Relating value-focused thinking and interactive planning

Value-focused thinking, using the dialogue decision process (DDP), and interactive planning appear to be two totally unrelated processes for making decisions. As this paper shows, new results on the interpretation of utility functions and new ways of thinking about downstream decisions allows us to reinterpret interactive planning as an ideal-focused decision process which is theoretically equivalent to DDP's value-focused decision process. But Ackoff's ideal-focused decision process may be more natural for certain organizational decision settings.     

A survey of decision support system applications (1988–1994)

To extend a previous survey of specific decision support system (DSS) applications over the period (January 1971–April 1988), we have conducted a follow-up survey of DSS applications published between May 1988 and December 1994. Two hundred seventy-one published applications are identified. This survey reveals that there appear to be more creative applications of optimisation and suggestion model-based DSS than simulation-based applications. This is evidenced by a proportional increase of optimisation and suggestion models and a decrease of representation models. Moreover, group decision support systems, executive support systems, and knowledge-based systems applications are becoming more prevalent in many organisations. Although management science (MS)/operational research (OR) models continue to play critical roles, there is a clear observable trend in the DSS model area that three non-MS/OR tools are emerging as powerful DSS tools: graphics, artificial intelligence, and visual interactive modeling.     

Optimal sequential forestry decisions under risk

This paper is a summary of central and typical concepts, ideas and results in the field of sequential optimization and stochastic phenomena in forestry. The sequential optimization methods can be applied to all forestry decisions. The text covers forestry decisions and forest economics issues that are based on sequential decision making. An illustration covers optimal decisions in the presence of stochastic market prices. Stochastic (and/or deterministic but for different reasons unpredictable) changes in the economic and physical environments can be considered in decision making over time as soon as they are revealed. For this reason, the information and decision processes are sequential.     

Executive views concerning decision support tools

Support tools for strategic-level decision-making have become increasingly popular. This study investigates the role of OR/MS tools in today’s strategic-level decision support tool market. Executives working in Finland’s 500 largest companies were asked about the decision support tools they use when making major decisions. The responses received indicated that executives actively use a variety of tools, and an average of five different strategic-level tools. Approximately 10% of the tools used could be identified as OR/MS type, these often suit the needs of larger companies with strategic logistical or production functions and compared to other tools, have a specific profile. Executives see advantages in using tools that provide cognitive, collaboration and communication possibilities, and also in using tools that make processes more efficient. OR methodologies have influenced some of the other tools on the market, but ‘soft OR’ tool usage could not be identified. Tools which support creativity are needed.     

Learning and transfer in dynamic decision environments

An important aspect of learning is the ability to transfer knowledge to new contexts. However, in dynamic decision tasks, such as bargaining, firefighting, and process control, where decision makers must make repeated decisions under time pressure and outcome feedback may relate to any of a number of decisions, such transfer has proven elusive. This paper proposes a two-stage connectionist model which hypothesizes that decision makers learn to identify categories of evidence requiring similar decisions as they perform in dynamic environments. The model suggests conditions under which decision makers will be able to use this ability to help them in novel situations. These predictions are compared against those of a one-stage decision model that does not learn evidence categories, as is common in many current theories of repeated decision making. Both models' predictions are then tested against the performance of decision makers in an Internet bargaining task. Both models correctly predict aspects of decision makers' learning under different interventions. The two-stage model provides closer fits to decision maker performance in a new, related bargaining task and accounts for important features of higher-performing decision makers' learning. Although frequently omitted in recent accounts of repeated decision making, the processes of evidence category formation described by the two-stage model appear critical in understanding the extent to which decision makers learn from feedback in dynamic tasks. Faison (Bud) Gibson is an Assistant Professor at College of Business, Eastern Michigan University. He has extensive experience developing and empirically testing models of decision behavior in dynamic decision environments.     

