Representations as Decision Aids: The Asymmetric Effects of Words and Numbers on Auditors' Inherent Risk Judgments*

Decision aids often change the representations used to express risk cue information and communicate risk judgments. In this study, we hypothesize that numbers and words may have asymmetric effects when used to represent risk cue information as opposed to when used to communicate risk judgments. Specifically, decision makers' agreement on cue weights and judgment consensus may be higher with cue information stated in words rather than numbers, since appropriately chosen words can more directly convey the risk implications of information cues. In contrast, decision makers' consistency in applying their judgment policies and judgment consensus may be higher with risk judgments communicated in numbers instead of words, because the greater precision available with numbers allows finer discriminations of risk. To test our hypotheses, we conducted an experiment in which experienced financial auditors made inherent risk judgments for a hypothetical manufacturing client. Cue and response representations were manipulated between numbers and words. The results generally support our arguments. In addition, representing cue information in words increased the time required to acquire cue information relative to numeric representation. In contrast, requiring auditors to express their risk judgments in numbers increased the time required to express risk assessments relative to     

Risk Assessment Can Be a Game‐Changing Information Technology—But Too Often It Isn't

The printing press was a game‐changing information technology. Risk assessment could be also. At present, risk assessments are commonly used as one‐time decision aids: they provide justification for a particular decision, and afterwards usually sit on a shelf. However, when viewed as information technologies, their potential uses are much broader. Risk assessments: (1) are repositories of structured information and a medium for communication; (2) embody evaluative structures for setting priorities; (3) can preserve information over time and permit asynchronous communication, thus encouraging learning and adaptation; and (4) explicitly address uncertain futures. Moreover, because of their “what‐if” capabilities, risk assessments can serve as a platform for constructive discussion among parties that hold different values. The evolution of risk assessment in the nuclear industry shows how such attributes have been used to lower core‐melt risks substantially through improved templates for maintenance and more effective coordination with regulators (although risk assessment has been less commonly used in improving emergency‐response capabilities). The end result of this evolution in the nuclear industry has been the development of “living” risk assessments that are updated more or less in real time to answer even routine operational questions. Similar but untapped opportunities abound for the use of living risk assessments to reduce risks in small operational decisions as well as large policy decisions in other areas of hazard management. They can also help improve understanding of and communication about risks, and future risk assessment and management. Realization of these opportunities will require significant changes in incentives and active promotion by the risk analytic community.     

Understanding the Positive Effects of Graphical Risk Information on Comprehension: Measuring Attention Directed to Written,Tabular, and Graphical Risk Information

Risk communications are an integral aspect of health education and promotion. However, the commonly used textual risk information is relatively difficult to understand for the average recipient. Consequently, researchers and health promoters have started to focus on so‐called decision aids, such as tables and graphs. Although tabular and graphical risk information more effectively communicate risks than textual risk information, the cognitive mechanisms responsible for this enhancement are unclear. This study aimed to examine two possible mechanisms (i.e., cognitive workload and attention). Cognitive workload (mean pupil size and peak pupil dilation) and attention directed to the risk information (viewing time, number of eye fixations, and eye fixation durations) were both measured in a between‐subjects experimental design. The results suggest that graphical risk information facilitates comprehension of that information because it attracts and holds attention for a longer period of time than textual risk information. Graphs are thus a valuable asset to risk communication practice for two reasons: first, they tend to attract attention and, second, when attended to, they elicit information extraction with relatively little cognitive effort, and finally result in better comprehension.     

Modeling Sequential Information Acquisition Behavior in Rational Decision Making

Most real‐life decisions are made with less than perfect information and there is often some opportunity to acquire additional information to increase the quality of the decision. In this article, we define and study the sequential information acquisition process of a rational decision maker (DM) when allowed to acquire any finite amount of information from a set of products defined by vectors of characteristics. The information acquisition process of the DM depends both on the values of the characteristics observed previously and the number and potential realizations of the remaining characteristics. Each time an observation is acquired, the DM modifies the probability of improving upon the products already observed with the number of observations available. We construct two real‐valued functions whose crossing points determine the decision of how to allocate each available piece of information. We provide several numerical simulations to illustrate the information acquisition incentives defining the behavior of the DM. Applications to knowledge management and decision support systems follow immediately from our results, particularly when considering the introduction and acceptance of new technological products and when formalizing online search environments.     

