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ABSTRACT This paper discusses a general design pattern that can provide systems engineering guidance for building adaptive systems. The Neocortex Adaptive System Pattern (NASP) architecture is an adaptive decision-making architecture. It is derived from the physical architecture observed within the neocortex. It allows different adaptive system components with diverse methodologies and techniques to coexist and cooperate within a single system. A literature review of relevant neuroscience literature is provided. An architectural view, along with a taxonomy of architecture features, is explained in detail. Experiment results that compare an implemented NASP system, a non-NASP adaptive system, and rule-based systems within a combative simulation are presented. The NASP design pattern unifies the diverse set of algorithms and technologies found across the spectrum of adaptive systems. Evidence from this study indicates that multiple methods and time windows for adaptation can result in superior performance of the overall system. The implications of this result infer a clear preference for Bayesian-based adaptive systems over neural network-based adaptive systems when the system faces time-sensitive problems. This paper supports the claim of a design pattern by providing an example system that shares the same architectural features as NASP. [ABSTRACT FROM AUTHOR]

The paper investigates the impact of trust on market efficiency and bilateral contracts. We prove that a market in which agents are trusted to the degree they deserve to be trusted is as efficient as a market with complete trustworthiness. In other words, complete trustworthiness is not a necessary condition for market efficiency. We prove that distrust could significantly reduce market efficiency, and we show how to solve the problem by using appropriately designed multiagent contracts. The problem of trust is studied in the context of a bilateral negotiation game between a buyer and a seller. It is shown that if the seller’s trust equals the buyer’s trustworthiness, then the social welfare, the amount of trade, and the agents’ utility functions are maximized. The paper also studies the efficiency of advance payment contracts as a tool for improving trustworthiness. It is proved that advance payment contracts maximize the social welfare and the amount of trade. Finally, the paper studies the problem of how to make agents truthfully reveal their level of trustworthiness. An incentive–compatible contract is defined, in which agents do not benefit from lying about their trustworthiness. The analysis and the solutions proposed in this paper could help agent designers avoid many market failures and produce efficient interaction mechanisms. [ABSTRACT FROM AUTHOR]

This paper discusses the state estimation and optimal control problem of a class of partially-observable stochastic hybrid systems (POSHS). The POSHS has interacting continuous and discrete dynamics with uncertainties. The continuous dynamics are given by a Markov-jump linear system and the discrete dynamics are defined by a Markov chain whose transition probabilities are dependent on the continuous state via guard conditions. The only information available to the controller are noisy measurements of the continuous state. To solve the optimal control problem, a separable control scheme is applied: the controller estimates the continuous and discrete states of the POSHS using noisy measurements and computes the optimal control input from the state estimates. Since computing both optimal state estimates and optimal control inputs are intractable, this paper proposes computationally efficient algorithms to solve this problem numerically. The proposed hybrid estimation algorithm is able to handle state-dependent Markov transitions and compute Gaussian- mixture distributions as the state estimates. With the computed state estimates, a reinforcement learning algorithm defined on a function space is proposed. This approach is based on Monte Carlo sampling and integration on a function space containing all the probability distributions of the hybrid state estimates. Finally, the proposed algorithm is tested via numerical simulations. [ABSTRACT FROM AUTHOR]

Recently, many claims have been made that new models of organization are emerging in the public and voluntary sector. These are partnerships, alliances, fora and consortia, to mention but a few, and they encompass both active participation of their members and/or users and the governance of their activities. The patterns of governance emerging, in my view, offer a different set of problems for the management sciences. Policy, strategy and accountability are now being developed within a complex network of different organizations drawn from the public and voluntary sector, aiming to govern or regulate the public sphere. This paper will explore how management scientists can address these patterns of governance. It will be argued that, if governance has supplanted management as an issue for the management sciences in this context, a new or different language is needed to ‘re-present’ the problems. In particular, it will be shown that management science can be employed to find a clear balance between good governance and effective management. This paper will also propose a framework that may be used to help the new organizational arrangements achieve effective governance. Copyright © 2001 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Peer‐to‐peer (P2P) energy trading is expected to be emerged as a new energy management paradigm that allows the transaction of energy from one prosumer to another prosumer without any dependency on a central controller. In Malaysia, first pilot test of P2P trading has been conducted recently, which exclusively participated by industrial prosumers and commercial consumers only. In order to investigate the impact of P2P energy trading to Malaysia market, this paper proposes an auction‐based two‐stage P2P trading market‐clearing strategy, for multi‐cities and intra‐city in Malaysia. A motivational game theory‐based price scheme is presented to ensure an unbiased market operation, to maximize the saving and payoff for each prosumer, at the same time benefits the power utility company. The proposed two‐stage market clearing model is solved by Linear Programming optimization approach. A realistic representation of P2P trading in Malaysia is constructed and tested out under multi‐cities and intra‐city energy trading test cases. Comparison between the proposed P2P energy trading with existing solar generation net metering scheme is also presented. The numerical results indicate the viability and potential of motivational game theory based P2P trading in future Malaysia transactive energy market. [ABSTRACT FROM AUTHOR]

