Models, knowledge creation and their limits

Instead of modelling socio-economic situations as mechanical systems with fixed, predictable behaviour, we now see that socio-economic systems are really complex systems, in which various possible structural changes can occur giving rise to a range of different possible futures. This necessary future uncertainty automatically imposes an uncertainty on the precise pay-off that any particular action or decision that an agent may take. Because of this, the decisions that agents will make are also uncertain and this poses limits to our ability to model socio-economic systems and therefore to the knowledge that we can have at any time about the future. Because of this constant knowledge decay, what matters in real world situations of markets and business is the generation of new, current knowledge. Contrary to traditional science in which the natural laws are independent of who knows them, in social and economic systems, knowledge of system behaviour decays over time, and is in any case used up when it triggers new behaviour in the system. Several examples of evolutionary market systems are presented which demonstrate how knowledge is constantly created and destroyed, and the problem of change, innovation and design are shown to be part of a ‘boundedly rational’ view in which imperfect search gives rise to ‘good enough’ behaviour. All of this is a radical departure from the traditional approach that falsely believe in the optimisation of designs, behaviours and profits. Complexity tells us that we must accept risk and uncertainty and work loosely, keeping our options open as much as possible.     

The knowledge of price and the price of knowledge

Social science is enmeshed from the outset in an interaction between individual agents, collective action and analytical response. The desire to implement policy and create a preferred outcome provides further complication. There is a fundamental confusion between knowledge in the system and knowledge about it. Classical market analysis divorces the two; in practice agents use both. Moreover, they use their knowledge to change the system. A given set of rules may therefore produce a variety of outcomes. Unless this phenomenon is better understood and analysed, policy-making will continue to produce unexpected and indeed undesired outcomes. Complex systems approaches offer a way forward into these issues which is beginning to bear fruit in thinking clearly about how systems can and should be analysed.     

IN-QUOTES: a knowledge-based system for supporting decision making in weakly structured domains

In complex and weakly structured domains, decision makers often employ multiple techniques, including quantitative modeling and reasoning from past experiences, to address the problem at hand. As such, there has been a call for more research on developing systems that merge problem-solving approaches, such as reasoning from past experiences, with other paradigms to provide support for both the unstructured and structured aspects of the decision-making process. Development of these systems is dependent on acquiring and modeling the knowledge and expertise inherent in the process and then representing and implementing it in an appropriate form. However, in weakly structured domains, knowledge acquisition may be better described as knowledge ‘co-creation’ in which the expert and system builder work together to understand the process and lend as much structure to it as possible. In this paper, we propose that the integration of principles drawn from the paradigms of case-base reasoning, expert systems, and object-oriented programming facilitates this process by providing a powerful approach to acquire and model knowledge in a weakly structured domain. We demonstrate this approach through the development of a system designed to assist a decision maker in the performance of a difficult, somewhat unstructured design and planning task. © 1998 John Wiley & Sons, Ltd.     

Refining the behavior of multiple expert systems: a concept and empirical results

Expert System Refinement (ESR) is introduced as a means to automatically refine the performance of one or more expert systems. The ESR concept is based on Holland’s learning classifier systems and a method for integrating multiple expert systems. Through users’ feedback about the usefulness/correctness of the integrated expert system’s recommendations, ESR enables behaviors of both individual expert systems as well as the integrated system to improve over time. The ESR concept is tested on a German Credit Database. This empirical evidence suggests that the ESR concept can be usefully applied in automating the process of expert system refinement and multiple expert systems integration. © 1998 John Wiley & Sons, Ltd.     

