The value of accuracy in information for planning and control

In the operation of planning and control systems we have to utilize information which in many categories possesses errors. It is important, therefore, to be able to assess the impact of inaccurate information on our plans. The author discusses some approaches to this problem when the planning system involves formulae of the management accounting type or models of the operational research variety. The value of information is clearly related to its accuracy and the most appropriate method for quantifying information value in management information systems is through Bayesian analysis and decision trees.     

A KNOWLEDGE-BASED DSS FOR MANAGERIAL PROBLEM DIAGNOSIS

A knowledge-based system supporting managerial problem diagnosis is described. The system provides the capability to monitor values of selected variables for problem situations. When problems are located, a list of problem symptoms is delivered to a problem processor for structuring and diagnosis. Problem structuring is based on a combination of concepts from expert systems and structural modeling. User assertions about cause-effect relationships between pairs of variables are maintained in a semantic network. Problem diagnosis uses the relationships in the semantic network to construct causation trees, the branches of which represent potential explanations of the problem symptoms. Mathematical models are constructed based on causation-tree branches, and values from the data base are used to test whether the model confirms the diagnosis. If so, the source of the problem has been located and it is then up to the user to resolve the problem. If the model fails to explain the problem, the model apparently is deficient and the user may perform “what if…” type scenarios in attempts to improve the model and search for problem causes. Realistic applications in the accounting and health care areas are discussed.     

Feedback Mechanisms,Judgment Bias,and Trust Formation in Online Auctions*

Online markets, like eBay, Amazon, and others rely on electronic reputation or feedback systems to curtail adverse selection and moral hazard risks and promote trust among participants in the marketplace. These systems are based on the idea that providing information about a trader's past behavior (performance on previous market transactions) allows market participants to form judgments regarding the trustworthiness of potential interlocutors in the marketplace. It is often assumed, however, that traders correctly process the data presented by these systems when updating their initial beliefs. In this article, we demonstrate that this assumption does not hold. Using a controlled laboratory experiment simulating an online auction site with 127 participants acting as buyers, we find that participants interpret seller feedback information in a biased (non‐Bayesian) fashion, overemphasizing the compositional strength (i.e., the proportion of positive ratings) of the reputational information and underemphasizing the weight (predictive validity) of the evidence as represented by the total number of transactions rated. Significantly, we also find that the degree to which buyers misweigh seller feedback information is moderated by the presentation format of the feedback system as well as attitudinal and psychological attributes of the buyer. Specifically, we find that buyers process feedback data presented in an Amazon‐like format—a format that more prominently emphasizes the strength dimension of feedback information—in a more biased (less‐Bayesian) manner than identical ratings data presented using an eBay‐like format. We further find that participants with greater institution‐based trust (i.e., structural assurance) and prior online shopping experience interpreted feedback data in a more biased (less‐Bayesian) manner. The implications of these findings for both research and practice are discussed.     

Framework for Microbial Food‐Safety Risk Assessments Amenable to Bayesian Modeling

Regulatory agencies often perform microbial risk assessments to evaluate the change in the number of human illnesses as the result of a new policy that reduces the level of contamination in the food supply. These agencies generally have regulatory authority over the production and retail sectors of the farm‐to‐table continuum. Any predicted change in contamination that results from new policy that regulates production practices occurs many steps prior to consumption of the product. This study proposes a framework for conducting microbial food‐safety risk assessments; this framework can be used to quantitatively assess the annual effects of national regulatory policies. Advantages of the framework are that estimates of human illnesses are consistent with national disease surveillance data (which are usually summarized on an annual basis) and some of the modeling steps that occur between production and consumption can be collapsed or eliminated. The framework leads to probabilistic models that include uncertainty and variability in critical input parameters; these models can be solved using a number of different Bayesian methods. The Bayesian synthesis method performs well for this application and generates posterior distributions of parameters that are relevant to assessing the effect of implementing a new policy. An example, based on Campylobacter and chicken, estimates the annual number of illnesses avoided by a hypothetical policy; this output could be used to assess the economic benefits of a new policy. Empirical validation of the policy effect is also examined by estimating the annual change in the numbers of illnesses observed via disease surveillance systems.     

