Abstract: Models of linguistic semantics can be viewed through representation and reasoning. This distinction concerns questions on how do we represent the world that we refer to by linguistic expressions and what kind of reasoning do we apply based on these representations. It has been commonplace to assume that each word or expression has one or a limited number of different, distinct senses. The classification task of disambiguation has been devised to find the right reference in each case. It is also possible to represent the world using a high-dimensional continuous space. In that case, we do not need to assume that the world is represented as a network of nodes and their connections. These mathematical representations go beyond the capacities provided by symbolic logic. The word embeddings has history that stems from vector space representations in information retrieval. When a framework of multidimensional continuous spaces is available, it is possible to study nuances of meaning that go beyond conducting disambiguation or choosing between alternatives within a logical framework. In the present work, it is postulated that semantic processes are essentially subjective and thus individual. When high-dimensional continuous spaces are used to represent meanings and defining contextual distributions, subjective aspects can be modelled. It is possible to measure subjectivity of meaning. This can be studied, for instance, in the framework of brain research (Saalasti et al. 2019) or motions tracking (Honkela & Förger 2013). The methodology or measuring subjective contextually grounded meaning has been been presented, for instance, in Raitio et al. 2014. Further methodological work and an empirical demonstration is presented in Sintonen et al. (2014). When it is possible to represent individually contextual meaning of expressions, it is consequently possible to analyse the differences of meaning between two individuals. A hypothesis is that suitable data for the purpose of meaning negotiation can be collected, computational algorithms devised and applied in real world contexts that helps in meaning negotiations. An alternative view is to aim at defining the meaning of words in a precise way and to teach all people to use these definition. In this present work, it is claimed that that objectivity can be reached only to a degree as it would require vast human cognitive and time resources and the mapping between words and the world is doomed to be partial. This concern has implications both in scientific and in real world communication and representation and has been applied in building the Peace Machine framework. Meaning negotiations as phenomena and as LT challenges Timo Honkela University of Helsinki with Iiro Jääskeläinen (Aalto University) on the Study of Individualized Meanings using Brain Research University of Helsinki, Topelia, F211 4th of April, 2019