Trust for accounting and accounting for trust

This paper combines insights from the sociology of knowledge and the emerging practice-based literature on learning and knowing to extend the institutional framework of accounting change developed by Burns and Scapens [Burns, J., Scapens, R.W., 2000. Conceptualising management accounting change: an institutional framework. Manage. Acc. Res., 11, 3–25]. In particular, it explores how management accounting systems (MAS) can be implicated in processes of learning and culture change, and used to identify ‘trustworthy’ solutions in the face of organisational crises. A case study of an Italian company, which was subject to massive change following its acquisition by General Electric, is used to discuss how, when crises arise and organisation members find themselves under intense pressure for change, their rationales and routinised behaviour, which are driven by the existing knowledge and cultural assumptions, are challenged. The case illustrates how MAS can act as sources of trust for the processes of change – i.e., accounting for trust; while at the same time being socially constructed objects of trust – i.e., trust for accounting. Drawing on the concept of personal trust and the notion of roles as access points to organisational (expert) systems, the paper discusses how, in this case, finance experts facilitated the acceptance and progressive sharing of new rationales and routines. Clearly, this does not guarantee that change will occur or occur in some ‘desired’ direction in other cases, but it increases the possibility of replacing trust in the predictability of routines with feelings of trust for change.     

An Analysis of Potential Legal Liability Incurred Through Audit Expert Systems

The introduction of expert systems technology into the audit environment has opened a new avenue of auditor legal liability. This paper examines the potential impact expert systems will have on auditor liability. The presentation of this new avenue of auditors' legal liability explores both the potential for litigation under failure of auditor/expert system collaboration to yield prudent decisions and the failure to use an available expert system. The risks evolving from failure to use an available expert system include the possibility that the system could be used against the auditor in the courtroom. While case law will ultimately determine the bounds of this liability, this paper acquaints the reader with the important legal issues involved and the varied outcomes that could emerge. It should also be noted that while the specific example presented in this paper relates to the audit profession, the legal concepts are of equivalent concern to other professions enduring broad implementation of expert systems.     

Knowledge Representation Issues in Complex Decision Domains: an Example from Inherent Audit Risk Assessment

There has been significant discussion in artificial intelligence and expert systems concerning different representation systems for complex domain knowledge. This article discusses the strengths, weaknesses and psychological validity of two common systems, associational or rule- and model-based, and presents a computer model which incorporates both approaches into a hybrid system. The computer model reasons in the complex decision domain of inherent audit risk assessment.     

Corporate Reporting in Central and Eastern Europe: Issues,Challenges and Research Opportunities

The purpose of this paper, building upon the papers included in this special section of Accounting in Europe on Corporate reporting in CEE countries and on our knowledge of the region, is to broaden out and open up dialogue and debate about how local institutions are evolving and impact the corporate reporting practices in this under-researched region. We begin by discussing the institutional context for conducting research on corporate reporting by entities in Central and Eastern Europe (CEE), within the broader context of emerging, transitional economies. We also reflect on how research conducted on CEE countries can make a relevant contribution to the international literature, and exemplify by summarizing the research questions and findings of the papers included in the special section. A future research agenda emerges, given the gaps in the international literature and the future research implications suggested in the papers constituting the special section.     

Evaluating Expert Systems in a Financial Domain: A Study Demonstrating Issues in Case Selection,Validation Standards and Evaluator Bias

This article describes and generalizes a validation study of four commercially available personal financial planning expert systems and the rationale for the research methodology used. Our evaluation of these systems adds to the understanding of verification and validation issues related to case selection, validation standards and evaluator bias. The article describes the systems, their domain and the empirical method—field tests using hypothetical cases—and relates that method to the literature. Comparing same-task systems combines multiple system perspectives and multiple models. Our methodology did efficiently and effectively identify conflicting terminology, omissions and system weaknesses but was inadequate for comparing the complex plan recommendations. The results re-emphasize the importance of continual knowledge base updating, formal system testing and the need for external evaluation. The results also show the value of comparing multiple, same-task systems.     

From “Broad Studies” to Internet-based “Expert Knowledge Aggregation”. Notes on the methodology and technology of knowledge integration

The article takes as a starting point Herman Kahn's concept of a process-based approach to future-oriented knowledge aggregation and integration and discusses the opportunities created by Internet technologies to achieve the goal envisioned by such a model. The article briefly introduces a general framework for analyzing knowledge aggregation, the use of various aggregation mechanisms as well as aggregation systems that rely either on human facilitators (meta-experts) or on computer algorithms. Available online instruments for knowledge aggregation and expert challenge are presented and the possibility of using them for setting up “virtual think tanks” for foresight studies is explored.     

