Estimation of Error and Bias in Bayesian Monte Carlo Decision Analysis Using the Bootstrap

Bayesian Monte Carlo (BMC) decision analysis adopts a sampling procedure to estimate likelihoods and distributions of outcomes, and then uses that information to calculate the expected performance of alternative strategies, the value of information, and the value of including uncertainty. These decision analysis outputs are therefore subject to sample error. The standard error of each estimate and its bias, if any, can be estimated by the bootstrap procedure. The bootstrap operates by resampling (with replacement) from the original BMC sample, and redoing the decision analysis. Repeating this procedure yields a distribution of decision analysis outputs. The bootstrap approach to estimating the effect of sample error upon BMC analysis is illustrated with a simple value-of-information calculation along with an analysis of a proposed control structure for Lake Erie. The examples show that the outputs of BMC decision analysis can have high levels of sample error and bias.     

Convergence Properties of the Likelihood of Computed Dynamic Models

This paper studies the econometrics of computed dynamic models. Since these models generally lack a closed‐form solution, their policy functions are approximated by numerical methods. Hence, the researcher can only evaluate an approximated likelihood associated with the approximated policy function rather than the exact likelihood implied by the exact policy function. What are the consequences for inference of the use of approximated likelihoods? First, we find conditions under which, as the approximated policy function converges to the exact policy, the approximated likelihood also converges to the exact likelihood. Second, we show that second order approximation errors in the policy function, which almost always are ignored by researchers, have first order effects on the likelihood function. Third, we discuss convergence of Bayesian and classical estimates. Finally, we propose to use a likelihood ratio test as a diagnostic device for problems derived from the use of approximated likelihoods.     

Eliciting Information from Experts on the Likelihood of Rapid Climate Change

The threat of so‐called rapid or abrupt climate change has generated considerable public interest because of its potentially significant impacts. The collapse of the North Atlantic Thermohaline Circulation or the West Antarctic Ice Sheet, for example, would have potentially catastrophic effects on temperatures and sea level, respectively. But how likely are such extreme climatic changes? Is it possible actually to estimate likelihoods? This article reviews the societal demand for the likelihoods of rapid or abrupt climate change, and different methods for estimating likelihoods: past experience, model simulation, or through the elicitation of expert judgments. The article describes a survey to estimate the likelihoods of two characterizations of rapid climate change, and explores the issues associated with such surveys and the value of information produced. The surveys were based on key scientists chosen for their expertise in the climate science of abrupt climate change. Most survey respondents ascribed low likelihoods to rapid climate change, due either to the collapse of the Thermohaline Circulation or increased positive feedbacks. In each case one assessment was an order of magnitude higher than the others. We explore a high rate of refusal to participate in this expert survey: many scientists prefer to rely on output from future climate model simulations.     

Missing consequences in multiattribute utility theory

This paper addresses how to deal with decision alternatives with missing consequences in multicriteria decision-making problems. We propose disregarding the attributes for which a decision alternative provides no consequence by redistributing their respective weights throughout the objective hierarchy in favor of a straightforward idea: the assignation of the respective attribute range as a default value for missing consequences due to possible uncertainty about the decision alternative consequences. In both cases, decision alternatives are evaluated by means of an additive multi-attribute utility model. An illustrative example of the restoration of radionuclide contaminated aquatic ecosystems is shown.     

Offshoring Business Process Services and Governance Control Mechanisms: An Examination of Service Providers from India

As emerging markets increasingly rely on service businesses through offshore outsourcing, we examine the role of governance control mechanisms in improving performance among business process outsourcing (BPO) service providers in India. Using data collected from 205 emerging market‐based BPO service providers in India, we examine the antecedents and consequences of establishing governance control mechanisms in BPO service providers. Specifically, we examine how structural (use of contracts with the client), administrative (effective allocation and demarcation of responsibilities within the firm), and relational (collaboration and information sharing with the client) mechanisms drive the performance of a BPO service provider operating in an emerging market. We also examine how key task‐related (task connectivity and task security) and client‐related (end customer orientation and global control) antecedents influence the use of different governance control approaches in this environment. Our analysis finds that both task connectivity and task security significantly impact use of structural and administrative mechanisms, whereas end customer orientation is significantly associated with the strength of the relational mechanisms governing the emerging market‐based BPO service provider and its client. Further global control significantly influences the strength of the structural mechanisms between the client and the BPO service provider. Finally, the three mechanisms have a complementary influence in driving the BPO service provider's performance.     

