the representation of uncertain information and inference with such information are some of the fundamental issues in uncertainty management. conventional methods for uncertainty management usually use a single value with the exception of interval-based approaches. in interval-based approaches, an interval is used to represent the uncertain information. it is assumed that the true, possibly unknown, value lies in an interval. however, in order to use interval-based methods, there must exist an order relation on the set of data values. the main objective of this thesis is to extend single-valued and interval-valued methods by introducing a framework of set-valued computations. in this model, uncertain information is described by a set without any further restrictions. basic issues of set-based computations are investigated. operations on set values are defined based on the corresponding point-based (i.e., single-value-based) operations on their members. the properties of set-based computations are examined in connection to the corresponding properties of the point-based computations. within the proposed framework, a critical analysis of a number of existing set-based computation methods is presented. this provides further evidence supporting the proposed model. to a large extent, the present study may be regarded as a more explicit re-examination of methods that have been implicitly used in many studies, using a unified notion. the results of such an investigation will be useful in establishing a framework for more systematic study of set-based computations.