Fuzzy Real Investment Valuation Model for Giga-Investments, and a Note on Giga-Investment Lifecycle and Valuation

Very large industrial real investments are different from financial investments and from small real investments, even so, their profitability is commonly valued with the same methods. A definition of a group of very large industrial real investments is made, by requiring three common characteristics. The decision support needs arising from these characteristics are discussed and a summary of existing methods to value and to provide decision support for large industrial investments is presented. A model built specifically to support investment decisions of very large industrial real investments and a numerical application of the model are presented. The model is discussed and commented. A note is made on an observation regarding the giga-investment lifecycle and its effect on giga-investment valuation.Large industrial investments; Profitability analysis; Fuzzy corporate finance; Capital Budgeting

Lazy User Behaviour

In this position paper we suggest that a user will most often choose the solution (device) that will fulfill her (information) needs with the least effort. We call this “lazy user behavior”. We suggest that the principle components responsible for solution selection are the user need and the user state. User need is the user’s detailed (information) need (urgency, type, depth, etc.) and user state is the situation, in which the user is at the moment of the need (location, time, etc.); the user state limits the set of available solutions (devices) to fulfill the user need. The context of this paper is the use of mobile devices and mobile services. We present the lazy user theory of solution selection, two case examples, and discuss the implications of lazy user behavior on user attachment to mobile services and devices, and to planning and execution of mobile services.User Attachment; Lazy User; Mobile Services; Mobile Devices; Adoption; Acceptance; Least Effort

New Method for Real Option Valuation Using Fuzzy Numbers

Real option analysis offers interesting insights on the value of assets and on the profitability of investments, which has made real options a growing field of academic research and practical application. Real option valuation is, however, often found to be difficult to understand and to implement due to the quite complex mathematics involved. Recent advances in modeling and analysis methods have made real option valuation easier to understand and to implement. This paper presents a new method for real option valuation using fuzzy numbers that is based on findings from earlier real option valuation methods and from fuzzy real option valuation. The method is intuitive to understand and far less complicated than any previous real option valuation model to date.Real Options, Fuzzy Numbers, New Method

Flexibility in Investments: Exploratory Survey on How Finnish Companies Deal with Flexibility in Capital Budgeting

Flexibility is an important issue when investments are being planned and valued. How flexibility inherent in investments is utilised and exploited is, therefore, of great importance to the accuracy of the plans and the valuation. This paper describes an exploratory survey, done with leading Finnish companies, exploring the use of Real Option Valuation (ROV), and the methods that Finnish corporations use to take flexibility into consideration, when planning and valuing investments. We found that real options exist in Finnish investments, but there are very few companies that have an established methodology of identifying, categorizing, or valuing them. We also found that Finnish managers have mixed views about the value of flexibility in investments. Very few Finnish managers seem to be aware of research done in the field, but most seem to have an intuitive understanding of how different variables affect the value of flexibility in an investment.flexibility; real options; capital budgeting

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

Educating Multi-disciplinary Student Groups in Entrepreneurship: Lessons Learned from a Practice Enterprise Project

The target audiences for entrepreneurial university studies are most often students of different fields of business studies, or economics; entrepreneurship studies are a part of their normal curriculum. Entrepreneurs, however, are not a group that consists only of business professionals, but a group of people from all walks of life. The basic procedures and laws governing the starting of a company are most often same for all companies and individuals. It is important to acknowledge these two facts, when designing curriculums for university studies: basic courses in entrepreneurship (starting a business) are important for students of all disciplines. This paper reports experiences from educating multi-disciplinary student groups in entrepreneurship, presents preliminary data about student background and attitudes towards entrepreneurship, and discusses some lessons learned from the experiences.Entrepreneurship education; multi-disciplinary groups; lessons learned

Tulokset talvikaalikokeista Hinnonmäen puutarhakoeasemalla vuosina 1923-25

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A Fuzzy Pay-off Method for Real Option Valuation

Real Options analysis offers interesting insights on the value of assets and on the profitability of investments, which has made real options a growing field of academic research and practical application. Real option valuation is, however, often found to be difficult to understand and to implement due to the quite complex mathematics involved. Recent advances in modeling and analysis methods have made real option valuation easier to understand and to implement. This paper presents a new method (fuzzy pay-off method) for real option valuation using fuzzy numbers that is based on findings from earlier real option valuation methods and from fuzzy real option valuation. The method is intuitive to understand and far less complicated than any previous real option valuation model to date. The paper also presents the use of number of different types of fuzzy numbers with the method and an application of the new method in an industry setting.Real Option Valuation; Fuzzy Real Options; Fuzzy Numbers

BENCHMARKING CLASSIFIERS - HOW WELL DOES A GOWA-VARIANT OF THE SIMILARITY CLASSIFIER DO IN COMPARISON WITH SELECTED CLASSIFIERS?

Digital data is ubiquitous in nearly all modern businesses. Organizations have more data available, in various formats, than ever before. Machine learning algorithms and predictive analytics utilize the knowledge contained in that data, in order to help the business related decision-making. This study explores predictive analytics by comparing different classification methods – the main interest being in the Generalize Ordered Weighted Average (GOWA)-variant of the similarity classifier. The target for this research is to find out how what is the GOWA-variant of the similarity classifier and how well it performs compared to other selected classifiers. This study also tries to investigate whether the GOWA-variant of the similarity classifier is a sufficient method to be used in the busi-ness related decision-making. Four different classical classifiers were selected as reference classifiers on the basis of their common usage in machine learning research, and on their availability in the Sta-tistics and Machine Learning Toolbox in MATLAB. Three different data sets from UCI Machine Learning repository were used for benchmarking the classifiers. The benchmarking process uses fitness function instead of pure classification accuracy to determine the performance of the classifiers. Fitness function combines several measurement criteria into a one common value. With one data set, the GOWA-variant of the similarity classifier per-formed the best. One of the data sets contains credit card client data. It was more complex than the other two data sets and contains clearly business related data. The GOWA-variant performed also well with this data set. Therefore it can be claimed that the GOWA-variant of the similarity classifi-er is a viable option to be used also for solving business related problems

Some heat capacity and thermometric investigations at ultralow temperatures

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