From vicious to virtuous circles: problem structuring for quantified decision making in operationalization of corporate social responsibility

There is no formalised approach for problem structuring and quantitative decision support to operationalise corporate social responsibility (CSR) implementation. In this paper, techniques for considering criteria relationships are outlined and a holistic, systematic framework combining a qualitative and quantitative method for practical CSR integration is provided. Cognitive mapping (CM) is applied to structure the problem picture, and the cause–effect relationships between decision elements. Soft CM methodology is employed to assess the cross-criteria interactions, at both an individual and a collective level. The interactions of criteria can have a significant impact upon CSR implementation. Such impacts can be direct or indirect through their close linkages to other criteria. The causal strategic map serves as an input to the analytic network process (ANP) to carry out the multi-criteria decision analysis. Then, CM and ANP are applied in a comparative analysis to verify whether the measures of criteria significance do correspond. The key criteria in networks are identified using centrality in CM and single limited priorities in ANP. This study demonstrates that using criteria without considering their interactions will result in shortcomings in the evaluation and assessment of CSR programmes. The holistic framework, combining CM and ANP proposed in this work, enhances the process of problem structuring and supports preference-based evaluation of decision alternatives. The results of our study yield that the mapping procedure has an influence on the criteria significance in networks. The correspondence between CM and ANP is stronger when cause relationships are rigidly interpreted. More unambiguous interpretations of causal relations can be achieved if methods are used jointly and common peaks of importance in both CM and ANP could potentially serve as indications of key decision elements.     

Integrating Bayesian networks and decision trees in a sequential rule-based transportation model

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. Some of these models use decision rules to support its decision-making instead of principles of utility maximization. Decision rules can be derived from different modelling approaches. In a previous study, it was shown that Bayesian networks outperform decision trees and that they are better suited to capture the complexity of the underlying decision-making. However, one of the disadvantages is that Bayesian networks are somewhat limited in terms of interpretation and efficiency when rules are derived from the network, while rules derived from decision trees in general have a simple and direct interpretation. Therefore, in this study, the idea of combining decision trees and Bayesian networks was explored in order to maintain the potential advantages of both techniques. The paper reports the findings of a methodological study that was conducted in the context of Albatross, which is a sequential rule based model of activity scheduling behaviour. To this end, the paper can be situated within the context of a series of previous publications by the authors to improve decision-making in Albatross. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of Albatross with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.     

Robust parameter design in resource-constrained environments: An investigation of trade-offs between costs and precision within variable processes

Engineers and scientists often identify robust parameter design as one of the most important process and quality improvement methods. Focused on statistical modeling and numerical optimization strategies, most researchers typically assume a process with reasonably small variability. Realistically, however, industrial processes often exhibit larger variability, particularly in mass production lines. In such cases, many of the modeling assumptions behind the robust parameter design models available in the literature do not hold. Accordingly, the results and recommendations provided to decision makers could generate suboptimal modifications to processes and products. As manufacturers seek improved methods for ensuring quality in resource-constrained environments, experimenters should examine trade-offs to achieve the levels of precision that best support their decision making. In contrast to previous research, this paper proposes a trade-off analysis between the cost of replication and the desired precision of generated solutions. We consider several techniques in the early stages of experimental design, using Monte Carlo simulation as a tool, for revealing potential options to the decision maker. This is perhaps the first study to show the avenue which may lead to more effective robust parameter design models with the optimal combination of cost constraints and desired precision of solutions.     

A refinement of the common cause principle

I study the interplay between stochastic dependence and causal relations within the setting of Bayesian networks and in terms of information theory. The application of a recently defined causal information flow measure provides a quantitative refinement of Reichenbach’s common cause principle.     

Recent advances in intuitionistic fuzzy information aggregation

Aggregation of intuitionistic fuzzy information is a new branch of intuitionistic fuzzy set theory, which has attracted significant interest from researchers in recent years. In this paper, we provide a survey of the aggregation techniques of intuitionistic fuzzy information, and their applications in various fields, such as decision making, cluster analysis, medical diagnosis, forecasting, and manufacturing grid. In addition, we analyze their characteristics and relationships. Finally, we discuss possible directions for future research in this area.     

