We examined the interaction in face-to-face selling situation. In particular, we examined how customer's nonverbal behaviors were related to their preference in a natural environment. We found that customers' body posture could be a good predictor about their preference. We also found that estimation on customer's preference could be achieved from two nonverbal behaviors better than single behavior. We discuss the present contributions toward constructing the efficient agent systems.

Many existing studies on human learning pay almost exclusive attention to how individuals learn. Unlike those studies, we examined influence of social structures on knowledge acquired by societies using computer simulations. We compared four types of social networks, namely regular, random, small world, and scale-free networks. When individual differences and the principle of homophily (i.e., people who have similar beliefs tend to have close relationships with each other) exist in societies, the societies would acquire pareto-optimal knowledge. We also investigated influences of highly connected individuals on knowledge acquired by societies. The results inarguably indicate that highly connected individuals play important roles in social learning, setting the standards for what type of knowledge to be acquired by societies.

Categorically organized knowledge is the main vehicle in high-level cognitive processes. The previous empirical and theoretical studies on categorization paid almost exclusive at-tention to how individuals learn categorical knowledge. In the real world, however, people acquire knowledge not only through individual learning, but also through interacting with others. In the present study, using computational modeling, we explored how social interactions would produce unique dynamics of knowledge acquisition that cannot be examined by studies on micro level processes. The results of simula-tion studies showed that when there were several clusters of individuals in a society where individuals held different be-liefs about what constitutes ”good ” knowledge, then the so-ciety as a whole formed Pareto-optimal knowledge. That is, there was no cluster of knowledge that was simultaneously worse in two important aspects of knowledge (i.e., accuracy and simplicity) as compared with those of other clusters in a mature society.

Particle swarm optimization (PSO) is a type of meta-heuristic optimization method built on the basis of the principle of collective behaviors exhibited by simple organisms. Although PSO is a model of social behaviors, the present research attempts to model learning behaviors of an individual human with PSO in order to evaluate our hypothesis that the dynamics of knowledge that are being acquired and updated in our mind resemble the dynamics of social interactions exhibited by swarms. A simulation study showed that a cognitive model with PSO was able to replicate not only manifested cognitive behaviors but also latent cognitive behaviors, resulting in the acquisition of at least two dissimilar yet functional solutions for a given task.