In this paper, I argue for a new normative theory of rational choice under risk, namely expected comparative utility (ECU) theory. I first show that for any choice option, a, and for any state of the world, G, the measure of the choiceworthiness of a in G is the comparative utility (CU) of a in G—that is, the difference in utility, in G, between a and whichever alternative to a carries the greatest utility in G. On the basis of this principle, I then argue that for any agent, S, faced with any decision under risk, S should rank his or her decision options (in terms of how choiceworthy they are) according to their comparative expected comparative utility (CECU) and should choose whichever option carries the greatest CECU. For any option, a, a's CECU is the difference between its ECU and that of whichever alternative to a carries the greatest ECU, where a's ECU is a probability‐weighted sum of a's CUs across the various possible states of the world. I lastly demonstrate that in some ordinary decisions under risk, ECU theory delivers different verdicts from those of standard decision theory. [ABSTRACT FROM AUTHOR]

This paper introduces a new methodology that mitigates the impact of interference produced by full‐duplexing transmission in the device‐to‐device (D2D) environment. One of the most significant issues is the approach with which the D2Ds' receivers recognise and decode the outcomes detected from the multi‐codewords sent by the peer D2Ds' transmitters. To address this issue, a novel proposed framework exchanged by the D2D users is applied and guided by two policies. Initially, all the D2Ds' transmitters expressly characterise and interchange the transmission policy regarding the modulation index, number of streams, and the order of the transmitted codewords. Specifically, this work introduces a novel 3GPP‐compliant binary integer optimisation model subject to several practical constraints for defining the transmission policy at the transmitters. Second, a new classification method is proposed, where the Bayesian decision theory is utilised at the D2Ds' receivers for acquiring the decision parameters from the exchanged transmission policy. Hence, this classification offers a determination for each codeword through enabling symbols' estimation and recognition in terms of posterior probability statements. A series of numerical and comprehensive simulations shows that using the proposed approach leads to several improvements on the conventional full‐duplex D2D schemes. [ABSTRACT FROM AUTHOR]

The aim of this paper is to highlight the role of the Decision Support System within the field of multi-criteria decision aid (MCDA). The MCDA tools have been incorporated into systems to create Multi-Criteria Decision Support Systems (MCDSSs). In our literature review, we noticed that more than 100 papers have been written over a 20-year period in which MCDSS was used as a decision-making tool. The present paper describes some real applications of MCDSS in different fields, harmoniously combined with decision-making methods such as analytic hierarchy process, Utility Additive, and Goal Programming. The present study proposes an integrative MCDSS evaluation through guidance on the tools most useful for a specific user with a particular decision problem. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Measurements of ambiguity attitudes have so far focused on artificial events, where (subjective) beliefs can be derived from symmetry of events and can be then controlled for. For natural events as relevant in applications, such a symmetry and corresponding control are usually absent, precluding traditional measurement methods. This paper introduces two indexes of ambiguity attitudes, one for aversion and the other for insensitivity/perception, for which we can control for likelihood beliefs even if these are unknown. Hence, we can now measure ambiguity attitudes for natural events. Our indexes are valid under many ambiguity theories, do not require expected utility for risk, and are easy to elicit in practice. We use our indexes to investigate time pressure under ambiguity. People do not become more ambiguity averse under time pressure but become more insensitive (perceive more ambiguity). These findings are plausible and, hence, support the validity of our indexes. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the Requirements Engineering process's vital role during systems development as the preparatory point and critical foundation of Systems Engineering. This paper proposes a framework for structuring decisions, based upon the INCOSE Decision Management Process that improves decisions early in the System Life Cycle. This paper introduces and applies the Nexus Engineering Execution Generation (NEX GEN) framework, a novel and unique capability to improve requirements by enabling a prediction of their impact on expected systems’ performance to gain efficiencies in budget and acquisition. Using this framework defines requirements in a quantitative manner to determine documented requirement issues. Regression analysis indicated a statistically significant relationship between the predictor variable of ambiguity (linguistic quality attribute) and the response variable of system performance (usability metric). This study provides a framework and metrics as a complementary method for the development of requirements in a theoretical manner for the reflective practitioner. This framework is demonstrated through multiple federal system program case studies. [ABSTRACT FROM AUTHOR]

