23,633 research outputs found

    Belief rule-base expert system with multilayer tree structure for complex problems modeling

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    Belief rule-base (BRB) expert system is one of recognized and fast-growing approaches in the areas of complex problems modeling. However, the conventional BRB has to suffer from the combinatorial explosion problem since the number of rules in BRB expands exponentially with the number of attributes in complex problems, although many alternative techniques have been looked at with the purpose of downsizing BRB. Motivated by this challenge, in this paper, multilayer tree structure (MTS) is introduced for the first time to define hierarchical BRB, also known as MTS-BRB. MTS- BRB is able to overcome the combinatorial explosion problem of the conventional BRB. Thereafter, the additional modeling, inferencing, and learning procedures are proposed to create a self-organized MTS-BRB expert system. To demonstrate the development process and benefits of the MTS-BRB expert system, case studies including benchmark classification datasets and research and development (R&D) project risk assessment have been done. The comparative results showed that, in terms of modelling effectiveness and/or prediction accuracy, MTS-BRB expert system surpasses various existing, as well as traditional fuzzy system-related and machine learning-related methodologie

    The Use of Marketing Knowledge in Formulating and Enforcing Consumer Protection Policy

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    The purpose of this first chapter of the handbook is to discuss how the findings and approaches offered by the marketing discipline are used in consumer protection policy

    Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods

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    Commercialization of non-timber forest products (NTFPs) has been widely promoted as a means of sustainably developing tropical forest resources, in a way that promotes forest conservation while supporting rural livelihoods. However, in practice, NTFP commercialization has often failed to deliver the expected benefits. Progress in analyzing the causes of such failure has been hindered by the lack of a suitable framework for the analysis of NTFP case studies, and by the lack of predictive theory. We address these needs by developing a probabilistic model based on a livelihood framework, enabling the impact of NTFP commercialization on livelihoods to be predicted. The framework considers five types of capital asset needed to support livelihoods: natural, human, social, physical, and financial. Commercialization of NTFPs is represented in the model as the conversion of one form of capital asset into another, which is influenced by a variety of socio-economic, environmental, and political factors. Impacts on livelihoods are determined by the availability of the five types of assets following commercialization. The model, implemented as a Bayesian Belief Network, was tested using data from participatory research into 19 NTFP case studies undertaken in Mexico and Bolivia. The model provides a novel tool for diagnosing the causes of success and failure in NTFP commercialization, and can be used to explore the potential impacts of policy options and other interventions on livelihoods. The potential value of this approach for the development of NTFP theory is discussed

    QUANTIFYING AND MANAGING RISK IN AGRICULTURE

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    Risk and Uncertainty,

    Predictive User Modeling with Actionable Attributes

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    Different machine learning techniques have been proposed and used for modeling individual and group user needs, interests and preferences. In the traditional predictive modeling instances are described by observable variables, called attributes. The goal is to learn a model for predicting the target variable for unseen instances. For example, for marketing purposes a company consider profiling a new user based on her observed web browsing behavior, referral keywords or other relevant information. In many real world applications the values of some attributes are not only observable, but can be actively decided by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose which type of a special offer to send to a client (actionable attribute), hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in predictive modeling. We emphasize that not all instances are equally sensitive to changes in actions. Accurate choice of an action is critical for those instances, which are on the borderline (e.g. users who do not have a strong opinion one way or the other). We formulate three supervised learning approaches for learning to select the value of an actionable attribute at an instance level. We also introduce a focused training procedure which puts more emphasis on the situations where varying the action is the most likely to take the effect. The proof of concept experimental validation on two real-world case studies in web analytics and e-learning domains highlights the potential of the proposed approaches

    Cognitive finance: Behavioural strategies of spending, saving, and investing.

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    Research in economics is increasingly open to empirical results. The advances in behavioural approaches are expanded here by applying cognitive methods to financial questions. The field of "cognitive finance" is approached by the exploration of decision strategies in the financial settings of spending, saving, and investing. Individual strategies in these different domains are searched for and elaborated to derive explanations for observed irregularities in financial decision making. Strong context-dependency and adaptive learning form the basis for this cognition-based approach to finance. Experiments, ratings, and real world data analysis are carried out in specific financial settings, combining different research methods to improve the understanding of natural financial behaviour. People use various strategies in the domains of spending, saving, and investing. Specific spending profiles can be elaborated for a better understanding of individual spending differences. It was found that people differ along four dimensions of spending, which can be labelled: General Leisure, Regular Maintenance, Risk Orientation, and Future Orientation. Saving behaviour is strongly dependent on how people mentally structure their finance and on their self-control attitude towards decision space restrictions, environmental cues, and contingency structures. Investment strategies depend on how companies, in which investments are placed, are evaluated on factors such as Honesty, Prestige, Innovation, and Power. Further on, different information integration strategies can be learned in decision situations with direct feedback. The mapping of cognitive processes in financial decision making is discussed and adaptive learning mechanisms are proposed for the observed behavioural differences. The construal of a "financial personality" is proposed in accordance with other dimensions of personality measures, to better acknowledge and predict variations in financial behaviour. This perspective enriches economic theories and provides a useful ground for improving individual financial services

