Narrow Results

Simulated virtual realities offer a promising but currently underutilized source of data in studying cultural and demographic aspects of dynamic decision-making (DDM) in small groups. This study focuses on one simulated reality, a clock-driven business simulation game, which is used to teach operations management. The purpose of our study is to analyze the characteristics of the decision-making groups, such as cultural orientation, education, gender and group size, and their relationship to group performance in a real-time processed simulation game. Our study examines decision-making in small groups of two or three employees from a global manufacturing and service operations company. We aim at shedding new light on how such groups with diverse background profiles perform as decision-making units. Our results reveal that the profile of the decision-making group influences the outcome of decision-making, the final business result of the simulation game. In particular, the cultural and gender diversity, as well as group size seem to have intertwined effects on team performance.

Robots might not act according to human expectations if they cannot anticipate how people make sense of a situation and what behavior they consider appropriate in some given circumstances. In many cases, understanding, expectations and behavior are constrained, if not driven, by culture, and a robot that knows about human culture could improve the quality level of human–robot interaction. Can we share human culture with a robot? Can we provide robots with formal representations of different cultures? In this paper, we discuss the (elusive) notion of culture and propose an approach based on the notion of trait which, we argue, permits us to build formal modules suitable to represent culture (broadly understood) in a robot architecture. We distinguish the types of traits that such modules should contain, namely behavior, knowledge, rule and interpretation traits, and how they could be organized. We identify the interpretation process that maps situations to specific knowledge traits, called scenarios, as a key component of the trait-based culture module. Finally, we describe how culture modules can be integrated in an existing architecture, and discuss three use cases to exemplify the advantages of having a culture module in the robot architecture highlighting surprising potentialities.