In this paper, we study the Bühlmann credibility model with constant interest rate and equal dependence structure over risks and time under balanced loss function. By means of orthogonal projection, the inhomogeneous and homogeneous credibility premium estimators are derived, which extend those for the existing models to slightly more general versions. Finally, we investigate the estimation of the structure parameters and present a numerical example to show the effectiveness of the inhomogeneous estimator. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we analysed classification rules under Bayesian decision theory. The setup we considered here is fairly general, which can represent all possible parametric models. The Bayes classification rule we investigated minimises the Bayes risk under general loss functions. Among the existing literatures, the 0‐1 loss function appears most frequently, under which the Bayes classification rule is determined by the posterior predictive densities. Theoretically, we extended the Bernstein–von Mises theorem to the multiple‐sample case. On this basis, the oracle property of Bayes classification rule has been discussed in detail, which refers to the convergence of the Bayes classification rule to the one built from the true distributions, as the sample size tends to infinity. Simulations show that the Bayes classification rules do have some advantages over the traditional classifiers, especially when the number of features approaches the sample size. The Bayes classification rule minimising the Bayes risk under general loss function, for data from any parametric model. [ABSTRACT FROM AUTHOR]

Presents lists of research papers on risk and insurance. "Effects of Quality and Quantity of Information on Decision Effectiveness," by Kevin Lane Keller; "Efficient Awards and Standards of Proof in Judicial Proceedings," by Daniel L. Rubinfeld and David E. M. Sappington; "Decision Theory As a Branch of Evolutionary Theory: A Biological Derivation of the Savage Axioms," by William S. Cooper; "Managerial Perspectives on Risk and Risk Taking," by James G. March and Zur Shapira; Others.

The indexing of a document is among the most crucial steps in preparing that document for retrieval. The adequacy of the indexing determines the ability of the system to respond to patron requests. This paper discusses this process, and document retrieval in general, on the basis of formal decision theory. The basic theoretical approach taken is illustrated by means of a model of word occurrences in documents in the context of a model information system; both models are fully defined in this paper. Though the main purpose of this paper is to provide insights into a very complex process, formulae are developed that may prove to be of value for an automated operating system. The paper concludes with an interpretation of recall and precision curves as seen from the point of view of decision theory. [ABSTRACT FROM AUTHOR]

This paper considers the effects of multicollinearity on the multiple coefficient of determination and on the standard errors of the estimated regression coefficients. Limiting results are well known for these problems, and some particular cases have been investigated. However, the limiting cases do not explain the nonmonotonic relationships that have been observed in practice. This paper explores these relationships and attempts to provide a unifying graphical explanation that includes the previous results as special cases. Implications for stepwise regression and for the interpretation of any multiple regression involving multicollinear independent variables are discussed. [ABSTRACT FROM AUTHOR]

The topic of behavioral objectives has recently received considerable attention in the academic community. This paper contains a comprehensive overview of issues and explores the relevance of using the behavioral objective framework for teaching quantitative methods (QM) to undergraduate business administration students. Included in the paper are (1) a discussion of the nature and meaning of behavioral objectives, (2) arguments for and against their use, (3) a rationale for their use in a QM course, and (4) a series of examples demonstrating the use of behavioral objectives in a quantitative course. The evidence—consisting of expert opinion from professionals who have successfully used behavioral objectives for teaching quantitative material, the logic of the arguments for behavioral objectives, consideration of arguments against objectives along with rebuttal to these arguments, and the available empirical studies—indicates that behavioral objectives do appear to offer considerable potential for improving QM teaching. The use of behavioral objectives is worthy of thoughtful consideration by QM teachers. [ABSTRACT FROM AUTHOR]

This paper examines some approaches to decision problems under uncertainty. Starr's domain criterion is presented and modified to take into account different philosophies concerning the desirability of winning versus the importance of avoiding losses. The concept of expected value of distribution information is defined and its computation is illustrated with a numerical example. Target values are then introduced into the model and a parametric procedure is used to maximize the chances of achieving a certain level of the given objective. Finally, we show how the concepts developed in this paper might provide further insight into some decision, situations reported in the literature. [ABSTRACT FROM AUTHOR]

The recent benefit–risk framework (BRF) developed by the Food and Drug Administration (FDA) is intended to improve the clarity and consistency in communicating the reasoning behind the FDA's decisions, acting as an important advancement in US drug regulation. In the PDUFA VI implementation plan, the FDA states that it will continue to explore more structured or quantitative decision analysis approaches; however, it restricts their use within the current BRF that is purely qualitative. By contrast, European regulators and researchers have been long exploring the use of quantitative decision analysis approaches for evaluating drug benefit–risk balance. In this paper, we show how quantitative modelling, backed by decision theory, could complement and extend the FDA's BRF to better support the appraisal of evidence and improve decision outcomes. After providing relevant scientific definitions for benefit–risk assessment and describing the FDA and European Medicines Agency (EMA) frameworks, we explain the components of and differences between qualitative and quantitative approaches. We present lessons learned from the EMA experience with the use of quantitative modelling and we provide evidence of its benefits, illustrated by a real case study that helped to resolve differences of judgements among EMA regulators. [ABSTRACT FROM AUTHOR]

