Online social networks have increased their impact on the real world, which motivates information senders to control the propagation process of information to promote particular actions of online users. However, the existing works on information provisioning seem to oversimplify the users' decision-making process that involves information reception, internal actions of social networks, and external actions of social networks. In particular, characterizing the best practices of information provisioning that promotes the users' external actions is a complex task due to the complexity of the propagation process in OSNs, even when the variation of information is limited. Therefore, we propose a new information diffusion model that distinguishes user behaviors inside and outside of OSNs, and formulate an optimization problem to maximize the number of users who take the external actions by providing information over multiple rounds. Also, we define a robust provisioning policy for the problem, which selects a message sequence to maximize the expected number of desired users under the probabilistic uncertainty of OSN settings. Our experiment results infer that there could exist an information provisioning policy that achieves nearly-optimal solutions in different types of OSNs. Furthermore, we empirically demonstrate that the proposed robust policy can be such a universally optimal solution.

Many countries have deregulated their electricity retail markets to offer lower electricity charges to consumers. However, many consumers have not switched their suppliers after the deregulation, and electricity suppliers do not tend to reduce their charges intensely. This paper proposes an electricity market model and evolutionary game to analyze the behavior of consumers in electricity retail markets. Our model focuses on switching costs such as an effort at switching, costs in searching for other alternatives, and so on. The evolutionary game examines whether consumers choose a strategy involving exploration of new alternatives with the searching costs as “cooperators” or not. Simulation results demonstrate that the share of cooperators was not improved by simply giving rewards for cooperators as compensation for searching costs. Furthermore, the results also suggest that the degree of cooperators in a network among consumers has a vital role in increasing the share of cooperators and switching rate.

The relationships between producers and consumers have changed radically by the recent growth of sharing economy. Promoting resource sharing can contribute to finding a solution to environmental issues (e.g. reducing food waste, consuming surplus electricity, and so on). Although prosumers have both roles as consumers and suppliers, matching between suppliers and consumers should be determined when the prosumers share resources. Especially, it is important to achieve envy-freeness that is a metric indicating how the number of prosumers feeling unfairness is kept small since the capacity of prosumers to supply resources is limited. Changing resource capacity and demand will make the situation more complex. This paper proposes a resource sharing model based on a temporal network and flows to realize envy-free resource sharing among prosumers. Experimental results demonstrate the deviation of envy among prosumers can be reduced by setting appropriate weights in a flow network.

This paper deals with a study of a problem for finding the minimum-cost spanning tree with a response-time bound. The relation of cost and response-time is given as a monotonous decreasing and convex function. Regarding communication bandwidth as cost in an information network, this problem means a minimum-cost tree shaped routing for response-time constrained broadcasting, where any response-time from a root vertex to other vertex is less than a given time bound. This problem is proven to be NP-hard and consists of the minimum-cost assignment to a rooted tree and the minimum-cost tree finding. A nonlinear programming algorithm solves the former problem for the globally optimal solution. For the latter problem, different types of heuristic algorithms evaluate to find a near optimal solution experimentally.