Raimo P. Hämäläinen

On the measurement of preferences in the analytic hierarchy process

In this paper we apply multiattribute value theory as a framework for examining the use of pairwise comparisons in the analytic hierarchy process (AHP). On one hand our analysis indicates that pairwise comparisons should be understood in terms of preference differences between pairs of alternatives. On the other hand it points out undesirable effects caused by the upper bound and the discretization of any given ratio scale. Both these observations apply equally well to the SMART procedure which also uses estimates of weight ratios. Furthermore, we demonstrate that the AHP can be modified so as to produce results similar to those of multiattribute value measurement; we also propose new balanced scales to improve the sensitivity of the AHP ratio scales. Finally we show that the so-called supermatrix technique does not eliminate the rank reversal phenomenon which can be attributed to the normalizations in the AHP.

On the convergence of multiattribute weighting methods

Abstract The convergent validity of five multiattribute weighting methods is studied in an Internet experiment. This is the first experiment where the subjects created the alternatives and attributes themselves. Each subject used five methods to assess attribute weights – one version of the analytic hierarchy process (AHP), direct point allocation, simple multiattribute rating technique (SMART), swing weighting, and tradeoff weighting. They can all be used following the principles of multiattribute value theory. Furthermore, SMART, swing, and AHP ask the decision makers to give directly the numerical estimates of weight ratios although the elicitation questions are different. In earlier studies these methods have yielded different weights. Our results suggest that the resulting weights are different because the methods explicitly or implicitly lead the decision makers to choose their responses from a limited set of numbers. The other consequences from this are that the spread of weights and the inconsistency between the preference statements depend on the number of attributes that a decision maker considers simultaneously.

How to benefit from decision analysis in environmental life cycle assessment (LCA)

Environmental life cycle assessment (LCA) is one method to support environmental information needs by multi-attribute product evaluations. LCA describes the environmental effects associated with a product, process or activity over its whole life cycle by calculating the material and energy requirements as well as emissions to air, water and soil and by assessing the environmental impacts of those. An LCA study has both objective and subjective steps. So far, LCA has been developed without much consideration of the literature on decision modelling. We want to show that approaches and tools from decision analysis would be beneficial both in the planning of an LCA study and in the interpretation and understanding of the results. We describe the LCA methodology and application, and discuss how the integration of decision analysis and LCA could improve LCA as a tool for decision making. We use an LCA study on beverage packaging systems to illustrate the new approach.

Preference programming through approximate ratio comparisons

Abstract In the context of hierarchical weighting, this paper operationalizes interval judgments which allow the decision maker to enter ambiguous preference statements by indicating the relative importance of factors as intervals of values on a ratio scale. Through such judgments the decision maker can capture the subjective uncertainty in his preferences and thus avoid the often cumbersome elicitation of exact ratio estimates. After each new statement the interval judgments are synthesized into dominance relations on the alternatives by solving a series of linear programming problems. This leads to an interactive process of preference programming which provides more detailed results as the decision maker gradually enters a more specific preference description. Moreover, the overall effort of preference elicitation is smaller than in the analytic hierarchy process because the most preferred alternative can usually be identified before all possible comparisons between pairs of factors have been completed.

Preference assessment by imprecise ratio statements

The PAIRS method developed in this paper introduces imprecise preference statements into value trees. The assessment of attribute weights in PAIRS extends the well known SMART technique so that in addition to exact statements the decision maker can enter interval judgments which indicate ranges for the relative importance of the attributes. The interval judgments and the possibly range-valued information about the outcomes of the alternatives are processed with linear programming into value intervals and dominance relations. As the decision maker refines the description of his preferences, either by entering new statements or by tightening his earlier judgments, these results become more detailed and convey more information about which alternatives are preferred. Throughout the interactive refinement process PAIRS supports the decision maker by deriving and displaying the consequences of his earlier judgments.