Summary: Small area estimation is concerned with methodology for estimating population parameters associated with a geographic area defined by a cross‐classification that may also include non‐geographic dimensions. In this paper, we develop constrained estimation methods for small area problems: those requiring smoothness with respect to similarity across areas, such as geographic proximity or clustering by covariates, and benchmarking constraints, requiring weighted means of estimates to agree across levels of aggregation. We develop methods for constrained estimation decision theoretically and discuss their geometric interpretation. The constrained estimators are the solutions to tractable optimisation problems and have closed‐form solutions. Mean squared errors of the constrained estimators are calculated via bootstrapping. Our approach assumes the Bayes estimator exists and is applicable to any proposed model. In addition, we give special cases of our techniques under certain distributional assumptions. We illustrate the proposed methodology using web‐scraped data on Berlin rents aggregated over areas to ensure privacy. [ABSTRACT FROM AUTHOR]

Leveraging preclinical animal data for a phase I oncology trial is appealing yet challenging. In this paper, we use animal data to improve decision‐making in a model‐based dose‐escalation procedure. We make a proposal for how to measure and address a prior‐data conflict in a sequential study with a small sample size. Animal data are incorporated via a robust two‐component mixture prior for the parameters of the human dose–toxicity relationship. The weights placed on each component of the prior are chosen empirically and updated dynamically as the trial progresses and more data accrue. After completion of each cohort, we use a Bayesian decision‐theoretic approach to evaluate the predictive utility of the animal data for the observed human toxicity outcomes, reflecting the degree of agreement between dose–toxicity relationships in animals and humans. The proposed methodology is illustrated through several data examples and an extensive simulation study. [ABSTRACT FROM AUTHOR]

The risk of a financial position is usually summarized by a risk measure. As this risk measure has to be estimated from historical data, it is important to be able to verify and compare competing estimation procedures. In statistical decision theory, risk measures for which such verification and comparison is possible, are called elicitable. It is known that quantile-based risk measures such as value at risk are elicitable. In this paper, the existing result of the nonelicitability of expected shortfall is extended to all law-invariant spectral risk measures unless they reduce to minus the expected value. Hence, it is unclear how to perform forecast verification or comparison. However, the class of elicitable law-invariant coherent risk measures does not reduce to minus the expected value. We show that it consists of certain expectiles. [ABSTRACT FROM AUTHOR]

The author reflects on Howard Raiffa's paper "Preferences for Multi-Attributed Alternatives," published as a RAND Corp. Research Memorandum. He observes that many of the ideas that permeate decision theory and the modern practice of decision analysis are present in the paper. He expresses disappointment for the little discussion of group decision making issues. He comments on the quantitative aspects of the report.

This paper axiomatizes the robust control criterion of multiplier preferences introduced by Hansen and Sargent (2001). The axiomatization relates multiplier preferences to other classes of preferences studied in decision theory, in particular, the variational preferences recently introduced by Maccheroni, Marinacci, and Rustichini (2006a). This paper also establishes a link between the parameters of the multiplier criterion and the observable behavior of the agent. This link enables measurement of the parameters on the basis of observable choice data and provides a useful tool for applications. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper highlights the omnipresence of the philosophy, theory, methodology and deployment of multiple criteria decision making (MCDM) in essentially every aspect of our lives. It also highlights the specific philosophical, theoretical, methodological, and deployment perspectives that MCDM has influenced, and how MCDM has thus shaped the decision-making process. The following five themes demonstrate the universality of multiple objectives and their impact on the complex decision-making process: (i) the centrality of the states of the system in MCDM; (ii) the centrality of trade-offs in MCDM and the !-constraint method; (iii) the centrality of the time frame in MCDM; (iv) the centrality of risk and uncertainty in MCDM; and (v) the contributions of MCDM to decision analysis. An epilogue concludes the paper. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The paper discusses the role of George L.S. Shackle in fostering an unconventional approach to individual decision making. Up until the early 1970s Shackle was the single critic of the probabilistic approach to decision making who proposed an alternative formal corpus for dealing with uncertainty. The main aim of the paper is to analyse Shackle's non-probabilistic conceptualization of individual decisions under uncertainty from a specific viewpoint, namely that of a possible connection between his theory and one of the most interesting recent approaches to decision under uncertainty, the so-called non-additive probability approach of Gilboa and Schmeidler. The paper shows that these developments in modern decision theory take Shackle's issue seriously and confirm that the reliance of strict Bayesian theory on probabilistic judgements based on point-probability estimates, a reliance that Shackle intended to oppose, is untenable. Non-additive decision theory also provides a usage of non-additive probability distributions in choice that is an alternative to Shackle's approach of using a qualitative notion of probability, such as potential surprise. [ABSTRACT FROM AUTHOR]