Decision analysis models in reinsurance

Decision analysis models are developed and illustrated for the reinsurance (risk transfer) decisions made by insurance companies. Decision analytic models were found to be useful tools both for structuring multistage reinsurance decisions and for comparing alternative options. The insurer is faced with many possible choices involving reinsurance type and extent, and an expected utility model provided insight both as a screening device and as an evaluation criterion. Decision analytic models appeared to be superior to other approaches such as mean/variance and risk of ruin models both because of their flexibility and their more comprehensive treatment the important elements of the decision, namely the complete claims distribution, the cost of reinsurance and the insurer's risk attitude.     

Financial modelling: Where to go? With an illustration for portfolio management

The definition of Financial Modelling chosen by the EURO working group on financial modelling is ‘the development and implementation of tools supporting firms, investors, intermediaries, governments and others in their financial-economic decision making, including the validation of the premises behind these tools and the measurement of the effectivity of the use of these tools’. Clearly, in this definition, the decision and its solution is central. Unlike financial modelling in our definition, the theory of finance is not so much concerned with individual decisions, but rather with the effects of the decisions and actions of many individuals on the formation of prices in financial markets. It is therefore no wonder that the assumptions underlying financial theory, which at best describe ‘average individuals’ and ‘average decision situations’, are not suited to describe specific individual decision problems. In our view it is the role of financial modelling to support individual decision making, taking account of the peculiarities of the actual case, where possible taking benefit from the results of the financial theory. This philosophy towards financial modelling is illustrated by a framework for portfolio management.     

A study of high-level managerial decision processes, with implications for MCDM research

We study six real-world major strategic decisions and discuss the role that analytic Multiple Criteria Decision Making (MCDM) models could play in helping decision makers structure and solve such problems. We have interviewed successful and well-educated managers who had access to quantitative decision models, but did not use them as part of their decision process. Our approach is a clinical one that takes a close look at the decision processes. We believe that the normative MCDM framework is oversimplified and does not always fit well with complex, real-world organizational decision processes. This may be one reason why decision tools are not used more widely for solving high-level decision problems. We believe that it would be worthwhile to revise some of the MCDM mainstream postulates and practices to make existing models and tools more suitable for practical purposes. The MCDM mainstream research has until today focused on the choice among alternatives. One should realize that MCDM models could also be used in creating alternatives, in assessing the importance of criteria, in providing the decision makers with “post-commitment support”, and as part of a devil's advocate approach.     

Mental representations of important real-life decisions

Two studies investigated how decision makers characterize alternatives in important real-life decisions, which they themselves had made (to leave a partner, to choose an education and to choose a home). First, the participants indicated a very high degree of involvement in the decisions studied and about half of the participants gave maximum involvement ratings for the partner decision. Second, the results showed that concepts that are essential in most decision theories, such as, consequence, probability and value were important characteristics of the decisions. Third, emotion, positive and negative affect were also important characteristics. Fourth, value and emotion were uncorrelated. Fifth, the patterns of characteristics of decisions made in the past did not differ markedly from the characteristics given to future decisions. Principal component analyses were performed on the ratings of applicability of the different characteristics across participants for each decision situation. Three factors were extracted. There was one factor for positive affect/emotions and another factor for negative affect/emotions verified in oblique solutions. Thus, different scales are needed to represent emotion/affect components (and not bipolar scales) in real-life important decisions. The third factor represented the way in which a decision was represented (moving pictures dialogue etc.). An analysis restricted to the participants who rated 100% involvement showed an additional fourth factor with “what others would think”, “similar situations”, “values” and “money” as the most prominent characteristics. This points to the importance of controlling for participant involvement in studies of human decision making to enable generalizations to real-life decisions.     

Stochastic dominance and mean–variance measures of profit and loss for business planning and investment

In this paper, we first extend the stochastic dominance (SD) theory by introducing the first three orders of both ascending SD (ASD) and descending SD (DSD) to decisions in business planning and investment to risk-averse and risk-loving decision makers so that they can compare both return and loss. We provide investors with more tools for empirical analysis, with which they can identify the first-order ASD and DSD prospects and discern arbitrage opportunities that could increase his/her utility as well as wealth and set up a zero dollar portfolio to make huge profit. Our tools also enable investors and business planners to identify the third order ASD and DSD prospects and make better choices.     

