Multi-attribute decision support and complexity: An evaluation and process analysis of aided versus unaided decision making

The present study addresses the effectiveness of a computerized decision aid (DECAID, Pitz 1987) based on the Multi-Attribute Utility Model (MAU). The effectiveness of DECAID was investigated for personnel selection problems varying in complexity (number of alternatives and number of attributes). Results show that the decision aid was most successful for problems of medium complexity. DECAID significantly affected the thoroughness of the decision, preference formation, and the perceived difficulty of the decision problem for medium complex problems. This was not the case for highly complex problems. Next, a process analysis is presented of unaided decision making focusing on the selection and evaluation of information. This process analysis of verbal protocols was based on the same decision problems as the first study. Results indicate that problem complexity had a significant impact on the decision process. Increasing complexity resulted in a less thorough evaluation, and selective information processing. Subjects tended to focus on one or two most promising alternatives. Furthermore, evaluation strategies diverged from those used in DECAID in terms of standard of comparison. The paper concludes with implications for the design of decision aids.     

The Effectiveness of Partial Information about Attribute Weights for Ranking Alternatives in Multiattribute Decision Making

Weighted additive evaluation functions are widely used to rank alternatives in decision making under certainty with multiple evaluation attributes. Some researchers have suggested that approximate attribute weights may be adequate to accurately rank alternatives. Use of approximate weights would simplify decision analysis since detailed elicitation of weights can be time consuming and controversial. This article investigates the degree to which partial information about the relative magnitudes of attribute weights is sufficient to rank alternatives as a function of the number of decision alternatives, the number of attributes, and the number of allowed levels for each attribute. A simulation analysis, as well as a reanalysis of an actual application, shows that partial information about weights is often not sufficient to determine the most preferred alternative for realistic decision problems. Hence, approximation procedures for specifying weights may lead to errors. However, our work also shows that a simple analysis procedure can be used to accurately determine whether partial information about weights is adequate to correctly specify the most preferred alternative. This procedure can be useful for identifying situations in which detailed elicitation of weights is not needed.     

Individual differences in the use of simplification strategies in a complex decision-making task

Decision time results were used to assess the strategies that 90 college undergraduates used in a complex decision-making task. Trend analyses revealed that the functions relating choice time to the number of choice alternatives in a set and the number of attributes comprising those alternatives contained linear (increasing) components. In addition, for a portion of the subjects, there was a quadratic effect of the number of attributes available to the decision maker on choice time, suggesting that these subjects adopted simplification strategies at high levels of task complexity. Reliable individual differences in these trend components were observed, consistent with individual differences in motivation and/or processing capacities. These individual differences were included in an information-processing model of decision behavior that captured the choice time data observed in this study. Subjects' ratings of apartments were used as a basis to assess the extent to which the use of simplification strategies resulted in preference reversals. Contrary to expectation, subjects whose choice times contained quadratic components demonstrated fewer preference reversals at high levels of information load.     

A Psychometric Analysis of the “Divide and Conquer” Principle in Multicriteria Decision Making

The principle of “Divide and Conquer” (DAC) suggests that: (1) complex decision problems should be decomposed into smaller, more manageable parts and (2) these smaller parts should be logically aggregated to derive an overall value for each alternative. Typically, decompositional procedures have been compared to holistic evaluations that require decision makers to simultaneously consider all of the relevant attributes in the evaluation of the objects under consideration. These comparisons between decompositional and holistic judgments have primarily used a variant of Multiattribute Utility Theory (MAUT).We presented a general experimental framework that allows for a more extensive assessment of the DAC principle, as well as the effects of decision complexity on both holistic and decompositional procedures. We illustrate this approach with a study that uses the Simple Multiattribute Rating Technique with Swing Weights (SMARTS; Edwards & Barron, 1994) and the Analytic Hierarchy Process (AHP; Saaty, 1980). We report data comparing the convergent validity (e.g the agreement between decompositional and holistic strategies) and the temporal stability for decompositional and holistic judgments on a variety of dependent measures. Decision complexity did not significantly affect the correspondence between decompositional and holistic judgments for both SMARTS and AHP judgments. Results from an ordinal measure of temporal stability indicated the DAC principle was violated for the AHP judgments. For a linear measure of temporal stability, trends in the data indicated that the predicted effects of decision complexity on the DAC principle was violated for the SMARTS judgments.     

