Multiknowledge for decision making

The representation of knowledge has an important effect on automated decision-making. In this paper, vector spaces are used to describe a condition space and a decision space, and knowledge is represented by a mapping from the condition space to the decision space. Many such mappings can be obtained from a training set. A set of mappings, which are created from multiple reducts in the training set, is defined as multiknowledge. In order to get a good reduct and find multiple reducts, the WADF (worst-attribute-drop-first) algorithm is developed through analysis of the properties of decision systems using rough set theory. An approach that combines multiknowledge and the naïve Bayes classifier is applied to make decisions for unseen instances or for instances with missing attribute values. Benchmark data sets from the UCI Machine Learning Repository are used to test the algorithms. The experimental results are encouraging; the prediction accuracy for unseen instances by using the algorithms is higher than by using other approaches based on a single body of knowledge.     

A model based on linguistic 2-tuples for dealing with multigranularhierarchical linguistic contexts in multi-expert decision-making

In those problems that deal with multiple sources of linguistic information we can find problems defined in contexts where the linguistic assessments are assessed in linguistic term sets with different granularity of uncertainty and/or semantics (multigranular linguistic contexts). Different approaches have been developed to manage this type of contexts, that unify the multigranular linguistic information in an unique linguistic term set for an easy management of the information. This normalization process can produce a loss of information and hence a lack of precision in the final results. In this paper, we shall present a type of multigranular linguistic contexts we shall call linguistic hierarchies term sets, such that, when we deal with multigranular linguistic information assessed in these structures we can unify the information assessed in them without loss of information. To do so, we shall use the 2-tuple linguistic representation model. Afterwards we shall develop a linguistic decision model dealing with multigranular linguistic contexts and apply it to a multi-expert decision-making problem.     

Induction of multiple fuzzy decision trees based on rough set technique

The integration of fuzzy sets and rough sets can lead to a hybrid soft-computing technique which has been applied successfully to many fields such as machine learning, pattern recognition and image processing. The key to this soft-computing technique is how to set up and make use of the fuzzy attribute reduct in fuzzy rough set theory. Given a fuzzy information system, we may find many fuzzy attribute reducts and each of them can have different contributions to decision-making. If only one of the fuzzy attribute reducts, which may be the most important one, is selected to induce decision rules, some useful information hidden in the other reducts for the decision-making will be losing unavoidably. To sufficiently make use of the information provided by every individual fuzzy attribute reduct in a fuzzy information system, this paper presents a novel induction of multiple fuzzy decision trees based on rough set technique. The induction consists of three stages. First several fuzzy attribute reducts are found by a similarity based approach, and then a fuzzy decision tree for each fuzzy attribute reduct is generated according to the fuzzy ID3 algorithm. The fuzzy integral is finally considered as a fusion tool to integrate the generated decision trees, which combines together all outputs of the multiple fuzzy decision trees and forms the final decision result. An illustration is given to show the proposed fusion scheme. A numerical experiment on real data indicates that the proposed multiple tree induction is superior to the single tree induction based on the individual reduct or on the entire feature set for learning problems with many attributes.     

概率区间值直觉犹豫模糊Maclaurin对称平均算子及决策方法

在概率区间值直觉犹豫模糊集(PIVIHFS)的基础上,引入Maclaurin对称平均算子和Archimedean范数,构建一种基于概率区间值直觉犹豫模糊Maclaurin对称平均(PIVIHFMSM)算子的多属性决策模型,用来刻画决策专家输入多个参数值的决策信息,决策者可根据风险偏好等主观意识选择合适的参数值进行决策,同时能保证决策信息的有效性和完整性,避免决策过程中的不确定性和决策信息缺失问题.首先,回顾PIVIHFS的定义和排序方法以及Archimedean范数;其次,提出概率区间值直觉犹豫模糊Maclaurin对称平均(PIVIHFMSM)算子,研究其优良性质及常见形式;最后,提出一种基于PIVIHFWMSM算子的多属性决策方法,并进行比较分析,通过实例验证该方法的可行性和有效性.拓展PIVIHFS理论和应用领域,为决策属性具有相关性和决策信息有可能缺失提供新思路.     

