Comparative analysis of the performance of humanitarian logistic structures using agent-based simulation

Unlike conventional crowd simulations for what-if analysis, agent-based crowd simulations for real-time applications are an emerging research topic and an important tool for better crowd managements in smart cities. Recent studies have attempted to incorporate the real-time crowd observations into crowd simulations for real-time crowd forecasting and management; however, crowd flow forecasting considering individual-level microscopic interactions, especially for large crowds, is still challenging. Here, we present a method that incorporates crowd observation data to forecast a large crowd flow, including thousands of individuals, using a microscopic agent-based model. By sequentially estimating both the crowd state and the latent parameter behind the crowd flows from the aggregate crowd density observation with the particle filter algorithm, the present method estimates and forecasts the large crowd flow using agent-based simulations that incorporate observation data. Numerical experiments, including a realistic evacuation scenario with 5000 individuals, demonstrated that the present method could successfully provide reasonable crowd flow forecasting for different crowd scenarios, even with limited information on crowd movements. These results support the feasibility of real-time crowd flow forecasting and subsequent crowd management, even for large but microscopic crowd problems.

In an agent-based simulation (ABS), a state update scheme carries out the transitions of agents from one state to the next. To produce correct simulation results, the update scheme must respect the cause-and-effect relationships defined by the agent-based model and ensure that the resulting overall simulation state is internally consistent. At the same time, the update scheme should be efficient enough to meet a simulationist's demand for timely results. Considering the common class of synchronous time-driven ABS, a number of update schemes have been employed in the literature and simulation frameworks. In this paper, various implementations of update schemes are analyzed and contrasted with respect to their ability to maintain the simulation correctness as well as their performance characteristics. A semantic model is formulated to define the reference behavior of synchronous time-driven ABS updates and model the dependencies among agent updates using a state access graph. Relying on the formalization, conditions under which different update schemes achieve causality are shown. Further, resolution methods are categorized according to their coordination mechanisms to achieve consistency by resolving conflicts among agent state updates. Through two case studies, the empirical performance of different update schemes and resolution methods are evaluated. For sequential execution, an update scheme based on the agent's dependencies achieves the highest performance, whereas in the parallel case, the choice of update scheme involves a tradeoff between execution time and memory usage. If deterministic simulation output is required, decentralized coordination generally outperforms centralized coordination. The results can assist implementers and researchers in their selection of appropriate methods in the design and implementation of agent-based simulators.

A major concern of many government agencies is to predict and control the behavior of crowds in different situations. Many times such gatherings are legal, legitimate, and peaceful. But there are times when they can turn violent, run out of control, result in material damages and even casualties. It then becomes the duty of governments to bring them under control using a variety of techniques, including non-lethal and lethal weapons, if necessary. In order to aid decision makers on the course of action in crowd control, there are modeling and simulation tools that can provide guidelines by giving programmed rules to computer animated characters and to observe behaviors over time in appropriate scenarios. A crowd is a group of people attending a public gathering, with some joint purpose, such as protesting government or celebrating an event. In some countries these kinds of activities are the only way to express public's displeasure with their governments. The governments' reactions to such activities may or may not be tolerant. For these reasons, such situations must be eliminated by recognizing when and how they occur and then providing guidelines to mitigate them. Police or military forces use non-lethal weapons (NLWs), such as plastic bullets or clubs, to accomplish their job. In order to simulate the results of such actions in a computer, there is a need to determine the physical effects of NLWs over the individuals in the crowd. In this dissertation, a fuzzy logic based crowd injury model for determining the physical effects of NLWs is proposed. Fuzzy logic concepts can be applied to a problem by using linguistic rules, which are determined by problem domain experts. In this case, a group of police and military officers were consulted for a set of injury model rules and those rules were then included in the simulation platform. As a proof of concept, a prototype system was implemented using the Repast Simphony agent based simulation toolkit. Simulation results illustrated the effectiveness of the simulation framework.

