Fuzzy Logic and Intelligent Agents: Towards the Next Step of Capital Budgeting Decision Support

The economic life of large investments is long and thus necessitates constant dynamic managerial actions. To be able to act in an optimal way in the dynamic management of large investments managers need the support of advanced analytical tools. They need to have constant access to information about the real time situation of the investment, as well as, access to up-to-date information about changes in the business environment. What is more challenging, they need to integrate qualitative information into quantitative analysis process, and to integrate foresight information into the capital budgeting process. In this paper we will look at how emerging soft computing technologies, specifically fuzzy logic and intelligent agents, will help to provide a better support in such a context and then to frame a support system that will make an integrated application of the aforementioned technologies. We will first develop a holistic framework for an agent-facilitated capital budgeting system using a fuzzy real option approach. We will then discuss how intelligent agents can be applied to collect decision information, both qualitative and quantitative, and to facilitate the integration of foresight information into capital budgeting process. Integration of qualitative information into quantitative analysis process will be discussed. Methods for integrating qualitative and quantitative information into fuzzy numbers, as well as, methods for using the fuzzy numbers in capital budgeting will be presented. A specification of how the agents can be constructed is elaborated.Intelligent Agents, Fuzzy Sets, Capital Budgeting, Real Options, DSS

Methods for Utilizing Connected Vehicle Data in Support of Traffic Bottleneck Management

The decision to select the best Intelligent Transportation System (ITS) technologies from available options has always been a challenging task. The availability of connected vehicle/automated vehicle (CV/AV) technologies in the near future is expected to add to the complexity of the ITS investment decision-making process. The goal of this research is to develop a multi-criteria decision-making analysis (MCDA) framework to support traffic agencies’ decision-making process with consideration of CV/AV technologies. The decision to select between technology alternatives is based on identified performance measures and criteria, and constraints associated with each technology. Methods inspired by the literature were developed for incident/bottleneck detection and back-of-queue (BOQ) estimation and warning based on connected vehicle (CV) technologies. The mobility benefits of incident/bottleneck detection with different technologies were assessed using microscopic simulation. The performance of technology alternatives was assessed using simulated CV and traffic detector data in a microscopic simulation environment to be used in the proposed MCDA method for the purpose of alternative selection. In addition to assessing performance measures, there are a number of constraints and risks that need to be assessed in the alternative selection process. Traditional alternative analyses based on deterministic return on investment analysis are unable to capture the risks and uncertainties associated with the investment problem. This research utilizes a combination of a stochastic return on investment and a multi-criteria decision analysis method referred to as the Analytical Hierarchy Process (AHP) to select between ITS deployment alternatives considering emerging technologies. The approach is applied to an ITS investment case study to support freeway bottleneck management. The results of this dissertation indicate that utilizing CV data for freeway segments is significantly more cost-effective than using point detectors in detecting incidents and providing travel time estimates one year after CV technology becomes mandatory for all new vehicles and for corridors with moderate to heavy traffic. However, for corridors with light, there is a probability of CV deployment not being effective in the first few years due to low measurement reliability of travel times and high latency of incident detection, associated with smaller sample sizes of the collected data

Intelligent Computer System of Decision Support for Optimizing the Control of Investment Analysis Processes and Projecting

For the successful operation of any business entity in the field of investment projecting, it is necessary to have a modern control tool for its processes. The article discusses the issues of development and creation of an intelligent computer system for decision support that allows one to optimize the control of investment analysis and projecting processes. The purpose of this work is to develop and create a decision support system for optimizing the control of investment analysis and projecting processes based on analysis of possible areas of use of intelligent systems. The development and creation of such a system is based on the technologies of computer expert decision support systems, neural networks, machine learning, as well as models and methods of network economic and mathematical modeling. In the paper, the basic stages of the creation of computer expert systems for optimization of control by processes of the investment analysis and projecting by the organization are considered. Specific examples of the development of logical rules in the production and clausal forms for the knowledge base of the proposed computer expert system are given. The work analyzes the feasibility of selecting specific models and technologies suitable for creating the proposed intellectual system. The presented results testify to the effectiveness of its application in the practical activities of economic entities when optimizing the control of investment analysis and projecting processes. This topic can be further developed in the directions of the application of various architectures of neural networks to solve many practical problems of investment analysis and projecting, as well as the use of large amounts of economic information suitable for neural network processing.Для успешной деятельности любого хозяйствующего субъекта в области инвестиционного проектирования необходимо иметь современный инструментарий управления его процессами. В статье обсуждаются вопросы разработки и создания интеллектуальной компьютерной системы поддержки принятия решений, позволяющей оптимизировать управление процессами инвестиционного анализа и проектирования. Целью данной работы является разработка и создание системы поддержки принятия решений для оптимизации управления процессами инвестиционного анализа и проектирования на основе анализа возможных направлений использования интеллектуальных систем. Разработка и создание такой системы основывается на технологиях компьютерных экспертных систем поддержки принятия решений, нейронных сетей, машинного обучения, а также моделях и методах сетевого экономико-математического моделирования. В работе рассмотрены основные этапы создания компьютерной экспертной системы для оптимизации управления процессами инвестиционного анализа и проектирования хозяйствующим субъектом. Приведены конкретные примеры разработки логических правил в продукционной и клаузальной формах для базы знаний предлагаемой компьютерной экспертной системы. В работе выполнен анализ целесообразности выбора конкретных моделей и технологий, подходящих для создания предлагаемой интеллектуальной системы. Представлены результаты, свидетельствующие об эффективности ее применения в практической деятельности хозяйствующих субъектов при оптимизации управления процессами инвестиционного анализа и проектирования. Дальнейшие направления развития данной темы могут быть направлены на применение различных архитектур нейронных сетей для решения многих практических задач инвестиционного анализа и проектирования, а также на использование больших объемов экономической информации, пригодной для нейросетевой обработки.Работа выполнена при финансовой поддержке РФФИ (проект № 17-01-00315

