GDSS及其绩效分析

GDSS——群体决策支持系统，是一种综合运用通讯技术、计算机技术、决策支持技术，交互式地对非结构化的问题寻求解决的技术和方法。文章作者初步探讨了GDSS和领导行为的关系，以及在GDSS情景下群体决策绩效的增损，并指出了以后GDSS研究方向和应该注意的问题。     

子群体间交互对动态群体决策观点收敛时间的影响研究

研究动态群体决策中子群体之间的交互对群体决策观点收敛时间及其稳定性的影响。设定合理的决策观点交互规则,即考虑子群体内部个体之间决策观点交互和不同子群体之间的决策观点交互,建立动态群体决策观点演化模型。仿真实验结果表明,子群体之间交互会促进整个群体达成共识;子群体的信任水平越高,群体决策观点的收敛时间越短,且收敛时间的稳定性越好;子群体间交互观点数目增多会缩短群体决策观点收敛时间。     

采用反向测试法避免定时环路

提出了在ＳＤＨ网络分配定时的过程中，增强Ｓ１字节功能，采用“ＤＵＳ ｆｏｒ Ｔｅｓｔ（ＤＵＳＴ）”的反向测试方法来避免定时环路的产生。     

群体决策与个体决策过分自信的比较研究

组织的群体决策理念与群体决策时群体极化理论中的风险转移之间存在着分歧。立足于这一分歧,研究采用“沙漠生存”游戏情境来检验个体决策与群体决策时的过分自信和决策质量。对96名被试的研究表明：群体决策在质量上优于个体决策,无论是群体决策还是个体决策都会出现过分自信的现象,群体对群体决策的过分自信程度高于个体对群体决策的过分自信程度。     

计算机应选配怎样的监视器

计算机应选配怎样的监视器屏幕尺寸工作质量的影响计算机监视器ＣＲＴ（阴极射线管）屏的尺寸加大，是用户的一个趋势，对于小系统的用户尤其如此。ＩＢＭ及与其兼容的个人计算机通常是在ＭＳ－ＤＯＳ或ＯＳ／２下配Ｗｉｎｄｏｗｓ运行的。适用于这些用户界面的应用程序，...     

群决策支持系统研究的现状值得注意的问题及将来的研究方向

对群决策支持系统的含义，组成、结构类型，实验研究及设计问题的研究现状进行了分析，提出了ＧＤＳＳ研究中值得注意的问题，并指出ＧＤＳＳ将来的研究方向。     

HDPE／PS／HDPE－g－PS合金的相容性和力学性能研究

用自制接枝共聚物ＧＲ－Ⅰ、ＧＲ－Ⅱ相容剂研究其对ＨＤＰＥ／ＰＳ共混物相容性和力学性能的影响。通过ＳＥＭ、ＤＭＡ、ＤＳＣ和力学性能测试表征，表明在ＨＤＰＥ／ＰＳ共混中加入这些相容剂其相容性和力学性能有一定提高     

HDPE／PS／HDPE—g—PS合金的相容性和力学性能研究

用自制接枝共聚物ＧＲ－Ｉ、ＧＲ－Ⅱ相容剂研究其对ＨＤＰＥ／ＰＳ共混物相容性和力学性能的影响。通过ＳＥＭ、ＤＭＡ和力学性能测试表征，表明在ＨＤＰＥ／ＰＳ共混中加入这些相容剂其相容性和力学性能有一定提高。     

LDPE／PS／LDPE—g—PS合金的相容性和力学性能研究

研究自制接枝共聚物ＧＲ－１，ＧＲ－Ⅱ相容剂对ＬＤＰＥ／ＰＳ共混物相容性和力学性能的影响，通过ＳＥＭ，ＤＭＡ和力学性能测试表征，表明在ＬＤＰＥ／ＰＳ共混中加入这些相容剂后，其相容性有一定提高，共混物结构形态呈精细分散结构。3     

新型集成电路器件与中、高档B超诊断仪的研制开发

本文扼要地介绍了新型器件在中、高档Ｂ超研制开发中的重要地位。首先介绍了ＰＬＤ、ＧＡ、ＳＣ等ＡＳＩＣ器件的简单原理、设计思想，指出了采用ＡＳＩＣ替代传统的ＳＳＩ／ＭＳＩ器件的优点以及在我国推广应用的困难。其后，文章又介绍了数字信号处理、图象处理及计算机专用芯片的基本情况。最后提出了对ＰＬＤ、ＳＣ、ＧＡ等ＡＳＩＣ器件和ＤＳＰ、图象处理以及计算机专用芯片在中、高档Ｂ超中进行应用研究的建议和设想。     

Understanding the role of intuition in decision-making when designing for experiences: contributions from cognitive psychology

In the cognitive sciences, intuition is described as a way of processing information based on automatic, affective and personal standards, but it is not the opposite of rationality. Designers generate solutions to daily issues, which forces them to make decisions that cannot be always understood rationally. Designing for experiences is a delicate practice in a rational perspective, since the designer's interpretation on how to trigger particular experiences can be highly influenced by intuition. Designers have a choice to use intuition in the design process without control, or to understand the process and use it consciously. In this context, self-knowing is a key factor in intentional intuiting. This paper aims at discussing how intuition works when professionals design for experiences and, by providing the basis to understand the process, offers inputs on how to deal with it, avoiding biases in the design practice and helping to develop self-knowledge.