Decision making using time‐dependent knowledge: knowledge augmentation using qualitative reasoning

In this paper we propose a method to enhance the performance of knowledge‐based decision‐support systems, knowledge of which is volatile and incomplete by nature in a dynamically changing situation, by providing meta‐knowledge augmented by the Qualitative Reasoning (QR) approach. The proposed system intends to overcome the potential problem of completeness of the knowledge base. Using the deep meta‐knowledge incorporated into the QR module, along with the knowledge we gain from applying inductive learning, we then identify the ongoing process and amplify the effects of each pending process to the attribute values. In doing so, we apply the QR models to enhance or reveal the patterns which are otherwise less obvious. The enhanced patterns can eventually be used to improve the classification of the data samples. The success factor hinges on the completeness of the QR process knowledge base. With enough processes taking place, the influences of each process will lead prediction in a direction that can reflect more of the current trend. The preliminary results are successful and shed light on the smooth introduction of Qualitative Reasoning to the business domain from the physical laboratory application. © 2001 John Wiley & Sons, Ltd.     

A fuzzy logic-driven multiple knowledge integration framework for improving the performance of expert systems

To maintain a high performance in an ill-structured situation, expert systems should depend on multiple sources of knowledge rather than a single type. For this reason, we propose multiple knowledge integration by using a fuzzy logic-driven framework. Types of knowledge being considered here are threefold: machine, expert and user. Machine knowledge is obtained by a back- propagation neural network model from historical instances of a target problem domain. Expert knowledge is related to interpreting the trends of external factors that seem to affect the target problem domain. User knowledge represents a user’s personal views about information given by both expert knowledge and machine knowledge. The target problem domain of this paper is one-week-ahead stock market stage prediction: Bull, Edged-up, Edged-down, and Bear. Extensive experiments with real data proved that the proposed fuzzy logic-driven framework for multiple knowledge integration can contribute significantly to improving the performance of expert systems. Copyright © 1998 John Wiley & Sons, Ltd.     

The General Motors' variation-reduction adviser: an example of grassroots knowledge management development

One approach to developing knowledge management systems is to seed the system in key communities of practice and then encourage its customization and spread throughout the enterprise by local (‘grassroots’) initiative. This has the benefit of worker buy-in and adaptation of the local systems to their workflows. The concept is that, in exchange for some loss of control and standardization, the grassroots systems will be used and appreciated and will grow into an enterprise-wide system. In this paper, we discuss this approach and how it is emerging in General Motors' Variation-Reduction Adviser, a manufacturing knowledgesharing and lessons-learned system. Copyright © 2003 John Wiley & Sons, Ltd.     

Can knowledge based systems be designed to counteract deskilling effects?

Recent calls in the information systems research community argue that we know intelligent systems deskill users, and future research should focus on how to design systems that do not deskill, rather than continue to examine whether the phenomenon occurs. This should be a wakeup call for public accounting firms focused on implementing restrictive audit support systems, which leads to de-skilling of novice accounting professionals. Our research focuses on redesigning knowledge-based systems to facilitate expertise development and counteract the de-skilling effects that result from use of such systems. Specifically, we manipulate the design of the system interface by providing information cues in a screen format consistent with expert knowledge representations and manipulate automatic provision versus voluntary use of explanations for users during task completion. Results show that after using the knowledge-based system to complete a series of reenacted client engagements over a three-day period, both the interface design manipulation and automatic provision of explanations had a positive effect on novice accounting professionals’ development of expert-like knowledge structures. The results of the study have important implications for the development of knowledge-based systems intended to support accounting professionals’ (and other knowledge workers’) expertise development processes.     

