DECISION SUPPORT SYSTEMS FOR ILL-STRUCTURED PROBLEMS: AN EMPIRICAL STUDY

Decision support systems (DSSs) are more complex than most other traditional decision-aid 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.     

PREDICTING STOCK MARKET BEHAVIOR THROUGH RULE INDUCTION: AN APPLICATION OF THE LEARNING-FROM-EXAMPLE APPROACH*

An artificial intelligence-based rule-induction approach to the analysis of stock market prediction is presented. A single investment analyst was used as the expert for this study. Predicting intermediate fluctuations in the movement of the market for nonconservative investors was selected as the decision to analyze. Commercially available rule-induction software was used to generate rules that predicted the market calls of the market analyst and the actual movements of the market. Rules predicting actual market movement performed better than rules predicting the analyst's calls and better than the analyst himself. Such an approach may prove useful in designing a decision support system for market analysts or in improving the decision-making processes of such analysts. The dynamic nature of the stock market represents a substantially different decision environment than those previously analyzed by learning-from-example (LFE) techniques. This study provides insights into the limits and applications of LFE approaches.     

USING ARTIFICIAL INTELLIGENCE TECHNIQUES TO ENHANCE THE CAPABILITIES OF MODEL MANAGEMENT SYSTEMS

Decision Support Systems (DSSs) originally were proposed as interactive problem-solving vehicles through which models and analytical techniques could be made available to decision makers. Model management represents a line of research within the DSS field that focuses on the design and implementation of software environments to support the use of DSSs for this purpose. This paper describes the tasks that must be supported within these environments and illustrates how existing prototype Model Management Systems (MMSs) implementations provide this support. The use of artificial intelligence techniques in such implementations are reveiwed, and three scenarios are presented to show how future MMSs could be constructed using these techniques.     

Spillover Impacts of a Reproductive Health Program on Elderly Women in Rural Bangladesh

This paper extends the program evaluation literature by investigating intra-household externalities generated by a reproductive health program, administered as a quasi-control experiment in rural Bangladesh. Although the program targeted only mothers and children in randomly selected treatment areas, using a reduced form demand approach and data from Matlab Health and Socio-economic Survey of 1996, we found a significantly positive spillover impact of this reproductive health program on the health of the never-targeted elderly women.

Decision Making,Scaling, and Number Crunching

The purpose of this note is to comment on the artificiality of using known scales of measurement (or conveniently improvised numbers) to make decisions. In particular, the pitfalls of trying to please the decision maker and of using normalization as a number crunching tool are discussed. Finally, an example is given to show what is needed to develop a workable and theoretically sound theory for multi-criteria decisions and to show that the analytic hierarchy process (AHP) meets these demands.     

中国林业扶持的依据

中国目前经济发展的程度和所处的阶段,已有能力和条件转向对林业的适度扶持.林业的供给与需求的重要性决定了必须对林业进行扶持.林业的弱质性是林业扶持的现实依据.尽管中国已消灭了森林资源赤字,实现了森林资源面积和蓄积双增长的局面,但中国林业的发展仍面临一系列严重问题,构成必然对林业进行扶持的迫切依据.     

流动人口的人际信任、社会支持和幸福感的比较及相互关系研究——以温州为例

比较了流动人口人际信任、社会支持、幸福感在人口学变量上的差异,并探讨了三者间的关系。结果发现:(1)在人际信任变量上,男性显著高于女性;25～30岁流动人口在人际信任上的得分最高,显著高于研究中的其它年龄组;在社会支持变量上,外出打工5年以上的流动人口要显著高于1年及以下(p<0.05)及1～2年(p<0.05);18～25岁流动人口社会支持得分最低,显著低于其它年龄组。在幸福感变量上,男性显著高于女性;签订劳动合同的流动人口显著高于没有签订劳动合同者。(2)在人际信任、社会支持和幸福感关系中,客观支持和主观支持对人际信任与幸福感之间的关系存在一定的调节作用。     

社会支持理论——一个文献的回顾

1970年代初,精神病学研究者首先从科学的角度,社会学家、流行病学家、心理学家等都从各自的理论立场出发,对社会支持加以研究.本文在梳理社会支持的内涵的基础上,详细阐释了社会支持体系的构成要素,最后就国内学术界对于该领域的研究做了一个简要的回顾.     

关于我国助老服务“时间银行”建设的思考

在综合国内外助老服务"时间银行"发展和现状的基础上,指出建设助老服务"时间银行"有助于建立社会化的养老体系,进一步推动我国的养老服务社会化进程。"时间银行"模式,适应我国低成本战略应对人口老龄化的要求,也是探索多层次养老服务体系、强化社区养老服务的要求,同时有利于推动我国精神文明建设的要。为此,助老服务"时间银行"的建设和发展需要依靠国家主导制定相关政策,自上而下全面开展,最终实现全国的系统构建和信息互通互联。     

构建依托社区服务的新型养老模式

人口老龄化是人类社会发展的必然结果。与发达国家不同的是 ,我国人口老龄化数量大、速度快 ,且城市人口老龄化的速度明显快于农村 ;我国经济尚未发达、社会保障体制尚未健全 ,现有的养老模式面临着严峻的挑战。自八十年代以来 ,城市社区服务蓬勃发展 ,为解决中国的养老问题提供了新的机遇 ,构建依托社区服务的养老模式是符合我国国情的城市人口老龄化对策之一。