Relevance to ergonomics theory

In recent decades, ergonomics has been concerned with the understanding of designing to evoke or prevent particular experiences among users. The designer's challenge is to interpret users’ bodily experiences, commonly without awareness of the situation or context in which they are perceived. The design process is often affected by intuition and its role needs to be understood by professionals in order to be used accurately. Theoretical inputs from cognitive psychology are discussed in relation to the design practice, to help improve ergonomics theory in the context of intuition in designing for experiences.

The paper indicates that a designer can make a conscious decision to use intuition when designing for experiences. The use of intentional intuiting in this context does not exclude rational thinking, but complements it and helps qualifying design decision-making, since it reduces the use of biased subjectiveness in the interpretation on how to evoke or prevent particular experiences. The training of intentional intuiting is possible but challenging since it takes time, and the keys to successful use of intuition lay in self-knowing and appropriate state of mind of a designer.     

Common biases in client involved decision-making in the AEC industry

Understanding of the constitution of client involved decisions is important for future improvements of the processes. Significant decisions in construction projects are reliant on heuristic processes where assumptions are developed from past experience. The paper presents a methodology to collect empirical data in an unstructured manner utilizing participant intuition and experience regarding project level collaboration, a term easily understood by practitioners. Empirical data collected from 6 focus group discussions in Norway and 18 individual interviews in Finland is associated with biases in decision making aimed at bridging the gap of understanding and literature’s insufficient coverage. An analytic framework was developed to suit the diverse emergence of concepts to allow application of psychological principles in a structured manner to empirical data. The paper contributes by identifying types of cognitive and motivational biases in client involved decisions. The biases are found to be alleviated by one another depending on the particular application of the decision. Findings suggest that normative beliefs exist developed from past experience and habitual thinking. A number of emerged biases in this domain are alleviated from normative beliefs which are discussed in this paper.     

Reducing the disruptive effects of interruption: A cognitive framework for analysing the costs and benefits of intervention strategies

Interruptions are ubiquitous, and they can lead to disastrous consequences. The goal of this paper is to describe remedies that have been proposed to reduce the disruption caused by interruptions based on an understanding of how principles of human cognitive processing bear on the sequence of events that take place during an interruption. We show that interruptions tap disparate cognitive operations, from attention to decision making to memory. We illustrate how these cognitive processes can lead to interruption-induced errors, and how they can help in understanding potential problems with remedies that have been proposed to ameliorate those effects. We present a framework in which the load imposed by the task and the cost of an error suggests the types of solutions that should be considered for a given domain. We then discuss the implications of this approach for understanding and reducing the negative effect of interruptions in transportation domains.     

认知需求在消费选项、时间与决策规避行为之间的调节

本研究以中学教师为被试,采用问卷调查的形式对选项数量、时间压力对消费者决策规避的影响以及消费者个体的认知需求水平在此过程中起到的作用进行研究。结果发现,选项数量为1比选项数量为2时消费者更倾向于规避决策;时间压力存在比时间压力不存在时消费者更倾向于规避决策。并且,个体的认知需求水平在选项数量与决策规避的关系中起到了调节作用。认知需求高的消费者的决策规避在两种选项数量之间差异较大,而认知需求低的消费者则差异较小。     

What you find is not always what you fix—How other aspects than causes of accidents decide recommendations for remedial actions

In accident investigation, the ideal is often to follow the principle “what-you-find-is-what-you-fix”, an ideal reflecting that the investigation should be a rational process of first identifying causes, and then implement remedial actions to fix them. Previous research has however identified cognitive and political biases leading away from this ideal. Somewhat surprisingly, however, the same factors that often are highlighted in modern accident models are not perceived in a recursive manner to reflect how they influence the process of accident investigation in itself. Those factors are more extensive than the cognitive and political biases that are often highlighted in theory. Our purpose in this study was to reveal constraints affecting accident investigation practices that lead the investigation towards or away from the ideal of “what-you-find-is-what-you-fix”. We conducted a qualitative interview study with 22 accident investigators from different domains in Sweden. We found a wide range of factors that led investigations away from the ideal, most which more resembled factors involved in organizational accidents, rather than reflecting flawed thinking. One particular limitation of investigation was that many investigations stop the analysis at the level of “preventable causes”, the level where remedies that were currently practical to implement could be found. This could potentially limit the usefulness of using investigations to get a view on the “big picture” of causes of accidents as a basis for further remedial actions.     

