The fuzzy set approach has progressively been introduced into many areas of organisational science in order to compensate for certain inadequacies in traditional tools. Indeed behaviourists and expected utility researchers have long been studying the role of ambiguity and vagueness in the human decision making process (e.g., Einhorn and Hogarth, 1986) and have highlighted the paradoxes linked to the use of probability theory (e.g., Tverski et al., 1984). The organisational sciences are particularly representative of systems with human interaction, in which information is affected by fuzziness (Zadeh, 1965). The areas of application for fuzzy set theory are characterised by: the importance of the role assigned to human judgement in decision making, the use of qualitative information, the dominant role of subjective evaluation and, more generally, the processing of information affected by non probabilistic uncertainty.We have dealt with two aspects of uncertainty in the field of accounting and controlling: • the creation of models which will accept imperfect information, • the effect of the use of imperfect information for decision making. The first point covers both imperfection in the data and imperfection in the relationships between the data. Indeed, we have seen that imperfect accounting data has led to a radical reappraisal and extension of the principal of double- entry accounting in order to obtain fuzzified financial statements. At the same time, auditing financial statements also raises the problem of the relevance of a precise evaluation of a judgement. We therefore put forward and tested an audit risk evaluation model based on fuzzy logic which allowed for a linguistic evaluation of the judgement. However, imperfect data cannot be the only dimension of modelled uncertainty in accounting and controlling. Indeed, the relationship between the variables is a major characteristic of management problems. We suggested, for instance, a formal model of financial valuation including synergy by using fuzzy measures. Moreover, the existence of a relationship between two variables of a model may be used to reduce the entropy of the resulting information as we have shown, by constructing an algorithm modelling a simple fuzzy relationship. Finally, the cognitive aspect of the use of imperfect information by managers should not be neglected in a consideration of decision-making. On the one hand, different forms of resistance to ambiguity have been found, which consequently prejudice the effectiveness of the information. But on the other hand, the replacement of the theory of measurement by fuzzy logic modifies the paradigmatic framework of the "management with imperfect information" model. When this becomes "representation of knowledge" and is no longer a "function of data", it seems to introduce behaviour which is less subject to cognitive bias, enabling a more rational treatment of the available information. This work, which originated in accounting and controlling, suggests research possibilities likely to find applications in other management in uncertainty disciplines (cost management, finance, etc.).