Managing Enterprise Risks of Technological Systems: An Exploratory Empirical Analysis of Vulnerability Characteristics as Drivers of Exploit Publication*

Enterprises experience opportunistic exploits targeted at vulnerable technology. Vulnerabilities in software‐based applications, service systems, enterprise platforms, and supply chains are discovered and disclosed on an alarmingly regular basis. A necessary enterprise risk management task concerns identifying and patching vulnerabilities. Yet it is a costly affair to develop and deploy patches to alleviate risk and prevent damage from exploit attacks. Given the limited resources available, technology producers and users must identify priorities for such tasks. When not overlooked, vulnerability‐patching tasks often are prioritized based on vulnerability disclosure dates, thus vulnerabilities disclosed earlier usually have patches developed and deployed earlier. We suggest priorities also should focus on time‐dependent likelihoods of exploits getting published. We analyze data on software exploits to identify factors associated with the duration between a vulnerability discovery date and the date when an exploit is publicly available, a time window for patching before exploit attack levels may escalate. Actively prioritizing vulnerability patching based on likelihoods of exploit publication may help lessen losses due to exploit attacks. Technology managers might apply the insights to better estimate relative risk levels, and better prioritize protection efforts toward vulnerabilities having higher risk of earlier exploitation.     

Addressing Climate Change as an Emerging Risk to Infrastructure Systems

The consequences that climate change could have on infrastructure systems are potentially severe but highly uncertain. This should make risk analysis a natural framework for climate adaptation in infrastructure systems. However, many aspects of climate change, such as weak background knowledge and societal controversy, make it an emerging risk where traditional approaches for risk assessment and management cannot be confidently employed. A number of research developments aimed at addressing these issues have emerged in recent years, such as the development of probabilistic climate projections, climate services, and robust decision frameworks. However, additional research is needed to improve the suitability of these methods for infrastructure planning. In this perspective, we outline some of the challenges in addressing climate change risks to infrastructure and summarize new developments aimed at meeting these challenges. We end by highlighting needs for future research, many of which could be well‐served by expertise within the risk analysis community.     

Confronting Deep Uncertainties in Risk Analysis

How can risk analysts help to improve policy and decision making when the correct probabilistic relation between alternative acts and their probable consequences is unknown? This practical challenge of risk management with model uncertainty arises in problems from preparing for climate change to managing emerging diseases to operating complex and hazardous facilities safely. We review constructive methods for robust and adaptive risk analysis under deep uncertainty. These methods are not yet as familiar to many risk analysts as older statistical and model‐based methods, such as the paradigm of identifying a single “best‐fitting” model and performing sensitivity analyses for its conclusions. They provide genuine breakthroughs for improving predictions and decisions when the correct model is highly uncertain. We demonstrate their potential by summarizing a variety of practical risk management applications.     

Bayesian Network Model with Application to Smart Power Semiconductor Lifetime Data

In this article, Bayesian networks are used to model semiconductor lifetime data obtained from a cyclic stress test system. The data of interest are a mixture of log‐normal distributions, representing two dominant physical failure mechanisms. Moreover, the data can be censored due to limited test resources. For a better understanding of the complex lifetime behavior, interactions between test settings, geometric designs, material properties, and physical parameters of the semiconductor device are modeled by a Bayesian network. Statistical toolboxes in MATLAB® have been extended and applied to find the best structure of the Bayesian network and to perform parameter learning. Due to censored observations Markov chain Monte Carlo (MCMC) simulations are employed to determine the posterior distributions. For model selection the automatic relevance determination (ARD) algorithm and goodness‐of‐fit criteria such as marginal likelihoods, Bayes factors, posterior predictive density distributions, and sum of squared errors of prediction (SSEP) are applied and evaluated. The results indicate that the application of Bayesian networks to semiconductor reliability provides useful information about the interactions between the significant covariates and serves as a reliable alternative to currently applied methods.     

Markets with Hierarchies and the Network Structure of Organizational Communities

Using data on supplier relationships among 106organizations involved in the production of means oftransportation located in southern Italy, I exploresome of the consequences of the progressive erosion ofboundaries around corporate actors in modernorganizational economies. The paper is built aroundthe claim that – as companies are forced by variouslearning imperatives to rely more on other companies– interorganizational differences are sustained andreproduced by role structures emerging from theconcatenation of different types of relationshipsacross multiple networks. The analysis illuminatesselected implications of network relations for thestructuring of market relations in interorganizationalfields.     