Web-HIPRE: Global decision support by value tree and AHP analysis

Abstract Web-HIPRE is a Java applet for multiple criteria decision analysis. Being located on the WWW, it can be accessed from everywhere in the world. This has opened up a completely new era and dimension in decision support. Web-HIPRE provides a common platform for individual and group decision making. The models can be processed at the same or at different times and the results can be easily shared and combined. There is a possibility to define links to other WWW addresses. These links can refer to any other kind of information such as graphics, sound or video describing the criteria or alternatives. This can improve the quality of decision support dramatically. The most common weighting methods including AHP, SMART, SWING, SMARTER and value functions are supported. Web-HIPRE is located on http://www.hipre.hut.fi/

Preference ratios in multiattribute evaluation (PRIME)-elicitation and decision procedures under incomplete information

This paper presents the preference ratios in multiattribute evaluation (PRIME) method which supports the analysis of incomplete information in multiattribute weighting models. In PRIME, preference elicitation and synthesis is based on 1) the conversion of possibly imprecise ratio judgments into an imprecisely specified preference model, 2) the use of dominance structures and decision rules in deriving decision recommendations, and 3) the sequencing of the elicitation process into a series of elicitation tasks. This process may be continued until the most preferred alternative is identified or, alternatively, stopped with a decision recommendation if the decision maker is prepared to accept the possibility that the value of some other alternative is higher. An extensive simulation study on the computational properties of PRIME is presented. The method is illustrated with a re-analysis of an earlier case study on international oil tanker negotiations.

Participatory multicriteria decision analysis with Web-HIPRE: a case of lake regulation policy

Environmental decision making typically concerns several stakeholders with conflicting views. Multicriteria decision analysis provides transparent ways to elicit and communicate individual preferences. When the stakeholders clearly understand each other’s views, a consensus can be reached more easily. Computer software provides a substantial enhancement to support participatory decision making processes, for example, in the preference elicitation and in the analysis of the results. In this paper, we describe the first web-based multicriteria decision support software called Web-HIPRE, and the use of it in participatory environmental modelling. The world wide web provides new possibilities to support the process, for example, by allowing distributed decision support. The stakeholders can be located in different geographical areas, especially in environmental problems. We illustrate the

On the importance of behavioral operational research: The case of understanding and communicating about dynamic systems

We point out the need for Behavioral Operational Research (BOR) in advancing the practice of OR. So far, in OR behavioral phenomena have been acknowledged only in behavioral decision theory but behavioral issues are always present when supporting human problem solving by modeling. Behavioral effects can relate to the group interaction and communication when facilitating with OR models as well as to the possibility of procedural mistakes and cognitive biases. As an illustrative example we use well known system dynamics studies related to the understanding of accumulation. We show that one gets completely opposite results depending on the way the phenomenon is described and how the questions are phrased and graphs used. The results suggest that OR processes are highly sensitive to various behavioral effects. As a result, we need to pay attention to the way we communicate about models as they are being increasingly used in addressing important problems like climate change.

Decision support by interval SMART/SWING - incorporating imprecision in the SMART and SWING methods

Interval judgments are a way of handling preferential and informational imprecision in multicriteria decision analysis (MCDA). In this article, we study the use of intervals in the simple multiattribute rating technique (SMART) and SWING weighting methods. We generalize the methods by allowing the reference attribute to be any attribute, not just the most or the least important one, and by allowing the decision maker to reply with intervals to the weight ratio questions to account for his/her judgmental imprecision. We also study the practical and procedural implications of using imprecision intervals in these methods. These include, for example, how to select the reference attribute to identify as many dominated alternatives as possible. Based on the results of a simulation study, we suggest guidelines for how to carry out the weighting process in practice. Computer support can be used to make the process visual and interactive. We describe the WINPRE software for interval SMART/SWING, preference assessment by imprecise ratio statements (PAIRS), and preference programming. The use of interval SMART/SWING is illustrated by a job selection example.