Intertemporal Choice of Fuzzy Soft Sets

This paper first merges two noteworthy aspects of choice. On the one hand, soft sets and fuzzy soft sets are popular models that have been largely applied to decision making problems, such as real estate valuation, medical diagnosis (glaucoma, prostate cancer, etc.), data mining, or international trade. They provide crisp or fuzzy parameterized descriptions of the universe of alternatives. On the other hand, in many decisions, costs and benefits occur at different points in time. This brings about intertemporal choices, which may involve an indefinitely large number of periods. However, the literature does not provide a model, let alone a solution, to the intertemporal problem when the alternatives are described by (fuzzy) parameterizations. In this paper, we propose a novel soft set inspired model that applies to the intertemporal framework, hence it fills an important gap in the development of fuzzy soft set theory. An algorithm allows the selection of the optimal option in intertemporal choice problems with an infinite time horizon. We illustrate its application with a numerical example involving alternative portfolios of projects that a public administration may undertake. This allows us to establish a pioneering intertemporal model of choice in the framework of extended fuzzy set theorie

Towards Understanding Life Cycle Saving Of Boundedly Rational Agents: A Model With Feasibility Goals - Replaced by CentER Discussion Paper 2010-138

This paper develops a new life cycle model that aims to describe the savings and asset allocation decisions of boundedly rational agents. The paper’s main theoretical contribution is the provision of a simple, tractable and parsimonious framework within which agents make forward looking decisions in the absence of full contingent planning. Instead, agents pursue two simple so-called feasibility goals. The paper uses this framework to shed light on important empirical patterns of asset allocation that are puzzling from the point of view of existing models.Behavioral economics;bounded rationality;equity shares;feasibility goals;life cycle saving;stock market participation

Constant Proportion Portfolio Insurance Strategies under Cumulative Prospect Theory with Reference Point Adaptation

Constant Proportion Portfolio Insurance (CPPI) is a significant and highly popular investment strategy within the structured product market. This has led to recent work which attempts to explain the popularity of CPPI by showing that it is compatible with Cumulative Prospect Theory (CPT). We demonstrate that this cannot explain the popularity of ratcheted CPPI products which lock-in gains during strong growth in the portfolio. In this paper we conjecture that CPPI investors not only follow CPT, but crucially that they also adapt their reference point over time. This important distinction explains investors preference for ratcheted product

A methodology for the selection of new technologies in the aviation industry

The purpose of this report is to present a technology selection methodology to quantify both tangible and intangible benefits of certain technology alternatives within a fuzzy environment. Specifically, it describes an application of the theory of fuzzy sets to hierarchical structural analysis and economic evaluations for utilisation in the industry. The report proposes a complete methodology to accurately select new technologies. A computer based prototype model has been developed to handle the more complex fuzzy calculations. Decision-makers are only required to express their opinions on comparative importance of various factors in linguistic terms rather than exact numerical values. These linguistic variable scales, such as ‘very high’, ‘high’, ‘medium’, ‘low’ and ‘very low’, are then converted into fuzzy numbers, since it becomes more meaningful to quantify a subjective measurement into a range rather than in an exact value. By aggregating the hierarchy, the preferential weight of each alternative technology is found, which is called fuzzy appropriate index. The fuzzy appropriate indices of different technologies are then ranked and preferential ranking orders of technologies are found. From the economic evaluation perspective, a fuzzy cash flow analysis is employed. This deals quantitatively with imprecision or uncertainties, as the cash flows are modelled as triangular fuzzy numbers which represent ‘the most likely possible value’, ‘the most pessimistic value’ and ‘the most optimistic value’. By using this methodology, the ambiguities involved in the assessment data can be effectively represented and processed to assure a more convincing and effective decision- making process when selecting new technologies in which to invest. The prototype model was validated with a case study within the aviation industry that ensured it was properly configured to meet the

Rules of Thumb in Life-Cycle Saving Decisions

We analyse life-cycle saving decisions when households use simple heuristics, or rules of thumb, rather than solve the underlying intertemporal optimization problem. We simulate life-cycle saving decisions using three simple rules and compute utility losses relative to the solution of the optimization problem. Our simulations suggest that utility losses induced by following simple decision rules are relatively low. Moreover, the two main saving motives re ected by the canonical life-cycle model { long-run consumption smoothing and short-run insurance against income shocks { can be addressed quite well by saving rules that do not require computationally demanding tasks such as backward induction