In 2001, Rama Cont introduced a now-widely used set of 'stylized facts' to synthesize empirical studies of financial time series, resulting in 11 qualitative properties presumed to be universal to all financial markets. Here, we replicate Cont's analyses for a convenience sample of stocks drawn from the U.S. stock market following a fundamental shift in market regulation. Our study relies on the same authoritative data as that used by the U.S. regulator. We find conclusive evidence in the modern market for eight of Cont's original facts, while we find weak support for one additional fact and no support for the remaining two. Our study represents the first test of the original set of 11 stylized facts against the same stocks, therefore providing insight into how Cont's stylized facts should be viewed in the context of modern stock markets.

Combat has seen fundamental changes over the past century—with new domains of warfighting (e.g., cyber, space); the introduction of nuclear and precision weapons; ubiquitous, all source intelligence; mixes of national, coalition, and private forces operating; and numerous other examples. Over this same period, combat simulations, and the attendant training, experimentation, and analyses they support, have increased in scale, scope, and resolution. But with respect to their fundamental effectiveness, have they substantively moved beyond the foundation created by Lanchester in the early 20th century? Differential equations of mean field approximations have been complemented by discrete-event simulations at many scales; however, the underlying conceptual framework of force-on-force conflict and tightly constrained, potentially fragile, service-specific command and control remains. In this position paper, we argue that new conceptualizations, better aligned with the modern warfighting landscape, are needed across all applications of military modeling. We examine the potential role of complex adaptive systems in this endeavor.

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.

Agent-based models can be a powerful tool for evaluating the impact of policy decisions on a population. However, analyses are traditionally beholden to one set of rules hypothesized at the conception of the model. Modelers must make assumptions of agent behavior that are not necessarily governed by data and the actual behavior of the true population can thusly vary. Evolutionary model discovery (EMD) seeks to provide a solution to this problem by leveraging genetic algorithms and genetic programming to explore the plausible set of rules that can explain agent behavior. Here we describe an initial use of the EMD system to develop robust policies in a resource constrained environment. In this instance, we extend the NetLogo implementation of the Epstein Rebellion model of civil violence as a sample problem. We use the EMD framework to generate 23 plausible populations and then develop policy responses for the government that are robust across the plausible populations.

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

In this study we use a simple case study of macaque fighting dynamics to develop and use the Evolutionary Model Discovery (EMD) framework [7, 8]. Rather than focus on the macaque populations EMD created, we will focus on our use of the EMD framework. Application of the EMD framework is not straight forward and involves making many decisions that may ultimately impact one's results. Here we highlight many of these and explain how we navigated this process.

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.

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.

Rigorous approaches to the engineering of decentralized, multi-agent systems are nascent. Providing contrast to these are hierarchical systems, in which central operational entities distribute tasks and aggregate results. We explore self-organized networks of sensors and effectors to understand how systems without centralized command and control can be created to accomplish an enterprise-wide mission when the individual agents do not have a concept of command or hierarchy. The individual agents do, however, have knowledge of how to act given their own embodied situational awareness, with an implicit understanding that these behaviors contribute to collective enterprise mission goals. When such local situation awareness is shared with appropriate other agents, we say the overall system is using distributed situational awareness (DSA). Our practical systems engineering challenges then are to determine what kinds, how much, and through what mechanisms such DSA should be shared, to enable the overall system to be most effective and efficient. The problem used to explore these questions is one of mitigating the damage caused by asteroid impacts within the continental United States with a set of autonomous vehicles that have limited capacities for sensing, communications, and information processing. We use this challenge problem to prototype an enterprise command and control system using an approach rooted in complexity science and computational social science methods. The experimental testbed we describe here allows us to study emergent engineering of decentralized multi-agent systems, by observing their enterprise-level mission performance as we apply stressors and instill agent-level behaviors enabled by DSA information sharing. Interestingly, we find that maximum system-level performance is obtained when 1) the vehicles are able to minimally coordinate mitigation efforts, and 2) are of limited capability with respect to sensor and communications ranges.

In the present work, we outline a set of coarse-grain analytical models that can be used by decision-makers to bound the potential impact of the COVID-19 pandemic on specific communities with known or estimated social contact structure and to assess the effects of various non-pharmaceutical interventions on slowing the progression of disease spread. This work provides a multi-dimensional view of the problem by examining steady-state and dynamic disease spread using a network-based approach. In addition, Bayesian-based estimation procedures are used to provide a realistic assessment of the severity of outbreaks based on estimates of the average and instantaneous basic reproduction number R 0 .

The U.S. stock market, more precisely known as the National Market System (NMS), is fragmented into various trading venues. The heterogeneity across this set of venues spans many dimensions; to include geographic location, price discovery mechanisms and fee structures. The prevailing models in the scientific community lag behind in replicating the complexity of today's NMS. In this study, we introduce a new generation of market model, with an explicit focus on an initial representation of the complexity and heterogeneity described above. As an extension of previous work we present the motivation and an overview of the literature relevant to the study of dynamics in multi-exchange markets. We also employ the ODD + D protocol to document our model formulation and its evolutionary trajectory from its predecessors. Experiments are described which show the relational, structural, equivalence between this model and real-world markets.