Modeling Dependencies in Operational Risk with Hybrid Bayesian Networks

This paper addresses the problem of quantifying and modeling financial institutions’ operational risk in accordance with the Advanced Measurement Approach put forth in the Basel II Accord. We argue that standard approaches focusing on modeling stochastic dependencies are not sufficient to adequately assess operational risk. In addition to stochastic dependencies, causal topological dependencies between the risk classes are typically encountered. These dependencies arise when risk units have common information- and/or work-flows and when failure of upstream processes imply risk for downstream processes. In this paper, we present a modeling strategy that explicitly captures both topological and stochastic dependencies between risk classes. We represent the operational-risk taxonomy in the framework of a hybrid Bayesian network (BN) and provide an intuitively compelling approach for handling causal relationships and external influences. We demonstrate the use of hybrid BNs as a tool for mapping causal dependencies between frequencies and severities of risk events and for modeling common shocks. Monte-Carlo simulations illustrate that the impact of topological dependencies on triggering overall system breakdowns can be substantial.     

A comprehensive method for comparing mental models of dynamic systems

Mental models are the basis on which managers make decisions even though external decision support systems may provide help. Research has demonstrated that more comprehensive and dynamic mental models seem to be at the foundation for improved policies and decisions. Eliciting and comparing such models can systematically explicate key variables and their main underlying structures. In addition, superior dynamic mental models can be identified. This paper reviews existing studies which measure and compare mental models. It shows that the methods used to compare such models lack to account for relevant aspects of dynamic systems, such as, time delays in causal links, feedback structures, and the polarities of feedback loops. Mental models without those properties are mostly static models. To overcome these limitations of the methods to compare mental models, we enhance the widely used distance ratio approach (Markóczy and Goldberg, 1995) so as to comprehend these dynamic characteristics and detect differences among mental models at three levels: the level of elements, the level of individual feedback loops, and the level of the complete model. Our contribution lies in a new method to compare explicated mental models, not to elicit such models. An application of the method shows that this previously non-existent information is essential for understanding differences between managers’ mental models of dynamic systems. Thereby, a further path is created to critically analyze and elaborate the models managers use in real world decision making. We discuss the benefits and limitations of our approach for research about mental models and decision making and conclude by identifying directions for further research for operational researchers.     

组织支持感及支持性人力资源管理对员工工作绩效的影响

通过对626名企事业单位大学以上文化程度知识员工的问卷调查,采用相关分析和结构方程模型的多重数据处理方法,构建和检验了多维组织支持感对于支持性人力资源管理影响员工工作绩效的中介作用模型。研究结果显示,支持性人力资源管理实践(具体包括上级支持、参与决策、组织公正等)对于提高员工组织支持感具有积极影响,同时,组织支持感在支持性人力资源管理实践与员工工作绩效(含任务绩效和情境绩效)之间起着重要中介作用。     

Visual-interactive decision modeling (VIDEMO) in policy management: Bridging the gap between analytic and conceptual decision modeling

Policy decision making is a process, rather than a means to an end, stretching over a long time span in a dynamic environment. The advent of easily accessible modeling paradigms promotes the use of sophisticated tools to support policy decision making. It is argued, however, that to be successful in practice, the analytic approaches must be flexible and their role in the problem solving process transparent. In this paper we discuss the concept of visual interactive decision modeling (VIDEMO) in policy management. After positioning decision modeling in the context of problem solving, a generic modeling environment is proposed. It provides the necessary flexibility at the structural level coupled with the required transparency at the formal and resolution levels. The system is based on the premise that policy decision makers can only benefit from the power of analytic modeling if they are supported where and how they want to be supported, without having the analytic tool posing a frame to problem perception, problem analysis, and decision making. In its final version, the proposed VIDEMO approach bridges the gap between analytic and conceptual decision modeling.     

