网范文：“FORMAL FRAMEWORKS” 两种形式满足不同程度的连通性和规律，两种措施是有用的，英语论文，在各种变量之间的关系及其随时间变化的作用。这篇范文讲述了这一问题。伦敦证交所处理定量变量，英国金融服务管理局主要是用于系统组成的定性变量。读者开始熟悉这两种措施,每一个简单地概述了之前的重要问题,如何使用这些框架探讨成果对人类决策和解决问题。在日常生活中,许多活动需要的监管和控制过程,包括定量变量。
经济和生态的情况下,我们首先需要了解系统面向目标的行动是可能的。在许多科学、系统和定量变量表示成功地通过了一般线性模型。下面的范文进行详述。
Introduction
The two formalisms which satisfy the aforementioned requirements—realising different degrees of connectivity and dynamics—are (1) the linear structural equation approach (LSE, see Funke, 1985, 1993; Vollmeyer & Funke, 1999) and (2) the theory of finite state automata (FSA, see Buchner & Funke, 1993; Funke & Buchner, 1992). Both approaches, LSE and FSA, are helpful in modelling the connection between various variables and their effects over time. Whereas the LSE deals with quantitative variables (measured on an interval scale), the FSA is primarily useful for systems consisting of qualitative variables (measured on a nominal scale). To familiarise the reader with the two approaches, each will be outlined briefly before moving on to the important question of how these frameworks can be used fruitfully in research on human decision making and problem solving.
In everyday life, a number of activities require the regulation and control of processes that consist of quantitative variables (e.g., driving a car, controlling a CAD machine). Not only technical but also economic and ecological situations require that we first understand the system before goal-oriented action is possible. In many sciences, systems with quantitative variables are represented successfully by means of the general linear model (see Stevens, 1992). How can such a linear model be used as a tool for analysing decision making and problem solving? The subject is instructed that she or he has to deal with a system that consists of some exogenous and some endogenous variables. The exogenous variables can be directly manipulated by the subject and, thus, can influence the endogenous variables which cannot be manipulated directly. The general task is (a) to find out how the exogenous and endogenous variables are related to each other, and (b) to control the variables in the system so that they reach certain goal values. Normally, these two sub tasks of system identification and system control are separated experimentally as two steps of the whole task (see Funke, 1993).4
In some systems, the endogenous variables have effects on other endogenous ones (in Figure 1, the effect from Y to Z); effects that one might label “indirect” which show up only when manipulating the exogenous variable A. Variable A itself has two effects, one being larger (“main effect” on Z), and one smaller (“side effect” on Y). Also, endogenous variables can influence themselves (shown with variable Z in the example), thus representing an effect one might call “eigendynamic ” because of the constant increase or decrease of this variable independent of other influences.5 As the reader might imagine, there are many possible ways to construct linear systems with a full range of effects of the kind just described, thus making identification and control of such systems a difficult problem.
Finite State Automata (FSA)
Many devices that we use every day, like coffee machines, video and fax machines, cameras, ticket machines, operating systems, any kind of software (and many more, see Weir, 1991), can be characterised on an abstract level by three qualities (see Buchner, 1999): (a) they can only be in a limited (finite) number of states; (b) from a given state they move to the following state either through user input (in the case of software, for example, by pressing a certain key) or through an autonomous process (for instance, in the case of a ticket machine where, after a certain period of no interactions, an automatic reset occurs); (c) normally, an output signal is produced dependent on the user’s input and the state reached. Systems with these attributes are represented on a more formal level as finite state automata.
A deterministic finite state automaton (Ashby, 1956; Hopcroft & Ullmann, 1979; Roberts, 1976; Salomaa, 1985) is defined by a finite set X of input signals, a finite set Z of states, a finite set Y of output signals, and two functions (see Hopcroft & Ullman, 1979, p. 15f): The transition function represents a mapping of Z × X on Z and determines which state will follow from a given state dependent on the input signal; the result function represents a mapping Z × X on Y and determines what kind of output signal will follow as a consequence of the input signal. A special case occurs if the output signal depends completely on the state reached and is independent of the input signal. In this case, the result function is replaced by a marker function which connects output signals with states. FSAs are frequently represented as state-transition matrices or as directed graphs. Each of the two representations illustrate certain aspects of the system in a special way. Table 1, for example, contains the state-transition matrix of a fictitious abstract system whose graphical structure is shown in Figure 2 (for a semantically labelled example, see Buchner & Funke, 1993).
