simulated organizational units is guided by assumptions of a learning model. Results indicate that innovation strategies sometimes have unintended effects that are both positive and negative in nature. Several lessons on managing innovation are offered.     

Product Line Rivalry and Market Segmentation—with an Application to Automobile Optional Engine Pricing

This paper addresses the questions whether and when the pricing practices on base products may differ from those of premium products, sold with options or add-ons. Various alternative models are considered: a monopoly model, a model of brand rivalry with full consumer information and a model of rivalry in which consumers are only well informed about base product prices. Only the brand rivalry model with limited consumer information predicts that premium products have larger percentage markups than base products, provided that brand rivalry is sufficiently intense. Empirical evidence on base and premium product pricing in the automobile market is consistent with the limited information model and inconsistent with the other two models.     

Potenziale zur Erhöhung des regionalen Markenkerns im Stromvertrieb am Beispiel der regionalen Grünstromkennzeichnung gemäß EEG 2017: Teil 2 (Multikriterien-Analyse)

In this paper – complementing an earlier article on this topic (Part 1: Basics and Analysis of Potentials) – we perform a Multicriteria Decision Analysis (MCDA), also engaging decision-makers in the analytical process. From the MCDA we derive recommendations to act with regard to the alternatives for regional green power marketing (cf. Part 1). For the MCDA we have developed a hybrid multicriteria approach, which enables to pool the advantages of two methods. Specifically, for the valuation of alternatives and criteria weighting we use the so-called Multi-Attribute Global Inference of Quality (MAGIQ) method, whereas the computation of the analytical results was performed with the help of an optimized Weighted Aggregated Sum Product Assessment (WASPAS) method. The results of the MCDA show that the regional green power labeling according to EEG 2017 can be used for upgrading the options already existing before the introduction of the EEG 2017 for the marketing of regionally generated electricity. From the MCDA conducted we find that those green power marketing models which combine the regional green power labeling according to the EEG 2017 with previously existing options rank first to third.     

Obfuscation and Rational Inattention*

We study the behavior of duopolistic firms that can obfuscate their prices before competing on price. Obfuscation affects the rational inattentive consumers' optimal information strategy, which determines the probabilistic demand. Our model advances related models by allowing consumers to update their unrestricted prior beliefs with an informative signal of any form. We show that the game may result in an obfuscation equilibrium with high prices or a transparency equilibrium with low prices and no obfuscation, providing an argument for market regulation. Obfuscation equilibria cease to exist for low information costs and if one firm seems a priori considerably more attractive.     

Modelling the objective function of managers in the presence of overlapping shareholding

The objective function of managers in the presence of overlapping shareholding may differ from the traditional own-firm profit maximization, as they may internalize the externalities their strategies impose on other firms. The dominant formulation of the objective function in such cases has, however, been criticised for yielding counter-intuitive profit weights when the ownership of non-overlapping shareholders is highly dispersed. In this paper, we examine this issue. First, we make use of a probabilistic voting model (in which shareholders vote to elect the manager) to microfound an alternative formulation of the objective function of managers, which solves the above-mentioned criticism. Second, we apply the two formulations to the set of S&P 500 firms. We show that ownership dispersion of non-overlapping shareholders is, in fact, a relevant empirical issue, which may induce an over-quantification of the profit weights computed from the dominant formulation, particularly under a proportional control assumption.     

A planning model of pharmaceutical research and development

Despite the popular view that pharmaceutical research is a random process, and by inference unplannable, the long-term survival and development of a pharmaceutical company must be based on a steady flow of new products with a full understanding by management of the relationships between research expenditure and the likely revenues they will produce. In this paper is developed a model of the R&D function which uses probabilistic assumptions to compute the expected stream of revenues from a portfolio of projects involving the major development routes from which pharmaceutical products usually arise. The model is based on expected values of market and technical research performance and hence gives expected or indicated sales in the future as output.     

Real Estate Valuation Models: Lender and Equity Investor Criteria

The use of valuation models that focus on lender criteria has been growing in the appraisal field. In the rush to build lender criteria into real estate valuation models, equity investor criteria, expectations, and requirements occasionally have been ignored. The specific criteria considered in this paper are the loan-to-value ratio and the debt coverage ratio for lenders and the equity dividend rate for equity investors. Each of these three criteria may be a binding constraint on value.
Graphical analysis provides a framework within which major real estate valuation models (i.e., Ellwood, McLaughlin, Gettel, Lusht-Zerbst, and Steele) are compared. A new valuation model (i.e., the Cannaday-Colwell model) is developed which utilizes the equity dividend rate.
The three definitional models (i.e., McLaughlin, Gettel, and Steele) are found to be relevant only by mere coincidence. Each of these models simultaneously considers two of the three key criteria, completely eliminating the possibility of consideration of anything else; i.e., the models become tautological.
It is shown that the discounted cash flow based models (i.e., Ellwood, Lusht-Zerbst, and Cannaday-Colwell) each tell one-third of the story. One of these models will be relevant depending upon whether the binding constraint is the maximum loan-to-value ratio, the minimum debt coverage ratio, or the minimum equity dividend rate. The relevant model is the one that yields the lowest value estimate of the three.     

所有权与自然垄断产业规制——组合模式及选择

对于自然垄断产业采用何种所有权结构的规制模式,在理论研究与实践操作中都存在着多样化的选择。本文主要比较分析了民营外生规制、国有内生规制和混合所有权规制三种不同组合模式,并认为混合所有权规制模式较好地兼容了其他模式的优点,可以成为自然垄断产业改革与治理进程中的一个可取路径。欧盟国家自然垄断产业的改革经验,从实践上证明了混合所有权规制模式的可行性,尽管其也存在一定的局限性。同时,本文认为对于转型改革时期的中国,针对自然垄断行业的国有企业,可以考虑采用混合所有权规制模式的改革路径,通过股权开放的形式放松民营资本进入自然垄断产业的行业管制,并有效、平稳地建立起自然垄断产业规制体系。     

Cost–raising Strategies in a Symmetric, Dynamic Duopoly

This paper provides a characterization of the set of dynamic models in which symmetric duopolists have incentives to raise a common cost. The advantage of the dynamic analysis over existing static models is that it extends the conditions (restrictive in static models) under which symmetric cost raising is profitable. The model is illustrated by standard examples from industrial organization: quantity and price adjustment, and learning–by–doing.     

光谱颜色复制模型概述

利用光谱复制技术提高颜色复制精度,减少同色异谱现象,是目前颜色高保真复制方法研究的一个主流方向。本文总结回顾了各经典光谱模型,并对打印机适用的基于回归的经典光谱模型和基于物理过程的关键光谱模型进行介绍。其中打印机适用的基于回归的经典光谱模型包括适用于单色打印机的MmTay-Davies模型、适用于网目调多色印刷的Neugebauer模型、添加n值优化的Yule-Nielsen光谱反射率模型、Clapper-Yule光谱反射率模型、细胞化Neugebauer模型、细胞化Yule-Nielsen光谱模型、YNSN光谱模型以及细胞化YNSN模型。通过控制模型中的各可变参数,不断优化模型,可提高光谱颜色复制的精度。