Choosing what to choose from: Preference for inclusion over exclusion when constructing consideration sets from large choice sets

Decision making is a two‐stage process, consisting of, first, consideration set construction and then final choice. Decision makers can form a consideration set from a choice set using one of two strategies: including the options they wish to further consider or excluding those they do not wish to further consider. The authors propose that decision makers have a relative preference for an inclusion (vs. exclusion) strategy when choosing from large choice sets and that this preference is driven primarily by a lay belief that inclusion requires less effort than exclusion, particularly in large choice sets. Study 1 demonstrates that decision makers prefer using an inclusion (vs. exclusion) strategy when faced with large choice sets. Study 2 replicates the effect of choice set size on preference for consideration set construction strategy and demonstrates that the belief that exclusion is more effortful mediates the relative preference for inclusion in large choice sets. Studies 3 and 4 further support the importance of perceived effort, demonstrating a greater preference for inclusion in large choice sets when decision makers are primed to think about effort (vs. accuracy; Study 3) and when the choice set is perceived as requiring more effort because of more information being presented about each alternative (vs. more alternatives in the choice set; Study 4). Finally, Study 5 manipulates consideration set construction strategy, showing that using inclusion (vs. exclusion) in large choice sets leads to smaller consideration sets, greater confidence in the decision process, and a higher quality consideration set.     

A parameter analysis method for the weight‐set to satisfy preference orders of alternatives in additive multi‐criteria value models

In the context of the additive multi‐criteria value model, this paper investigates how the set of criteria weights (weight‐set hereafter) can be determined according to the preference orders of alternatives given by the decision maker. A construction method is proposed for the weight‐set for different intervals of β, where β is a differential amount of value between the preference information on two alternatives. The results of this paper are important for sensitivity analysis in multi‐criteria decision making (MCDM) problems and multi‐criteria group decision analysis. Copyright © 2000 John Wiley & Sons, Ltd.     

Unstable Interaction in Multiple Criteria Decision Problems

In this paper, we present a decision‐aid method with interacting criteria. The fuzzy measures used to aggregate the criteria depend on the considered alternatives. Pairwise comparisons on the importance of criteria and some levels of veto (or favor) degrees, specified by the decision maker, are taken into consideration. For each alternative, we search for the fuzzy measure that makes it the best. The orness degrees of the resulting fuzzy measures are compared and the alternative associated with the minimum orness degree is selected. Copyright © 2014 John Wiley & Sons, Ltd.     

An Approach to Multi‐Criteria Decision Problems Under Severe Uncertainty

Solving complex decision problems is a demanding task; it requires determining and evaluating the consequences of decision alternatives. To this end, uncertain factors that can only partly be influenced by the decision makers, and their interdependencies need to be considered. Scenarios focus on this part of the decision problem; they enable a systematic exploration of a multitude of possible future developments that are relevant for the decision including external events and decisions made. Scenarios are particularly useful when the problem is pervaded by severe uncertainties that cannot be quantified. For the evaluation of alternatives, multiple objectives and the potentially diverging preferences of the involved actors need to be respected. Multi‐criteria decision analysis aims at structuring the problem, evaluating the alternatives and supporting decision makers pursuing multiple goals. We propose an approach integrating scenarios and multi‐criteria decision analysis that focuses on the robustness of alternatives in complex, dynamic, uncertain and time‐bound situations. In this integrated framework, the scenarios provide the basis for evaluating a set of alternatives. Ideally, the set of scenarios considered captures all possible future developments. To appropriately explore this set, formal or analytical approaches to scenario construction generate a large number of scenarios. This challenges the decision makers' information‐processing capacity. To support them in managing the richness of information, a two‐fold approach that uses selection and aggregation is presented. By using a selection method, the scenarios that are deemed most relevant are identified, and their evaluations are presented in detail to decision makers. This approach is complemented by an aggregation of scenario evaluations on the basis of the decision makers' preferences. We present two approaches to facilitate the preference elicitation process. Copyright © 2013 John Wiley & Sons, Ltd.     