Using dynamic models to support inferences of insider threat risk

Two modeling approaches were integrated to address the problem of predicting the risk of an attack by a particular insider. We present a system dynamics model that incorporates psychological factors including personality, attitude and counterproductive behaviors to simulate the pathway to insider attack. Multiple runs of the model that sampled the population of possible personalities under different conditions resulted in simulated cases representing a wide range of employees of an organization. We then structured a Bayesian belief network to predict attack risk, incorporating important variables from the system dynamics model and learning the conditional probabilities from the simulated cases. Three scenarios were considered for comparison of risk indicators: An average employee (i.e., one who scores at the mean of a number of personality variables), an openly disgruntled malicious insider, and a disgruntled malicious insider who decides to conceal bad behaviors. The counterintuitive result is that employees who act out less than expected, given their particular level of disgruntlement, can present a greater risk of being malicious than other employees who exhibit a higher level of counterproductive behavior. This result should be tempered, however, considering the limited grounding of some of the model parameters. Nevertheless, this approach to integrating system dynamics modeling and Bayesian belief networks to address an insider threat problem demonstrates the potential for powerful prediction and detection capability in support of insider threat risk mitigation.     

A survey of quantum-like approaches to decision making and cognition

There has always been a steady interest in how humans make decisions amongst researchers from various fields. Based on this interest, many approaches such as rational choice theory or expected utility hypothesis have been proposed. Although these approaches provide a suitable ground for modeling the decision making process of humans, they are unable to explain the corresponding irrationalities and existing paradoxes and fallacies. Recently, a new formulation of decision theory that can correctly describe these paradoxes and possibly provide a unified and general theory of decision making has been proposed. This new formulation is founded based on the application of the mathematical structure of quantum theory to the fields of human decision making and cognition. It is shown that by applying these quantum-like models, one can better describe the uncertainty, ambiguity, emotions and risks involved in the human decision making process. Even in computational environments, an agent that follows the correct patterns of human decision making will have a better functionality in performing its role as a proxy for a real user. In this paper, we present a comprehensive survey of the researches and the corresponding recent developments. Finally, the benefits of leveraging the quantum-like modeling approaches in computational domains and the existing challenges and limitations currently facing the field are discussed.     

A multiobjective decomposition-coordination framework with an application to vehicle layout and configuration design

This short paper addresses both researchers in multiobjective optimization as well as industrial practitioners and decision makers in need of solving optimization and decision problems with multiple criteria. To enhance the solution and decision process, a multiobjective decomposition-coordination framework is presented that initially decomposes the original problem into a collection of smaller-sized subproblems that can be solved for their individual solution sets. A common solution for all decomposed and, thus, the original problem is then achieved through a subsequent coordination mechanism that uses the concept of epsilon-efficiency to integrate decisions on the desired tradeoffs between these individual solutions. An application to a problem from vehicle configuration design is selected for further illustration of the results in this paper and suggests that the proposed method is an effective and promising new solution technique for multicriteria decision making and optimization. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

A Bayesian network approach to making inferences in causal maps

The main goal of this paper is to describe a new graphical structure called ‘Bayesian causal maps’ to represent and analyze domain knowledge of experts. A Bayesian causal map is a causal map, i.e., a network-based representation of an expert’s cognition. It is also a Bayesian network, i.e., a graphical representation of an expert’s knowledge based on probability theory. Bayesian causal maps enhance the capabilities of causal maps in many ways. We describe how the textual analysis procedure for constructing causal maps can be modified to construct Bayesian causal maps, and we illustrate it using a causal map of a marketing expert in the context of a product development decision.     

Application of structural equation modeling to evaluate the intention of shippers to use Internet services in liner shipping

Operations research (OR) is the application of modeling techniques to formulate and analyze systems and problems for management decision-making. Structural equation modeling (SEM) is a modeling technique applied to social or behavioral systems to understand and explain relationships that may exist among elements of systems. Recently, the measurement of unobservable variables has gained increasing attention in operations management (OM) research, and the OR discipline has begun to recognize the value of applying SEM to analyze behavioral-related OR problems. To provide OR researchers with a better understanding of the application of this useful statistical modeling technique, this paper presents a tutorial on the application of SEM. Specifically, we investigate the key factors that affect the adoption of Internet services in the context of liner shipping services. Although [Fishbein, M.A., Ajzen, I., 1975. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA; Davis, F.D., 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (3), 319–339; Ajzen, I., 1985. From intention to actions: A theory of planned behavior. In: Kuhl, J., Bechmann, J. (Eds.), Action Control: From Cognition to Behavior. Springer Verlag, New York, pp. 11–39; Ajzen, I., 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, 179–211] have made important contributions to understanding users’ behavior of technology acceptance, shippers’ resistance to end-user systems is still a common problem in the liner shipping industry. To better predict, explain, and increase shippers’ acceptance of technology, we need to understand why shippers accept or reject Internet services provided by their liner shipping carriers. Another objective of this paper is to propose and empirically test a theoretical framework that relates the intention of shippers to use Internet services in liner shipping with its antecedents such as perceived usefulness, perceived ease of use, and the perceptions of security protection. Tests of the structural model confirm Davis’s (1989) notion that perceived ease of use explains the intention of shippers to use Internet services, and that perceived ease of use has a strong positive effect on perceived usefulness. The results also indicate that security protection influences perceived ease of use. The SEM analyses in this study offer OR researchers a methodological guide on how to assess the efficacy of both a measurement model that relates observed indicators to latent factors and a structural model that poses relationships between constructs.     