Euclid: Strategic alternative assessment matrix

The vast amount of information that must be considered to solve inherently ill‐structured and complex strategic problems creates a need for tools to help decision makers (DMs) recognize the complexity of this process and develop a rational model for strategy evaluation. Over the last several decades, a philosophy and a body of intuitive and analytical methods have been developed to assist DMs in the evaluation of strategic alternatives. However, the intuitive methods lack a structured framework for the systematic evaluation of strategic alternatives while the analytical methods are not intended to capture intuitive preferences. Euclid is a simple and yet sophisticated multiobjective value analysis model that attempts to uncover some of the complexities inherent in the evaluation of strategic alternatives. The proposed model uses a series of intuitive and analytical methods including environmental scanning, the analytic hierarchy process (AHP), subjective probabilities, and the theory of displaced ideal, to plot strategic alternatives on a matrix based on their Euclidean distance from the ideal alternative. Euclid is further compared to the quantitative strategic planning matrix (QSPM) in a real world application. The information provided by the users shows that Euclid can significantly enhance decision quality and the DM's confidence. Euclid is not intended to replace the DMs, rather, it provides a systematic approach to support, supplement, and ensure the internal consistency of their judgments through a series of logically sound techniques. Copyright © 2003 John Wiley & Sons, Ltd.     

Information search and decision making: The effects of information displays

Information display boards may be used to test assumptions about underlying cognitive processes in decision situations. However, the question may be raised whether information processing in a complex decision situation is indepemdent of the structure of the information display board used. In a study employing 96 subjects, the design of the information display board, the number of alternatives, and the number of attributes were varied. Twelve groups of eight subjects were presented with one hypothetical choice among riskless multiattribute decision alternatives (apartments). Results showed that information search patterns were not influenced by the design of the information display board, but that the relative number of attributes included in the decision process was. The number of available attributes appeared to be the strongest determinant of information search patterns. A higher number of attributes induced information search patterns consistent with the noncompensatory conjunctive decision rule. It is suggested that the decision situation employed, which forced the decision maker to remember each piece of information acquired, underlies the difference between the results in the present study and previous findings.     

An Evaluation Study of the Effectiveness of Multi-attribute Decision Support as a Function of Problem Complexity

This study focuses on the evaluation of multi-attribute (MAU) decision support for problems differing in complexity. In this study we compared decision making with a computerized decision support system (DECAID) with the unaided process. Overall, results indicate that MAU support led to a more thorough decision process, had more impact on preference formation, and led to a larger decrease of perceived difficulty of the decision problem as compared to the no-support condition. The MAU decision aid turned out to be less lime efficient than the unaided mode, especially with increasing numbers of alternatives. Results suggest that the MAU decision aid is effective for problems of low and moderate complexity only. For highly complex problems, results are less favorable.     

An Optimized Design of Choice Experiments: A New Approach for Studying Decision Behavior in Choice Task Experiments

In this paper, we present a new approach for the optimal experimental design problem of generating diagnostic choice tasks, where the respondent's decision strategy can be unambiguously deduced from the observed choice. In this new approach, we applied a genetic algorithm that creates a one‐to‐one correspondence between a set of predefined decision strategies and the alternatives of the choice task; it also manipulates the characteristics of the choice tasks. In addition, this new approach takes into account the measurement errors that can occur when the preferences of the decision makers are being measured. The proposed genetic algorithm is capable of generating diagnostic choice tasks even when the search space of possible choice tasks is very large. As proof‐of‐concept, we used this novel approach to generate respondent‐specific choice tasks with either low or high context‐based complexity that we operationalize by the similarity of alternatives and the conflict between alternatives. We find in an experiment that an increase in the similarity of the alternatives and an increase in the number of conflicts within the choice task lead to an increased use of non‐compensatory strategies and a decreased use of compensatory decision strategies. In contrast, the size of the choice tasks, measured by the number of attributes and alternatives, only weakly influences the strategy selection. Copyright © 2014 John Wiley & Sons, Ltd.     