Reducing Risk Through Real Options in Systems Design: The Case of Architecting a Maritime Domain Protection System

Complex engineering systems are usually designed to last for many years. Such systems will face many uncertainties in the future. Hence the design and deployment of these systems should not be based on a single scenario, but should incorporate flexibility. Flexibility can be incorporated in system architectures in the form of options that can be exercised in the future when new information is available. Incorporating flexibility comes, however, at a cost. To evaluate if this cost is worth the investment a real options analysis can be carried out. This approach is demonstrated through analysis of a case study of a previously developed static system-of-systems for maritime domain protection in the Straits of Malacca. This article presents a framework for dynamic strategic planning of engineering systems using real options analysis and demonstrates that flexibility adds considerable value over a static design. In addition to this it is shown that Monte Carlo analysis and genetic algorithms can be successfully combined to find solutions in a case with a very large number of possible futures and system designs.     

A Methodology for Constructing Collective Causal Maps*

This article develops a new approach for constructing causal maps called the Collective Causal Mapping Methodology (CCMM). This methodology collects information asynchronously from a group of dispersed and diverse subject‐matter experts via Web technologies. Through three rounds of data collection, analysis, mapping, and interpretation, CCMM constructs a parsimonious collective causal map. The article illustrates the CCMM by constructing a causal map as a teaching tool for the field of operations management. Causal maps are an essential tool for managers who seek to improve complex systems in the areas of quality, strategy, and information systems. These causal maps are known by many names, including Ishikawa (fishbone) diagrams, cause‐and‐effect diagrams, impact wheels, issue trees, strategy maps, and risk‐assessment mapping tools. Causal maps can be used by managers to focus attention on the root causes of a problem, find critical control points, guide risk management and risk mitigation efforts, formulate and communicate strategy, and teach the fundamental causal relationships in a complex system. Only two basic methods for creating causal maps are available to managers today—brainstorming and interviews. However, these methods are limited, particularly when the subject‐matter experts cannot easily meet in the same place at the same time. Managers working with complex systems across large, geographically dispersed organizations can employ the CCMM presented here to efficiently and effectively construct causal maps to facilitate improving their systems.     

A Comparative Analysis of PRA and Intelligent Adversary Methods for Counterterrorism Risk Management

In counterterrorism risk management decisions, the analyst can choose to represent terrorist decisions as defender uncertainties or as attacker decisions. We perform a comparative analysis of probabilistic risk analysis (PRA) methods including event trees, influence diagrams, Bayesian networks, decision trees, game theory, and combined methods on the same illustrative examples (container screening for radiological materials) to get insights into the significant differences in assumptions and results. A key tenent of PRA and decision analysis is the use of subjective probability to assess the likelihood of possible outcomes. For each technique, we compare the assumptions, probability assessment requirements, risk levels, and potential insights for risk managers. We find that assessing the distribution of potential attacker decisions is a complex judgment task, particularly considering the adaptation of the attacker to defender decisions. Intelligent adversary risk analysis and adversarial risk analysis are extensions of decision analysis and sequential game theory that help to decompose such judgments. These techniques explicitly show the adaptation of the attacker and the resulting shift in risk based on defender decisions.     

Strategy Alternatives for Homeland Air and Cruise Missile Defense

Air and cruise missile defense of the U.S. homeland is characterized by a requirement to protect a large number of critical assets nonuniformly dispersed over a vast area with relatively few defensive systems. In this article, we explore strategy alternatives to make the best use of existing defense resources and suggest this approach as a means of reducing risk while mitigating the cost of developing and acquiring new systems. We frame the issue as an attacker‐defender problem with simultaneous moves. First, we outline and examine the relatively simple problem of defending comparatively few locations with two surveillance systems. Second, we present our analysis and findings for a more realistic scenario that includes a representative list of U.S. critical assets. Third, we investigate sensitivity to defensive strategic choices in the more realistic scenario. As part of this investigation, we describe two complementary computational methods that, under certain circumstances, allow one to reduce large computational problems to a more manageable size. Finally, we demonstrate that strategic choices can be an important supplement to material solutions and can, in some cases, be a more cost‐effective alternative.     

