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The purpose of this paper is to study the dynamical group consensus of heterogeneous multi-agent systems with fixed topologies.

The tool used in this paper to model the topologies of multi-agent systems is algebraic graph theory. The matrix theory and stability theory are applied to research the group consensus of heterogeneous multi-agent systems with fixed topologies. The Laplace transform and Routh criterion are utilized to analyze the convergence properties of heterogeneous multi-agent systems.

It is discovered that the dynamical group consensus for heterogeneous multi-agent systems with first-order and second-order agents can be achieved under the reasonable hypothesizes. The group consensus condition is only relied on the nonzero eigenvalues of the graph Laplacian matrix.

The novelty of this paper is to investigate the dynamical group consensus of heterogeneous multi-agent systems with first-order and second-order agents and fixed topologies and obtain a sufficient group consensus condition.

The increasing need to access and elaborate dynamic and heterogeneous information sources distributed over the Internet calls for new models and paradigms for application design and development. The mobile agent paradigm promotes the design of applications where agents roam through Internet sites to locally access and elaborate information and resources, possibly co‐operating with each other. Focuses on mobile agent co‐ordination, and presents the TuCSoN co‐ordination model for Internet applications based on mobile information agents. TuCSoN exploits a notion of local tuple‐based interaction space, called a tuple centre. A tuple centre is a tuple space enhanced with the capability of programming its behaviour in response to communication events. This enables properties to be embedded into the interaction space, and a mobile agent to be designed independently of the peculiarities of the information sources. Several issues critical to Internet applications can then be charged on tuple centres transparently to agents. The effectiveness of the TuCSoN model is first shown by means of an application example in the area of Internet information retrieval, then discussed in the context of workflow management and electronic commerce.

The purpose of this article is to provide a comprehensive literature review on the utilisation of intelligent agent technology in the library environment.

Research papers since 1990 on the use of various intelligent agent technologies in libraries are divided into two main application areas: digital library (DL), including agent‐based DL projects, multi‐agent architecture for DLs, intelligent agents for distributed heterogeneous information retrieval and agent support to information search process in DLs; and services in traditional libraries, including user interface for library information systems, automatic reference services and multi‐agent architecture for library services. For each paper on the topic, its new ideas or models, referred work, analyses, experiments, findings and conclusions are addressed.

The majority of the literature covers DLs and there have been fewer studies about services in traditional libraries. A variety of architecture, framework and models integrating agent technology in library systems or services are proposed, but only a few have been implemented in the practical environment. The application of agent technology is still at the research and experimentation stage. Agent technology has great potential in many areas in the library context; however it presents challenges to libraries that want to be involved in its adoption.

The survey has practical implications for libraries, librarians and computer professionals in developing projects that employ intelligent agent technology to meet end‐users' expectations as well as to improve information services within limited resources in library settings.

The paper provides a comprehensive survey on the development and research of intelligent agents in libraries in literature.

The purpose of this paper is to explore the manner in which the degree of substitutability between public and private health expenditures contributes towards the distribution of wealth and political economy outcomes in the long run.

An overlapping generations model with heterogeneous agents where a person’s probability of survival into old age is determined by a variable elasticity of substitution (VES) health production function with public and private expenditures as inputs is developed. Public expenditure on health is determined through a political economy process.

Analytical and numerical results reveal that higher substitutability between private and public expenditures at the aggregate level and a higher share of public spending in the production of health lead to higher long run wealth levels and lower inequality. In the political equilibrium, higher aggregate substitutability between public and private health expenditures is associated with more tax revenue allocated towards public health. For most parameter combinations, the political economy and welfare maximising proportions of tax revenue allocated towards public health care converge in the long run.

The paper is a theoretical investigation of how substitutability between public and private health expenditures affect transitional and long run macroeconomic outcomes. These results are amenable to further empirical investigation.

The findings indicate that policies to improve institutional aspects that yield higher substitutability between public and private health expenditures and returns to public health spending could lead to better long run economic outcomes.

The results provide a political economy explanation for the low investments in public health care in developing countries, where aggregate substitutability between public and private health expenditures is likely to be lower. Furthermore, comparing the political economy and welfare maximising paradigms broadens the scope of the framework developed herein to provide potential explanations for cross-country differences in health outcomes.