The development of an expert system for managerial evaluation of internal controls

Both practitioners and researchers have devoted signi?cant effort to the study of decision aids, especially expert systems, to assist auditors in internal control evaluations. In addition to being used as a decision aid, researchers have long contended that expert systems could be used to train non‐expert users. Even though the professional accounting literature makes it clear that responsibility for maintaining an effective internal control system rests with management rather than auditors, the focus to date has been on expert systems aimed at assisting/training auditors, not an organization's management. In contrast, this study focuses on management as users of an expert system for internal control evaluation. We describe the development process, explain how the resulting system was evaluated, and discuss results of that evaluation. These results suggest that such a system gives a new way to help managers increase effectiveness and ef?ciency of a critical organizational process: the evaluation of internal controls. Copyright © 2004 John Wiley & Sons, Ltd.     

Futures signals sense-making framework (FSSF): A start-up tool to analyse and categorise weak signals, wild cards, drivers, trends and other types of information

This article introduces future signals sense-making framework (FSSF), an alternative philosophy towards weak signals, emerging issues, drivers, and trends, that is in contrast to the traditional single signal or path extrapolation approach. The philosophy of FSSF is based on the principles of environmental scanning and pattern management, which state that if there is a grand transformation process on the way or if there is a new emerging pattern or phenomenon, such a process will certainly be reflected in many different ways. Therefore, in this philosophy, futures knowledge is believed to be fragmented between various simultaneous and overlapping sources. Here, a researcher's task is to carry out a sufficient environmental scanning process and to cluster and sense how to create the emerging future through a pattern management process where FSSF plays a role as the first start-up tool. Alternatively, FSSF can also be used as a general knowledge management and sense-making tool for any kind of analyses.     

An Audit Framework for Expert Systems

Expert systems moved from research laboratories to widespread use in the business world during the previous decade. Current data indicate that use will continue to increase in the foreseeable future. However, auditors have not comprehensively addressed the need for and feasibility of independent audits of these systems. This paper discusses factors that will soon create a demand for independent audits of expert systems. It then identifies a framework present in current professional standards that appears to be appropriate for proceeding with the auditing of expert systems. A hypothetical case is subsequently presented to illustrate one possibility of how measurement criteria, audit procedures and an audit report developed under these professional standards might appear. Potential practice problems and research issues are identified throughout the paper.     

PowerPoint and Concept Maps: A Great Double Act

Abstract

This article explores how concept maps can provide a useful addition to PowerPoint slides to convey interconnections of knowledge and help students see how knowledge is often non-linear. While most accounting educators are familiar with PowerPoint, they are likely to be less familiar with concept maps and this article shows how the tool can be used by students to construct their own concept maps and how educator-constructed concept maps can be used as quizzes to encourage more student participation in class.     

How much discretion for risk regulators?

We analyze the regulation of firms that undertake socially risky activities but can reduce the probability of an accident inflicted on third parties by carrying out non verifiable effort. Congress delegates regulation to an agency, although these two bodies may have different preferences toward the industry. The optimal level of discretion left to the agency results from the following trade‐off: the agency can tailor discretionary policies to its expert knowledge about potential harm, but it implements policies that are too “pro‐industry.” The agency should be given full discretion when the firm is solvent; partial discretion is preferred otherwise. We then investigate how this trade‐off changes as the political and economic landscapes are modified.     

Performance measurement systems and their relation to strategic learning: A case study in a software-developing organization

Authors have often suggested that expert systems for measuring and assessing intangible and tangible resources fulfill strategic learning purposes. This role requires further theoretical discussion and empirical investigation since strategic learning does not automatically result from the implementation of performance measurement systems (PMS). PMS can be used for internal or external control purposes in addition their development and usage can lead to strategic learning processes. Depending on how PMS are conceptualized they can be related to different modes of learning. Thus, this article links different types of PMS enactment with specific modes of learning. Strategic learning is thereby defined as specific mode within the organizational learning approach by Bateson (1972) and Argyris and Schön (1978). Orlikowski's work on technologies (2000) provides a framework for the different types of PMS enactment. A strategic learning process initiated by the development of PMS is illustrated through a case study in the software development industry.