Extending the MAD Portfolio Optimization Model to Incorporate Downside Risk Aversion

The mathematical model of portfolio optimization is usually expected as a bicriteria optimization problem where a reasonable trade-off between expected rate of return risk is sought. In a classical Markowitz model the risk is measured by a variance, thus resulting in a quadratic programming model. As an alternative, the MAD model was proposed where risk is measured by (mean) absolute deviation instead of a variance. The MAD model is computationally attractive, since it is transformed into an easy to solve linear programming program. In this paper we present an extension to the MAD model allowing to account for downside risk aversion of an investor, and at the same time preserving simplicity and linearity of the original MAD model

Psychological Stability of Solutions in the Multiple Criteria Decision Problems

In interactive programming, a choice behavior of the decision maker may differ depending on a proximity of current solution to satisfactory values of the objectives. An interactive approach proposed in this paper allows the decision maker to use different search principles depending on his/her perception of the achieved values of the objectives and trade-offs. While an analysis of values of the objectives may guide the initial search for a final solution, it can be replaced by trade-off evaluations at some later stages of interactive decision making. Such an approach allows the decision maker to change search principles, and to identify a psychologically stable solution of the multiple criteria decision problem

Establishing Regret Attitude of a Decision Maker within the MCDM Modeling Framework

The paper describes the MCDM modeling framework can be extended to account for the notion of regret. The Non-regrettable decisions are generated in accordance with a DM's regret attitude which is established through an analysis of the trade-offs. Decisional validity of a proposed modeling framework is illustrated with a simple example

Using Trade-off Information in Attributes' Investing

The paper describes the use of trade-off information to create effective stock portfolio characterized by the desired values of selected stock attributes. The basic notions behind such a process of portfolio creation are discussed and related to multi attribute analysis which is done by evaluating compensations among the attributes' values. A framework to construct a portfolio using only compensatory information is presented and applied to the analysis of stocks traded on the Toronto Stock Exchange

Dimensional Consistency Analysis in Complex Algebraic Models

Relations in complex algebraic models include numerous variables and parameter that capture the physical dimensions of the objects represented in models (such as "mass", or "volume" of an object). A model developer must ensure the semantic correctness of the model, which includes consistency across physical dimensions and their units of measure in the model relations. Such dimensional consistency analysis is the subject of the research described in this paper. We propose a new methodological framework for this type of analysis which comprises: - a two-level structure for representing knowledge about physical dimensions and units of measure; and - the dimensional analysis algorithm that uses this structured knowledge for the verification of consistency. The proposed methodology allows us to resolve issues related to handling complex non-decomposable units of measure and the situation when instances of the same physical dimension are associated with different physical quantities. We illustrate the proposed methodological framework using mathematical relations from a comprehensive environmental model developed at IIASA

Using PICO to Align Medical Evidence with MDs Decision Making Models

Modern medicine is characterized by an “explosion” in clinical research information making practical application of Evidence-Based Medicine (EBM), problematic for many clinicians. We have developed a PICO-(evidence based search strategy focusing on Patient/Population, Intervention, Comparison and Outcome)-based framework for (indexing and retrieving medical evidence and we posit that the use of PICO allows for organizing evidence that is aligned with an MD’s decision making model. We describe a study where medical students evaluated our PICO-based approach and results show that students are eager to apply EBM but are hindered by a lack of specialist skills. Students reported that the PICO-based framework for organizing evidence provided an intuitive way of accessing and evaluating evidence and would be useful for their clinical tasks

A recursive procedure for selecting optimal portfolio according to the MAD model

The mathematical model of portfolio optimization is usually represented as a bicriteria optimization problem where a reasonable trade-off between expected rate of return and risk is sought. In a classical Markowitz model the risk is measured by a variance, thus resulting in a quadratic programming model. As an alternative, the MAD model was proposed where risk is measured by (mean) absolute deviation instead of a variance. The MAD model is computationally attractive, since it is transformed into an easy to solve linear programming program. In this paper we present a recursive procedure which allows to identify optimal portfolio of the MAD model depending on investor's downside risk aversion

Clinical decision support system for point of care use--ontology-driven design and software implementation

OBJECTIVES: The objective of this research was to design a clinical decision support system (CDSS) that supports heterogeneous clinical decision problems and runs on multiple computing platforms. Meeting this objective required a novel design to create an extendable and easy to maintain clinical CDSS for point of care support. The proposed solution was evaluated in a proof of concept implementation. METHODS: Based on our earlier research with the design of a mobile CDSS for emergency triage we used ontology-driven design to represent essential components of a CDSS. Models of clinical decision problems were derived from the ontology and they were processed into executable applications during runtime. This allowed scaling applications' functionality to the capabilities of computing platforms. A prototype of the system was implemented using the extended client-server architecture and Web services to distribute the functions of the system and to make it operational in limited connectivity conditions. RESULTS: The proposed design provided a common framework that facilitated development of diversified clinical applications running seamlessly on a variety of computing platforms. It was prototyped for two clinical decision problems and settings (triage of acute pain in the emergency department and postoperative management of radical prostatectomy on the hospital ward) and implemented on two computing platforms--desktop and handheld computers. CONCLUSIONS: The requirement of the CDSS heterogeneity was satisfied with ontology-driven design. Processing of application models described with the help of ontological models allowed having a complex system running on multiple computing platforms with different capabilities. Finally, separation of models and runtime components contributed to improved extensibility and maintainability of the system

Identification of Biodiversity and Other Forest Attributes for Sustainable Forest Management: Siberian Forest Case Study

This paper attempts to identify characteristics of biodiversity and other (forest) ecosystem conditions that are considered essential for a description of ecosystem functioning and development of sustainable forest management practices in the Siberian forests. This is accomplished through an analysis of net primary production of phytomass (NPP) which acts as a proxy for ecosystem functioning. Rough Sets (RS) analysis is applied to study the Siberian ecoregions classified into compact and cohesive NPP performance classes. Through a heuristic procedure, a reduced set of attributes is generated for a NPP classification problem. In order to interpret relationships between various forest characteristics, so-called "interesting rules" are generated on a basis of reduced problem description. These "interesting rules" provide means to draw conclusions in the form of knowledge statements about functioning of the Siberian forests