Introducing decision-aware business processes

Business processes are designed to smoothly operate under multiple contexts (or business situations). Each context technically implies taking a different course of action. Be that as it may, going for the most appropriate action is still left up to the business process participant without any kind of assistance. Such a situation demonstrates that there is a lack of a context-aware decision-making feature. This paper addresses the issue of enabling a context-aware decision-making within the frame of business processes. We combine the concepts of business process, context-awareness and decision-making to introduce a new concept of Decision-Aware Business Processes in which decision partitions are the cornerstones. A decision partition reacts to the collected contextual parameters by selecting or recommending the most appropriate decision(s). In fact, the focus of this research is to introduce a new formalism for designing these partitions by means of patterns. Throughout our approach, each proposed pattern leads to building decision partitions in a straight-forward fashion. An overall example is proposed to illustrate our approach. It is inspired from the banking industry and introduces a decision-aware business process that handles loan applications. To sum up, whether seasoned, novice or in-between, business process participants will be able to save time in taking action(s). Moreover, the workflow becomes no longer stagnant across the business process. Instead, it dynamically adapts itself to each new set of business requirements imposed by the collected contextual input(s).     

A GP-AHP method for solving group decision-making fuzzy AHP problems

The analytic hierarchy process (AHP) elicits a corresponding priority vector interpreting the preferred information from the decision-maker(s), based on the pairwise comparison values of a set of objects. Since pairwise comparison values are the judgments obtained from an appropriate semantic scale, in practice the decision-maker(s) usually give some or all pair-to-pair comparison values with an uncertainty degree rather than precise ratings. By employing the property of goal programming (GP) to treat a fuzzy AHP problem, this paper incorporates an absolute term linearization technique and a fuzzy rating expression into a GP-AHP model for solving group decision-making fuzzy AHP problems. In contrast to current fuzzy AHP methods, the GP-AHP method developed herein can concurrently tackle the pairwise comparison involving triangular, general concave and concave–convex mixed fuzzy estimates under a group decision-making environment.

Scope and purpose

Many real world decision problems involve multiple criteria in qualitative domains. As expected, such problems will be increasingly modeled as multiple criteria decision-making problems, which involve scoring on subjective/qualitative domains. This results in a class of significant problems for which an evaluation framework, which handles occurrences of seeming intransitivity and inconsistency will be required. Another interesting issue of group decision-making analysis is how to deal with disagreements between two or more different rankings within an alternative set. These phenomena are likely to appear in qualitative/subjective domains where the decision-making environment is ambiguous and vague. Therefore, this study proposes a GP-AHP model that is sufficiently robust to permit conflict and imprecision. Numerical examples demonstrate the effectiveness and applicability of the proposed models in deriving the most promising priority vector from a fuzzy AHP problem within a group decision-making environment.     

Group decision-making model with incomplete fuzzy preference relations based on additive consistency.

In decision-making problems there may be cases in which experts do not have an in-depth knowledge of the problem to be solved. In such cases, experts may not put their opinion forward about certain aspects of the problem, and as a result they may present incomplete preferences, i.e., some preference values may not be given or may be missing. In this paper, we present a new model for group decision making in which experts' preferences can be expressed as incomplete fuzzy preference relations. As part of this decision model, we propose an iterative procedure to estimate the missing information in an expert's incomplete fuzzy preference relation. This procedure is guided by the additive-consistency (AC) property and only uses the preference values the expert provides. The AC property is also used to measure the level of consistency of the information provided by the experts and also to propose a new induced ordered weighted averaging (IOWA) operator, the AC-IOWA operator, which permits the aggregation of the experts' preferences in such a way that more importance is given to the most consistent ones. Finally, the selection of the solution set of alternatives according to the fuzzy majority of the experts is based on two quantifier-guided choice degrees: the dominance and the nondominance degree.     

A family of dominance rules for multiattribute decision making under uncertainty

Multiattribute decision-making involves choosing from a set of alternatives each of which is evaluated along multiple criteria that reflect the dimensions of interest to the goals and values of the decision-maker. Dominance-based decision-making narrows down the focus of the decision to the Pareto optimal set. The elimination of dominated alternatives is a compelling principle of rationality since each dominated alternative is logically inferior to its dominating alternative, given the criteria of evaluation. One kind of uncertainty in multiattribute decision making arises out of noisy or inaccurate criteria evaluations. The application of the principle of dominance is not quite rational if the criteria evaluations are known to be noisy. In this paper, we see how dominance-based decision-making can be applied to multiattribute decision-making problems with uncertainty due to noisy criteria values. In particular it will be shown that, for bounded uncertainty it is possible to produce the smallest sufficient subset that is guaranteed to contain all of the nondominated alternatives, and the largest necessary subset that contains only nondominated alternatives. For unbounded uncertainty, we will see how these notions of sufficiency and necessity can be adapted to varying degrees of probabilistic assurances desired by the decision-maker, and that the varying degrees of user assurance map naturally to a family of dominance rules.     

