The role of social robots as advisors for decision making is investigated. It has been consistently shown that when asked to rank options, people often make fallacious judgements. Furthermore, such fallacies can be sensitive to presentation mode. We study whether having social robot advisors presenting options can mitigate and reduce the fallacy rates of participants. For this purpose we explored a novel presentation mode of options with conjunction judgmental fallacy, namely, choosing among different rank-orders, as opposed to rank the options themselves. We first show that the mere presentation mode has a significant mitigating effect on the fallacy rates. We then further show that when social robot advisors present the rank-orders, the fallacy rates of participants significantly decrease even further. Moreover, participants perceive the fallacious robot as more likeable and intelligent, but assign the non-fallacious robot to trustworthy roles, such as jury and analyst. These results suggest that social robot advisors may be used to influence and mitigate human fallacious judgmental decision making.

In this paper, an offline approximate dynamic programming approach using neural networks is proposed for solving a class of finite horizon stochastic optimal control problems. There are two approaches available in the literature, one based on stochastic maximum principle (SMP) formalism and the other based on solving the stochastic Hamilton-Jacobi-Bellman (HJB) equation. However, in the presence of noise, the SMP formalism becomes complex and results in having to solve a couple of backward stochastic differential equations. Hence, current solution methodologies typically ignore the noise effect. On the other hand, the inclusion of noise in the HJB framework is very straightforward. Furthermore, the stochastic HJB equation of a control-affine nonlinear stochastic system with a quadratic control cost function and an arbitrary state cost function can be formulated as a path integral (PI) problem. However, due to curse of dimensionality, it might not be possible to utilize the PI formulation for obtaining comprehensive solutions over the entire operating domain. A neural network structure called the adaptive critic design paradigm is used to effectively handle this difficulty. In this paper, a novel adaptive critic approach using the PI formulation is proposed for solving stochastic optimal control problems. The potential of the algorithm is demonstrated through simulation results from a couple of benchmark problems.

Social influence in social networks has been extensively researched. Most studies have focused on direct influence, while another interesting question can be raised as whether indirect influence exists between two users who’re not directly connected in the network and what affects such influence. In addition, the theory of complex contagion tells us that more spreaders will enhance the indirect influence between two users. The authors’ observation of intensity of indirect influence, propagated by n parallel spreaders and quantified by retweeting probability in two Twitter social networks, shows that complex contagion is validated globally but is violated locally. In other words, the retweeting probability increases non-monotonically with some local drops. A quantum cognition based probabilistic model is proposed to account for these local drops.

In the global economy, design of digital media often involves teams of individuals from a variety of cultures who must function together. Similarly, products must be designed and marketed taking specific cultural characteristics into account. Much is known about decision processes, culture and cognition, design of products and interfaces for human interaction with machines, and organizational processes, but this knowledge is dispersed across several disciplines and research areas. This article reviews current work in these areas and proposes a research agenda for fostering increased understanding of the ways in which cultural differences influence decision making and action in design and use of digital media.