Knowledge-Based Representations of Risk Beliefs

Beliefs about risks associated with two risk agents, AIDS and toxic waste, are modeled using knowledge-based methods and elicited from subjects via interactive computer technology. A concept net is developed to organize subject responses concerning the consequences of the risk agents. It is found that death and adverse personal emotional and sociological consequences are most associated with AIDS. Toxic waste is most associated with environmental problems. These consequence profiles are quite dissimilar, although past work in risk perception would have judged the risk agents as being quite similar. Subjects frequently used causal semantics to represent their beliefs and "% of time" instead of "probability" to represent likelihoods. The news media is the most prevalent source of risk information although experiences of acquaintances appear more credible. The results suggest that "broadly based risk" communication may be ineffective because people differ in their conceptual representation of risk beliefs. In general, the knowledge-based approach to risk perception representation has great potential to increase our understanding of important risk topics.     

HRD standards and standardization: where now for human resource development?

This paper examines standard-setting and standardization processes currently being undertaken in the human resources field and makes a ‘call to action’ for human resource development (HRD) scholars and practitioners to influence these developments. The paper provides a reflexive ‘insider account’ of HR standards development combining personal experience with theoretical perspectives; ‘grey’ and practitioner literatures; and secondary data sources. Drawing on scholarly literature sources, opportunities and dilemmas of standardization processes in the HR field are discussed. Grounded in the standardization literature, alternative approaches to system-wide (meta) standards are identified. Drawing on publically available information, different standardization approaches in USA and UK are discussed. The paper critiques the dominant performance-orientated paradigm and ‘rules-based’ approach to standards and argues for an alternative, principles-based approach for HR standardization to support sustainable individual and organizational performance. These issues have important consequences for HRD identity, pedagogy, education, and practice. In addition to the development of an original typology of emerging HR standardization, the paper contributes a new perspective to debates about the identity, values, purpose, and contribution of HRD and the relationship between HRD and human resource management (HRM).     

Sovereign Cat Bonds and Infrastructure Project Financing

We examine the opportunities for using catastrophe‐linked securities (or equivalent forms of nondebt contingent capital) to reduce the total costs of funding infrastructure projects in emerging economies. Our objective is to elaborate on methods to reduce the necessity for unanticipated (emergency) project funding immediately after a natural disaster. We also place the existing explanations of sovereign‐level contingent capital into a catastrophic risk management framework. In doing so, we address the following questions. (1) Why might catastrophe‐linked securities be useful to a sovereign nation, over and above their usefulness for insurers and reinsurers? (2) Why are such financial instruments ideally suited for protecting infrastructure projects in emerging economies, under third‐party sponsorship, from low‐probability, high‐consequence events that occur as a result of natural disasters? (3) How can the willingness to pay of a sovereign government in an emerging economy (or its external project sponsor), who values timely completion of infrastructure projects, for such instruments be calculated? To supplement our treatment of these questions, we use a multilayer spreadsheet‐based model (in Microsoft Excel format) to calculate the overall cost reductions possible through the judicious use of catastrophe‐based financial tools. We also report on numerical comparative statics on the value of contingent‐capital financing to avoid project disruption based on varying costs of capital, probability and consequences of disasters, the feasibility of strategies for mid‐stage project abandonment, and the timing of capital commitments to the infrastructure investment. We use these results to identify high‐priority applications of catastrophe‐linked securities so that maximal protection can be realized if the total number of catastrophe instruments is initially limited. The article concludes with potential extensions to our model and opportunities for future research.     

(Re) Defining Emerging Risks

The concept of emergence in risk management can be seen as a revealing symptom of the increasing need for organizations to update their portfolio of risks and opportunities in a rapidly changing and highly competitive environment. Accordingly, the concept of emerging risks has been widely discussed in both scientific and business communities, with, however, a lack of agreement as to whether we should distinguish these risks from others and, if so, what should be the adopted approach for their governance. After reviewing a large set of definitions and conceptions of emerging risks, this article aims at exploring the existence of distinctive features allowing the characterization of a risk as emerging or not. First, we will demonstrate that the features used in the various definitions are ineffective to achieve this distinction. Furthermore, we will argue that all events and consequences associated with risks are or have been states of nature that emerged from complex interactions involving combinations of hazardous activities and stakes. Accordingly, emerging risks are no longer a specific category of risks; they are rather an early step in every risk life cycle that deserves specific governance approaches.     