An interactive computer-based interface to support the discovery of individuals’ mental representations and preferences in decisions problems: An application to travel behavior

Growing emphasis is currently given in decision modeling on process data to capture behavioral mechanisms that ground decision-making processes. Nevertheless, advanced applications to elicit such data are still lacking. The Causal Network Elicitation Technique interview and card-game, both face-to-face interviews, are examples of a behavioral process method to obtain individuals’ decision-making by eliciting temporary mental representations of particular problems. However, to portray and model these representations into formal modeling approaches, such as Bayesian decision networks, an extensive set of parameters has to be gathered for each individual. Thus, data collection procedures for large sample groups can be costly and time consuming. This paper reports on the methodological conversion and enhancement of the existing elicitation methods into a computer-based interface that allows to not only uncover individuals’ mental representations but also to automate the generation of preference parameter elicitation questions. Results of such studies can be used to understand individuals’ constructs and beliefs with respect to decision alternatives, predict individuals’ decision behavior at a disaggregate level, and to assess behavioral changes due to differences in contexts and constraints.     

EXPERIENCE-BASED KNOWLEDGE REPRESENTATION: SOEKS

When managers make decisions, they use previous, similar, or equal experiences to help themselves in a new decision-making situation. Thus, keeping record of previous decision events appears to be of the utmost importance as part of the decision making process.

For us, every formal decision event has to be collected and stored as experienced knowledge, and any technology able to do this will allow us to improve the decision-making process by reducing decision time, as well as by avoiding duplication in the process. However, one of the most complicated issues about knowledge is its representation. Developing a knowledge structure that stores and administers experience from the day-to-day decision processes would improve decision-making quality and efficiency. We are proposing such a knowledge structure and have named it set of experience knowledge structure. A set of experience knowledge structure (SOEKS) is a combination of organized information obtained from a formal decision event. Fully applied, the set of experience knowledge structure would advance the notion of administering knowledge in the current decision-making environment.     

A method to aggregate crisp values into interval-valued intuitionistic fuzzy information for group decision making

Owing to the increasing complexity in modern society, aggregating group's knowledge and experiences to make an appropriate decision is an important research topic. The aim of this article is to present a soft computing model for multiple attribute group decision-making problems. This model aggregates all individual decisions on an attribute into an interval-valued intuitionistic fuzzy number (IVIFN), in which each individual decision as an attribute value is expressed in crisp value. Furthermore, we obtain a collective decision matrix, in which the attribute values are expressed by the aggregated IVIFNs. Then make a decision under intuitionistic fuzzy environment. To illustrate the decision process of the developed approach, we give an example for supplier selection and a sensitivity analysis with different attribute weights. Finally, we show a comparison with another group decision-making method from relevant literature.     

集对优势关系下多粒度决策粗糙集的可变三支决策模型

多粒度决策粗糙集是从多角度来处理不确定数据和风险决策问题的重要模型.针对不完备信息系统下的决策分析问题,在多粒度决策粗糙集中引入集对优势关系,对优势度进行了改进,使结果更加合理.然后对多粒度近似空间进行了拓展,提出了集对优势关系下的乐观、悲观、均值、乐观-悲观和悲观-乐观5种多粒度决策粗糙集模型,并讨论了其相关性质以及...     

Minimizing deviations of input and output weights from their means in data envelopment analysis

In this paper, we propose a model that minimizes deviations of input and output weights from their means for efficient decision-making units in data envelopment analysis. The mean of an input or output weight is defined as the average of the maximum and the minimum attainable values of the weight when the efficient decision making unit under evaluation remains efficient. Alternate optimal weights usually exist in the linear programming solutions of efficient decision-making units and the optimal weights obtained from most of the linear programming software are somewhat arbitrary. Our proposed model can yield more rational weights without a priori information about the weights. Input and output weights can be used to compute cross-efficiencies of decision-making units in peer evaluations or group decision-making units, which have similar production processes via cluster analysis. If decision makers want to avoid using weights with extreme or zero values to access performance of decision-making units, then choosing weights that are close to their means, may be a rational choice.     

The effect of system feedback and decision context on value-based decision-making behavior

Relatively few studies in MIS research have examined systems to support value-based decision-making behavior. The increasing complexity of the decision environment necessitates more reliance on personal values by decision-makers, thus making it an important component to study when considering the design of systems to aid decision-making. This paper describes an exploratory experiment that was conducted to determine how individual value-based decision-making behavior can be influenced by an information system through the use of value specific feedback. It also examines the role of decision context on value-based decisions. The results indicate that value-based decision-making behavior can be influenced and discusses operant theory and reactance theory as useful predictors of decision-maker response to feedback in different decision contexts.     