This paper argues that true ‘systemic’ epistemology should reveal multiple perspectives, conflicting realities and various other contexts due to its inherent pluralistic nature. Therefore, generalization in systems research occurs not across one linear conceptual framework but across the variety of nonlinear, conflicting, dialectal ‘realities’. It is therefore a misunderstanding of systems research to search for linear, singular and non-conflicting versions of events as though there is one overarching ‘mega concept’ that will explain world events. The paper presents the hallmarks of a systemic epistemology. Using an example the nature of a systems epistemology is demonstrated and explained. The paper concludes that a ‘systemic’ epistemology is one that should reflect a ‘systemic’ reality. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

It is well known, in structural break problems, that it is much easier to detect the existence of a break in a data set than to determine the location of such a break in the sample span. This paper investigates why, in the context of Gaussian linear regressions, using a decision theory framework. The nub of the problem, even for moderately sized breaks, is that the posterior probability distribution of the possible break points is usually not very informative about the true break location. The information content is measured here by a proper scoring rule. Hence, even a locally optimal break location procedure, as introduced here, is ineffective. In the regression context, it turns out to be quite common, indeed the norm, for break location procedures to misidentify the true break position up to 100 per cent of the time. Unfortunately too, the magnitude of the difference between the misidentified and true break locations is usually not small. [ABSTRACT FROM AUTHOR]

This paper considers passive fund selection from an individual investor's perspective. The growth of the passive fund market over the past decade is staggering. Individual investors who wish to buy these funds for their retirement and brokerage accounts have many options and are faced with a difficult selection problem. Which funds do they invest in, and in what proportions? We develop a novel statistical methodology to address this problem by adapting recent advances in posterior summarization. A Bayesian decision‐theoretic approach is presented to construct optimal sparse portfolios for individual investors over time. [ABSTRACT FROM AUTHOR]

Many sample size criteria exist. These include power calculations and methods based on confidence interval widths from a frequentist viewpoint, and Bayesian methods based on credible interval widths or decision theory. Bayesian methods account for the inherent uncertainty of inputs to sample size calculations through the use of prior information rather than the point estimates typically used by frequentist methods. However, the choice of prior density can be problematic because there will almost always be different appreciations of the past evidence. Such differences can be accommodated a priori by robust methods for Bayesian design, for example, using mixtures or ϵ-contaminated priors. This would then ensure that the prior class includes divergent opinions. However, one may prefer to report several posterior densities arising from a "community of priors," which cover the range of plausible prior densities, rather than forming a single class of priors. To date, however, there are no corresponding sample size methods that specifically account for a community of prior densities in the sense of ensuring a large-enough sample size for the data to sufficiently overwhelm the priors to ensure consensus across widely divergent prior views. In this paper, we develop methods that account for the variability in prior opinions by providing the sample size required to induce posterior agreement to a prespecified degree. Prototypic examples to one- and two-sample binomial outcomes are included. We compare sample sizes from criteria that consider a family of priors to those that would result from previous interval-based Bayesian criteria. [ABSTRACT FROM AUTHOR]

The author presents views on Howard Raiffa's paper "Preferences for Multi-Attributed Alternatives," published as a RAND Corp. Research Memorandum. He discusses two fundamentally important preference concepts introduced in the paper. He summarizes key issues for implementing multiattribute utility in the memorandum. He comments on the importance of interpreting and applying multiattribute utility theory.