Down the Garden Path: The Role of Conditional Inference Processes in Self‐Persuasion

In a controlled experiment, information format and prior knowledge of risk‐related decision attributes altered the response to decision conflict produced by negatively correlated attributes (NCA) in choice under uncertainty. When dominated choice alternatives were presented in a format in which the displayed attributes were not negatively correlated, decision makers used a simple choice process based on the displayed information. This allowed them to detect the dominated alternatives, regardless of their prior knowledge. However, when the same alternatives were presented in a format in which the displayed attributes were negatively correlated, the more‐knowledgeable decision makers used a compensatory choice process based on the displayed attributes and, consequently, missed the dominated alternatives. The less‐knowledgeable participants, who were unable to use the displayed attributes because of their lack of expertise, calculated the nondisplayed attributes and detected the dominated alternatives. With NCA, the more‐knowledgeable participants followed a conflict‐resolution strategy using the displayed NCAs, whereas the less‐knowledgeable individuals followed a conflict‐avoidance strategy using the uncorrelated, nondisplayed attributes that inadvertently revealed the dominance structure of the decision. The implications of person factors for the effects of NCA and in other types of decisions with NCA are discussed.     

Limits to decision-making ability in direct multiattribute alternative evaluation

In recent years there has been considerable interest in how decision strategies in choice problems depend on such characteristics as the number of available criteria and the number of available alternatives. Along with multicriteria choice problems, classification tasks are treated where alternatives are classified into several decision (evaluative) classes. We believe that in classification problems one's ability in information processing is limited and depends on task complexity. In this paper we describe two experiments involving categorization of a large number of multiattribute alternatives into two or four evaluative classes. The results demonstrate that the subjects use different decision strategies and that there is a limit in human ability to implement these two tasks.     

Political ideology and labor arbitrators' decision making in work-family conflict cases

Labor arbitrators were asked to render decisions about grievances brought by employees who had been fired because of problems created by work conflicts with family responsibilities. The study examined the effects of experimentally manipulated grievant attributes (gender, type of work-family conflict) as well as arbitrator attributes (gender, political ideology) on decision making. When employees were depicted as having had child care problems, liberal arbitrators tended to favor female over male grievants, and political conservatism predicted more favorable judgments rendered toward male grievants. Overall, the data suggest that child care responsibilities cue different patterns of gender bias among liberal and conservative decision makers.     

Less may be more when choosing is difficult: Choice complexity and too much choice

Although consumers readily seek choice and abundance, the so-called too-much-choice effect suggests that having many alternatives to choose from eventually leads to negative consequences, such as decreased post-choice satisfaction. The present research extends this research by highlighting the role of choice complexity. It is argued that too-much-choice effects are associated with choice complexity, which is influenced not only by the number of alternatives, but also by other features of the choice set, such as the number of attributes that alternatives are differentiated upon. These other influences of choice complexity may propel or hinder the emergence of too-much-choice effects. Two experiments tested this hypothesis by orthogonally manipulating the number of alternatives and the number of attributes. Results revealed a too-much-choice effect when alternatives were differentiated on many attributes, but not when alternatives were differentiated on few attributes. Implications for theory and practice are discussed.     

Relative and absolute duration judgments under prospective and retrospective paradigms

A dual-process contingency model of short duration judgment is proposed and tested. The first process, or P(t), is a timer that uses cognitive capacity to keep track of units of time. If capacity is directed toward other tasks, P(t) will record fewer units and produce lower time judgments than when capacity is not directed toward other tasks. This timing process is most likely to affect performance when people know in advance (prospective judgments) that time judgments will be required and when absolute, rather than relative, judgments are made. The second process, or P(m), which is used for retrospective and relative judgments, judges duration on the basis of the number of remembered high priority events (HPEs) occurring during the interval. When this process is used, time judgments increase with the amount of HPEs that can be retrieved at the moment of judgment. Two experiments are reported. Tactual stimuli were presented, and nontemporal information processing load (simple or complex stimuli), type of judgment (absolute or relative), and judgment paradigm (prospective or retrospective)were manipulated. The results obtained support the proposed dual-process contingency model.     