The Effects of Task Information and Outcome Feedback on Individuals' Insight into Their Decision Models

When making business decisions, people generally receive some form of guidance. Often, this guidance might be in the form of instructions about which inputs to the decision are most important. Alternatively, it might be outcome feedback concerning the appropriateness of their decisions. When people receive guidance in making difficult judgments, it is important that they do not confuse this guidance with insight into their own decision models. This study examined whether people confuse their actual decision model with task information and outcome feedback. Subjects predicted the likelihood that various hypothetical companies would experience financial distress and then reported the decision models they believed they had used. Their reported models were compared with their actual models as estimated by a regression of the subjects' predictions on the inputs to their decisions. In a 2times2 factorial design, some subjects were provided with task information regarding the relative importance of each input to their decisions while others were not. Some subjects were provided with outcome feedback regarding the quality of their decisions while others were not. The subjects tended to confuse the task information and outcome feedback with their actual decision models. Implications for the results are discussed.     

Framing Effects in Narrative and Non‐Narrative Risk Messages

Narrative messages are increasingly popular in health and risk campaigns, yet gain/loss framing effects have never been tested with such messages. Three experiments examined framing in narrative messages. Experiment 1 found that only the character's decision, not framing, influenced judgments about characters in a narrative derived from a prospect theory context. Experiment 2 found that a framing effect that occurred when presented in a decision format did not occur when the same situation was presented as a narrative. Using a different story/decision context, Experiment 3 found no significant difference in preference for surgery over radiation therapy in a narrative presentation compared to a non‐narrative presentation. The results suggest that health and risk campaigns cannot assume that framing effects will be the same in narrative messages and non‐narrative messages. Potential reasons for these differences and suggestions for future research are discussed.     

信息不对称下供应链成员的决策顺序研究

市场需求通常是不确定的，而供应链成员之间关于需求信息会存在不对称性。本文采用"信号"博弈（Signaling）的方法，探讨信息不对称结构下供应链成员的最优决策顺序。考虑由两个成员组成的供应链，各成员需要决定自己的边际收益，后决策方可以观测到先决策方的决策，并推测（Infer）市场需求信息。研究表明：1）当信息精确度较低时，拥有信息的一方偏向于先决策，而当信息精确度较高时，其会偏向于后决策；2）无论信息精确度的高低，不拥有信息的一方总是偏向于先决策；3）对于整个供应链而言，当信息精确度较低时，拥有信息的一方先决策较优，而当信息精确度较高时，拥有信息的一方后决策较优。一些结论与直观相悖，取决于"信号"成本（Signaling cost）、先行者优势（First-mover advantage）以及后行者对先行者的反应之间的权衡。     

The Influence of Feelings While Driving Regular Cars on the Perception and Acceptance of Self‐Driving Cars

Self‐driving vehicles will affect the future of transportation, but factors that underlie perception and acceptance of self‐driving cars are yet unclear. Research on feelings as information and the affect heuristic has suggested that feelings are an important source of information, especially in situations of complexity and uncertainty. In this study (N = 1,484), we investigated how feelings related to traditional driving affect risk perception, benefit perception, and trust related to self‐driving cars as well as people's acceptance of the technology. Due to limited experiences with and knowledge of self‐driving cars, we expected that feelings related to a similar experience, namely, driving regular cars, would influence judgments of self‐driving cars. Our results support this assumption. While positive feelings of enjoyment predicted higher benefit perception and trust, negative affect predicted higher risk and higher benefit perception of self‐driving cars. Feelings of control were inversely related to risk and benefit perception, which is in line with research on the affect heuristic. Furthermore, negative affect was an important source of information for judgments of use and acceptance. Interest in using a self‐driving car was also predicted by lower risk perception, higher benefit perception, and higher levels of trust in the technology. Although people's individual experiences with advanced vehicle technologies and knowledge were associated with perceptions and acceptance, many simply have never been exposed to the technology and know little about it. In the absence of this experience or knowledge, all that is left is the knowledge, experience, and feelings they have related to regular driving.     

