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This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

This paper aims to focus on the rise and decline of the Qing dynasty in Chinese history, and tries to explain the evolutionary phenomenon that when a dynasty became strong, it replaced the former established but deteriorating one, and then at the end of its development, it disappeared eventually by using interest models developed herein.

Systemic interest models are introduced to the study of Chinese history quantitatively. First, by briefly going over the history of Qing, the reasons for its rise and fall are analyzed qualitatively. Second, the concept of interest is generalized under some proper assumptions so that several interest models are established. At the end, intriguing conclusions are drawn by analyzing the numerical solutions of these interest models.

Comparing this paper's results of numerical solutions with the Qing's history, we can see that the stability of a country was essentially an external appearance of the conflict of interests between the ruling and ruled classes. Usually, the eventual social turbulence happened when the balance of interests deteriorated and was tilted excessively to one social class, and ended when the imbalance reached another state of equilibrium. Moreover, the stability of a country always appeared to be a cycle of “turbulence→peace→turbulence→ċ” which is similar to the evolutionary characteristics of general systems indicated by the systemic yoyo model. Furthermore, the cycle can be found in all the feudal dynasties throughout Chinese history.

The interest models presented in this article can be applied to the study of other social problems, such as corporation governance, the analysis of the national economic relationships, and others.

The concept of interest is generalized in this paper, and the relevant interest models provide good conclusions in our analysis of social and historical phenomena.

The Covid-19 pandemic spread rapidly around the world and required strict restriction plans and policies. In most countries around the world, the outbreak of the disease has been serious and has greatly affected the health system and the economy. The factors such as the number of patients with chronic diseases, the number of people over 65 years old, hospital facilities, the number of confirmed Covid-19 cases, the recovering Covid-19 cases and the number of deaths affect the rate of spread of Covid-19. This study aims to evaluate the performances of 21 Organisation for Economic Co-operation and Development (OECD) countries against the Covid-19 outbreak using three data envelopment analysis (DEA) models.

In this study, the performance of 21 OECD countries to manage the Covid-19 process has been analysed weekly via DEA which is widely used in various practical problems and provides a general framework for efficiency evaluation problems using the inputs and outputs of decision-making units.

The analysis showed that 11 countries out of 21 countries were efficient for selected weeks. According to the DEA results from the 20-week review (09 April 2020–20 August 2020), information about the course of the epidemic prevention and the normalization process for any country can be obtained.

In this study, due to the problem of the discrimination power of DEA, the cross-efficiency model and the super-efficiency model also used. In addition, the output-oriented model was preferred in this study for Covid-19 management efficiency.