A Language for Agent-based Discrete-event Modeling and Simulation of Linked Lives

In agent-based modeling and simulation, discrete-time methods prevail. While there is a need to cover the agents’ dynamics in continuous time, commonly used agent-based modeling frameworks offer little support for discrete-event simulation. Here, we present a formal syntax and semantics of the language ML3 (Modeling Language for Linked Lives) for modeling and simulating multi-agent systems as discrete-event systems. The language focuses on applications in demography, such as migration processes, and considers this discipline’s specific requirements. These include the importance of life courses being linked and the age-dependency of activities and events. The developed abstract syntax of the language combines the metaphor of agents with guarded commands. Its semantics is defined in terms of Generalized Semi-Markov Processes. The concrete language has been realized as an external domain-specific language. We discuss implications for efficient simulation algorithms and elucidate benefits of formally defining domain-specific languages for modeling and simulation.

Agents.jl: a performant and feature-full agent-based modeling software of minimal code complexity

Agent-based modeling is a simulation method in which autonomous agents interact with their environment and one another, given a predefined set of rules. It is an integral method for modeling and simulating complex systems, such as socio-economic problems. Since agent-based models are not described by simple and concise mathematical equations, the code that generates them is typically complicated, large, and slow. Here we present Agents.jl, a Julia-based software that provides an ABM analysis platform with minimal code complexity. We compare our software with some of the most popular ABM software in other programming languages. We find that Agents.jl is not only the most performant but also the least complicated software, providing the same (and sometimes more) features as the competitors with less input required from the user. Agents.jl also integrates excellently with the entire Julia ecosystem, including interactive applications, differential equations, parameter optimization, and so on. This removes any “extensions library” requirement from Agents.jl, which is paramount in many other tools.

Heterogeneity: method and applications for complex systems analysis

Socio-ecological systems like another physical systems are complex systems in which are required methods for analyzes their non-linearities, thresholds, feedbacks, time lags, and resilience. This involves understanding the heterogeneity of the interactions in time and space. In this article, we carry out the proposition and demonstration of two methods that allow the calculation of heterogeneity in different contexts. The practical effectiveness of the methods is presented through applications in sustainability analysis, land transport, and governance. It is concluded that the proposed methods can be used in various research and development areas due to their ease of being considered in broad modeling frameworks as agent-based modeling, system dynamics, or machine learning, although it could also be used to obtain point measurements only by replacing values.

Exploring S-shape curves and heterogeneity effects of rumor spreading in online collective actions

<abstract> <p>Nowadays online collective actions are pervasive, such as the rumor spreading on the Internet. The observed curves take on the S-shape, and we focus on evolutionary dynamics for S- shape curves of online rumor spreading. For agents, key factors, such as internal aspects, external aspects, and hearing frequency jointly determine whether to spread it. Agent-based modeling is applied to capture micro-level mechanism of this S-shape curve. We have three findings: (a) Standard S-shape curves of spreading can be obtained if each agent has the zero threshold; (b) Under zero-mean thresholds, as heterogeneity (SD) grows from zero, S-shape curves with longer right tails can be obtained. Generally speaking, stronger heterogeneity comes up with a longer duration; and (c) Under positive mean thresholds, the spreading curve is two-staged, with a linear stage (first) and nonlinear stage (second), but not standard S-shape curves either. From homogeneity to heterogeneity, the spreading S-shaped curves have longer right tail as the heterogeneity grows. For the spreading duration, stronger heterogeneity usually brings a shorter duration. The effects of heterogeneity on spreading curves depend on different situations. Under both zero and positive-mean thresholds, heterogeneity leads to S-shape curves. Hence, heterogeneity enhances the spreading with thresholds, but it may postpone the spreading process with homogeneous thresholds.</p> </abstract>