Integration of Analytic Hierarchy Process with Regression Analysis to Identify Attractive Locations for Market Expansion

Selection of new geographies in which to expand is a key decision for businesses aspiring to go beyond the opportunities in the existing markets. The conventional approaches of market selection can only provide a set of systematic steps for problem solving without considering the relationships between the decision factors. Decision models based on statistical techniques are able to examine the relationship between decision factors but are unable to effectively assist decision makers in identifying the most promising market, particularly in terms of prioritizing across decision factors. Analytic Hierarchy Process (AHP) is a commonly used approach for choosing alternatives by prioritizing across multiple decision factors. The typical AHP modelling requires knowledge of criteria and/or alternatives along with their relative weights, generally elicited from field experts. Quite often, firms encounter situations where decision makers are aware of only the overall objective and a set of earmarked geographies for setting up market locations while being relatively unaware of decision criteria and relative weights. This precludes using AHP to identify promising market locations. This paper conceptualizes a market selection decision model that integrates AHP with statistical modelling techniques to identify the attractive market locations for the purpose of expansion. The model first uses principal component analysis and multiple regression to determine significant decision criteria and their weights. Thereafter, it applies AHP to prioritize the market locations across the decision criteria. This integrative approach is illustrated for identifying the attractive locations in rural markets for a steel firm in India. The major advantage of this approach is that unlike the existing models, it works in situations when firms have not enough knowledge about factors for evaluating alternative market locations. Another key advantage of the proposed model is that of economizing resources for data collection on variables representing decision factors. Copyright © 2012 John Wiley & Sons, Ltd.     

The Effect of Decision Strategy on Deciding to Defer Choice

Recent research has examined consumer decision making when the option of not choosing any of the alternatives is also provided. The findings from this research suggest that the decision to defer choice is sensitive to the uncertainty of choosing the most preferred option from the set of alternatives provided. Building on this research, the author tests whether the decision to defer choice is also influenced by task variables that influence decision uncertainty. In the first experiment, this proposition is tested for choice problems in which information on three relatively equally attractive alternatives is presented either sequentially or simultaneously. As predicted, the preference for the defer-choice option was greater when the three alternatives were presented simultaneously. A second study forced subjects into using one of four decision strategies in order to choose between two non-dominated alternatives. The preference for the no-choice option was found to be higher when the rule required explicit attribute tradeoffs and lower when it simplified choice. These results suggest that choice uncertainty is influenced by the decision strategy used to determine the preference among alternatives. We conclude with a discussion of the implications of the results for marketers' communication strategies.     

The modified star graph and the petal diagram: two new visual aids for discrete alternative multicriteria decision making

Many studies have looked into the provisions of visual aids to multicriteria decision making. However, most of them have separated the display of alternative profiles and criteria weight information into two displays. This makes the analysis of the relationship between the criteria and alternatives and the effect of changing the criteria weights on the decision difficult. In this study, displays that can incorporate the display of both alternative profiles and criteria weight information for discrete alternative multicriteria decision-making problems are examined. The simple additive model is the multicriteria analysis method used. The result is two new visual aids for representing data of multicriteria decision-making problems, the modified star graph and the petal diagram. This paper discusses the two displays and compares their strengths and weaknesses. The results of a preliminary test conducted to evaluate the effectiveness of the displays are also included. © 1998 John Wiley & Sons, Ltd.     

Multicriteria approval: application of approval voting concepts to MCDM problems

Multicriteria decision making (MCDM) methods generally require information that is difficult and expensive to obtain. It is shown in this paper that given an ordering of criteria by importance, and whether each alternative is above the average evaluation of the alternatives for each criterion, considerable information can be extracted concerning the overall rankings of the alternatives. This is done by recognizing the similarity between criteria in MCDM, and voters in social choice theory. Multicriteria approval, proposed in this paper, is an MCDM adaptation of approval voting which can be used to classify decisions into one of several categories, depending on whether, and the manner in which, a superior alternative can be identified. An exhaustive examination of all possible decision situations of tractable size is made to measure the applicability of multicriteria approval, and the approach is demonstrated in an industrial procurement case. In addition to requiring minimal information from a decision maker, multicriteria approval satisfies several desirable criteria for MCDM techniques. © 1998 John Wiley & Sons, Ltd.     

Multiattribute Choice for the Lazy Decision Maker: Let the Alternatives Decide!