Stochastic linear programs with restricted recourse

Stochastic programs with recourse provide an effective modeling paradigm for sequential decision problems with uncertain or noisy data, when uncertainty can be modeled by a discrete set of scenarios. In two-stage problems the decision variables are partitioned into two groups: a set of structural, first-stage decisions, and a set of second-stage, recourse decisions. The structural decisions are scenario-invariant, but the recourse decisions are scenario-dependent and can vary substantially across scenarios. In several applications it is important to restrict the variability of recourse decisions across scenarios, or to investigate the tradeoffs between the stability of recourse decisions and expected cost of a solution.We present formulations of stochastic programs with restricted recourse that trade off recourse stability with expected cost. The models generate a sequence of solutions to which recourse robustness is progressively enforced via parameterized, satisficing constraints. We investigate the behavior of the models on several test cases, and examine the performance of solution procedures based on the primal-dual interior point method.     

Weak coupling of thin-walled multi-patch NURBS structures in the framework of isogeometric analysis

In the framework of isogeometric analysis, models are typically derived by the use of computer-aided geometric design (CAGD) tools which often results in a large number of non-conforming NURBS patches that are connected along arbitrary curved boundaries. A strong coupling on the basis of matching control meshes is rarely possible and limits the modeling process for practical applications. Weak coupling according to the principles introduced by Nitsche is the method of choice if a stable and variationally consistent method is favored which does not require the solution of additional equations to enforce the coupling constraints. We concentrate on the weak coupling of thin-walled shell structures modeled according to the theory of Kirchhoff-Love. The proposed concept is free of ad-hoc decisions for stabilization thus truly supporting a design-through-analysis idea. (© 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Scheduling and control,performance measures and discrete event simulation

There are a growing number of finite capacity scheduling tools which are in use and available to industry. To be fully effective, a scheduling system needs to be incorporated within a control methodology which enables shop-floor performance to be analysed and, when necessary, corrective actions to be formulated. Manufacturing system performance, however, is not only dependent upon short-term planning decisions, but is also constrained by the capability for which it is designed. Hence, any control methodology should be based on a consistent set of performance measures and well defined procedures which help to integrate decision making at all levels in the order fulfilment process. The paper examines the distinctive roles of process optimisation and process control in the decision hierarchy; it classifies a set of measures of performance and other system variables according to the functions they serve in manufacturing control, and suggests a hierarchical process control methodology. The role of discrete event simulation techniques in linking decisions on manufacturing system design and production planning/control is also discussed.     

考虑双边服务水平的网购服务供应链协调契约

本文考虑了由单个网络商店和单个第三方物流企业(TPL)所构成的二级网购服务供应链,在双方服务水平同时影响市场需求的情况下,研究了网购服务供应链的服务质量协调问题。通过对比分散式和集中式供应链中各方的均衡决策,指出传统价格契约的不足,提出采用收益共享契约对供应链进行协调,并分析了该契约实现供应链协调的条件和局限。研究结果表明:收益共享契约能激励双方提高服务水平、实现供应链协调;协调契约实施前后各方的基本服务水平对各自的收益和服务决策均有正向影响,且相应的影响在协调契约下更明显。此外,研究还指出供应链系统可能会因TPL服务能力不足发生服务拥塞,使得系统运营效率降低。最后,通过算例验证了收益共享契约的有效性,并对主要参数的灵敏度进行了分析。     

On some new discrete inequalities

Some new discrete inequalities involving higher order differences have been obtained here. These inequalities can be used in the analysis of a class of summary difference equations as handy tools. Some applications are also given.     

Environmental Impact Assessment and Decision Analysis

A brief introduction to environmental impact assessment (E.I.A.) is given. An E.I.A. requires, as a final stage, the aggregation of diverse and highly subjective impacts, though in practice this is often done implicitly. Explicit aggregation techniques have their origins in cost-benefit analysis or decision analysis, and the appropriateness of these two techniques for applications in E.I.A.s is considered.An illustration of decision analysis is given in which multi-attribute value analysis is used to assist in the siting of new mines. The method can be presented as a development of the simple visual sieving technique, and by employing colour graphics facilities and microcomputers, a portable, flexible and powerful decision aid emerges: but powerful tools require careful handling.     