Risk strategy under task complexity: a multivariate analysis of behaviour in a naturalistic setting

This study complements the existing literature on decision-making processes and outcomes in complex settings by exploring the impact of different types of complexity on risk strategies in a naturalistic setting. The study analyses a large sample of decisions made by individuals in UK offcourse betting markets, a fertile environment for observing both a variety of risk strategies and a range of task complexities. Specifically, the investigation focuses on the comparative impacts of complexity defined in terms, respectively, of alternatives and attributes. The results suggest that the risk strategy employed is affected by task complexity. Complexity does not affect the size of risk accepted but alternative- and attribute-based complexity together influence the propensity to accept greater degrees of risk. In addition, the effect of attribute-based complexity on risk taking appears to be modified by the use of risk-hedging strategies. The results are observed to corroborate some earlier work on decision process and outcome; where differences with earlier findings are identified, some possible explanations are offered. © 1998 John Wiley & Sons, Ltd.     

Generating consensus priority interval vectors for group decision‐making in the AHP

Many practical and important decision‐making problems are complicated by at least two factors: (1) the qualitative/subjective nature of some criteria often results in uncertainty in the individual ratings; and (2) group decision‐making is involved and some means of aggregating individual ratings is required. Traditionally, both individual and group priorities have been represented as point estimates, but this approach presents severe limitations for accommodating imprecision in the decision‐making process. This paper examines the group decision‐making problem in the context where priorities are represented as numeric intervals. A set of techniques that could be used at some of the phases of an analytic hierarchy process (AHP)‐based group decision‐making process, which has the objective of generating a ‘consensus’ priority that represents the group's opinion with regards to the relative importance of a set of N objects (e.g. criteria, alternatives), is presented. Copyright © 2000 John Wiley & Sons, Ltd.     

Random error reduction in analytic hierarchies: a comparison of holistic and decompositional decision strategies

The principle of ‘divide and conquer’ (DAC) suggests that complex decision problems should be decomposed into smaller, more manageable parts, and that these parts should be logically aggregated to derive an overall value for each alternative. Decompositional procedures have been contrasted with holistic evaluations that require decision makers to simultaneously consider all the relevant attributes of the alternatives under consideration (Fischer, 1977 ). One area where decompositional procedures have a clear advantage over holistic procedures is in the reduction of random error (Ravinder, 1992 ; Ravinder and Kleinmuntz, 1991 ; Kleinmuntz, 1990 ). Adopting the framework originally developed by Ravinder and colleagues, this paper details the results of a study of the random error variances associated with another popular multi‐criteria decision‐making technique, the Analytic Hierarchy Process (AHP); (Saaty, 1977 , 1980 ), as well as the random error variances of a holistic version of the Analytic Hierarchy Process (Jensen, 1983 ). In addition, data concerning various psychometric properties (e.g. the convergent validity and temporal stability) and values of AHP inconsistency are reported for both the decompositional and holistic evaluations. The results of the study show that the Ravinder and Kleinmuntz ( 1991 ) error‐propagation framework extends to the AHP and decompositional AHP judgments are more consistent than their holistic counterparts. Copyright © 2001 John Wiley & Sons, Ltd.     

The Characteristic Objects Method: A New Distance‐based Approach to Multicriteria Decision‐making Problems

Multicriteria decision‐making (MCDM) methods are concerned with the ranking of alternatives based on expert judgements made using a number of criteria. In the MCDM field, the distance‐based approach is one popular method for obtaining a final ranking. The technique for order preference by similarity to the ideal solution (TOPSIS) is a commonly used example of this kind of MCDM method. The TOPSIS ranks the alternatives with respect to their geometric distance from the positive and negative ideal solutions. Unfortunately, two reference points are often insufficient, especially for nonlinear problems. As a consequence of this situation, the final result ranking is prone to errors, including the rank reversals phenomenon. This study proposes a new distance‐based MCDM method: the characteristic objects method. In this approach, the preferences of each alternative are obtained on the basis of the distance from the nearest characteristic objects and their values. For this purpose, we have determined the domain and Fuzzy number set for all the considered criteria. The characteristic objects are obtained as the combination of the crisp values of all the Fuzzy numbers. The preference values of all the characteristic object are determined on the basis of the tournament method and the principle of indifference. Finally, the Fuzzy model is constructed and is used to calculate preference values of the alternatives, making it a multicriteria model that is free of rank reversal. The numerical example is used to illustrate the efficiency of the proposed method with respect to results from the TOPSIS method. The characteristic objects method results are more realistic than the TOPSIS results. Copyright © 2014 John Wiley & Sons, Ltd.