Models and Methods to Support a New Type of Inventory Performance Measure: The ESWSO

When analyzing a reorder point, order quantity (r, Q) inventory systems, one important question that often gets very little, if any, attention is: When a stockout occurs, how large is it? This paper is directed at researchers and practicing inventory planners with two objectives. First, we provide several models and algorithms to compute the Expected Shortages When a Stockout Occurs (ESWSO) for a variety of stochastic environments. We show that when ESWSO, is used in conjunction with the traditional fill rate measures it greatly enhances a planners ability to plan for shortages. Second, we develop two cost‐minimizing inventory models—one addressing the backorder and the other the shortage scenario—to show how the ESWSO can be seamlessly integrated into an inventory‐cost framework to specify lot sizes and safety stocks.     

Implementing corporate strategy: Using management education and training

This article surveys a wide area—all of it relevant to the chief executive and the corporate planner—in linking management training and education to corporate strategy. Some companies are well aware of the benefits of using training as a means of implementing strategy. Unfortunately the management of many firms does not seem to be aware that the option exists and they are not able to use training this way because of their organizational and control systems. Training is a powerful weapon for implementing strategy. All the evidence suggests that it works, so who can afford to ignore it?     

Bayesian and Dominant‐Strategy Implementation in the Independent Private‐Values Model

We prove—in the standard independent private‐values model—that the outcome, in terms of interim expected probabilities of trade and interim expected transfers, of any Bayesian mechanism can also be obtained with a dominant‐strategy mechanism.     

When Are Local Incentive Constraints Sufficient?

We study the question of whether local incentive constraints are sufficient to imply full incentive compatibility in a variety of mechanism design settings, allowing for probabilistic mechanisms. We give a unified approach that covers both continuous and discrete type spaces. On many common preference domains—including any convex domain of cardinal or ordinal preferences, single‐peaked ordinal preferences, and successive single‐crossing ordinal preferences—local incentive compatibility (suitably defined) implies full incentive compatibility. On domains of cardinal preferences that satisfy a strong nonconvexity condition, local incentive compatibility is not sufficient. Our sufficiency results hold for dominant‐strategy and Bayesian Nash solution concepts, and allow for some interdependence in preferences.     

On “Black Swans” and “Perfect Storms”: Risk Analysis and Management When Statistics Are Not Enough

Two images, “black swans” and “perfect storms,” have struck the public's imagination and are used—at times indiscriminately—to describe the unthinkable or the extremely unlikely. These metaphors have been used as excuses to wait for an accident to happen before taking risk management measures, both in industry and government. These two images represent two distinct types of uncertainties (epistemic and aleatory). Existing statistics are often insufficient to support risk management because the sample may be too small and the system may have changed. Rationality as defined by the von Neumann axioms leads to a combination of both types of uncertainties into a single probability measure—Bayesian probability—and accounts only for risk aversion. Yet, the decisionmaker may also want to be ambiguity averse. This article presents an engineering risk analysis perspective on the problem, using all available information in support of proactive risk management decisions and considering both types of uncertainty. These measures involve monitoring of signals, precursors, and near‐misses, as well as reinforcement of the system and a thoughtful response strategy. It also involves careful examination of organizational factors such as the incentive system, which shape human performance and affect the risk of errors. In all cases, including rare events, risk quantification does not allow “prediction” of accidents and catastrophes. Instead, it is meant to support effective risk management rather than simply reacting to the latest events and headlines.     

A Three‐Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property

In the United Kingdom, dwelling fires are responsible for the majority of all fire‐related fatalities. The development of these incidents involves the interaction of a multitude of variables that combine in many different ways. Consequently, assessment of dwelling fire risk can be complex, which often results in ambiguity during fire safety planning and decision making. In this article, a three‐part Bayesian network model is proposed to study dwelling fires from ignition through to extinguishment in order to improve confidence in dwelling fire safety assessment. The model incorporates both hard and soft data, delivering posterior probabilities for selected outcomes. Case studies demonstrate how the model functions and provide evidence of its use for planning and accident investigation.     

Towards an integrated model of leadership and self regulation

Most organizations strive to recruit employees who can accommodate unpredictable changes, demonstrate adaptability, and interact effectively with a diversity of individuals. Nevertheless, recent advances in self regulation imply that leaders can instead cultivate such qualities in employees rather than merely attract individuals with these capacities. This paper delineates some of the leadership characteristics and behaviors that could diminish the fragility of employees to foster these qualities. First, a framework is developed that integrates four theories of self regulation—self discrepancy theory, personality systems interaction theory, optimal self esteem, and self salience. Second, this framework is applied to demonstrate how fragility undermines adaptive performance. Finally, this framework is used to show how many leadership constructs, such as self sacrificing, moral management, and transformational behavior, can curb this fragility and thus improve adaptive performance.     