基于前景理论的移动理财决策体验引导设计

目的研究决策体验引导的交互设计,减少用户认知成本,帮助用户进行移动理财决策。方法基于前景理论中关于决策者在风险决策过程中的心理和行为机制的研究,通过定性分析得出决策行为中的影响因素的价值与权重,提出了移动理财的决策体验引导设计模型,结合交互设计方法、流程、要素,对引导用户的决策行为进行研究。结论通过移动理财产品首页的设计案例,验证了基于前景理论的决策体验引导设计模型的可行性。在实现可用性目标的同时,也提升了用户在理财决策过程中的体验。     

Optimal use of simplified social information in sequential decision-making

Social animals can improve their decisions by attending to those made by others. The benefit of this social information must be balanced against the costs of obtaining and processing it. Previous work has focused on rational agents that respond optimally to a sequence of prior decisions. However, full decision sequences are potentially costly to perceive and process. As such, animals may rely on simpler social information, which will affect the social behaviour they exhibit. Here, I derive the optimal policy for agents responding to simplified forms of social information. I show how the behaviour of agents attending to the aggregate number of previous choices differs from those attending to just the most recent prior decision, and I propose a hybrid strategy that provides a highly accurate approximation to the optimal policy with the full sequence. Finally, I analyse the evolutionary stability of each strategy, showing that the hybrid strategy dominates when cognitive costs are low but non-zero, while attending to the most recent decision is dominant when costs are high. These results show that agents can employ highly effective social decision-making rules without requiring unrealistic cognitive capacities, and indicate likely ecological variation in the social information different animals attend to.     

Challenges of human–machine collaboration in risky decision-making

The purpose of this paper is to delineate the research challenges of human–machine collaboration in risky decision-making. Technological advances in machine intelligence have enabled a growing number of applications in human–machine collaborative decision-making. Therefore, it is desirable to achieve superior performance by fully leveraging human and machine capabilities. In risky decision-making, a human decision-maker is vulnerable to cognitive biases when judging the possible outcomes of a risky event, whereas a machine decision-maker cannot handle new and dynamic contexts with incomplete information well. We first summarize features of risky decision-making and possible biases of human decision-makers therein. Then, we argue the necessity and urgency of advancing human–machine collaboration in risky decision-making. Afterward, we review the literature on human–machine collaboration in a general decision context, from the perspectives of human–machine organization, relationship, and collaboration. Lastly, we propose challenges of enhancing human–machine communication and teamwork in risky decision-making, followed by future research avenues.     

An analytical approach to quantitative effect estimation of operation advisory system based on human cognitive process using the Bayesian belief network

The design of instrumentation and control (I&C) systems for nuclear power plants (NPPs) is rapidly moving towards fully digital I&C systems and is trending towards the introduction of modern computer techniques into the design of advanced main control rooms (MCRs) of NPPs. In the design of advanced MCRs, human–machine interfaces have improved and various types of decision support systems have been developed. It is important to design highly reliable decision support systems in order to adapt them in actual NPPs. In addition, to evaluate decision support systems in order to validate their efficiency is as important as to design highly reliable decision support systems. In this paper, an operation advisory system based on the human cognitive process is evaluated in order to estimate its effect. The Bayesian belief network model is used in the evaluation of the target system, and a model is constructed based on human reliability analysis event trees. In the evaluation results, a target system based on the operator's cognitive process showed better performance compared to independent decision support systems.     

Analytic Cognitive Task Allocation: a decision model for cognitive task allocation

This paper introduces Analytic Cognitive Task Allocation (Analytic CTA) as a decision model for cognitive task allocation. Analytic CTA can support system designers systematically analyse alternative allocation choices, evaluate them through criterion-based quantitative judgements and integrate partial judgements towards a final allocation decision. Analytic CTA was applied in the design of cognitive task allocation by testing three hypotheses. The hypotheses postulated that Analytic CTA can effectively support system designers in three ways: (a) in the identification of task requirements; (b) in the definition of design trade-offs between human allocation and automation; and (c) in the derivation of the allocation decision. Two groups of system designers allocated cognitive functions from three task scenarios in production planning and control. The experimental group, which was supported by the decision model, made allocation decisions which expressed weak preference for either human allocation or automation, consistent with the experimental tasks which had been selected as to elicit weak preferences. The control group relied on the early selection of an automation technology as the means of structuring the design problem. This involved reducing the task to a version suitable for automation, introduced a pro-automation bias and resulted in allocation decisions expressing strong preference for automation. These results were found in support of the experimental hypotheses.