# A New AI Approach by Acquisition of Characteristics in Human Decision-Making Process
## 5 Results and Discussion  
The application example and its data processing demonstrated the process of estimating the function \(\phi\) based on the initial decisions made by a group of people. The result of estimation was called a \"Decision Map\", where all possible decisions, i.e., initial decisions and their variations, were characterized by a probability distribution, as presented in Figure 7. The amplitude of the probability distribution reveals the commonness among all possible decisions, i.e., a higher amplitude corresponds to more commonness of the decision. The meanings of each process are explained as following: (1) The process of the feature space mapping interpreted the sequence of each participant's decisions (the chosen number) into a \(df\) value in the feature space. The \(df\) value indicates how each participant decided to partition the current range to narrow down the searching area of the targeted number for the next attempt. Moreover, the \(df\) value is a comparable measurement among all decisions regardless of the dynamic range. (2) To find the common characteristics between those decisions made by each participant, their decision sequence was clustered and each cluster was considered as a decision strategy. For instance, in Figure 5a right, the \(df\) values were around 0.5, meaning the participant always used the strategy that partitioned the targeted range at around middle point in each attempt. However, each cluster was just a preliminary representation of a decision strategy that only contained limited decisions, but other possible variations of decision in this strategy might not be observed in the limited attempts during the application example. (3) To include those possible variations of decision into the decision strategies, ambiguous clusters were generated. The generation of an ambiguous cluster was carried out by modeling the uncertainty of initial clusters as a Gaussian distribution or Student's t-distribution. Note that the choices of modeling type depended on the attribute of initial data in the cluster and subjective preference that had impact on the decision-making process. In this study, the Gaussian distribution and Student's t-distribution were used for the generation of ambiguous clusters by assuming that possible decisions, i.e., the initial decision and its variations, were normally distributed within a strategy. By generation of an ambiguous cluster, each decision strategy was represented by a probability distribution, where the characteristics of possible decisions within the strategy was reflected by the probability density. Furthermore, the weighting of the normalized ambiguous cluster reflected the commonness of each decision strategy; in other words, the higher-weighted ambiguous cluster meant more inclusions of initial decisions, thereby, the more commonness among decisions was integrated into the relevant strategy. However, each ambiguous cluster only revealed the characteristics of possible decisions within the decision strategy. (4) To understand the characteristics of all possible  
Figure 6: Example of generating two ambiguous clusters and operation of their association. decisions among the different decision strategies, first, each two ambiguous clusters were associated with their relative weights. As Figure 6 presents, the overlapping part shows the common decisions between two strategies, their amplitudes in the corresponding positions are increased after association. Then, after accumulating all associations of two ambiguous clusters, the commonness of decisions was displayed by amplitudes in the decision map, and the curve also indicated the characteristics of the decision making in this group of people. For example, in Figure 7, the most common decisions were around 0.5; according to this, we can conclude a characteristic of decision making that most participants in this group made decisions cautiously since they preferred to control the consequent risk of decisions (results in the searching ranges in the next attempt) equally.