Validation is the process of determining if a model adequately represents the system under study for the model's intended purpose. Validation is a critical component in building the credibility of a simulation model with its end-users. Effectively conducting validation can be a daunting task for both novice and experienced simulation developers. Further compounding the difficult task of conducting validation is that there is no universally accepted approach for assessing a simulation. These challenges are particularly relevant to the paradigm of Agent-Based Modeling and Simulation (ABMS) because of the complexity found in these models' mechanisms and in the real-world situations they attempt to represent. To aid both the novice and expert in conducting a validation process for an agent-based simulation, this article reviews nine methods that are useful for this process, including foundational topics of docking, empirical validation, sampling, and visualization, as well as advanced topics of bootstrapping, causal analysis, inverse generative social science, and role-playing. Each method is reviewed with respect to its benefits and limitations as a validation-supporting method for ABMS. Suggestions that may support a validation plan for an agent-based simulations, are also provided. This article is an introductory guide for understanding and conducting ABMS validation for developers of all experience levels.

The structure and dynamics of modern United States Federal Case Law are examined here. The analyses utilize large-scale network analysis tools, natural language processing techniques, and information theory to examine all the federal opinions in the Court Listener database, containing approximately 1.3 million judicial opinions and 11.4 million citations. The analyses are focused on modern United States Federal Case Law, as cases in the Court Listener database range from approximately 1926–2020 and include most Federal jurisdictions. We examine the data set from a structural perspective using the citation network, overall and by time and space (jurisdiction). In addition to citation structure, we examine the dataset from a topical and information theoretic perspective, again, overall and by time and space.

Both the scientific community and the popular press have paid much attention to the speed of the Securities Information Processor—the data feed consolidating all trades and quotes across the US stock market. Rather than the speed of the Securities Information Processor (SIP), we focus here on its accuracy. Relying on Trade and Quote data, we provide various measures of SIP latency relative to high-speed data feeds between exchanges, known as direct feeds. We use first differences to highlight not only the divergence between the direct feeds and the SIP, but also the fundamental inaccuracy of the SIP. We find that as many as 60% or more of trades are reported out of sequence for stocks with high trade volume, therefore skewing simple measures, such as returns. While not yet definitive, this analysis supports our preliminary conclusion that the underlying infrastructure of the SIP is currently unable to keep pace with the trading activity in today’s stock market.

This is a guest editors' statement accompanying the publication of a special issue on "Inverse Generative Social Science", published in volume 26, issue 2, 2023 of JASSS-Journal of Artificial Societies and Social Simulation

In the many cases where there are no solvable closed-form solutions for the dynamics of Systems of Systems (SOS), stochastic simulation may be the only practical solution to gaining insight into the system's interactions, processes and outcomes. Gaining this insight often requires producing many instantiations of the simulation, which, depending of the variability of the simulation, may produce huge amounts of data. Statistical techniques exist for summarizing the range of outcomes and identifying and analyzing various model states of interest. Often however, visualization techniques provide a more effective tool in gaining insight in the scope and result of the dynamic processes and interactions. The authors describe and present the use of Density Plotting (DP) and Trajectory Storyboarding (TS), visualization techniques for potentially extracting insights from SOS models.

For many system level questions jurisprudential data has grown to a size and scale that no longer lends itself to traditional analytic techniques driven by human examination and direct analysis. While there will always be vast numbers of specific questions well within the capabilities of humans, an understanding of the system as a whole is no longer among them. Over the past several decades jurisprudence has begun to use mathematical and other analytic techniques many of which were developed in the physical sciences. It is now time for jurisprudence to embrace more fully the analytic tools of these other disciplines, specifically those coming out of physics, in order to continue to produce new insights to aid in the structure, function, design of judicial systems and the analysis of judicial dynamics.