Context, Composition, Automation, and Communication -- The C2AC Roadmap for Modeling and Simulation

TL;DR

The basis and interdependencies evident in current research and practice are explored and future research directions based on these considerations are delineated.

Abstract

Simulation has become, in many application areas, a sine-qua-non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work for addressing these limitations. The first is to provide better support for capturing, representing, and evaluating the context of simulation studies, including research questions, assumptions, requirements, and activities contributing to a simulation study. In addition, the composition of simulation models and other simulation studies' products must be supported beyond syntactical coherence, including aspects of semantics and purpose, enabling their effective reuse. A higher degree of automating simulation studies will contribute to more systematic, standardized simulation studies and their efficiency. Finally, it is essential to invest increased effort into effectively communicating results and the processes involved in simulation studies to enable their use in research and decision-making. These goals are not pursued independently of each other, but they will benefit from and sometimes even rely on advances in other subfields. In the present paper, we explore the basis and interdependencies evident in current research and practice and delineate future research directions based on these considerations.

Figures (7)

• Figure 1: The roadmap proposes to support the entire life cycle of modeling and simulation by a) enriching context information beyond the conceptual model that can be deployed for developing the simulation model, executing simulation experiments, and interpreting results \ref{['sec:context']}, b) providing means for composition and reuse of the different artifacts of the simulation study, such as simulation model, simulation experiments, or behavioral requirements, as well as the needed software and methods \ref{['sec:composition']}, c) automating large portions of the modeling and simulation cycle, also by exploiting recent developments in artificial intelligence \ref{['sec:automation']}, and d) fine-tuning the representation of results, models, and activities involved in the simulation study to the mental models and needs of the different users and stakeholders.
• Figure 2: Model code snippet from a rule-based Repast implementation of an agent-based simple epidemic (susceptible, infected, recovered) model. An adaptation layer enables a compact description of agent-based CTMC models in a style that resembles rule-based languages and allows the execution of agents in Repast Simphony warnke2016population. The addRule method is provided by an abstract Agent class. It is called in the constructor of a concrete agent class. For the definition of the condition, waiting-time distribution, and effect, the anonymous functions of Java 8 are exploited.
• Figure 3: To move forward, the context of simulation studies needs to be explicitly and unambiguously represented. Therefore, the various artifacts play a role (here depicted with their individual life cycle), including conceptual model (research questions, assumptions, requirements, data, etc.), simulation experiments, and simulation models inspired by ruscheinski2019artifact. Methods are needed to interrelate those with provenance retrospectively as well as to exploit them prospectively, guiding (based on a workflow-based view) or even automatically generating the next steps (see Section \ref{['sec:automation']}).
• Figure 4: Community efforts are required to maintain repositories, to develop standards and ontologies - not only for simulation models (SM) but also for other artifacts of simulation studies (Assumptions (A), Simulation experiments (SE), and Requirements (R)), - to enhance composition and reuse. These efforts need to be accompanied by methodological advances that support unambiguous and succinct annotations with suitable meta-information (e.g., a simulation model's context, see section \ref{['sec:context']}), flexible (e.g., white-box) and powerful (e.g., pragmatic level) composition and analysis methods (e.g., comparing and interpreting variations and automatically testing assumptions and requirements, see section \ref{['sec:automation']}).
• Figure 5: Enriching the different tasks of the modeling and simulation life cycle (adapted from balci2012life) with intelligent methods to enhance the automation in generating the conceptual model, the simulation model, and simulation experiments, as well as in interpreting and communicating the simulation results.