Agent-based modelling and simulation (ABMS) is a relatively new approach to modelling systems composed of autonomous, interacting agents. Agent-based modelling is a way to model the dynamics of complex systems and complex adaptive systems. Such systems often self-organize themselves and create emergent order. Agent-based models also include models of behaviour (human or otherwise) and are used to observe the collective effects of agent behaviours and interactions. The development of agent modelling tools, the availability of micro-data, and advances in computation have made possible a growing number of agent-based applications across a variety of domains and disciplines. This article provides a brief introduction to ABMS, illustrates the main concepts and foundations, discusses some recent applications across a variety of disciplines, and identifies methods and toolkits for developing agent models.

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Tutorial on agent-based modelling and simulation

Networks are ubiquitous in science and have become a focal point for discussion in everyday life. Formal statistical models for the analysis of network data have emerged as a major topic of interest in diverse areas of study, and most of these involve a form of graphical representation. Probability models on graphs date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active "network community" and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning network literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online "networking communities" such as Facebook, MySpace, and LinkedIn, and a host of more specialized professional network communities has intensified interest in the study of networks and network data.

Our goal in this review is to provide the reader with an entry point to this burgeoning literature. We begin with an overview of the historical development of statistical network modeling and then we introduce a number of examples that have been studied in the network literature. Our subsequent discussion focuses on a number of prominent static and dynamic network models and their interconnections. We emphasize formal model descriptions, and pay special attention to the interpretation of parameters and their estimation. We end with a description of some open problems and challenges for machine learning and statistics.

https://dash.harvard.edu/bitstream/handle/1/4645865/Goldenberg_NetSurvey.pdf?sequence=1

A Survey of Statistical Network Models

Agent-based modeling and simulation (ABMS) is a new approach to modeling systems comprised of autonomous, interacting agents. ABMS promises to have far-reaching effects on the way that businesses use computers to support decision-making and researchers use electronic laboratories to support their research. Some have gone so far as to contend that ABMS is a third way of doing science besides deductive and inductive reasoning. Computational advances have made possible a growing number of agent-based applications in a variety of fields. Applications range from modeling agent behavior in the stock market and supply chains, to predicting the spread of epidemics and the threat of bio-warfare, from modeling consumer behavior to understanding the fall of ancient civilizations, to name a few. This tutorial describes the theoretical and practical foundations of ABMS, identifies toolkits and methods for developing ABMS models, and provides some thoughts on the relationship between ABMS and traditional modeling techniques.

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Tutorial on agent-based modeling and simulation

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

Social computing represents a new computing paradigm and an interdisciplinary research and application field. Undoubtedly, it strongly influences system and software developments in the years to come. We expect that social computing's scope continues to expand and its applications multiply. From both theoretical and technological perspectives, social computing technologies moves beyond social information processing towards emphasizing social intelligence. As we've discussed, the move from social informatics to social intelligence is achieved by modeling and analyzing social behavior, by capturing human social dynamics, and by creating artificial social agents and generating and managing actionable social knowledge

Social Computing: From Social Informatics to Social Intelligence

While structured by social and institutional networks, disease outbreaks are modulated by physical, economical, technological, communication, health, and governmental infrastructures. To systematically reason about the nature of outbreaks, the potential outcomes of media, prophylaxis, and vaccination campaigns, and the relative value of various early warning devices, social context, and infrastructure, must be considered. Numerical models provide a cost-effective ethical system for reasoning about such events. BioWar, a scalable citywide multiagent network numerical model, is described in this paper. BioWar simulates individuals as agents who are embedded in social, health, and professional networks and tracks the incidence of background and maliciously introduced diseases. In addition to epidemiology, BioWar simulates health-care-seeking behaviors, absenteeism patterns, and pharmaceutical purchases, information useful for syndromic and behavioral surveillance algorithms.

BioWar: scalable agent-based model of bioattacks

: BioWar is scalable city-wide simulation, capable of simultaneously simulating the impact of background diseases, natural outbreaks and bioterrorism attacks on the population s behavior within a city. The multi-agent simulator includes social and institutional networks, weather and climate conditions, and the physical, economical, technological, communication, health, and governmental infrastructures which modulate disease outbreaks and individual behavior. Individual behaviors include health seeking, entertainment and work/school behavior. A wide variety of reports are generated based on user needs including absenteeism patterns, pharmaceutical purchases, doctor's office insurance claims reports, and hospital/emergency room reports. Sub-reports are available for specific sentinel groups including military personnel, first responders and health workers. Reports matching real world data streams and reports can be created for analyst or public health personnel including appropriate delays in generating said reports. This paper provides an overview of BioWar's current capabilities and information on the algorithms and data used to drive the simulation as of the Challenge 5 (C5) version.