TASK DEMANDS AND ASSESSMENT PROCEDURES
To use the two presented formalisms in research on complex problem solving and decision making, we need to specify in more detail the task requirements, as well as the assessment procedures by which the performance of decision makers and problem solvers can be evaluated. I will start with a description of the two main tasks: knowledge acquisition and knowledge application, illuminating these topics by an example.
Knowledge acquisition The term “knowledge acquisition” (system identification) describes a complex learning situation during which the subject has to find out details about the connectivity of the variables and their dynamics. The structural aspects of the system (= connectivity) cannot easily be separated from the dynamic aspects, because the system itself can only be analysed interactively over the time course. In the LSE situation, this identification problem requires an identification strategy; that is, a certain way of manipulating the exogenous variables so that you can derive from the consequences (in terms of values of the endogenous variables) the causal structure of the system, or at least to come to hypotheses about this structure which could be tested subsequently. Identification of system relation can occur at different levels: (a) as identification of the existence or non-existence of a relation, (b) as specification of a direction, (c) as specification of qualitative aspects of this (either positive or negative) relation, and (d) as the exact quantitative specification of the weight of this relation. In the FSA situation, the task is similar because the effects of the input signals on the output signals and on the states of the system have to be discovered. Instead of searching for direction, qualitative or quantitative aspects, one has to detect conditions that have to be satisfied in order to make certain state transitions possible.
Task demand 2: Knowledge application The term “knowledge application” (system control) describes the situation of applying previously acquired knowledge in order to reach a certain goal state within the system. The goal specifications are normally given by the experimenter. 80 FUNKE In the LSE situation, knowledge application requires two subgoals: first, to transform a given state of the endogenous variables by means of an input vector into the goal state, and second, to keep this goal state on a stable level because in a dynamic system the goal state—once reached—may disappear quickly due to “eigendynamics ” (the ability of some variables to influence themselves). In the FSA situation, the task of knowledge application simply requires finding a path from the initial to the goal state; that is, to find a sequence of input signals that leads to the goal. This task might become fairly difficult if a number of pre-conditions have to be fulfilled in order to allow for a certain critical sequence. (For example, to change the waking time on a clock-radio alarm may require a “set-up sequence” to be passed successfully before a time change is accepted.)
The relationship between knowledge acquisition and knowledge application
In the preceding section it was assumed that the two processes of knowledge acquisition and knowledge application can be separated from each other. Formally, one can realise this distinction by different instructions to the subject for each of the two tasks. For example, a subject could be instructed to explore the system in a first phase for a certain period of time, in order to control the system in a second phase for certain goal states presented by the experimenter. However, there is one problem: even under such clear-cut instructions, during the first (exploration) phase subjects could try to reach self-generated goals in order to apply this knowledge without being evaluated. Even during the second (application) phase, subjects can learn about the system because of the feedback they receive from the system for their interventions.
One might argue that learning models (such as neural networks) do not separate out knowledge acquisition and knowledge application into distinct stages (or even processes), and that the acquisition of further knowledge is always a result of applying the knowledge that is already accumulated to the new situation. But even though there is truth in this, it has to be acknowledged that after some learning period in a neural network, feedback is removed so that the knowledge application can be tested without contamination by further knowledge acquisition. Concerning the causal relationship between knowledge acquisition and application , a common assumption holds that acquisition is a necessary and sufficient condition for application. Moreover, a number of older studies (e.g., Berry & Broadbent, 1984, 1987, 1988) ed dissociation between verbalisable knowledge and control performance. These results have since been interpreted in a somewhat different fashion (cf. Buchner et al., 1995): instead of making the assumption of two different modes of learning (an “implicit” one for contingencies and an “explicit” one for hypotheses and rules), Buchner et al. explain the dissociation effects in terms of different degrees of system exploration. Here, different types of knowledge acquisition (in terms of explored state transitions) were made responsible for a specific pattern of response during the application phase. Also, more recent studies show substantial positive correlations between knowledge acquisition and performance, if subjects are encouraged to acquire knowledge, if they have time, and if an assessment of the acquired knowledge is differentiated enough (e.g., Beckmann, 1994; Beckmann & Guthke, 1995; Funke, 1993; Kersting, 1999; Müller, 1993; Preußler, 1996, 1998; Sanderson, 1989; Süß, 1999).
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