Many types of financial time series, most notably market returns, have been found to exhibit long-range memory as well as dramatic day-to-day swings that cannot be adequately represented by light-tailed distributions such as the normal distribution. In particular, this means that for such time series, the usual variance parameter (i.e., the sum of covariances at all time lags) is not defined because the covariance function does not converge to zero fast enough as the time lag increases. Moreover in such time series, often the tails of the marginal density converge to zero so slowly that higher-order marginal moments such as skewness and kurtosis fail to exist. Therefore conventional methods for analyzing simulation-generated time series cannot generally be applied to high-fidelity simulations of financial markets.Building on earlier work in fractal geometry and fractal time series, Mandelbrot et al. proposed the multifractal model of asset returns (MMAR) as an alternative to the ARCH models for analyzing time series exhibiting volatility clustering, long-range dependence, and heavy-tailed returns (Mandelbrot et al. 1997). They defined the multifractal spectrum as the renormalized probability density function of the Holder exponents observed in the time series; and they used the multifractal spectrum to measure the ability of MMAR to match the statistical properties of real data. In 2002 Kantelhardt et al. formulated multifractal detrended fluctuation analysis (MF-DFA), an algorithm for extracting the multifractal spectrum from a time series (Kantelhardt et al. 2002).Many economists have recently adopted agent-based simulation for modeling financial markets. Fads based on new products, shocks related to world events, scandals involving company leaders, or outright criminal activity can drastically change the processes and relationships governing a market. Already there is evidence that agent-based models yield more accurate approximations to the true or observed behavior in financial markets. Agent-based models exhibit emergent behaviors that have been linked to non-Gaussian interaction metrics and singularities in the time series they generate (Chan 2011). Farmer et al. constructed a agent-based model that is capable of exhibiting many of the statistical properties observed in real financial data simply due to the rules governing a double auction order book (Farmer et al. 2005).During the 20th century, economists relied heavily on Brownian motion to construct theoretical models of finance. The advantages of using Brownian motion stem from its foundation in the Gaussian distribution; and with advances in stochastic calculus and differential equations, it became a staple in models of empirical finance. The well-known Black-Scholes model for options pricing is a prime example of this application. Multifractal analysis has the ability to illuminate the underlying difference between a monofractal like Brownian motion and a considerably more diverse construction like the multifractal binomial measure. Thus, combining multifractal analysis with an agent-based model has the potential to provide insight into the underlying processes and behaviors of a dynamic economic system that is constantly changing and evolving.To analyze market-return time series exhibiting volatility clustering, long-range dependence, or heavy-tailed marginals, we exploit multifractal analysis and agent-based simulation. We develop a robust, automated software tool for extracting the multifractal spectrum of a time series based on MF-DFA. Guidelines are given for setting MF-DFA's parameters in practice. The software is tested on simulated data with closed-form monofractal and multifractal spectra as well as on observed data, and the results are analyzed. We also present a prototype agent-based financial market model based on the zero-intelligence model of Farmer et al. and analyze its output using MF-DFA. The figure below compares the multifractal spectrum of the output from the prototype model with that of the multifractal binomial measure. The ultimate objective is to expand this model to study the effects of microlevel agent behaviors on the macrolevel time series output as analyzed by MF-DFA. Finally we explore the potential for validating agent-based models using MF-DFA and thus being able to tune these models to the multifractal spectrum of empirical data.

Self-sustainability of micro grids is an important challenge to be addressed, particularly when they operate disconnected from the utility grid. The multi-agent systems paradigm allows implementing intelligent and predictive strategies towards an efficient management of the power flow. In this paper, an agent-based model is introduced, optimizing the load demand, through load prioritization, by shedding and rescheduling, and using forecasting mechanisms to improve the micro grids self-sustainability. The designed agent-based model and strategies were implemented and tested in the Repast agent-based modelling and simulation framework. Experimental results show improvements in the self-sustainability of the micro grid under analysis when operating in critical mode.

A formal model of educational decision-making is presented, the aim of which is to generate the essential characteristics of the empirical aggregate patterns of educational stratification. The so-called “Interdependent Educational Choice Model” (IECM) purports to integrate three kinds of social mechanisms: a/ generative mechanisms of the link between actor’s origin group and his individual evaluations of the social returns, costs and risks of educational investment; b/ generative mechanisms of the link between actor’s origin group, social networks and individual evaluation of educational degrees; c/ generative mechanisms of the link between certain elements of the actor’s decision-making function and social aggregates that actors themselves create dynamically. The mathematical form of the IECM fits into the framework of discrete choice models with externalities. The claim is that agent-based computational simulation is the most suitable technique for studying aggregate implications of models that formalize such kind of generative mechanisms. The educational stratification generated by agent-based simulation of the IECM is then presented and compared to cross-sectional empirical data for Italy and France in the 1990s. It is suggested that the mechanisms formalized in IECM generate educational stratification that is structurally homologous to empirical stratification of these two countries by triggering an “amplification process” driven by a “cumulative advantage process”. Lastly, the author studies the artificial aggregate educational stratification generated by manipulating characteristics of the network that IECM agents are located in, particularly, locale density, structure of heterophily links and proportion of these links. In conclusion, the main limitations of the IECM and, more generally, the “validity problem” for results achieved with agent-based simulation are discussed.

Agent based simulation (ABS) is efficient for the simulation of objects whose mathematical modeling is not easy. There are many studies on ABS and several frameworks for ABS have already been published. However, there are few frameworks that can enable agent-based simulation using large numbers of agents. We have been developing a Java-based platform for Massive Agent-Based Simulation (MABS) called “Zillions of Agents-based Simulation Environment” or ZASE. The purpose of ZASE is to develop MABS applications on multiple general computers such as PCs or workstations where they are connected with a high performance but generally available network such as gigabit Ethernet. ZASE is designed to host over millions of agents. In this paper, we introduce ZASE and explain the fundamental capabilities for MABS provided by ZASE. We evaluated ZASE for the agent-based auction simulation where the number of agents varied from ten to a million. The results indicate that the number of agents affects the final bid prices and their distributions. Performance measurement results on both an SMP computer environment and a multiple-computer environment are shown. The results in the both cases were about 3.5 millions of times of that agents handle messages in a second. The scalability ratio on the SMP computer and the multiple-computers environment were 78% and 85% respectively. Those scalability ratios are such good values for the simulations.KeywordsAgent-based SimulationMassively Multi-Agent Systems

