Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy

Science and technology for renewable and sustainable energy are indispensable for our future society and economics. To meet the goal of sustainable energy development, there is a growing body of research efforts world wide. The planner of energy research has to grasp the broader coverage of scientific and technological research, and make decisions on effective investment in promising and emerging technologies especially under circumstances of limited resources. In this paper, we track emerging research domains in energy research by using citation network analysis. Our analysis confirms that the fuel cell and solar cell are rapidly growing domains in energy research. We further investigate the detailed structure of these two domains by clustering publications in these domains. Each citation cluster has characteristic research topics, and there is a variety of growth trends among the clusters. By using citation network analysis, we can track emerging research domains among a pile of publications efficiently and effectively.     

Citation network analysis of organic LEDs

The field of organic light-emitting diodes (OLEDs) is an emergent research domain because of both scientific interest in chemistry and condensed matter physics and industrial importance as flat panel displays. In this paper, we analyzed a citation network of OLED papers and used a topological clustering method to investigate the structure of research and to detect emerging research domains. We found that most papers belong to two main clusters: organics and polymers. These two clusters have distinctive differences in subcluster structures and journals where papers are published. Supposing this discrepancy to indicate research progress, organics are in the applied research stage, while polymers are in the basic research stage.     

Evolutionary game dynamics and distributed recency-weighted learning

This paper presents a relationship between evolutionary game dynamics and distributed recency-weighted Monte Carlo learning. After reviewing some existing theories of replicator dynamics and agent-based Monte Carlo learning, we provide proofs of the formulation-level equivalence between these two models. The relationship will be revealed not only from a theoretical viewpoint, but also by computational simulations of the models. As a consequence, macro dynamic patterns generated by distributed micro-decisions can be explained by parameters defined at an individual level. In particular, given the equivalent formulations, we investigate how the rate of agents’ recency weighting in learning affects the emergent evolutionary game dynamic patterns. An increase in this rate negatively affects the inertia, making the evolutionary stability condition more strict, and positively affecting the evolutionary speed toward equilibrium.JEL Classification: C63, C73Supervisions and advice given by Arthur J. Caplan have greatly contributed to this paper. I am also grateful to the anonymous reviewers for their valuable comments redirecting my presentation to a more appropriate one.     

Extracting the commercialization gap between science and technology — Case study of a solar cell

In this paper, we compared structures of the citation network of scientific publications with those of patents, and discussed the differences between them. A case study was performed in a solar cell to develop a method of detecting gaps between science and technology. Scientific research has tended to be more basic, especially in terms of cell design, whereas patents have focused on more applied technology used in solar cell modules. Of the major citation clusters of scientific publications, only two, namely silicon and compound solar cells, corresponded semantically with patent clusters. Conversely, there were no patent clusters corresponding to the other two scientific research fronts, namely dye-sensitized and polymer solar cells. These research areas could be regarded as opportunities for industrial commercialization because scientific activities exist but not technological applications. Our results could offer an intellectual basis for discovering potential opportunities for industrial commercialization.     

Productivity growth,on-the-job search,and unemployment

The impact of long-run productivity growth on unemployment is studied. We incorporate disembodied technological progress and on-the-job search into the endogenous job separation model of Mortensen and Pissarides (1994). Because we include on-the-job search, faster growth reduces unemployment by decreasing job separation and inducing job creation. The incorporation of on-the-job search substantially improves the ability of the Mortensen and Pissarides model to explain the effect of growth on labor market variables. Specifically, our model generates not only an empirically consistent sign of the effect, but also a larger impact of growth on unemployment than the standard matching model.     

Modeling collective adaptive agent design and its analysis in Barnga game

This paper explores the collective adaptive agent that adapts to a group in contrast with the individual adaptive agent that adapts to a single user. For this purpose, this paper starts by defining the collective adaptive situation through an analysis of the subject experiments in the playing card game, Barnga, and investigates the factors that lead the group to the collective adaptive situation. Intensive simulations using Barnga agents have revealed the following implications: (1) the leader who takes account of other players’ opinions contributes to guide players to the collective adaptation situation, and (2) an appropriate role balance among players (i.e., the leader, the claiming and quiet players, which make the most and least number of corrections of the leader’s decision) is required to derive the collective adaptive situation.     

Detecting emerging research fronts in regenerative medicine by the citation network analysis of scientific publications

In this paper, we detect emerging research fronts in a huge number of academic papers related to regenerative medicine, a field of radically innovative research. We divide citation networks into clusters using the topological clustering method, track the positions of papers in each cluster, and visualize citation networks with characteristic terms for each cluster. Analyzing the clustering results with the average published year and parent-child relationship of each cluster could be helpful in detecting recent trends. In addition, tracking topological measures, within-cluster degree z and participation coefficient P, enables us to determine whether there are emerging knowledge clusters. Our results show the success of our method in detecting emerging research fronts in regenerative medicine, and these results are confirmed as reasonable by experts. Finally, we predict the future core papers, with the potential of many citations, via the betweenness centralities in the citation network of the research into adult and somatic stem cells.     

An Exploratory Look at Supply Chains in Japan from Multiscale Network Perspectives

In social network analysis, advances in social networking and computing techniques have increasingly become a popular approach for extracting features and rules of real-world networks. The network language—\(G=\{V, E \}\) provides a common representation of various networks, where G, V, and E denote the system, components, and interactions, respectively. In this study, we employ this emerging technique to discuss supply chains in Japan. We construct the supply network (i.e., system) based on the firms (i.e., components) and their transactional relationships (i.e., interactions). In comparison with the traditional approaches of industrial sectors and regional clusters, this study represents an exploratory look at supply networks, and investigates different scales of supply networks from three perspectives. (1) In the macro-scale perspective, we evaluate the “small-world” separation of supply networks using average path length. (2) In the meso-scale perspective, we detect communities of the supply networks, which can be marked for cross-location and cross-industry features. (3) In the micro-scale perspective, we investigate the “scale-free” nature of supply networks and each community using node degree-prior connections, which can find “hub” firms and simultaneously estimate the robustness of supply networks using a sequential elimination choice strategy of these hubs.