利用贝叶斯网络分析基因表达数据

利用基因表达数据提出一种新的网络模型—贝叶斯网络,发现基因的互作.一个贝叶斯网络是多变量联合概率分布的有向图模型,表示变量间的条件独立属性.首先我们阐明贝叶斯网络如何表示基因间的互作,然后介绍从基因芯片数据学习贝叶斯网络的方法.     

Artificial intelligence and human decision making

Decision is obviously related to reasoning. One of the possible definitions of artificial intelligence (AI) refers to cognitive processes and especially to reasoning. Before making any decision, people also reason, it is therefore natural to explore the links between AI and decision making. This paper distinguishes between two aspects of decision making: diagnosis and look-ahead. It is shown that, on the one hand, AI has many relationships with diagnosis (expert systems, case-based reasoning, fuzzy set and rough set theories). On the other hand, AI has not paid enough attention to look-ahead reasoning, whose main components are uncertainty and preferences. These aspects of AI and decision making are reviewed in the paper.     

Qualitative chain graphs and their application

For many problem domains, such as medicine, chain graphs are more attractive than Bayesian networks as they support representing interactions between variables that have no natural direction. In particular, interactions between variables that result from certain feedback mechanisms can be represented by chain graphs. Using qualitative abstractions of probabilistic interactions is also of interest, as these allow focusing on patterns in the interactions rather than on the numerical detail. Such patterns are often known by experts and sufficient for making decisions. So far, qualitative abstractions of probabilistic interactions have only been developed for Bayesian networks in the form of qualitative probabilistic networks. In this paper, such qualitative abstractions are developed for chain graphs with the practical aim of using qualitative knowledge as constraints on the hyperspace of probability distributions. The usefulness of qualitative chain graphs is explored for modelling and reasoning about the interactions between diseases.     

Dealing with external actions in belief causal networks

Graphical models are efficient and simple ways to represent dependencies between variables. We introduce in this paper the so-called belief causal networks where dependencies are uncertain causal links and where the uncertainty is represented by belief masses. Through these networks, we propose to represent the results of passively observing the spontaneous behavior of the system and also evaluate the effects of external actions. Interventions are very useful for representing causal relations, we propose to compute their effects using a generalization of the “do” operator. Even if the belief chain rule is different from the Bayesian chain rule, we show that the joint distributions of the altered structures to graphically describe interventions are equivalent. This paper also addresses new issues that are arisen when handling interventions: we argue that in real world applications, external manipulations may be imprecise and show that they have a natural encoding under the belief function framework.     

Forecasting seasonal time series data: a Bayesian model averaging approach

A flexible Bayesian periodic autoregressive model is used for the prediction of quarterly and monthly time series data. As the unknown autoregressive lag order, the occurrence of structural breaks and their respective break dates are common sources of uncertainty these are treated as random quantities within the Bayesian framework. Since no analytical expressions for the corresponding marginal posterior predictive distributions exist a Markov Chain Monte Carlo approach based on data augmentation is proposed. Its performance is demonstrated in Monte Carlo experiments. Instead of resorting to a model selection approach by choosing a particular candidate model for prediction, a forecasting approach based on Bayesian model averaging is used in order to account for model uncertainty and to improve forecasting accuracy. For model diagnosis a Bayesian sign test is introduced to compare the predictive accuracy of different forecasting models in terms of statistical significance. In an empirical application, using monthly unemployment rates of Germany, the performance of the model averaging prediction approach is compared to those of model selected Bayesian and classical (non)periodic time series models.