Information systems and managerial decision making

The role of an information system is to support managerial decision processes. Some of these processes can be completely specified in advance and, as long as the specification remains acceptable to management, resolution can be done automatically, within the framework of the information system. However, in other cases, managers are involved in all stages of the decision process, which cannot be completely specified in advance. The task of the information system in these cases is to provide information to the managers engaged in making the decisions. This paper considers these problems from the point of view of the information system designer and makes recommendations about design and implementation.     

A Simulation of Partial Information Use in Decision Making: Implications for DSS Design

Information matrices are often the output produced by a decision support system. These matrices are a common method for expressing a decision situation under different decision-making scenarios. The decisions involved in designing a decision support system to generate the information matrix are important and involve several cost and benefit components. A designer needs guidance in making effective design decisions in this context. Such guidance can be provided by considering the relationships among specific design decisions, costs, and benefits. The general objective of this study is to provide a comprehensive framework for this purpose. This study is the first to develop and present a comprehensive cost-benefit framework for evaluating design decisions for a variety of scenarios. The specific objective of this research is to provide guidance regarding the number of available information dimensions to incorporate in a computer-based decision aid. Simulation experiments are conducted with a completely specified model based on the cost-benefit framework (including needed assumptions) to evaluate how many information dimensions to include for a specific information matrix size to achieve a balance between information use costs and decision quality. Based upon extensive simulation analyses for a hypothetical decision maker, the practical guideline found for designers is to include only the top half of the relevant information dimensions in any specific decision support system. Over a large number of repeating choice decisions, the savings in cognitive effort and information gathering costs clearly offset relatively minor losses in decision quality.     

AN EMPIRICAL ASSESSMENT OF COMPUTER-ASSISTED DECISION ANALYSIS*

Decision analysis tools often are used in semistructured and ill-structured situations. While some researchers have argued that computerized decision analysis programs may improve decision quality in such situations, research support for this assertion is weak. In this laboratory study, business students used a computerized decision-analysis program with short strategic-management cases to prepare decision reports. Independent raters' perceptions of aided and unaided decision performance were contrasted, attitudes of students toward the program were assessed, individual differences were correlated with attitudes, and the program's impact on students' perceptions of their decision processes and performance was examined. Student attitudes toward the computerized aid were favorable, and individual differences in reactions generally were as predicted. However, the program did not improve the independent ratings of students' decision reports and recommendations. These findings suggest that computerized decision aids should be adopted cautiously. If such aids result in positive user affect and heightened decision confidence without corresponding improvements in decision quality, they may be dysfunctional. Rigorous research methodologies which incorporate independent evaluations of analyses and decisions are recommended for use in future research on computerized decision-analysis programs.     

Representation,Propagation, and Decision Issues in Risk Analysis Under Incomplete Probabilistic Information

This article tries to clarify the potential role to be played by uncertainty theories such as imprecise probabilities, random sets, and possibility theory in the risk analysis process. Instead of opposing an objective bounding analysis, where only statistically founded probability distributions are taken into account, to the full‐fledged probabilistic approach, exploiting expert subjective judgment, we advocate the idea that both analyses are useful and should be articulated with one another. Moreover, the idea that risk analysis under incomplete information is purely objective is misconceived. The use of uncertainty theories cannot be reduced to a choice between probability distributions and intervals. Indeed, they offer representation tools that are more expressive than each of the latter approaches and can capture expert judgments while being faithful to their limited precision. Consequences of this thesis are examined for uncertainty elicitation, propagation, and at the decision‐making step.     

