This systematic review provides a comprehensive overview of tsunami evacuation models. The review covers scientific studies from the last decade (2012–2021) and is explicitly focused on models using an agent-based approach. The PRISMA methodology was used to analyze 171 selected papers, resulting in over 53 studies included in the detailed full-text analysis. This review is divided into two main parts: (1) a descriptive analysis of the presented models (focused on the modeling tools, validation, and software platform used, etc.), and (2) model analysis (e.g., model purpose, types of agents, input and output data, and modeled area). Special attention was given to the features of these models specifically associated with an agent-based approach. The results lead to the conclusion that the research domain of agent-based tsunami evacuation models is quite narrow and specialized, with a high degree of variability in the model attributes and properties. At the same time, the application of agent-specific methodologies, protocols, organizational paradigms, or standards is sparse.

Various organizations and institutions store large volumes of tsunami-related data, whose availability and quality should benefit society, as it improves decision making before the tsunami occurrence, during the tsunami impact, and when coping with the aftermath. However, the existing digital ecosystem surrounding tsunami research prevents us from extracting the maximum benefit from our research investments. The main objective of this study is to explore the field of data repositories providing secondary data associated with tsunami research and analyze the current situation. We analyze the mutual interconnections of references in scientific studies published in the Web of Science database, governmental bodies, commercial organizations, and research agencies. A set of criteria was used to evaluate content and searchability. We identified 60 data repositories with records used in tsunami research. The heterogeneity of data formats, deactivated or nonfunctional web pages, the generality of data repositories, or poor dataset arrangement represent the most significant weak points. We outline the potential contribution of ontology engineering as an example of computer science methods that enable improvements in tsunami-related data management.

The objective of the chapter is to identify and analyze key aspects and possibilities of Ambient Intelligence (AmI) applications in educational processes and institutions (universities), as well as to present a couple of possible visions for these applications. A number of related problems are discussed as well, namely agent-based AmI application architectures. Results of a brief survey among optional users of these applications are presented as well.

The more criteria a human decision involves, the more inconsistent the decision. This study experimentally examines the effect on the degree of pairwise comparison inconsistency by using the (im)possibility of selecting the criteria for the evaluation and the size of the decision-making problem. A total of 358 participants completed objective and subjective tasks. While the former was associated with one possible correct solution, there was no single correct solution for the latter. The design of the experiment enabled the acquisition of eight groups in which the degree of inconsistency was quantified using three inconsistency indices (the Consistency Index, the Consistency Ratio and the Euclidean distance) and these were analysed by the repeated measures ANOVA. The results show a significant dependence of the degree of inconsistency on the method of determining the criteria for pairwise evaluation. If participants are randomly given the criteria, then with more criteria, the overall inconsistency of the comparison decreases. If the participants can themselves choose the criteria for the comparison, then with more criteria, the overall inconsistency of the comparison increases. This statistical dependence exists only for males. For females, the dependence is the opposite, but it is not statistically significant.