重大传染病疫情演化情境下应急物资配置决策建模分析:以新冠肺炎疫情为例

基于对新冠肺炎疫情时空分布的分析,从时段、关键事件、传播动力学、空间分布、感染规模、信息特征、医疗资源等7个维度构建了重大传染病疫情演化的5种情境,提出了各种情境下需要解决的5个关键应急物资配置决策问题。综合考虑应急物资配置的空间、信息、物资、供应、需求和网络等特性,分析了每一个决策问题进行建模优化的关键因素。基于这些关键因素构建了一个多周期贝叶斯序贯决策模型,给出了求解步骤和解析解,并结合武汉疫情进行了算例分析,验证了模型的有效性。在重大传染病疫情演化情境下,综合考虑这些关键因素,应用贝叶斯决策分析进行应急物资配置决策建模有利于建立更加符合实际的决策模型,减少决策损失。     

Comparison and Validation of Statistical Methods for Predicting Power Outage Durations in the Event of Hurricanes

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out‐of‐sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy.     

基于贝叶斯网络的并发型突发事件链建模方法

现实情形中，由于致灾因子和作用对象的相似性，初始突发事件的发生易引发多个次生事件并发及耦合，致使事件的演化发展及可能造成的损失具有更大的不确定性。然而，现有的突发事件链式演化分析多运用串发型事件链，对于并发型突发事件存在适用性较低的问题。针对上述问题，本文将突发事件抽象描述为以输入、状态和输出属性为组成要素，通过属性要素间的作用关系构成的复杂系统，进而从属性层面分析事件间的关联关系；以贝叶斯网络为建模工具，识别并发型突发事件间具有的因果关系和耦合关系，给出事件贝叶斯网络关联方法，构建并发型突发事件链模型；基于历史数据获取网络节点间的先验概率信息，运用贝叶斯网络推理算法实现并发型突发事件的演化分析；最后，通过实例验证本文方法在实际应用中的科学性及可行性，并通过对比分析阐明其在提高灾害损失预测精度方面具有一定的优势。     

Integrating information systems into business strategies

The role of information systems (IS) in business is changing rapidly. The information revolution, driven by dramatic improvements in cost and performance of the technology is radically altering the business environment of many firms— restructuring whole industries, re-aligning the balance of power and leverage of industry component firms and enabling competitive strategies to be implemented or sustained more effectively. This transformation of role requires strategies for information systems to become an integral part of business strategy formulation. Traditional approaches to the management of IS activities are inappropriate for the determination of business strategies for systems. However, the techniques of corporate strategic analysis and formulation can be used to determine how IS should be managed to gain maximum business benefit.     

Scale invariance of incident size distributions in response to sizes of their causes.

Incidents can be defined as low-probability, high-consequence events and lesser events of the same type. Lack of data on extremely large incidents makes it difficult to determine distributions of incident size that reflect such disasters, even though they represent the great majority of total losses. If the form of the incident size distribution can be determined, then predictive Bayesian methods can be used to assess incident risks from limited available information. Moreover, incident size distributions have generally been observed to have scale invariant, or power law, distributions over broad ranges. Scale invariance in the distributions of sizes of outcomes of complex dynamical systems has been explained based on mechanistic models of natural and built systems, such as models of self-organized criticality. In this article, scale invariance is shown to result also as the maximum Shannon entropy distribution of incident sizes arising as the product of arbitrary functions of cause sizes. Entropy is shown by simulation and derivation to be maximized as a result of dependence, diversity, abundance, and entropy of multiplicative cause sizes. The result represents an information-theoretic explanation of invariance, parallel to those of mechanistic models. For example, distributions of incident size resulting from 30 partially dependent causes are shown to be scale invariant over several orders of magnitude. Empirical validation of power law distributions of incident size is reviewed, and the Pareto (power law) distribution is validated against oil spill, hurricane, and insurance data. The applicability of the Pareto distribution, in particular, for assessment of total losses over a planning period is discussed. Results justify the use of an analytical, predictive Bayesian version of the Pareto distribution, derived previously, to assess incident risk from available data.