The Impact of Information Sharing and Advance Order Information on a Supply Chain with Balanced Ordering

This paper considers a supply chain with one supplier and multiple retailers that face exogenous heterogeneous end‐customer demands, where all parties utilize base‐stock policies. Each retailer is restricted to order once in every order cycle and their orders are replenished in a balanced manner within the cycle. Our study investigates the impact of information sharing and advance order information (AOI) on the supply chain. We find that the supplier benefits from the two mechanisms via two important factors, the information about observed end‐customer demands and the decision on re‐establishing the replenishment sequence. We derive the supplier's optimal sequence for stochastically comparable end‐customer demands with AOI and propose a sequencing rule for the setting with information sharing. Our numerical study examines the cost impacts of two proposed mechanisms on the entire supply chain.     

Deciding with Thresholds: Importance Measures and Value of Information

Risk‐informed decision making is often accompanied by the specification of an acceptable level of risk. Such target level is compared against the value of a risk metric, usually computed through a probabilistic safety assessment model, to decide about the acceptability of a given design, the launch of a space mission, etc. Importance measures complement the decision process with information about the risk/safety significance of events. However, importance measures do not tell us whether the occurrence of an event can change the overarching decision. By linking value of information and importance measures for probabilistic risk assessment models, this work obtains a value‐of‐information‐based importance measure that brings together the risk metric, risk importance measures, and the risk threshold in one expression. The new importance measure does not impose additional computational burden because it can be calculated from our knowledge of the risk achievement and risk reduction worth, and complements the insights delivered by these importance measures. Several properties are discussed, including the joint decision worth of basic event groups. The application to the large loss of coolant accident sequence of the Advanced Test Reactor helps us in illustrating the risk analysis insights.     

Quantifying Cyberinfrastructure Resilience against Multi‐Event Attacks

This article introduces a general approach for characterizing cyberinfrastructure resilience in the face of multiple malicious cyberattacks, such as when a sequence of denial‐of‐service attacks progressively target an already weakened information system. Although loss assessment frequently focuses on a single overall measure such as cost or downtime, the proposed technique considers both the timing and the amount of loss associated with each individual attack, as well as whether this loss is incurred suddenly or is “slow‐onset.” In support of this, an underlying mathematical model is developed to represent the relative impact of each attack and the corresponding length of time that its effects persist within the system, as well as to illustrate the trade‐offs between these two factors. The model is extended to represent uncertainty in its parameters and thus to support comparative analyses among various security configurations with respect to a baseline estimate of resilience. Monte Carlo simulation is then used to illustrate the model's capabilities and to support a discussion of its ability to provide for more effective decision making in the context of disaster planning and mitigation. [Submitted: March 21, 2011. Revised: July 14, 2011; November 4, 2011. Accepted: December 19, 2011.]     

An Empirical Study of Equivalence Judgments vs. Ratio Judgments in Decision Analysis*

Two commonly used elicitation modes on strength of preference, equivalence and ratio judgments, were compared in an experiment. The result from the experiment showed that ratio judgments were less effective than equivalence judgments. Based on an iterative design for eliciting multiattribute preference structures, equivalence judgments outperformed ratio judgments in estimating single‐attribute measurable value functions, while being nearly more effective than ratio judgments in assessing multiattribute preference structures. The implications of the results from the experiment are that multiattribute decision‐making techniques should take advantage of the decision maker's inclination of making effective equivalence trade‐off judgments, and that useful techniques should be devised to incorporate different commonly used techniques, such as multiattribute utility theory and the Analytic Hierarchy Process, to elicit and consolidate equivalence trade‐off judgments.     