    Trust based marketing on the internet

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    Cover title. "October 15, 1998."Includes bibliographical references (p. 18-20).Partial support from the MIT International Motor Vehicle Project.by Glen L. Urban, Fareena Sultan, William Qualls

    Innovation attributes and managers' decisions about the adoption of innovations in organizations: A meta-analytical review

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    The adop­tion of in­no­va­tions has emerged as a dom­i­nant re­search topic in the man­age­ment of in­no­va­tion in or­ga­ni­za­tions, al­though in­ves­ti­ga­tions of­ten yield mixed re­sults. To help man­agers and re­searchers im­prove their ef­fec­tive­ness, the au­thors em­ployed a meta-analy­sis in­te­grated with struc­tural equa­tion mod­el­ing to an­a­lyze the as­so­ci­a­tions be­tween the at­trib­utes of in­no­va­tions, man­agers' be­hav­ioral pref­er­ences, and or­ga­ni­za­tions' in­no­va­tion adop­tion de­ci­sions in a me­di­ated-mod­er­ated frame­work. Our find­ings of­fer ev­i­dence that at­trib­utes of in­no­va­tions in­flu­ence man­agers' be­hav­ioral pref­er­ences and, con­se­quently, adop­tion de­ci­sions in or­ga­ni­za­tions. We also ob­serve the sig­nif­i­cance of the con­text in which the adop­tion de­ci­sion oc­curs as well as the re­search set­tings em­ployed by schol­ars. Fi­nally, we dis­cuss the the­o­ret­i­cal con­tri­bu­tion and prac­ti­cal im­pli­ca­tions of our meta-an­a­lyt­i­cal re­sults

