Fusion Techniques for Reliable Information: A Survey <s> Cognitive Decision Making (CDM) model <s> Abstract This study contributes to the new and growing body of research on shared cognition by examining how individuals entering a group decision-making context with different perspectives of the issues to be discussed arrive at cognitive consensus. Cognitive consensus refers to similarity among group members regarding how key matters are conceptualized and was operationalized as shared assumptions underlying decision issues in the present research. Utilizing 37 student groups participating in a multi-issue decision-making exercise, the study investigated antecedents and correlates of cognitive consensus. Results revealed that unanimity decision rule groups achieved more cognitive consensus than majority rule groups. In addition, group members inquiring concerning the reasons underlying others' decision preferences, accepting others' viewpoints as legitimate, and incorporating others' perspectives into their own interpretations of the issues was positively related to arriving at a greater degree of cognitive consensus. Cognitive consensus also positively influenced expectations regarding decision implementation and satisfaction. <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Cognitive Decision Making (CDM) model <s> In this paper, we propose an agent-based model of human decision making: CODAGE (Cognitive Decision AGEnt). Classical Decision Theories have been widely used in multi-agent systems, but imply a too rational behavior when faced with real-world human data. Moreover, classical model usually exceeds human capabilities. Therefore, we derived our decision model from several cognitive psychological theories (e.g. Simon's decision theory, Montgomery's search of dominance structure, etc.) to take human bounded rationality into account. While most of existing cognitive agents use the BDI framework, we propose a new kind of architecture. In the CODAGE model, the decision maker is modeled by an entire multi-agent system, where each agent is in charge a particular sub-process of the whole decision. The architecture is intended to be as generic as possible. It could be viewed as an agent-based decision framework, in which different decision heuristics and biases could be implemented. We illustrate this approach with a simulation of a small experimental financial market, for which our model was able to replicate some human decision behaviors. <s> BIB002
CDM model has a human-like decision making that does not include some complex optimization methods and numerical calculations BIB001 . CDM model includes information about the possibility of their applications. This model uses a continuous integrated process for the approximation of comprehension and prediction in the interaction with the environment BIB002 . 