This paper adopts an innovative approach to exploring this issue of substitutability in health expenditures by introducing a VES health production function. In an environment where agents have heterogeneous wealth endowments, this specification enables a distinction to be made between substitutability of these expenditures at the aggregate and individual levels, which introduces a rich set of dynamics that feeds into long run outcomes and political economy results.

Expectations play a crucial role in finance, macroeconomics, monetary economics, and fiscal policy. In the last decade a rapidly increasing number of laboratory experiments have been performed to study individual expectation formation, the interactions of individual forecasting rules, and the aggregate macro behavior they co-create. The aim of this article is to provide a comprehensive literature survey on laboratory experiments on expectations in macroeconomics and finance. In particular, we discuss the extent to which expectations are rational or may be described by simple forecasting heuristics, at the individual as well as the aggregate level.

The purpose of this paper is to critically analyze whether supply networks may be validly treated as complex adaptive systems (CAS). Finding this to be true, the paper turns into the latest concerns of complexity science like Pareto distributions to explain well‐known phenomena of extreme events in logistics, like the bullwhip effect. It aims to introduce a possible solution to handle these effects.

The method is a comparative analysis of current literature in the fields of logistics and complexity science. The discussion of CAS in supply networks is updated to include recent complexity research on power laws, non‐linear dynamics, extreme events, Pareto distribution, and long tails.

Based on recent findings of complexity science, the paper concludes that it is valid to call supply networks CAS. It then finds that supply networks are vulnerable to all the nonlinear and extreme dynamics found in CAS within the business world. These possible outcomes have to be considered in supply network management. It is found that the use of a neural network model could work to manage these new challenges.

Since, smart parts are the future of logistics systems, managers need to worry about the combination of human and smart parts, resulting design challenges, the learning effects of interacting smart parts, and possible exacerbation of the bullwhip effect. In doing so, the paper suggests several options concerning the design and management of supply networks.

The novel contribution of this paper lies in its analysis of supply networks from a new theoretical approach: complexity science, which the paper updates. It enhances and reflects on existing attempts in this field to describe supply networks as CAS through the comprehensive theoretical base of complexity science. More specifically, it suggests the likely vulnerability to extreme outcomes as the “parts” in supply networks become smarter. The paper also suggests different ways of using a neural network approach for their management – depending on how smart the logistics parts actually are.

Governments in both developing and developed economies play an active role in labor markets in the form of providing both formal public sector jobs and employment through public workfare programs. The authors refer to this as employment targeting. The purpose of the paper is to consider different labor market effects of employment targeting in a stylized model of a developing economy. In the context of a simple search and matching friction model, the authors show that the propensity for the public sector to target more employment can increase the unemployment rate in the economy and lead to an increase in the size of the informal sector.

The model is an application of a search and matching model of labor market frictions, where agents have heterogeneous abilities. The authors introduce a public sector alongside the private sector in the economy. Wage in the private sector is determined through Nash bargaining, whereas the public sector wage is exogenously fixed. In this setup, the public sector hiring rate influences private sector job creation and hence the overall employment rate of the economy. As an extension, the authors model the informal sector coupled with the other two sectors. This resembles developing economies. Then, the authors check the overall labor market effects of employment targeting through public sector intervention.

In the context of a simple search and matching friction model with heterogeneous agents, the authors show that the propensity for the public sector to target more employment can increase the unemployment rate in the economy and lead to an increase in the size of the informal sector. Employment targeting can, therefore, have perverse effects on labor market outcomes. The authors also find that it is possible that the private sector wage falls as a result of an increase in the public sector hiring rate, which leads to more job creation in the private sector.

What is less understood in the literature is the impact of employment targeting on the size of the informal sector in developing economies. The authors fill this gap and show that public sector intervention can have perverse effects on overall job creation and the size of the informal sector. Moreover, a decrease in the private sector wage due to a rise in public sector hiring reverses the consensus findings in the search and matching literature which show that an increase in public sector employment disincentivizes private sector vacancy postings.