Agent-based Modeling and Simulation for Evacuation of Landslides Natural Disaster

Computational Social Science

Crowds are a feature of large cities, occurring not only at mass gatherings but also at routine events such as the journey to work. To address extreme crowding, various computer models for crowd movement have been developed in the past decade, and we review these and show how they can be used to identify health and safety issues. State-of-the-art models that simulate the spread of epidemics operate on a population level, but the collection of fine-scale data might enable the development of models for epidemics that operate on a microscopic scale, similar to models for crowd movement. We provide an example of such simulations, showing how an individual-based crowd model can mirror aggregate susceptible-infected-recovered models that have been the main models for epidemics so far.

There are numerous biological scenarios in which populations of cells migrate in crowded environments. Typical examples include wound healing, cancer growth, and embryo development. In these crowded environments cells are able to interact with each other in a variety of ways. These include excluded-volume interactions, adhesion, repulsion, cell signaling, pushing, and pulling. One popular way to understand the behavior of a group of interacting cells is through an agent-based mathematical model. A typical aim of modellers using such representations is to elucidate how the microscopic interactions at the cell-level impact on the macroscopic behavior of the population. At the very least, such models typically incorporate volume-exclusion. The more complex cell-cell interactions listed above have also been incorporated into such models; all apart from cell-cell pulling. In this paper we consider this under-represented cell-cell interaction, in which an active cell is able to "pull" a nearby neighbor as it moves. We incorporate a variety of potential cell-cell pulling mechanisms into on- and off-lattice agent-based volume exclusion models of cell movement. For each of these agent-based models we derive a continuum partial differential equation which describes the evolution of the cells at a population level. We study the agreement between the agent-based models and the continuum, population-based models and compare and contrast a range of agent-based models (accounting for the different pulling mechanisms) with each other. We find generally good agreement between the agent-based models and the corresponding continuum models that worsens as the agent-based models become more complex. Interestingly, we observe that the partial differential equations that we derive differ significantly, depending on whether they were derived from on- or off-lattice agent-based models of pulling. This hints that it is important to employ the appropriate agent-based model when representing pulling cell-cell interactions.

A review of capabilities of agent-based simulation and modelling for analyzing Supply Chains and a review of related scientific literature are being conducted. It is shown that agent-based simulation and modelling makes it possible to describe the behavior, processes of cooperation, coordination and inter-organizational interaction of participants of a supply chain and reconfigurable network structures of the supply chain. The paper deals with the genesis, key factors and mechanisms of inter-organizational coordination as objects of representation in dynamic models of supply chain, such as behavior, motivation and alignment of partner interests, integrated collaborative planning, and related information and knowledge sharing, and formation of coalitions and flexible network structure, and so on. The authors have proposed an agent-based simulation model of a supply chain.

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.

This article argues that the agent-based computational model permits a distinctive approach to social science for which the term “generative” is suitable. In defending this terminology, features distinguishing the approach from both “inductive” and “deductive” science are given. Then, the following specific contributions to social science are discussed: The agent-based computational model is a new tool for empirical research. It offers a natural environment for the study of connectionist phenomena in social science. Agent-based modeling provides a powerful way to address certain enduring—and especially interdisciplinary—questions. It allows one to subject certain core theories—such as neoclassical microeconomics—to important types of stress (e.g., the effect of evolving preferences). It permits one to study how rules of individual behavior give rise—or “map up”—to macroscopic regularities and organizations. In turn, one can employ laboratory behavioral research findings to select among competing agent-based (“bottom up”) models. The agent-based approach may well have the important effect of decoupling individual rationality from macroscopic equilibrium and of separating decision science from social science more generally. Agent-based modeling offers powerful new forms of hybrid theoretical-computational work; these are particularly relevant to the study of non-equilibrium systems. The agentbased approach invites the interpretation of society as a distributed computational device, and in turn the interpretation of social dynamics as a type of computation. This interpretation raises important foundational issues in social science—some related to intractability, and some to undecidability proper. Finally, since “emergence” figures prominently in this literature, I take up the connection between agent-based modeling and classical emergentism, criticizing the latter and arguing that the two are incompatible. ! 1999 John Wiley & Sons, Inc.

Background/purpose: This paper discusses the application of ABMS - agent-based modelling and simulation in the analysis of customer behaviour on B2C e-commerce websites as well as in the analysis of various business decisions upon the effects of on-line sales. The continuous development and dynamics in the field of e-commerce requires application of advanced decision-making tools. These tools must be able to process, in a short time period, a large amount of data generated by the e-commerce systems and enable the use of acquired data for making quality business decisions. Methodology: The methodology of the agent-based simulation used in this paper may significantly enhance the speed and quality of decision making in electronic trade. The models developed for the needs of this research aim to improve the use of practical tools for the evaluation of the B2C online sales systems in that they allow for an investigation into the outcomes of varied strategies in the e-commerce site management as regards customer behaviour, website visits, scope of sales, income earned, etc. Results: An agent-based simulation model developed for the needs of this research is able to track the interactions of key subjects in online sales: site visitors - prospective consumers, sellers with different business strategies, and suppliers. Conclusion: Simulation model presented in this paper can be used as a tool to ensure a better insight into the problem of consumer behavior on the Internet. Companies engaged in the B2C e-commerce can use simulation results to better understand their consumers, improve market segmentation and business profitability and test their business policies.