In this article we present a method of multi-attribute choice, based on an application of linear programming called Data Envelopment Analysis (DEA). The first part of the method, which is straightforward DEA, can be thought of as an idealized process of self-evaluation in which each alternative weights the attributes in order to maximize its own desirability relative to the other alternatives. These weights are taken as defining the preferences of a fragment of the market. The second step is to use each alternative′s optimal weights to reconstruct the entire market and thus to infer the preferences of an average decision maker (DM) who needs to choose from among these alternatives. We show how this process is equivalent to an idealized peer evaluation; each alternative applies its own DEA-derived best weights to each of the other alternatives (alternative cross-evaluation, or AXE), then the average of the cross-evaluations that get placed on an alternative is taken as an index of its overall desirability (i.e., we are "letting the alternatives decide"). We use a large data set to examine the workings of the method and to compare our results with the published results of the data set′s compiler. AXE is also able to make use of partial information about the ordering of importance of the attributes specific to a particular DM. Taking an ecological perspective we argue that the method is sensitive to cues already in the data and uses them to amplify helpful bias and attenuate unhelpful bias. We also show how different kinds sensitivity analysis may be performed to check the robustness of the results.     

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.     

Time pressure and the application of decision rules: Choices and judgments among multiattribute alternatives

The effects of time pressure on decisions and judgments were studied and related to the use of different decision rules in a multiattribute decision task. The decision alternatives were students described by their high school grades in Swedish, Psychology and Natural Science. The subjects were asked to choose the student they thought would be most able to follow a university program and graduate as a school psychologist. On the basis of earlier findings using the same kind of decision task (Svenson et al., 1990) it was hypothesised that subjects under time pressure would prefer candidates having the maximum grade across all attributes to a greater extent than subjects under no time pressure. Furthermore, it was hypothesised that subjects under time pressure would also focus more on the most important attribute and choose the alternatives being best on that attribute. The results supported these hypotheses.     

Numerical vs Cardinal Measurements in Multiattribute Decision Making: How Exact Is Enough?

Multiattribute decision making can involve consideration of both quantitative and qualitative measures of criteria attaintment. Some decision support systems (decision aids) to help multiattribute decision making quantify value functions. One of the most popular of these systems, multiattribute utility theory (MAUT), requires two types of input. Decision makers need to express the relative value of different attainment levels on each criterion, as well as express the relative importance of these criteria. Some systems (such as DECAID) require simple direct graphical input of value and criterion importance. Other systems (such as LOGICAL DECISION) use more complex means of expressing relative value. Either way, MAUT converts expressions of criterion importance into quantitative form. This study compares the relative stability of numerical results obtained through two decision support systems, DECAID and LOGICAL DECISION (LD), used in the task of evaluation of multiattribute alternatives. Additionally the relative stability of results was measured by comparison with results obtained using an ordinal method, ZAPROS. ZAPROS is a decision support system for construction of a partial order over the set of alternatives. It does not require conversion of qualitative measures into quantitative form. The relations among alternatives are close to those based on ordinal dominance. The results of experiments show that ordinal relationships between task parameters are much more stable than those obtained from quantitative measures. Results from DECAID and LD are much less coincident with each other than with results obtained through ZAPROS. Many inconsistencies were found in subject responses. It is concluded that more attention should be given to the means of testing judgment consistency, and that in some cases, attempts to solve decision tasks through more "exact" judgments of value function parameters may lead to erroneous results.     

The Modes of Value

Contrary to the received view, decision theory is not primarily devoted to instrumental (ends-to-means) reasoning. Instead, its major preoccupation is the derivation of ends from other ends. Given preferences over basic alternatives, it constructs preferences over alternatives that have been modified through the addition of value object modifiers (modes) that specify probability, uncertainty, distance in time etc. A typology of the decision-theoretical modes is offered. The modes do not have (even extrinsic) value, but they transform the value of objects to which they are applied. A rational agent's total set of preferences should be coherent, but from this it does not follow that her preferences over mode-containing objects have to be derivable from her preferences over mode-free objects.     

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.     