IMENSE: An e-infrastructure environment for patient specific multiscale data integration,modelling and clinical treatment

Secure access to patient data and analysis tools to run on that data will revolutionize the treatment of a wide range of diseases, by using advanced simulation techniques to underpin the clinical decision making process. To achieve these goals, suitable e-Science infrastructures are required to allow clinicians and researchers to trivially access data and launch simulations. In this paper we describe the open source Individualized MEdiciNe Simulation Environment (IMENSE), which provides a platform to securely manage clinical data, and to perform wide ranging analysis on that data, ultimately with the intention of enhancing clinical decision making with direct impact on patient health care. We motivate the design decisions taken in the development of the IMENSE system by considering the needs of researchers in the ContraCancrum project, which provides a paradigmatic case in which clinicians and researchers require coordinated access to data and simulation tools. We show how the modular nature of the IMENSE system makes it applicable to a wide range of biomedical computing scenarios, from within a single hospital to major international research projects.     

Heterogeneous uncertainties in cholesterol management

Physicians use clinical guidelines to inform judgment about therapy. Clinical guidelines do not address three important uncertainties: (1) uncertain relevance of tested populations to the individual patient, (2) the patient’s uncertain preferences among possible outcomes, and (3) uncertain subjective and financial costs of intervention. Unreliable probabilistic information is available for some of these uncertainties; no probabilities are available for others. The uncertainties are in the values of parameters and in the shapes of functions. We explore the usefulness of info-gap decision theory in patient-physician decision making in managing cholesterol level using clinical guidelines. Info-gap models of uncertainty provide versatile tools for quantifying diverse uncertainties. Info-gap theory provides two decision functions for evaluating alternative therapies. The robustness function assesses the confidence—in light of uncertainties—in attaining acceptable outcomes. The opportuneness function assesses the potential for better-than-anticipated outcomes. Both functions assist in forming preferences among alternatives. Hypothetical case studies demonstrate that decisions using the guidelines and based on best estimates of the expected utility are sometimes, but not always, consistent with robustness and opportuneness analyses. The info-gap analysis provides guidance when judgment suggests that a deviation from the guidelines would be productive. Finally, analysis of uncertainty can help resolve ambiguous situations.     

Chisini means and rational decision making: equivalence of investment criteria

A plethora of tools are used for investment decisions and performance measurement, including net present value, internal rate of return, profitability index, modified internal rate of return, average accounting rate of return. All these and other known metrics are generally considered non-equivalent and some of them are regarded as unreliable or even naïve. Building upon Magni (Eng Econ 55(2):150–180, 2010a, Eng Econ 58(2):73–111, 2013)’s average internal rate of return, we show that the notion of Chisini mean enables these tools to be used as rational decision criteria. Specifically, we focus on 11 metrics and show that, if properly used, they all provide equivalent accept–reject decisions and equivalent project rankings. Therefore, the intuitive notion of mean is the founding basis of investment decision criteria.     

Multiobjective optimization and marginal pollution abatement cost in the electricity sector – An Israeli case study

The multiple objective optimization models for capacity expansion problem of power generation system in the long run as a base for setting up the marginal abatement cost were examined. In the optimization model the objective function is considered as the weighted sum of several objective functions. Air pollutants are taken into account in both the objective function and the constraints. Different scenarios of pollutant reduction were analyzed. The periods of the years 2003–2013 were taken into account and the results are based on the real data of the Israel electricity sector. Several environmental policies were considered by using the CAPEX system to evaluate the environmental and economic deficiencies in different abatement cost scenarios. The following are obtained: abatement cost for each pollutant, amount of emissions and additional cost connected with the pollutants. Modern decision tools are implemented, such as data envelopment analysis (DEA) and reasonable goal method/interactive decision maps (RGM/IDM) technique as a base for decision-makers to make decisions on energy and environmental policy.