In this paper a decision-making process is applied to a Chilean forestry firm that must deal with a host of environmental variables as well as profit making. At the core of this process is a ‘dynamic objective–subjective structure’ founded on the approach that decision making is about comprehending the preferences of the various parties involved and expanding the set of alternatives open to the decision makers. This is in contrast to many models that aim at finding the ‘best alternative’ and to decision analysis approaches that focus on presenting preferences via value functions. The paper describes in chronological order, following the sessions of a decision-making process, how the various components of the structure emerged. In doing so it demonstrates how the process led to an understanding of the real effects of dealing with the environment and how comprehending the preferences led to the introduction of new decision variables. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper investigates two different yet related research questions about stock management in feedback environments. The first purpose is to analyse the effects of selected experimental factors on the performances of subjects (players) in a stock management simulation game. In light of these results, our second objective is to evaluate the adequacy of standard decision rules typically used in dynamic stock management models and to seek improvement formulations. To carry out the research, the generic stock management problem is chosen as the interactive gaming platform. In the first part, gaming experiments are designed to test the effects of three factors on decision-making behaviour: different patterns of customer demand, minimum possible order decision (‘review’) interval and, finally, the type of receiving delay. ANOVA results of these three-factor, two-level experiments show which factors have significant effects on 10 different measures of behaviour (such as max–min range of orders, inventory amplitudes, periods of oscillations and backlog durations). In the second phase of research, the performances of subjects are compared against some selected ordering heuristics (formulations). First, the patterns of ordering behaviour of subjects are classified into three basic types. Comparing these three pattern types with simulation results using different decision rules, we observe that the common linear ‘anchoring and adjustment rule’ can represent the smooth and gradually damping type of behaviour, but cannot generate the non-linear and/or discrete ordering dynamics. Thus, several alternative non-linear rules are formulated and tested against subjects' behaviour patterns. Some standard discrete inventory control rules (such as (s, S)) common in the inventory management literature are also formulated and tested. These non-linear and/or discrete rules, compared with the linear stock adjustment rule, are found to be more representative of the subjects' ordering behaviour in many cases, in the sense that these rules can generate non-linear and/or discrete ordering behaviours. Another major finding is the fact that the well-documented oscillatory dynamic behaviour of the inventory is a quite general result, not just an artifact of the linear anchor and adjust rule. When the supply line is ignored or underestimated, large inventory oscillations result also with the non-linear rules, as well as the standard inventory management rules. Furthermore, depending on parameter values, non-linear ordering rules are more prone to yield unstable oscillations—even if the supply line is taken into account. Further methodological and experimental research questions are suggested. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a decision rule to rank actions under strict uncertainty, the available information being limited to the states of nature, the set of alternative rows, and the consequence of choosing every row if a given state occurs. This rule is suitable to moderately pessimistic individuals and social groups, these agents being neither maximax nor maximin decision makers but people who assume that the best outcome from the action will not occur. For these decision makers the paper shows the existence of a consistent weight system in which one and only one weight is attached to each state of the world under plausible conditions of domination. Most of the traditional axioms are satisfied by the proposed ranking approach. In the frame of disappointment (measured by ranges of column dispersion), the meaning of some controversial postulates used in the literature is explained. The proposed criterion is a departure from Laplace's (1825) rule and from the remaining standard criteria. Only in the special case of equal column dispersion do both Laplace's rule and the proposed weights lead to the same solution. [ABSTRACT FROM AUTHOR]

Examines the debate between evidential and causal decision theory. Reactions to papers by Clark Glymour and Christopher Meek (1994) and by Christopher Hitchcock (1996); Argument that the papers are seduced by a plausible line of thought into a mistaken sympathy for evidentialism; Account of non-causal probabilistic rationality developed earlier with Helen Beebee to deal with causal contexts as well.

ABSTRACT This paper introduces a conceptual, yet quantifiable, architecture framework by extending the notion of system modularity in its broadest sense. Acknowledging that modularity is not a binary feature and comes in various types and levels, the proposed framework introduces higher levels of modularity that naturally incorporate decentralized architecture on the one hand and autonomy in agents and subsystems on the other. This makes the framework suitable for modularity decisions in Systems of Systems and for analyzing the impact of modularity on broader surrounding ecosystems. The stages of modularity in the proposed framework are naturally aligned with the level of variations and uncertainty in the system and its environment, a relationship that is central to the benefits of modularity. The conceptual framework is complemented with a decision layer that makes it suitable to be used as a computational architecture decision tool to determine the appropriate stage and level of modularity of a system, for a given profile of variations and uncertainties in its environment. We further argue that the fundamental systemic driving forces and trade-offs of moving from monolithic to distributed architecture are essentially similar to those for moving from integral to modular architectures. The spectrum, in conjunction with the decision layer, could guide system architects when selecting appropriate parameters and building a system-specific computational tool from a combination of existing tools and techniques. To demonstrate the applicability of the framework, a case for fractionated satellite systems based on a simplified demo of the DARPA F6 program is presented where the value of transition from a monolithic architecture to a fractionated architecture, as two consecutive levels of modularity in the proposed spectrum, is calculated and ranges of parameters where fractionation increases systems value are determined. [ABSTRACT FROM AUTHOR]