An agent-based model for analyzing the impact of business interoperability on the performance of cooperativeindustrial networks

This article aims at contributing to the discussion about the relationships between ICT, computer science and policy-making by focusing on agent-based social simulation. Enabled, from a technical point of view, by the developments of Distributed Artificial Intelligence in the 1990s and by the features of the object-oriented programming paradigm, agent-based social simulations are a tool for the analysis of social dynamics that can be used also to support the design and the evaluation of public policies. After a brief description of social simulation paradigm and of its impact on social sciences, the paper presents a simple agent-based model devised to analyze, even if in a very abstract way, a phenomenon that can rouse the interest of legal scientists: the interplay between damaging behaviors, punishment and social mechanisms of learning and imitation. Our goal is to show how agent-based simulation can be used not only to illuminate basic mechanisms underlying social phenomena but also to reflect, in an innovative way, on how society can deal with them.

Cascading effects describe risk interdependencies, whereby the occurrence of one risk may trigger one or more risks with potential propagation chains in complex systems. In this study, on the basis of a formalized model namely danger-risk-consequence chain, a generic simulation framework is proposed to characterize risk causal processes and cascading effects within collaborative networks. Risk-related components and the causal relationships between them are visualized by abstractly representing the instantaneous state of the considered collaborative network as a directed graph. Furthermore, the simulation of trajectories of the state evolution over time is realized by knowledge-driven automatic inference of causal chains and propagation chains, thus enabling the tracing of cascading effects within complex systems. The presented simulation framework provides a solid foundation for a systemic understanding of risks, which implies an innovative tool that helps decision-makers to identify, prevent and mitigate cascading effects within collaborative networks (e.g., supply chains).KeywordsSimulationCascading effectRisk interdependencyCollaborative networkFramework

Quantifying the social costs of power outages and restoration disparities across four U.S. hurricanes

Agent-based modeling and simulation is a method well suited for studying individual behavior and interactions among members of a population connected by social networks. Although the development of such simulation models can be relatively complex, it is even more challenging to develop models that are empirically valid. In the case of reverse logistics, the sophisticated and difficult-to-predict behavior of consumers must be modeled. In this paper, an agent-based simulation model of consumer behavior and interactions was configured to conduct a case study of the voluntary deposit collection program for wine bottles in the Val-Saint-François region of Quebec. As this collection program was officially launched in 2019, two empirical samples were obtained to test the validity of the model and study how social interactions such as word of mouth contributes to the success of the collection program. The first sample represents the amount of glass collected during the last 26 weeks of 2019, while the second sample covers the first 13 weeks of 2020. Having observed an increase in collection rates between 2019 and 2020, word of mouth was introduced into the model to explain this phenomenon. Statistical tests show that the model is indeed valid with the inclusion of diffusion of awareness, as the simulation results are significantly consistent with the empirical data. The validation of the model demonstrates the viability of using multiple heterogeneous data-sources to configure a simulation model based on the Theory of Planned Behavior without using Structural Equation Modeling.

This paper discusses basic findings on crowd movement and their application to simulation models. This includes empirical and experimental results concerning group behavior, pedestrian motion, and emergency egress. Next to a literature review, we will present own empirical investigations on walking speed distribution and the dependency of walking speed on group size.

To date the work done in the UK to assess the loss of life and evacuation times for flood risk areas has been limited.To provide the most accurate assessment of loss of life and evacuation times a complex model is required.This paper details the application of a prototype, agent-based Life Safety Model (LSM) to estimate the loss of life in two areas of the Thames Estuary.The LSM models individual receptors (e.g.people and cars) and their dynamic interaction with the floodwater.The LSM estimates deaths from: drowning; exhaustion; building collapse; and vehicles being swept away, as well as evacuation times.The LSM offers a scientifically robust method of assessing residual risk behind flood defences and downstream of dams in terms of fatalities.Importantly, it allows the comparison of different emergency management strategies (e.g. the use of safe havens) that can assist in reducing the loss of life during future floods and dam breaks.The model was validated against historical data from the Canvey Island flood in 1953, during which 58 people lost their lives.The LSM was then applied to the Thamesmead embayment to assess the results for a range of scenarios.

Agent-based simulation is a powerful tool that allows emulating the complex behaviour of individuals within a system in order to assess its global performance. In this context, this paper develops a modular agent-based simulation framework for being applied to vehicle-sharing systems. The modular structure of the simulator implies that each sub-system is treated separately, so that each module can be replaced or modified without affecting the others. In this way, the simulator can be used to analyse specific problems such as the optimal location of stations, the design of repositioning strategies, or different vehicle usage policies, by focusing only on the relevant module. The simulation framework proposed has been implemented and tested in the analysis of two different systems: a station-based bike-sharing system and a mixed (i.e., station-based and free-floating) car-sharing initiative. The paper shows some of the results obtained from such implementations as an example of its possible applications.