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BioWar: A City-Scale Multi-Agent Network Model of Weaponized Biological Attacks

Benchmarks that measure time values using a standard system clock often employ a dual loop design. One of the important assumptions of this design is that textually identical loop statements will take the same amount of time to execute. This assumption was tested on two bare computers with Ada test programs and has been demonstrated to be inaccurate in these specific test cases. 1. Dual Loop Benchmarks: Purpose and Assumptions Benchmarks are tests designed to measure the capabilities of a computer system. They are used to compare different computer systems and determine the suitability of a computer system for particular tasks. Benchmarks show a wide variation in what they are intended to measure, how they are designed, and how they are implemented. Many benchmarks produce outputs that are measurements of the time required to perform some task. A common technique is to write a program that performs some interesting bit of work (e.g., a rendezvous) sandwiched between calls to a system timer. Benchmarks that use time as a unit of measure vary widely in the time they require to run. Some tasks are brief and can take fractional parts of a second to execute. Others measure durations of minutes or hours. The ability of computer clocks to measure this range of times also varies widely. A system clock available to a benchmark designer may be accurate only to a tenth of a second, far too slow to measure an event in the millisecond or microsecond range. More accurate timing devices are often available, but as an option rather than as a standard component. For benchmarks intended for general use, a dual loop benchmark design is often used to permit the benchmark to execute on an unmodified system. Dual loop benchmarks handle the problem of imprecise clocks by extending the duration of the test to a length that the clock can readily measure. The time required for a test is extended by repeating the test numerous times between calls to the system timer. Repetition is usually programmed by inserting the test in a loop, where the number of repetitions may be conveniently changed. The increased time duration of the test series can be measured easily, and the time for the individual test can be determined by computing the average value for the test series. Introducing a loop construct into the test adds time, which must be factored out. This is done with a second loop, a control loop, which contains only the loop construct and not the actual test. The time required for the benchmark is assumed to be the value obtained by subtracting the control loop time from the test loop time. An Ada skeleton for a dual loop benchmark appears as follows:1 1This Ada program fragment requires that the constant SOME_VALUE and a procedure TEST be added before the program can actually be run. In actual practice, precautions must be taken to ensure optimization by the compiler does not alter the essential program structure. For example, because the empty loop contains no executable statements, it might be removed by a compiler.

Timing variation in dual loop benchmarks

: One of the goals of the Ada Embedded Systems Testbed (AEST) Project is to assess the readiness of the Ada programming language and Ada tools for developing embedded system. The benchmarking and instrumentation subgroup within AEST Project is running various suites of Ada benchmarks to obtain data on the real-time performance of Ada on a number of different target systems. The purpose of this report is to categorize the factors have been observed, while others are more speculative in nature. All these factors should be understood if accurate, comparable, and repeatable performance data are to be produced by Ada benchmarks. Keywords: Compilers; Timing anomalies; System software effects; Hardware effects.

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Factors Causing Unexpected Variations in Ada Benchmarks.

When pandemics, chemical spills, and bio-warfare attacks occur cities must respond quickly to mitigate loss of life. Which interventions should be used? How can we assess intervention policies for novel and low frequency events? Reasoning about such events is difficult for people due to the high level of complexity and the multitude of interacting factors. Computational models, however, are a particularly useful tool for reasoning about complex systems. In this paper, we describe a multi-agent dynamic-network model and demonstrate its use for policy assessment. BioWar is a city-level multi-agent dynamic-network model of the impact of epidemiological events on a city’s population. Herein, we describe BioWar and then use it to examine the impact of school closures and quarantine on the spread and impact of pandemic influenza. Key aspects of the model include utilization of census data to set population characteristics, imputed social networks among agents, and flexible disease modeling at the symptom level. This research demonstrates that high-fidelity models can be effectively used to assess policies.

Neal Altman

Papers

BioWar: scalable agent-based model of bioattacks

BioWar: A City-Scale Multi-Agent Network Model of Weaponized Biological Attacks

Timing variation in dual loop benchmarks

Factors Causing Unexpected Variations in Ada Benchmarks.

Multi-Agent Modeling of Biological and Chemical Threats