Attention and attribute overlap in preferential choice

Attributes that are common, or overlapping, across alternatives in two-alternative forced preferential choice tasks are often non-diagnostic. In many settings, attending to and evaluating these attributes does not help the decision maker determine which of the available alternatives is the most desirable. For this reason, many existing behavioural theories propose that decision makers ignore common attributes while deliberating. Across six experiments, we find that decision makers do direct their attention selectively and ignore attributes that are not present in or associated with either of the available alternatives. However, they are as likely to attend to common attributes as they are to attend to attributes that are unique to a single alternative. These results suggest the need for novel theories of attention in preferential choice.     

Reduction of pairwise comparisons in decision making via a duality approach

Although pairwise comparisons have been seen by many as an effective and intuitive way for eliciting qualitative data for multi‐criteria decision making problems, a major drawback is that the number of the required comparisons increases quadratically with the number of the entities to be compared. Thus, often even data for medium size decision problems may be impractical to be elicited via pairwise comparisons. The more the comparisons are, the higher is the likelihood that the decision maker will introduce erroneous data. This paper introduces a dual formulation to a given multi‐criteria decision making problem, which can significantly alleviate the previous problems. Some theoretical results establish that this is possible when the number of alternatives is greater than the number of decision criteria plus one. Copyright © 1999 John Wiley & Sons, Ltd.     

航空决策模型及其影响因素分析

航空决策指在航空飞行驾驶过程中,飞行员面对特定情境做出最佳决策时的心理过程。情景评估和风险评估是航空决策模型中最重要的两个阶段。个体认知因素、疲劳和动机因素以及知识经验的限制会导致不良航空决策,组织压力和社会因素则通过混淆飞行员对飞行安全的看法间接影响航空决策。在未来的研究中,研究者应当重视构建更合理的航空决策模型,从辩证的角度全面看待知识经验与飞行安全的关系,加强对动机因素和环境因素的调查分析和实验性研究。     

Probabilistic Subset Conjunction

The authors introduce subset conjunction as a classification rule by which an acceptable alternative must satisfy some minimum number of criteria. The rule subsumes conjunctive and disjunctive decision strategies as special cases. Subset conjunction can be represented in a binary-response model, for example, in a logistic regression, using only main effects or only interaction effects. This results in a confounding of the main and interaction effects when there is little or no response error. With greater response error, a logistic regression, even if it gives a good fit to data, can produce parameter estimates that do not reflect the underlying decision process. The authors propose a model in which the binary classification of alternatives into acceptable/unacceptable categories is based on a probabilistic implementation of a subset-conjunctive process. The satisfaction of decision criteria biases the odds toward one outcome or the other. The authors then describe a two-stage choice model in which a (possibly large) set of alternatives is first reduced using a subset-conjunctive rule, after which an alternative is selected from this reduced set of items. They describe methods for estimating the unobserved consideration probabilities from classification and choice data, and illustrate the use of the models for cancer diagnosis and consumer choice. They report the results of simulations investigating estimation accuracy, incidence of local optima, and model fit. The authors thank the Editor, the Associate Editor, and three anonymous reviewers for their constructive suggestions, and also thank Asim Ansari and Raghuram Iyengar for their helpful comments. They also thank Sawtooth Software, McKinsey and Company, and Intelliquest for providing the PC choice data, and the University of Wisconsin for making the breast-cancer data available at the machine learning archives.     

Information search and mental representation in risky decision making: The advantages first principle

In three experiments the problem is investigated how people identify early in the decision process those alternatives that are worthwhile to be examined in more detail. We assume that decision makers employ the Advantages first Principle: They first search for information about positive outcomes and then focus their information search (e.g., for negative consequences or for risk defusing operators) on those alternatives that appear attractive after this initial evaluation. In Experiment 1 (120 participants), initial information about consequences was varied for eight alternatives (no information, positive consequences, negative, or mixed for four alternatives). In all conditions, the great majority of participants followed both aspects of the Advantages first Principle. In Experiment 2, 60 participants decided in two quasi‐realistic scenarios with two alternatives each. Initial information was presented so that one alternative had better positive consequences, worse negative consequences, or both. In all conditions, more information was searched for in the initially better alternative. In Experiment 3 (20 participants) the Advantages first Principle was not only confirmed for a scenario but also for choices in traditional gambling tasks with two and eight alternatives, respectively. Participants could win or lose real money. Copyright © 2010 John Wiley & Sons, Ltd.