网范文：“Dynamic systems as tools for analysing human judgement” 随着计算机的出现，在实验室、动态系统已成为一个新的探讨工具。允许系统措施在这一领域有作用的变量,除了正式的背景,这篇范文提出了系统辨识的任务要求，英语论文，系统控制可以意识到在这些环境中,如何派生心理测量学的可接受的因变量。使用计算机模拟的场景在解决问题的探讨已成为越来越受欢迎，在过去的25年里，这种新措施解决问题似乎有吸引力的原因有几个。
与静态问题相比,计算机模拟场景提供了一个独特的机会来探讨人类解决问题和决策行为，下面的范文进行详述。
Introduction
With the advent of computers in the experimental labs, dynamic systems have become a new tool for research on problem solving and decision making. A short review of this research is given and the main features of these systems (connectivity and dynamics) are illustrated. To allow systematic approaches to the influential variables in this area, two formal frameworks (linear structural equations and finite state automata) are presented. Besides the formal background, the article sets out how the task demands of system identification and system control can be realised in these environments, and how psychometrically acceptable dependent variables can be derived.
The use of computer-simulated scenarios in problem-solving research has become increasingly popular during the last 25 years (for a representative collection of s see, e.g., the two editions from Sternberg & Frensch, 1991, and Frensch & Funke, 1995). This new approach to problem solving seems attractive for several reasons. In contrast to static problems, computer-simulated scenarios provide a unique opportunity to study human problem-solving and decision-making behaviour when the task environment and subjects’ actions change concurrently. Subjects can manipulate a specific scenario via a number of input variables (typically ranging from 2 to 20, and in some exceptional instances even up to 2017), and they observe the system’s state changes in a number of output variables. In exploring and/or controlling a system, subjects have to continuously acquire and use knowledge about the internal structure of the system.
Research on dynamic systems was motivated partly because traditional IQ tests turned out to be weak predictors in non-academic environments (see Rigas & Brehmer, 1999, p. 45). According to their proponents, computer-simulated “microworlds ” seem to possess what is called “ecological validity”. Simulations of (simplified) industrial production (e.g., Moray, Lootsteen, & Pajak, 1986), medical systems (e.g., Gardner & Berry, 1995), or political processes (e.g., Dörner, 1987) have the appeal of bringing “real-world tasks” to the laboratory. Brehmer and Dörner (1993) argue that these scenarios escape both the narrow straits of the laboratory and the deep blue sea of the field study, because scenarios allow for a high degree of fidelity with respect to reality and at the same time allow systematic control of influential factors.
These and other arguments have stimulated the use of a great diversity of dynamic systems as experimental task environments, each of which is designed to relate to a different aspect of “reality”. The problem, however, is that such vastly different experimental tasks and, hence, the results of experiments using these tasks, are very difficult to compare. In particular, it becomes unclear whether one should attribute experimental findings to the experimenter’s manipulation or to the peculiarities of the task employed. Most systems do not differ only with respect to surface features (i.e., the semantics implied by the labelling of their input and output variables) which we know to have strong influences on problem-solving behaviour in both static (e.g., Blessing & Ross, 1996; Hesse, Kauer, & Spies, 1997; Kotovsky & Fallside, 1989; Novick, 1988; Wagenaar, Keren, & Lichtenstein, 1988) and dynamic tasks (e.g., Hesse, 1982; Luc, Marescaux, & Karnas, 1989; Preußler, 1997; Putz-Osterloh, 1993).
Equally important, for most systems it is not clear how to compare them with respect to the underlying formal structure. There are two possible solutions to the latter problem. One possibility is to define a set of formal dynamic system characteristics and use this set to systematically compare the tasks used in various experiments (e.g., Funke, 1990). Such an analysis will at least give a rough idea of whether or not two dynamic tasks could yield comparable results.
The other possibility is to derive different dynamic task environments from the same formal background. The formal homogeneity of different task environments facilitates comparisons between experiments and increases the chances of discovering effects that are not just “local”. In the following sections I will illustrate this second solution. Before going into detail, let me add that dynamic systems are not only useful and necessary as task environments. With even more emphasis I would argue that we also need dynamic systems on the side of the theories (for example, van Gelder, 1998). The world is dynamic and we have to adapt continually. Static theories do not provide adequate explanations for how we cope in most of life’s activities. But that is another story—the current is primarily about tools and less about theories.
The plan of this is first to give a short review of the history of complex problem solving (CPS), second to elaborate in more detail the important features of a complex problem, third to demonstrate the use of two formal frameworks (linear structural equation systems and the theory of finite state automata) for constructing dynamic decision situations, and fourth to show what task demands result from such situations and what measures could be derived from the subjects’ interactions with these scenarios. A final conclusion will also mention some of the unsolved problems of this approach.