Determining the level of detail necessary to a modeling effort is fundamental to the discipline. Insufficient detail can limit a model's utility. Likewise, extraneous detail may impact the runtime performance of the model, increase its maintenance burden, impede the model validation process by making the model harder to understand than necessary, or overfit the model to a specific scenario. Intuition suggests that resolving this tension is an intractable challenge that reflects the art of modeling and is without promise for general solution. Most analytic communities accept that a truly rigorous, repeatable, engineering solution to the construction of an arbitrary model is unattainable. But the long history of research in modeling methodology suggests there are useful steps communities can make in that direction. Through the lens of current modeling challenges, practices and methods in several domains, we hope to add to this important discussion at the intersection of philosophy and engineering.

NetLogo and agent-based models more generally are traditionally implemented as time-stepped simulations. This means that everything is done at every increment of time. While this can be necessary at times, a potentially far more efficient simulation method is known as event-driven simulation. This type of simulation uses a scheduler for the times particular events will take place. Rather than doing everything every time step the simulation moves forward in time based upon the need for an event to occur. In this paper we show how to use NetLogo Tables and Arrays to create a time ordered queue and develop a simple NetLogo model implemented both as a traditional time-stepped simulation and as an event-driven simulation. We also present performance results.

Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in calendar year 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity marketplace are relatively common and persist for longer than what physical constraints may suggest. Information feeds reported different prices for the same equity more than 120 million times, with almost 64 million dislocation segments featuring meaningfully longer duration and higher magnitude. During this period, roughly 22% of all trades occurred while the SIP and aggregated direct feeds were dislocated. The current market configuration resulted in a realized opportunity cost totaling over $160 million, a conservative estimate that does not take into account intra-day offsetting events.

Chapter 10 APPLIED COMPLEXITY SCIENCE: ENABLING EMERGENCE THROUGH HEURISTICS AND SIMULATIONS Michael D. Norman, The MITRE Corporation, Bedford, MA, USASearch for more papers by this authorMatthew T.K. Koehler, The MITRE Corporation, Bedford, MA, USASearch for more papers by this authorRobert Pitsko, The MITRE Corporation, McLean, VA, USASearch for more papers by this author Michael D. Norman, The MITRE Corporation, Bedford, MA, USASearch for more papers by this authorMatthew T.K. Koehler, The MITRE Corporation, Bedford, MA, USASearch for more papers by this authorRobert Pitsko, The MITRE Corporation, McLean, VA, USASearch for more papers by this author Book Editor(s):Saurabh Mittal, The MITRE Corporation, McLean, VA, USASearch for more papers by this authorSaikou Diallo, Virginia Modeling, Analysis & Simulation Center Old Dominion University, Suffolk, VA, USASearch for more papers by this authorAndreas Tolk, The MITRE Corporation, Hampton, VA, USASearch for more papers by this author First published: 16 April 2018 https://doi.org/10.1002/9781119378952.ch10Citations: 5 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Summary This chapter introduces a set of heuristics to engineer for emergence, explores a number of them with a simulation of unmanned vehicle (UxV) swarms, and provides taxonomy to understand the levels of autonomy in engineered UxVs. It describes the practical integration of complexity science and traditional engineering. The chapter explores one method of using simulation to leverage emergence, the essence of designing a complex system by applying complexity science. It focuses on the use of the tools and techniques of complexity science and the corresponding feedback into the traditional engineering process that may occur. The chapter also focuses on developing the understanding required to begin to engineer how UxVs interact to enable emergence and set conditions for the swarm to be resilient to perturbations. The chapter further presents other governmental efforts to create "swarms" have been distributed systems of very small scale, not decentralized swarms. Citing Literature Biographies Michael D. Norman is the Applied Complexity Science Capability lead and a founder of the Applied Complexity Science Capability at The MITRE Corporation. MITRE is a nonprofit corporation that operates multiple Federally Funded Research and Development Centers (FFRDCs). Michael is responsible for leading the development of MITRE's Applied Complexity Science Capability. As such, his time is divided between work program development, workforce development, individual contribution, and external engagement activities. Michael's research interests are in applying generative methods from complexity science to US Federal Government agencies facing challenges of emergence, nonlinearity, and unpredictability. Michael has authored several journal articles and peer-reviewed conference papers on complexity science as applied to the topics of psychotherapeutic dynamics, affective computing, antifragile design, and cyber defense by using techniques such as dynamical systems theory and agent-based modeling. Michael has been an invited chair and speaker on complex systems, systemic risk, and enterprise-scale engineering at multiple peer-reviewed science, and engineering conferences. Michael received his BS in Computer Systems Engineering from the College of Engineering at the University of Massachusetts at Amherst and his PhD in Complex Systems and Brain Sciences from the Charles E. Schmidt College of Science at Florida Atlantic University. Matthew T.K. Koehler is the Applied Complexity Science Capability Lead for the US Treasury/Internal Revenue Service, US Commerce, and Social Security Administration Program Division within the Center for Connected Government at The MITRE Corporation, a nonprofit corporation that operates multiple Federally Funded Research and Development Centers (FFRDCs). As a founder of MITRE's Applied Complexity Science Capability, Matthew is responsible for guiding, growing, and technically contributing to MITRE's work program in complex systems. During his time at MITRE, Matt has concentrated on decision support using agent-based models and simulations and the analysis and visualization of large datasets coming from complex systems. Matt uses these tools and techniques across MITRE's FFRDCs and collaborates with other National Labs and FFRDCs, private companies, and educational institutions to address hard problems facing the US Federal Government. He has authored several journal articles, peer-reviewed conference papers, and book chapters on applied agent-based modeling from tax evasion and combat to fraud detection and legal institution design. Matt received his AB in Anthropology from Kenyon College, his MPA from Indiana University's School of Public and Environmental Affairs, his JD from George Washington University's Law School, and his PhD in Computational Social Science from George Mason University's Krasnow Institute for Advanced Study, Department of Computational Social Science. Robert Pitsko is the Department Head of the End to End Systems Engineering Department of the Systems Engineering Technical Center for the MITRE Corporation, a nonprofit corporation that operates multiple Federally Funded Research and Development Centers (FFRDCs). Rob, a founder of the MITRE Applied Complexity Science Capability, has focused on the union of complex systems modeling and systems design. He was pivotal in initiating the MITRE Innovation Program Agile Enterprises research portfolio, funding over 20 active research projects exploring application of modeling, visualization, organizational, and acquisition agility. He received the ME and PhD in Systems Engineering from Stevens Institute of Technology and his BS in Computer Science from the Pennsylvania State University. As an Adjunct Professor with Stevens Institute of Technology, he teaches the graduate courses on the Foundation of Systems Engineering and System Architecture and Design. As a Doctoral Fellow of the Systems Engineering Research Center (SERC), he routinely provides talks describing Complex Systems research to the academic and Department of Defense sponsor community. His professional and research activities emphasize systems engineering and design with a focus on conceptual design evaluation, early application of modeling, adaptability, complex system design, system architecture, and System of Systems Engineering. Emergent Behavior in Complex Systems Engineering: A Modeling and Simulation Approach RelatedInformation