Presenting Geographic Information: Effects of Data Aggregation,Dispersion, and Users' Spatial Orientation

Geographic information systems (GIS) have taken on an increasingly important role supporting decision making in many organizations. GIS have been used to support a breadth of tasks including oil and mineral exploration, facility location, logistics support, and facilities management decisions. The effectiveness of GIS as a decision support tool comes primarily from the visual display of data in the form of maps. When presenting information as a geographic map, the level of data aggregation potentially affects aspects of task complexity such as information load and the potential for pattern recognition by the user. Other task attributes expected to be related to data aggregation effects include problem size, the degree of data dispersion, and users' spatial orientation skills. We conducted an experiment to study these effects and their interactions. Subjects used a GIS including map-based information characterized by different levels of problem size, data dispersion, and data aggregation. Spatial orientation skill was examined as a covariate in the experimental treatments. The results indicate that all of these geographic information characteristics have significant impacts on decision performance. Moreover, many interactions are present among the factors. We evaluate these interactions in order to derive implications for practice and for future research.     

Capturing and Integrating Knowledge for Managing Risks in Tunnel Works

Risk‐related knowledge gained from past construction projects is regarded as potentially extremely useful in risk management. This article describes a proposed approach to capture and integrate risk‐related knowledge to support decision making in construction projects. To ameliorate the problem related to the scarcity of risks information often encountered in construction projects, Bayesian Belief Networks are used and expert judgment is elicited to augment available information. Particularly, the article provides an overview of judgment‐based biases that can appear in the elicitation of judgments for constructing Bayesian Networks and the provisos that can be made in this respect to minimize these types of bias. The proposed approach is successfully applied to develop six models for top risks in tunnel works. More than 30 tunneling experts in the Netherlands and Germany were involved in the investigation to provide information on identifying relevant scenarios than can lead to failure events associated with tunneling risks. The article has provided an illustration of the applicability of the developed approach for the case of “face instability in soft soils using slurry shields.”     

The Effects of Information Overload on Software Project Risk Assessment*

Managers are responsible for providing effective information technology governance of the software development process. Ineffective governance leads to serious resource misallocations and negative consequences concerning Sarbanes‐Oxley compliance. In order for managers to make informed decisions about software development projects, they often need more information than is available through normal information channels, that is, they need an in‐depth review of the at‐risk project. Such in‐depth reviews, however, are costly. Hence, accurate identification of at‐risk projects for in‐depth review is critical to management's ability to govern. This research considers how two factors, information load and time pressure, affect the quality of the project‐selection process. We examine quality by observing the decision strategies involved and then relating these strategies to subsequent decision making. An experiment was conducted with experienced information systems auditors using a combination of policy‐capturing and computerized process‐tracing techniques. The participants in our study cope with information overload by accelerating their decision‐making process and adopting noncompensatory decision processes. Contrary to prior research, our process‐tracing analysis suggests that participants rarely filter information, thus implying that decision makers are unable to process all the information. Coping by resorting to noncompensatory strategies did not decrease decision quality unless combined with accelerated information processing. Participants also increase their weight on the software project risk factors that they repeatedly access and that they view for longer periods of time. The theoretical contributions and practical implications regarding what actions managers can take to reduce the negative impact of information overload are discussed.     

The Design of a Knowledge-Based Decision Support System to Support the Information Analyst in Determining Requirements

Decision support system (DSS) researchers and designers continue to look for unstructured organizational tasks where there is a critical need for intelligent computer-based support. One such decision task is information requirements determination. Requirements determination is recognized as the most crucial phase of the systems development life cycle. Unfortunately, most methodologies and CASE tools focus only on how to specify the requirements once they are determined. There is very little computer support for the process of determining requirements. This paper discusses the conceptual design and development of a knowledge-based DSS to support information analysts in the critical decision task of determining requirements for the design of effective information systems. The expert modeling support system has the expertise to assist the analyst in studying the organization as a whole and in modeling the system under study in the context of the overall organization's goals and needs. The focus of the paper is on the problems associated with building the knowledge base component of the intelligent decision support system. A prototype implementation of the system is described.