Sensitivity or importance analysis has been widely used for identifying system weaknesses and supporting system improvement and maintenance activities. Despite the rich literature on the sensitivity analysis of many mission-critical and safety-critical systems, no existing work has been devoted to wireless sensor networks (WSN). In this paper, we first analyze link and node importance with respect to the infrastructure communication reliability of WSN systems. The binary decision diagrams based algorithms are implemented to evaluate and compare three importance measures: structural importance measure, Birnbaum's measure, and criticality importance measure. The effects of node degree, choice of the destination node, data delivery models, as well as mission time on the importance analysis results are investigated through examples. Results from this work can facilitate the design, deployment, and maintenance of reliable WSN for critical applications. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Decision science emphasizes necessary elements required for robust decision‐making. By incorporating decision science principles, frameworks, and tools, it has been demonstrated that decision‐makers can increase the chances of achieving conservation aims. Setting measurable objectives, clearly documenting assumptions about the impact of available actions on a specific threat or problem, explicitly considering constraints, exploring and characterizing uncertainty, and structured deliberation on trade‐offs have been identified as key elements of successful decision‐making. We quantify the extent to which these five elements were utilized in published examples of decision making in conservation in both academic and conservation practice between 2009 and 2018. We found that less than 50% of identified examples included all five elements, with differences in the degree of decision science applied across five commonly used decision support approaches: adaptive management (AM), systematic conservation planning (SCP), structured decision making (SDM), multi‐criteria decision analysis, and cost‐effectiveness analysis. Example applications that utilized the SDM framework were limited in numbers but used on average more than 50% of the five key elements we considered. Although SCP and AM constituted the majority of examples, they were more prevalent in academic studies rather than management applications. SCP and AM examples were widespread in protected area planning, threat abatement, and restoration. Strong geographic bias exists in documented conservation activities that deploy all five decision science elements. [ABSTRACT FROM AUTHOR]

The Swets model of information retrieval, based on a decision theory approach, is discussed, with the overall performance measure being the crucial element reexamined in this paper. The Neyman-Pearson criterion from statistical decision theory, and based on likelihood ratios, is used to determine an optimal range of Z, the variable assigned to each document by the retrieval system in an attempt to discriminate between relevant and nonrelevant documents. This criterion is shown to be directly related to both precision and recall, and is equivalent to the maximization of the expected value of the retrieval decision for a specific query and a given document under certain conditions. Thus, a compromise can be reached between those who advocate precision as a measure, due partially to its ability to be easily measurable empirically, and those who advocate consideration of recall. Several cases of the normal and Poisson distributions for the variable Z are discussed in terms of their implications for the Neyman-Pearson decision rule. It is seen that when the variances are unequal, the Swets rule of retrieving a document if its Z value is large enough is not optimal. Finally, the situation of precision and recall not being inversely related is shown to be possible under certain conditions. Thus, this paper attempts to extend the understanding of the theoretical foundations of the decision theory approach to information retrieval. [ABSTRACT FROM AUTHOR]

Three conceptual and methodological issues of the preceding paper by Bonoma and Johnston are identified and discussed. A theoretical integration of interpersonal trust and locus of control is made, and some necessary conditions for an interpersonal trust-relevant decision-making situation are pointed out. Implications for the design of future research into trust-relevant decision making are drawn. [ABSTRACT FROM AUTHOR]

Beginning with the information economics framework and a multi-period decision model, this paper considers the use of computer simulation methods within an information system choice environment. Actual decision behavior is replaced by optimal decision rules, and simulation is used to evaluate the effects of parameter changes in the environmental model. Simulation is shown to be functional (1) in estimating the value of alternative information structures within a fifteen period decision model and (2) in providing sensitivity and statistical data which would be useful both for different decision maker utility functions and for a variety of information system design questions. [ABSTRACT FROM AUTHOR]

Most of the initial work on perishable asset revenue management (PARM) situations assumed no possibility of customer diversion from one product class to another when the former is made unavailable. In this paper, an evaluation is made of the few published decision rules that incorporate the realistic and common behavior of customer diversion (or sell-up). We present a new heuristic approach that incorporates diversion and could be used by airlines and other relevant industries to achieve improvements in expected contribution of 0.25 to 2.5% over the approach currently used. The sensitivity of the difference in expected contribution between these rules is tested relative to changes in the input parameters, and managerial insights are presented. [ABSTRACT FROM AUTHOR]

Most models of investor behavior assume a time-state independent utility function and result in a deterministic solution where a given set of inputs uniquely specifies the decision. In contrast, a state preference model using a time-state dependent utility function is derived in this paper. The model allows the investment choice decision to be analyzed in a game theoretic context. The general solution is a mixed strategy which allows for a probabilistic interpretation of the decision. The approach presented in this paper can accommodate anomalies such as intransitivity of preference and satisficing as rational behavior. An example of a possible implementation is given along with interpretations of the outcomes. [ABSTRACT FROM AUTHOR]