    - Case of next-generation transportation market -

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    학위논문 (박사) -- 서울대학교 대학원 : 공과대학 협동과정 기술경영·경제·정책전공, 2020. 8. 이종수.The present dissertation aims to provide insights into the application of different artificial neural network models in the analysis of consumer choice regarding next-generation transportation services (NGT). It categorizes consumers decisions regarding the adoption of new services according to Deweys buyer decision process and then analyzes these decisions using a variety of different methods. In particular, various artificial neural network (ANN) models are applied to predict consumers intentions. Also, the dissertation proposes an attention-based ANN model that identifies the key features that affect consumers choices. Consumers preferences for different types of NGT services are analyzed using a hierarchical Bayesian model. The analyzed consumer preferences are utilized to forecast demand for NGT services, evaluate government policies within the transportation market, and provide evidence regarding the social conflicts among traditional and new transportation services. The dissertation uses the Multiple Discrete-Continuous Extreme Value (MDCEV) model to analyze consumers decisions regarding the use of different transportation modes. It also utilizes this MDCEV model analysis to estimate the effect of NGT services on consumers travel mode selection behavior and the environmental effects of the transportation sector. Finally, the findings of the dissertations analyses are combined to generate marketing and policy insights that will promote NGT services in Korea.본 연구는 기계학습 기반의 인공지능망과 기존의 통계적 마케팅 선택모형을 통합적으로 활용하여 제품 및 서비스 수용 이론으로 정의된 소비자들의 제품 수용 행위를 분석하였다. 기존의 제품 수용 이론들은 소비자들의 선택에 끼치는 영향을 단계별로 정의하였지만, 대부분의 이론은 제품 특성이 소비자 선택에 미치는 영향을 분석하기 보다는 소비자들의 의향, 제품의 대한 의견, 지각 수준과 소비자 선택의 관계 분석에 집중하였다. 따라서 본 연구는 소비자의 제품 수용 의향, 대안 평가 그리고 제품 및 사용량 선택을 포함하여 더욱 포괄적인 측면에서 소비자 제품 수용 행위를 분석하였다. 본 연구에서는 소비자의 제품 수용 관련 선택을 총 세 단계로 분류하였다. 첫 번째는 소비자의 제품 사용 의향을 결정하는 단계, 두 번째는 제품들의 대안을 평가하는 단계, 세 번째는 제품의 사용량을 선택하는 단계로, 각 단계를 분석하기 위해서 본 연구는 인공지능망과 통계적 마케팅 선택모형을 활용하였다. 인공지능망은 예측과 분류하는 작업에서 월등한 성능을 가진 모형으로 소비자들의 제품 수용 의향을 예측하고, 의향 선택에 영향을 주는 주요 변수들을 식별하는 데 활용되었다. 본 연구에서 제안한 주요 변수 식별을 위한 인공지능망은 기존의 변수 선택 기법 보다 모형 추정 적합도 측면에서 높은 성능을 보였다. 본 모형은 향후 빅데이터와 같이 많은 양의 소비자 관련 데이터를 처리하는데 활용될 가능성이 클 뿐만 아니라, 기존의 설문 설계 기법을 개선하는데 용이한 방법론으로 판단된다. 소비자 선호를 기반으로 한 대안 평가 및 사용량을 분석하기 위해서 통계적 선택 모형 중 계층적 베이지안 모형과 혼합 MDCEV 모형을 활용하였다. 계층적 베이지안 모형은개별적인 소비자 선호를 추정할 수 있는 장점이 있고, 혼합 MDCEV 모형의 경우 소비자들의 선호를 기반하여 선택된 대안들로 다양한 포트폴리오를 구성할 수 있고, 각 대안에 대한 사용량을 분석할 수 있다. 제안된 모형들의 실증 연구를 위해 차세대 자동차 수송 서비스에 대한 소비자들의 사용 의향, 서비스 대안에 대한 선호, 수송 서비스별 사용량을 분석하였다. 실증 연구에서는 차세대 자동차 수송 서비스를 수용하기까지 소비자들이 경험하는 단계별 선택 상황을 반영하였으며, 각 단계에서 도출된 결과를 통해 향후 차세대 자동차 수송 서비스의 성장 가능성과 소비자들의 이동 행위 변화에 대해 예측하였다. 본 연구를 통해 인공지능망이 소비자 관련 연구에서 유용하게 활용될 수 있음을 보였으며, 인공지능망과 통계적 마케팅 선택모형이 결합될 경우 소비자들의 제품 선택 행위뿐만 아니라, 제품 선택 의사결정 과정 전반에 걸쳐 소비자 선호를 포괄적으로 분석할 수 있음을 확인하였다.Chapter 1. Introduction 1 1.1 Research Background 1 1.2 Research Objective 7 1.3 Research Outline 12 Chapter 2. Literature Review 14 2.1 Product and Technology Diffusion Theory 14 2.1.1. Extension of Adoption Models 19 2.2 Artificial Neural Network 22 2.2.1 General Component of the Artificial Neural Network 22 2.2.2 Activation Functions of Artificial Neural Network 26 2.3 Modeling Consumer Choice: Discrete Choice Model 32 2.3.1 Multinomial Logit Model 32 2.3.2 Mixed Logit Model 34 2.3.3 Latent Class Model 37 2.4 Modeling Consumer Heuristics in Discrete Choice Model 39 2.4.1 Consumer Decision Rule in Discrete Choice Model: Compensatory and Non-Compensatory Models 39 2.4.2 Choice Set Formation Behaviors: Semi-Compensatory Models 42 2.4.3 Modeling Consumer Usage: MDCEV Model 50 2.5 Difference between Artificial Neural Network and Choice Modeling 53 2.6 Limitations of Previous Studies and Research Motivation 58 Chapter 3. Methodology 63 3.1 Artificial Neural Network Models for Prediction 63 3.1.1 Multiple Perceptron Model 63 3.1.2 Convolutional Neural Network 69 3.1.3 Bayesian Neural Network 72 3.2 Feature Identification Model through Attention 77 3.3 Hierarchical Bayesian Model 83 3.4 Multiple Discrete-Continuous Extreme Value Model 86 Chapter 4. Empirical Analysis: Consumer Preference and Selection of Transportation Mode 98 4.1 Empirical Analysis Framework 98 4.2 Data 101 4.2.1 Overview of the Survey 101 4.3 Empirical Study I: Consumer Intention to New Type of Transportation 110 4.3.1 Research Motivation and Goal 110 4.3.2 Data and Model Setup 114 4.3.3 Result and Discussion 123 4.4 Empirical Study II: Consumer Choice and Preference for New Types of Transportation 142 4.4.1 Research Motivation and Goal 142 4.4.2 Data and Model Setup 144 4.4.3 Result and Discussion 149 4.5 Empirical Study III: Impact of New Transportation Mode on Consumers Travel Behavior 163 4.5.1 Research Motivation and Goal 163 4.5.2 Data and Model Setup 164 4.5.3 Result and Discussion 166 Chapter 5. Discussion 182 Bibliography 187 Appendix: Survey used in the analysis 209 Abstract (Korean) 241Docto
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