Dealing with incomplete information in linguistic group decision making by means of Interval Type-2 Fuzzy Sets

Nowadays, in the social network–based decision-making processes, like the ones involved in e-commerce and e-democracy, multiple users with different backgrounds may take part and diverse alternatives might be involved. This diversity enriches the process, but at the same time, increases the uncertainty of opinions. This uncertainty can be considered from two different perspectives: (i) the uncertainty in the meaning of the words given as preferences, that is, motivated by the heterogeneity of the decision makers; and (ii) the uncertainty inherent to any decision-making process that may lead to an expert not being able to provide all their judgments. The main objective of this study is to address these two types of uncertainty. To do so, the following approaches are proposed: First, to capture, process, and keep the uncertainty in the meaning of the linguistic assumption, the Interval Type-2 Fuzzy Sets are introduced as a way to model the experts' linguistic judgments. Second, a measure of the coherence of the information provided by each decision maker is proposed. Finally, a consistency-based completion approach is introduced to deal with the uncertainty presented in the expert judgments. The proposed approach is tested in an e-democracy decision-making scenario.     

Decisions,decisions: analysis of age,cohort, and time of testing on framing of risky decision options

Decision making in uncertain environments is a daily challenge faced by adults of all ages. Framing decision options as either gains or losses is a common method of altering decision-making behavior. In the experiment reported here, benchmark decision-making data collected in the 1970s by Tversky and Kahneman (1981, 1988) were compared with data collected from current samples of young and older adults to determine whether behavior was consistent across time. Although differences did emerge between the benchmark and the present samples, the effect of framing on decision behavior was relatively stable. The present findings suggest that adults of all ages are susceptible to framing effects. Results also indicated that apparent age differences might be better explained by an analysis of cohort and time-of-testing effects. Actual or potential applications of this research include an understanding of how framing might influence the decision-making behavior of people of all ages in a number of applied contexts, such as product warning interactions and medical decision scenarios.     

Decider: A fuzzy multi-criteria group decision support system

Multi-criteria group decision making (MCGDM) aims to support preference-based decision over the available alternatives that are characterized by multiple criteria in a group. To increase the level of overall satisfaction for the final decision across the group and deal with uncertainty in decision process, a fuzzy MCGDM process (FMP) model is established in this study. This FMP model can also aggregate both subjective and objective information under multi-level hierarchies of criteria and evaluators. Based on the FMP model, a fuzzy MCGDM decision support system (called Decider) is developed, which can handle information expressed in linguistic terms, boolean values, as well as numeric values to assess and rank a set of alternatives within a group of decision makers. Real applications indicate that the presented FMP model and the Decider  software are able to effectively handle fuzziness in both subjective and objective information and support group decision-making under multi-level criteria with a higher level of satisfaction by decision makers.     

Multi-period multi-attribute group decision-making under linguistic assessments

In fuzzy environments, decision information is more suitable to be expressed in linguistic labels than exact numerical values. Group decision-making with linguistic assessments has received more and more attention over the last decades. Most research on this topic has focused on situations where all the original decision information is provided at the same time and refers to one and same period. However, in many decision areas, such as multi-period investment decision-making, medical diagnosis, personnel dynamic examination, military system efficiency dynamic evaluation, etc., the original decision information is usually collected at different periods and/or refers to different moments in time. This paper investigates the multi-period multi-attribute group decision-making problems where all decision information is expressed by decision-makers in multiplicative linguistic labels at different periods. The paper first introduces a new operator called a dynamic linguistic weighted geometric (DLWG) operator and uses the minimum variability model to derive the time series weights associated with the DLWG operator, and then utilises, respectively, the linguistic weighted geometric (LWG) operator and the DLWG operator to aggregate the given linguistic labels. Moreover, the paper develops an approach to multi-period multiple attribute group decision-making under linguistic assessments so as to derive the final ranking of alternatives, and finally, gives an illustrative example and extends the above results to uncertain linguistic environments.     

Scenario relaxation algorithm for finite scenario-based min–max regret and min–max relative regret robust optimization

Most practical decision-making problems are compounded in difficulty by the degree of uncertainty and ambiguity surrounding the key model parameters. Decision makers may be confronted with problems in which no sufficient historical information is available to make estimates of the probability distributions for uncertain parameter values. In these situations, decision makers are not able to search for the long-term decision setting with the best long-run average performance. Instead, decision makers are searching for the robust long-term decision setting that performs relatively well across all possible realizations of uncertainty without attempting to assign an assumed probability distribution to any ambiguous parameter. In this paper, we propose an iterative algorithm for solving min–max regret and min–max relative regret robust optimization problems for two-stage decision-making under uncertainty (ambiguity) where the structure of the first-stage problem is a mixed integer (binary) linear programming model and the structure of the second-stage problem is a linear programming model. The algorithm guarantees termination at an optimal robust solution, if one exists. A number of applications of the proposed algorithm are demonstrated. All results illustrate good performance of the proposed algorithm.