The Use of Latent Semantic Analysis in Operations Management Research

In this article, we introduce the use of Latent Semantic Analysis (LSA) as a technique for uncovering the intellectual structure of a discipline. LSA is an emerging quantitative method for content analysis that combines rigorous statistical techniques and scholarly judgment as it proceeds to extract and decipher key latent factors. We provide a stepwise explanation and illustration for implementing LSA. To demonstrate LSA's ability to uncover the intellectual structure of a discipline, we present a study of the field of Operations Management. We also discuss a number of potential applications of LSA to show how it can be used in empirical Operations Management research, specifically in areas that can benefit from analyzing large volumes of unstructured textual data.     

Strategic Alliance Research in the Era of Digital Transformation: Perspectives on Future Research

The emerging digital transformation in the twenty‐first century is rapidly and significantly changing the business landscape. The fast‐changing activities, expectations and new modes of collaboration suggest it is time to review the current theoretical insights from strategic alliance (SA) research, which are based on assumptions from a different era. We therefore aim to stimulate multidisciplinary debate and theoretical reflections to better understand emerging paradoxes and challenges that contemporary firms face in the formation, evolution and dissolution of strategic alliances. Specifically, we offer alternative visions of SA research and suggest fresh applications or supplements of existing theoretical perspectives and research methods that can better address the research questions emerging from an era of digital transformation.     

Management science models for evaluating regional government policies

This paper discusses the use of econometric models in evaluating alternative courses of action for public investment and governmental programs. Included are an extensive statewide and regional input-output analysis of the Texas economy, a simulation model to be used by government officials for fiscal policy-making and a model which simulates the demand for and use of water resources. These models provide a means whereby government planners and policy makers can plan and understand the consequences of investing limited resources in various public programs.     

Decision Making in Child Protective Services: A Risky Business?

Child Protective Services (CPS) in the United States has received a torrent of criticism from politicians, the media, child advocate groups, and the general public for a perceived propensity to make decisions that are detrimental to children and families. This perception has resulted in numerous lawsuits and court takeovers of CPS in 35 states, and calls for profound restructuring in other states. A widely prescribed remedy for decision errors and faulty judgments is an improvement of risk assessment strategies that enhance hazard evaluation through an improved understanding of threat potentials and exposure likelihoods. We examine the reliability and validity problems that continue to plague current CPS risk assessment and discuss actions that can be taken in the field, including the use of receiver operating characteristic (ROC) curve technology to improve the predictive validity of risk assessment strategies.     

Augmenting the Deliberative Method for Ranking Risks

The Department of Homeland Security (DHS) characterized and prioritized the physical cross‐border threats and hazards to the nation stemming from terrorism, market‐driven illicit flows of people and goods (illegal immigration, narcotics, funds, counterfeits, and weaponry), and other nonmarket concerns (movement of diseases, pests, and invasive species). These threats and hazards pose a wide diversity of consequences with very different combinations of magnitudes and likelihoods, making it very challenging to prioritize them. This article presents the approach that was used at DHS to arrive at a consensus regarding the threats and hazards that stand out from the rest based on the overall risk they pose. Due to time constraints for the decision analysis, it was not feasible to apply multiattribute methodologies like multiattribute utility theory or the analytic hierarchy process. Using a holistic approach was considered, such as the deliberative method for ranking risks first published in this journal. However, an ordinal ranking alone does not indicate relative or absolute magnitude differences among the risks. Therefore, the use of the deliberative method for ranking risks is not sufficient for deciding whether there is a material difference between the top‐ranked and bottom‐ranked risks, let alone deciding what the stand‐out risks are. To address this limitation of ordinal rankings, the deliberative method for ranking risks was augmented by adding an additional step to transform the ordinal ranking into a ratio scale ranking. This additional step enabled the selection of stand‐out risks to help prioritize further analysis.     

The Financial Management of Catastrophic Flood Risks in Emerging-Economy Countries

This article examines the potential of pre- and post-disaster instruments for funding disaster response and recovery and for creating incentives for flood loss mitigation in countries with emerging or transition economies. As a concrete case, we discuss the disaster recovery arrangements following the 1997 flood disaster in Poland. We examine the advantages and limitations of hedging instruments, which are instruments for transferring the risk to investors either through insurance or capital market-based securities. We compare these mechanisms with financing instruments whereby the government sets aside funds prior to a disaster or taps its own funding sources after the event occurs. We show how hedging instruments can be designed to create incentives for the mitigation of damage to public infrastructure using the flood proofing of a water-treatment plant on the hypothetical Topping River as an illustrative example. We conclude that hedging instruments can be an attractive alternative to financing instruments that have been traditionally used in the poorer, emerging-economy countries to fund disaster recovery. Since very poor countries are likely to have difficulty paying the price of protection prior to a disaster, we suggest that international lending institutions consider innovations for subsidizing these payments.