Innovation in the services area — especially in the electronic services (e-services) domain — can be systematically developed by first considering the strategic drivers and foci, then the tactical principles and enablers, and finally the operational decision attributes, all of which constitute a process or calculus of services innovation. More specifically, there are four customer drivers (i.e., collaboration, customization, integration and adaptation), three business foci (i.e., creation-focused, solution-focused and competition-focused), six business principles (i.e., reconstruct market boundaries, focus on the big picture not numbers, reach beyond existing demand, get strategic sequence right, overcome organizational hurdles and build execution into strategy), eight technical enablers (i.e., software algorithms, automation, telecommunication, collaboration, standardization, customization, organization, and globalization), and six attributes of decision informatics (i.e., decision-driven, information-based, real-time, continuously-adaptive, customer-centric and computationally-intensive). It should be noted that the four customer drivers are all directed at empowering the individual — that is, at recognizing that the individual can, respectively, contribute in a collaborative situation, receive customized or personalized attention, access an integrated system or process, and obtain adaptive real-time or just-in-time input. The developed process or calculus serves to identify the potential white spaces or blue oceans for innovation. In addition to expanding on current innovations in services and related experiences, white spaces are identified for possible future innovations; they include those that can mitigate the unforeseen consequences or abuses of earlier innovations, safeguard our rights to privacy, protect us from the always-on, interconnected world, provide us with an authoritative search engine, and generate a GDP metric that can adequately measure the growing knowledge economy, one driven by intangible ideas and services innovation.

Company bankruptcies cost billions of dollars in losses to banks each year. Thus credit risk prediction is a critical part of a bank’s loan approval decision process. Traditional financial models for credit risk prediction are no longer adequate for describing today’s complex relationship between the financial health and potential bankruptcy of a company. In this work, a multiple classifier system (embedded in a multiple intelligent agent system) is proposed to predict the financial health of a company. In our model, each individual agent (classifier) makes a prediction on the likelihood of credit risk based on only partial information of the company. Each of the agents is an expert, but has limited knowledge (represented by features) about the company. The decisions of all agents are combined together to form a final credit risk prediction. Experiments show that our model out-performs other existing methods using the benchmarking Compustat American Corporations dataset.

Formal systems engineering approaches to modeling misperceptions and attitudes are employed within the framework of the graph model for conflict resolution to systematically study the War of 1812 between the United States of America and Great Britain in order to provide enhanced insights into the causes of the war. More specifically, relational definitions for preferences, movements and stability concepts are defined for describing the attitudes and associated behavior of decision makers involved in a conflict. To capture misperceptions of decision makers in the War of 1812, attitudes are studied within the structure of a hypergame. Combining attitudes and misperceptions within the paradigm of the graph model furnishes the flexible analytical tool which demonstrates that misunderstanding of attitudes by Great Britain and the United States may have contributed to the outbreak of this nasty war.

Motivated by the small world network research of Watts & Strogatz, this paper studies relationships between topology and cascading line outages in electric power grids. Cascading line outages are a type of cascading collapse that can occur in power grids when the transmission network is congested. It is characterized by a self-sustaining sequence of line outages followed by grid breakup, which generally leads to widespread blackout. The main findings of this work are twofold: On one hand, the work suggests that topologies with more disorder in their interconnection topology tend to be robust with respect to cascading line outages in the sense of being able to support greater generation and demand levels than more regularly interconnected topologies. On the other hand, the work suggests that topologies with more disorder tend to be more fragile in that should a cascade get started, they tend to break apart after fewer outages than more regularly interconnected topologies. Thus, as has been observed in other complex networks, there appears to be a tradeoff between robustness and fragility. These results were established using synthetically generated power grid topologies and verified using the IEEE 57 bus and 188 bus power grid test cases.

Complex problem solving requires diverse expertise and multiple techniques. In order to solve such problems, complex multi-agent systems that include both of human experts and autonomous agents are required in many application domains. Most complex multi-agent systems work in open domains and include various heterogeneous agents. Due to the heterogeneity of agents and dynamic features of working environments, expertise and capabilities of agents might not be well estimated and presented in these systems. Therefore, how to discover useful knowledge from human and autonomous experts, make more accurate estimation for experts’ capabilities and find out suitable expert(s) to solve incoming problems (“Expert Mining”) are important research issues in the area of multi-agent system. In this paper, we introduce an ontology-based approach for knowledge and expert mining in hybrid multi-agent systems. In this research, ontologies are hired to describe knowledge of the system. Knowledge and expert mining processes are executed as the system handles incoming problems. In this approach, we embed more self-learning and self-adjusting abilities in multi-agent systems, so as to help in discovering knowledge of heterogeneous experts of multi-agent systems.

This paper focuses on the question how to build an electronic support environment for knowledge creation in a research institute (JAIST). In order to assess the importance of diverse conditions of scientific creativity, we performed a survey in JAIST, and extracted useful knowledge from the database of survey results. Following the analysis of the theory of academic processes of knowledge creation and the survey findings in JAIST, a computer-based integrated system is proposed. In the aspect of the system design, we postulate that an electronic support environment for academic creativity can be achieved through a seamless integration with Internet, Application Server, Middle Ware, Database and Data Warehouse. The paper addresses issues of knowledge representation in the Electronic Support System for academic research, testing and evaluation issues and conclusions.