SHORT HISTORY OF PLEX PROBLEM SOLVING (CPS)
According to Buchner (1995) the history of CPS has two roots in the European countries, one resulting from Donald Broadbent’s research on different memory systems (Berry & Broadbent, 1984, 1988; Broadbent, 1977; Broadbent & Aston, 1978; Broadbent, Fitzgerald, & Broadbent, 1986; Hayes & Broadbent, 1988), the other from Dietrich Dörner’s research on the structure of intelligent behaviour (Dörner, 1987; Dörner, Kreuzig, Reither, & Stäudel, 1983; Dörner & Wearing, 1995). The first was dedicated to the experimental approach and therefore used very simple dynamic tasks (e.g., a simple transportation system or a simple sugar factory); the second one was a kind of exploratory work using a dynamic system with more than 2017 variables (for the simulated town called “Lohhausen”).
Broadbent’s work has led to the elaboration of explicit and implicit modes of learning and could be classified as one of the early dissociation studies which have been conducted many times in many different ways (for a review see Berry & Broadbent, 1995).1 Dörner’s work has led to an intensive discussion about the shortcomings of current IQ testing and to an action-theoretical analysis of acting in complex environments (Dörner, 1986, 1996; Dörner & Kreuzig, 1983; Dörner, Schaub, & Strohschneider, 1999). In the remaining part of the , I will concentrate on the latter issues, because from a human judgement point of view these topics seem to be more fruitful than elaborating on the explicit/implicit distinction (which has been well documented, for example, in the handbook edited by Stadler & Frensch, 1998). In the early work of Dörner and his associates, disappointment was expressed with the low predictive power of traditional IQ testing for problem solving in everyday situations.
Instead of using tasks that might be seen as rather academic, Dörner proposed an alternative approach: constructing complex everyday problems as simulated scenarios with which subjects had to interact under controlled conditions in the lab (see Brehmer & Dörner, 1993; Brehmer, Leplat, & Rasmussen, 1991). Subjects acting in these scenarios did indeed face a lot more tasks than in the IQ tests: (a) the complexity of the situation and (b) the connectivity between a huge number of variables forced the actors to reduce a large amount of information and anticipate side effects; (c) the dynamic nature of the problem situation required the prediction of future developments (a kind of planning) as well as long-term control of decision effects; (d) the intransparency (opaqueness) of the scenarios required the systematic collection of information; (e) the existence of multiple goals (polytely) required the careful elaboration of priorities and a balance between contradicting, conflicting goals.
FEATURES OF CPS
Before dealing with two formal frameworks more precisely, I will first discuss the main features of complex problems. In the previous section describing the early phases of CPS research, the following five typical qualities of a CPS scenario were mentioned (see Dörner, 1980; Dörner et al., 1983): (a) complexity, (b) connectivity, (c) dynamics, (d) intransparency (opaqueness), and (e) polytely (i.e., a problem situation having many goals at the same time).
This list remained more or less unchanged in the later studies; at most the former controversy about a taxonomy of systems and tasks (see Funke, 1990; Hussy, 1984, p. 122f; Strohschneider , 1991) dealt with this question in more detail.3 A closer examination of the five CPS features just listed allows three conclusions: First, the (a) complexity and (b) connectivity features are hardly to be distinguished. Considering the unclear definition of complexity (often understood as “number of variables with a system”; for a critique of this simple definition of complexity see Funke, 1984; Kotkamp, 1999, p. 27; Strauß, 1993, p. 38; Wallach, 1998, p. 130), I suggest we concentrate on the more comprehensible term “connectivity ”, understood as dependency between two or more variables. Connectivity is an important feature of complex systems (see Casti, 1979) and requires a subject to figure out the connections between the variables; that is, to construct a causal model of the system under consideration. Second, the feature (c) dynamics is a second important characteristic of a CPS system, making it clearly distinct from a static problem, which does not change its state over time. Whereas the aspect of connectivity is related to the structural relationships within a system, the aspect of dynamics is related to processes within a system. Therefore, a subject has to find out how the system develops or changes over time and what the short- and long-term effects of specific interventions are.
网站原创范文除特殊说明外一切图文作品权归所有；未经官方授权谢绝任何用途转载或刊发于媒体。如发生侵犯作品权现象，保留一切法学追诉权。()
更多范文欢迎访问我们主页 当然有需求可以和我们 联系交流。-X()
，英语论文题目 |
