In this article, we will investigate the properties of a compromise solution selection method based on modelling the consequences of a decision as factors influencing the decision making in subsequent problems. Specifically, we assume that the constraints and preference structures in the (k þ 1)st multicriteria optimisation problem depend on the values of criteria in the k-th problem. To make a decision in the initial problem, the decision maker should take into account the anticipated outcomes of each linked future decision problem. This model can be extended to a network of linked decision problems, such that causal relations are defined between the time-ordered nodes. Multiple edges starting from a decision node correspond to different future scenarios of consequences at this node. In addition, we will define the relation of anticipatory feedback, assuming that some decision makers take into account the anticipated future consequences of their decisions described by a network of optimisers � a class of information processing units introduced in this article. Both relations (causal and anticipatory) form a feedback information model, which makes possible a selection of compromise solutions taking into account the anticipated consequences. We provide constructive algorithms to solve discrete multicriteria decision problems that admit the above preference information structure. An illustrative example is presented in Section 4. Various applications of the above model, including the construction of technology foresight scenarios, are discussed in the final section of this article.
Abstract. This paper presents new approaches to formulating and solving complex real-life decision-making problems, making use of the creativity concept. We assume that the decision-making process is embedded in the system of views and mutual relations between the decision-makers and their surrounding environment, so that creativity, as defined formally in Sec. 2, could play a primary role in the decision-making process. We will investigate multicriteria decision problems, where the decision-maker is unable to fully follow decisionmaking rules resulting from a standard mathematical formulation of multicriteria optimization problem. This is either due to external conditions (such as the need to make a quick decision, loss of data, or lack of data processing capabilities) or when the decision-maker can manifest creativity related to the hidden internal states of the decision-making process. We will provide a formal definition of freedom of choice (FOC), specifying three levels of FOC for multicriteria decision-making (MCDM) problems. Then we will point out that creativity in decision-making can be explained within the framework of autonomous and free decisions, and that decision-making freedom is a necessary prerequisite for creativity. The methods presented here can be applied to analyzing human decision-making processes and conditions allowing the expression of creativity as well as to designing pathways leading to creative decision-making in artificial autonomous decision systems (AADS). The applications of the latter include visual information retrieval, financial decision-making with feature identification, intelligent recommenders, to name just a few.
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