While the formal study of tax evasion began with a seminal paper by M.G. Allingham and A. Sandmo titled “Income Tax Evasion: A Theoretical Analysis” in 1972, scholars and practitioners continue to be challenged with designing and implementing policies and incentives to mitigate tax evasion. While early theoretical studies provide a baseline from which to evaluate hypotheses, the methodology underlying the classical formulation; that is, the use of utility functions and the assumptions of taxpayer homogeneity and rationality, fall short in characterizing taxpayer behaviors observed in practice. In this book, we seek to advance the state of the art in the study of tax evasion by presenting an alternative computational approach based on simulating individual agents. These so-called agent-based models (ABM) aim to take into account individual preferences and can accommodate a larger variety of intrinsic and extrinsic variables to help explore a broader space of compliance outcomes. In this introductory chapter, we present a formal definition of tax evasion in Section 1.2 and outline the case for why its analysis is a priority not only for tax administrators, but also for society at large. The classical theoretical models of tax evasion are then summarized

Natural complex adaptive systems are of particular scientific interest in many domains, as they may produce something new, like structures, patterns, or properties, that arise from the rules of self-organization. These novelties are emergent if they cannot be understood as any property of the components, but as a new property of the system. One of the leading methods to better understand complex adaptive systems is the use of their computational representation. In this paper, we make the case that emergence in computational complex adaptive systems can only be epistemological, as the constraints of computer functions do not allow for the creation of something new, as required for ontological emergence. As such, computer representations of complex adaptive systems are limited in producing emergence, but nonetheless useful to better understand the relationship between emergence and complex adaptive systems.

The application of Complexity Science, an undertaking referred to here as Complex Systems Engineering, often presents challenges in the form of agent-based policy development for bottom-up complex adaptive system design and simulation. Determining the policies that agents must follow in order to participate in an emergent property or function that is not pathological in nature is often an intensive, manual process. Here we will examine a novel path to agent policy development in which we do not manually craft the policies, but allow them to emerge through the application of machine learning within a game engine environment. The utilization of a game engine as an agent-based modeling platform provides a novel mechanism to develop and study intelligent agent-based systems that can be experienced and interacted with from multiple perspectives by a learning agent. In this paper we present results from an example use-case and discuss next steps for research in this area.

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.