Mikael Collan

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.

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.

A multi-expert system for ranking patents: An approach based on fuzzy pay-off distributions and a TOPSIS-AHP framework

The aim of this paper is to introduce a decision support system that ranks patents based on multiple expert evaluations. The presented approach starts with the creation of three value scenarios for each considered patent by each expert. These are used for the construction of individual fuzzy pay-off distribution functions for the patent value; a consensual fuzzy pay-off distribution is then determined starting from the individual distributions. Possibilistic moments are calculated from the consensus pay-off distribution for each patent and used in ranking them with TOPSIS. It is further showed how the analytic hierarchy process (AHP) can be used to include additional decision variables into the patent selection, thus allowing for a two-tier decision making process. The system is illustrated with a numerical example and the usability of the system and the combination of methods it includes for patent portfolio selection in the real world context is discussed.

A Practical Approach to R&D Portfolio Selection Using the Fuzzy Pay-Off Method

The objective of this research is to develop a practical research and development (R&D) portfolio selection model that addresses the effective R&D project valuation issue, while tackling R&D uncertainty in portfolio optimization. Fuzzy set theory is employed to capture and model the uncertain project information. To evade the well-known complexities of fuzzy real option valuation, the recently developed fuzzy pay-off method is used to more effectively valuate R&D projects. The resulting problem is formulated as a fuzzy zero-one integer programming model that handles uncertainty of input data in order to determine the optimal portfolio. Two satisfaction measures, which are based on possibility and necessity theory, are employed to solve the resulting fuzzy model. An example is presented to compare these measures, as well as to illustrate the simplicity and applicability of the proposed approach.

A Fuzzy Pay-Off Method for Real Option Valuation

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 triangular and trapezoidal fuzzy numbers with the method.

IT and institutional constraints: effects of legal and administrative constraints to use it in production of health care services - focus on Finland

This paper looks into the legal and administrative frameworks governing the different aspects of using IT as a medium and a tool in production of health care services. The administrative framework is described from the point of view of the status quo in Finland. The issues are elaborated by a discussion of a number of legal aspects and a short description of normative insights from actors in the health care production collected through a survey.

Fuzzy black and scholes real options pricing

Giga-investments are very large projects with long life cycles, which make them both risk investments and endeavours built on imprecise and uncertain assumptions. Still, the classical approach to judging the profitability of the investments is the calculation of the net present values (NPV) of estimated cash flows. This involves the use of fairly precise numbers, which somehow are estimated for life cycles of 15-25 years in an uncertain context. Real option valuation (ROV) has been proposed as an alternative and complement to the NPV calculations. Flexibility introduced with the ROV methods can be used for strategic purposes and better investment decisions can be made, when there are some degrees of flexibility available for the process of deciding on investment alternatives. Still, the problem with unrealistic expectations on precision has not been solved. It appears clear that a high level of precision in cash flow estimates will be misleading for the decision makers, but the methods used have required the precision used. We introduce a new method, the fuzzy ROV (or FROV), which is built on the use of fuzzy numbers and possibility distributions, and which has the capability to tackle imprecision and uncertainty in a direct and explicit way.

On the usability of real option valuation model types under different types of uncertainty

This paper discusses the usability of real option valuation model types under different types of uncertainty. Three types of uncertainty are presented (parametric, structural and procedural), followed by a presentation of six different real option valuation types that are variably suitable for different uncertainties that in turn characterise real world decision-making situations. A usability-based mapping of real option valuation models with each type of uncertainty is made and discussed. Implications concerning decision-making are direct: wrong model selection may lead to unreliable or wrong results and, consequently, poor and often expensive investment decisions.

Usability Evaluation of a Mobile Medical Information System for Military Physicians

A mobile medical information system, developed primarily for the purposes of civilian medicine, is expanded to be used in military medicine. A usability study with 7 military physicians at the Finnish Defense Forces was conducted in field conditions in December 2005. Their evaluation of the characteristics and the features of the system, and properties and the features of the mobile device-Nokia 9210 communicator were investigated. The results show that both the mobile system and the device have some degree of readiness for military medicine. Further usability improvements of the system and the device are discussed

On teaching business decision-making in complex domains

This paper treats teaching decision-making in business as a separate issue from teaching business decision support. We consider business decision-making ability to be a combination of theory and practice (experience) and hence, perceive teaching decision-making an exercise that has to take both, business theory and business experience into consideration; this can be achieved with experiential learning. Complexity in the business environment and different learning environments are discussed. Selected instances of teaching decision-making under complexity are presented for illustration.