An intelligent system for risk classification of stock investment projects

The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange

Managing stimulation of regional innovation subjects’ interaction in the digital economy

The reported study was funded by RFBR according to the research project No. 18-01000204_a, No. 16-07-00031_a, No. 18-07-00975_a.Purpose: The article is devoted to solving fundamental scientific problems in the scope of the development of forecasting modeling methods and evaluation of regional company’s innovative development parameters, synthesizing new methods of big data processing and intelligent analysis, as well as methods of knowledge eliciting and forecasting the dynamics of regional innovation developments through benchmarking. Design/Methodology/Approach: For regional economic development, it is required to identify the mechanisms that contribute to (or impede) the innovative economic development of the regions. The synergetic approach to management is based on the fact that there are multiple paths of IS development (scenarios with different probabilities), although it is necessary to reach the required attractor by meeting the management goals. Findings: The present research is focused on obtainment of new knowledge in creating a technique of multi-agent search, collection and processing of data on company’s innovative development indicators, models and methods of intelligent analysis of the collected data. Practical Implications: The author developed recommendations before starting the process of institutional changes in a specific regional innovation system. The article formulates recommendations on the implementation of institutional changes in the region taking into account the sociocultural characteristics of the region’s population. Originality/Value: It is the first time, when a complex of models and methods is based on the use of a convergent model of large data volumes processing is presented.peer-reviewe

Model of cybersecurity means financing with the procedure of additional data obtaining by the protection side

The article describes the model of cybersecurity means financing strategies of the information object with incomplete information about the financial resources of the attacking side. The proposed model is the core of the module of the developed decision support system in the problems of choosing rational investing variants for information protection and cybersecurity of various information objects. The model allows to find financial solutions using the tools of the theory of multistep games with several terminal surfaces. The authors proposed an approach that allows information security management to make a preliminary assessment of strategies for financing the effective cybersecurity systems. The model is distinguished by the assumption that the protection side does not have complete information, both about the financing strategies of the attacking side, and about its financial resources state aimed at overcoming cybersecurity lines of the information object. At the same time, the protection side has the opportunity to obtain additional information by the part of its financial resources. This makes it possible for the protection side to obtain a positive result for itself in the case when it can not be received without this procedure. The solution was found using a mathematical apparatus of a nonlinear multistep quality game with several terminal surfaces with alternate moves. In order to verify the adequacy of the model there was implemented a multivariate computational experiment. The results of this experiment are described in the article. © 2005 - ongoing JATIT & LL

An agent-based fuzzy cognitive map approach to the strategic marketing planning for industrial firms

This is the post-print version of the final paper published in Industrial Marketing Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Industrial marketing planning is a typical example of an unstructured decision making problem due to the large number of variables to consider and the uncertainty imposed on those variables. Although abundant studies identified barriers and facilitators of effective industrial marketing planning in practice, the literature still lacks practical tools and methods that marketing managers can use for the task. This paper applies fuzzy cognitive maps (FCM) to industrial marketing planning. In particular, agent based inference method is proposed to overcome dynamic relationships, time lags, and reusability issues of FCM evaluation. MACOM simulator also is developed to help marketing managers conduct what-if scenarios to see the impacts of possible changes on the variables defined in an FCM that represents industrial marketing planning problem. The simulator is applied to an industrial marketing planning problem for a global software service company in South Korea. This study has practical implication as it supports marketing managers for industrial marketing planning that has large number of variables and their cause–effect relationships. It also contributes to FCM theory by providing an agent based method for the inference of FCM. Finally, MACOM also provides academics in the industrial marketing management discipline with a tool for developing and pre-verifying a conceptual model based on qualitative knowledge of marketing practitioners.Ministry of Education, Science and Technology (Korea

Knowledge dependencies in fuzzy information systems evaluation

Experience and research within the field of Information Systems Evaluation (ISE), has traditionally centered on providing tools and techniques for investment justification and appraisal, based upon explicit knowledge which encodes financial and other direct situational factors (such as accounting, costing and risk metrics). However, such approaches tend not to include additional causal interdependencies that are based upon tacit knowledge and are inherent within such a decision-making task. The authors show the results of applying a cognitive mapping approach, in the guise of a Fuzzy Cognitive Mapping (FCM) simulation, i.e. Fuzzy Information Systems Evaluation (F-ISE), in order to highlight the usefulness of applying such a technique. The authors highlight those contingent and necessary knowledge dependencies, in an exploratory sense, which relate to the investment appraisal decision-making task, in terms of the interplay between tacit and explicit knowledge, in this regard