Fundamental assumptions behind qualitative modelling are critically considered and some inherent problems in that modelling approach are outlined. The problems outlined are due to the assumption that a sufficient set of symbols representing the fundamental features of the physical world exists. That assumption causes serious problems when modelling continuous systems. An alternative for intelligent system building for cases not suitable for qualitative modelling is proposed. The proposed alternative combines neural networks and quantitative modelling.

This article considers current research in computer‐supported cooperative work (CSCW), computer‐mediated communication (CMC), and distributed artificial intelligence (DAl). These areas need an articulated model of social contexts to bridge the persisting gap between social and technological dimensions in computer system design and use.A conceptual model of context is presented to account for both cooperation‐conflict and communication‐negotiation processes. The model conceives of contexts as including not only physical objects and other people but also social norms which influence both individuals and organizations. (...) It assumes that computer system use occurs in social scenarios in which the features cannot be reduced to any type of input or data in the world that designers and users can process along with other information coming from the current task. The model is built on three levels: from social contexts as normative order (Level 1), to specific and intrinsically complex situations (Level 2), to person‐computer interactions for the performance of particular tasks (Level 3).The model has three main implications. First, Human‐Computer Interaction (HCl) studies—especially scenario‐based design—may profit from a fresh topdown approach to designers' and users' mental models taking into account normative social processes which have been neglected in previous research. Second, CSCW may realize how deeply discrepant perspectives affect multi‐agent environments and why in real working life negotiation is intertwined with cooperation. Designers may use this insight to design systems allowing more place for negotiation among actors. Third, we should dismiss the view that CMC lacks adequate social cues and fosters impulsive behavior. Cognitive processes such as categorization, stereotype construction, and social identification can make electronic environments even more strongly sensible to social norms than face‐to‐face communication. Context, according to our model, is not restricted to the physical copresence of other people but consists mainly of processes providing situations with socially recognizable meaning. (shrink)

Theoretical commentaries on AI often operate as a metadiscourse on the way in which science represents itself to a wider public. The sciences and humanities do the same kind of work but in different fields that encourage them to talk about their work differently: science refers to a natural world that does not talk back, and the humanities refer continually to a world with communicative people in it. This paper suggests that much AI commentary is misconceived because it models itself (...) on the way that science represents itself, rather than on the actual practice of science.AI theorists have become increasingly worried about the lack of evaluation in AI, the lack of reflexivity, and the lack of contact with society. Frequently these writers turn to concepts of tacit knowledge to work through these worries. In doing so they are recognising the problem of AI's second-order representation of science and trying to deal with it. However, this recognition of a problem with the representations of science simply turns back to the legitimation crisis of Western politics where many commentators use science precisely as a ‘model’ for western political institutions. They do so because science is one of the few areas of knowledge where it has been legitimate to use plausible methodology for representation that allows for arbitrary designations of authority as well as parallel systems of different authority. However, the plausible rejects any control on reflexivity, assumes an ethnocentric club culture and does not address social context.It is in this sense that the problems of legitimation in political liberalism are similar to those of legitimation in sciences, both are rooted in their uses of representation. AI's link with the representation of science places it in the heart of this debate about legitimacy. This paper suggests that AI does need to learn about reflexivity and that it might well do so by looking at the recent work on experimentation and representation by historians of science, and by looking to the debates about representation by historians of science, and by looking to the debates about representation within the humanities. However, reflexivity may not be enough. Devising rules of thumb for the appropriate halting of reflexivity, is also needed to address social context and take action. (shrink)