Drawing upon the choice models developed in the multiple criteria decision making (MCDM) area, this paper proposes an architecture for designing an intelligent decision support system (DSS) that is intended to aid in making choices among multiple alternatives along multiple dimensions. It argues that effective support can be provided to the decision maker when the knowledge-based DSS is capable of dynamically selecting choice models appropriate to the domain and context of a particular problem being specified by the decision maker, and of properly applying them to the problem solution. Development of a prototype intended to partially represent application of the architecture is described. The paper concludes with suggestions for research extensions. [ABSTRACT FROM AUTHOR]

This paper defines and solves a set of new problems for two-level hierarchical transportation networks. Given a set of points, the objective is to find the path of a primary link such that a weighted sum of the lengths of the perpendiculars from each point to the primary link and the distance between the feet of the two extreme perpendiculars is minimized. The perpendiculars are the connecting or secondary links and the primary link could be a straight line or a curve. The paper provides an algorithm and computational results for the case in which the primary link is a straight line. Implementation approaches are also discussed. [ABSTRACT FROM AUTHOR]

Decision support systems (DSSs) are more complex than most other traditional decisionaid systems. For what types of problems are they more effective, and what design characteristics make them more effective? The laboratory experiment reported here examined the effect of three design characteristics of these systems in the context of decision makers faced with ill-structured problems. The characteristics were presence or absence of decision-aid heuristics, degree of interaction between the user and the system, and whether or not the system was computerized. The dependent variables were (1) quality of user performance, (2) user productivity of ideas, (3) user confidence in the quality of his/her performance, (4) user satisfaction with the decision aid or support system, (5) changes in user attitude toward the problem addressed, and (6) changes in user attitude toward computers. Use of heuristics and increased interaction had positive effects on decision quality, user productivity, and attitude toward computers; they had negative effects on user confidence, satisfaction, and attitude toward the problem addressed. Whether or not the system was computerized did not have a significant effect on any dependent variable The findings concerning negative effects, in particular, suggest the need for research on the design of heuristics for addressing ill-structured problems—heuristics that will deliver the positive but not the negative effects observed in this study. The findings also suggest the need for research on how to benefit from computers in the context of solving ill-structured problems. [ABSTRACT FROM AUTHOR]

This paper explores linear programming-like sensitivity analysis in decision theory. In particular, the paper considers the sensitivity of an optimal decision to changes in probabilities of the states of nature and the development of "confidence spheres" to bound arbitrary parametric changes in the probability vector. Such information can be used to assess the accuracy required in assigning probabilities and the confidence in the maximum utility decision. [ABSTRACT FROM AUTHOR]

The views of Robert Winkler on research directions in decision making under uncertainty has properties that reflect his own research background, as show in his article "Research Directions in Decision Making Under Uncertainty." His decision to disregard references allows the paper to be free of value judgments about past work. The range of topics and emphasis make the paper a good outline for a textbook or a course which could follow on from introductory material on probability, decision trees and utility axioms. The focus of the article's reviewer is on the process of decision analysis rather than on technical or methodological developments.

The paper addresses the problem of plant location in the formal context of decision making under uncertainty and presents a framework employing Bayesian analysis in the collection and assessment of information. As a general model, the Bayesian approach is shown to subsume two practical approaches common to plant-location literature: satisficinS and spatial hierarchy of plant-site characteristics. [ABSTRACT FROM AUTHOR]

This paper seeks to identify variables which explain variations in budget allocations in a UK university using a pooled database relating to two periods: one of resource availability, the other of resource scarcity. The results over both periods indicate, consistent with previous research, that a combination of workload and power proxies explains a very high proportion of budget allocations. However, analysis of the two periods revealed that: (i) incremental budgeting, which was observed to be in operation during the period of resource availability, was not supplanted by comprehensive budgeting during the period of resource scarcity, (ii) the impact of power proxies on budget allocation did not materially change in response to changes in levels of resource availability, and (iii) compared with internally based power, externally based power (as proxied) had neither a greater impact on resource allocation during the period of resource scarcity nor a lower impact during the period of resource availability as hypothesized. [ABSTRACT FROM AUTHOR]

The perceived importance of seven power factors to two types of intradepartmental decision are examined: those which a manager takes alone and those which are taken in a group composed of departmental members only. The major findings from this examination are that while formal authority is important in the former situation it is of lesser importance in the latter situation; the reverse is true for personality. We conclude that decision method has a pronounced effect upon the relative importance of power factors. Secondary analysis of the data suggests that group composition also has a marked effect but that issue domain has a negligible effect. Nevertheless, managerial expertise remains dominantly important throughout the whole range of decision-making situations; this finding also concurs with the results from our companion paper (Kenny and Wilson, 1984). [ABSTRACT FROM AUTHOR]