 These various theoretical and statistical assumptions form the basis for the construction of one or more models of the measurement process. Unlike mathematical theories of measurement, where the term “model” denotes a set-theoretical structure that interprets a formal language, here the term “model” denotes an abstract and local representation of a target system that is constructed from simplifying  assumptions.[17]  The relevant target system in this case is a measurement process, that is, a system composed of a measuring instrument, objects or events to be measured, the environment (including human operators), secondary instruments and reference standards, the time-evolution of these components, and their various interactions with each other. Measurement is viewed as a set of procedures whose aim is to coherently assign values to model parameters based on instrument indications. Models are therefore seen as necessary preconditions for the possibility of inferring measurement outcomes from instrument indications, and as crucial for determining the content of measurement outcomes. As proponents of model-based accounts emphasize, the same indications produced by the same measurement process may be used to establish different measurement outcomes depending on how the measurement process is modeled, e.g., depending on which environmental influences are taken into account, which statistical assumptions are used to analyze noise, and which approximations are used in applying background theory. As Luca Mari puts it, 