In this paper, we describe what we believe to be the simplest agent-based model. More specifically we define the class of the simplest agent-based simulation model formulations, referred to as SABM. We describe the nine essential elements of agent-based simulation models in the class SABM and give an example of such a model. We find that the simplest ABM formulation provides a basis for a transparent, compact and elegant description of agent-based modeling. The design illustrates the essential characteristics of agent-based modelling as a field of research and application and facilitates the explanation of agent-based modeling. We hope that the simplest formulation demystifies agent-based modeling for those within the simulation community, as well as those new to the field such as researchers from other disciplines and application communities.

According to recent studies, Vietnam is one of the twenty countries most affected by natural disasters in the world, and particularly by floods either on the low elevation coastal zones (risk of submersion) or along the Red River and the Mekong River (risk of flooding). In this context, dams are both means of mitigation but also threats given the possible failures and ruptures. The authorities must, therefore, prepare warning systems and evacuation plans for the downstream population to avoid loss of life. Agent-based models are now the approach of choice to support such preparedness by considering the system as a whole and integrating dynamics of different natures: hydrology, population behavior, evacuation, crisis management, etc. To design such a decision-support tool, modelers generally need to couple different formalisms, such as diffusion equations when considering the hydrodynamic part, and agent-based modeling when considering inhabitants’ behaviors. This is the goal of the ESCAPE project, which uses agent-based simulations to explore evacuation strategies and contribute to the development and evaluation of evacuation plans. In this study, to improve the ESCAPE framework, we propose to combine a hydraulic dam failure model with an agent-based evacuation model using the GAMA platform. We focus on the evacuation of a Hanoi city (Vietnam) district, threatened by flooding due to the failure of the Hoa Binh dam located more than 80 km upstream of the city. We demonstrate how to methodologically and operationally couple a hydrodynamic water diffusion model (implemented using the HEC-RAS software) and a multi-paradigm evacuation model (using the ESCAPE framework). Our goal is to extend and enrich this population evacuation model by coupling it with flood simulation.

Purpose: In order to improve the safety, capacity, economy, and sustainability of air transportation, revolutionary changes are required. These changes might range from the introduction of new technology and operational procedures to unprecedented roles of human operators and the way they interact. Implementing such changes can introduce both negative and positive emergent behaviour. i.e. behaviour that arises from the interactions between system entities as proposed in innovative concepts. Currently, the inability to understand and control such behaviour prevents us from avoiding undesired negative emergent behaviours and promoting positive ones. In order to address this problem, this thesis aims to understand emergent behaviour in the complex socio-technical air transportation system. Methods: The thesis proposes Agent-Based Modelling and Simulation (ABMS) as a method for capturing emergent behaviour of the socio-technical air transportation system, and evaluating novel system designs. The popularity of ABMS is driven by its capability of handling the increasing complexity of real world socio-technical systems that exhibit emergent behaviour. This thesis focuses on two main applications namely: 1) the identification of emergent safety risk of an active runway crossing operation; and 2) the evaluation of the role of coordination in Airline Operations Control (AOC) resilience. In both applications, ABMS has emerged as a key method because it is widely used in complexity science to understand how interactions give rise to emergent behavior. The agent-based models include all relevant human and technical agents, such as pilots and controllers and the decision support systems involved. Simulation of these agents interacting together is conducted to predict the impact of both existing and future concepts of operation. Results: The applications in this thesis highlight that ABMS has the capability to reveal unexpected emergent behaviour and provide novel insights in air transportation. For the airport safety application, various types of emergent behaviour have been revealed due to the development and simulation of the agent-based model that covers the totality of interactions of components and their variability in performance over time. The Monte Carlo simulations make it possible to understand the potential of agents in restricting the risk in off-nominal scenarios, through capturing their stochastic nature and accounting for uncertainty. For the airline resilience application, novel insights were gained about the role of coordination in airline resilience. Capitalizing on established airline practice and research about human coordination from the psychology domain, the agent-based simulations evaluated the operational effects of AOC coordination policies on a challenging disruption scenario. Conclusions & possible applications and implications: This thesis demonstrates that ABMS of air transport operations is a viable approach in gaining knowledge about emergent behaviour which was unknown before. This knowledge includes both bottlenecks of system designs and identified opportunities, and hence can be used to control and further optimize the socio-technical air transportation system. This also implies that ABMS can be a cost-effective method for evaluating new concepts during the early design phase of air transport operations.

An agent-based modeling and simulation of consumers’ purchase behavior for wine consumption

Agent-based modelling has emerged as a powerful tool for modelling systems that are driven by discrete, heterogeneous individuals and has proven particularly popular in the realm of pedestrian simulation. However, real-time agent-based simulations face the challenge that they will diverge from the real system over time. This paper addresses this challenge by integrating the ensemble Kalman filter (EnKF) with an agent-based crowd model to enhance its accuracy in real time. Using the example of Grand Central Station in New York, we demonstrate how our approach can update the state of an agent-based model in real time, aligning it with the evolution of the actual system. The findings reveal that the EnKF can substantially improve the accuracy of agent-based pedestrian simulations by assimilating data as they evolve. This approach not only offers efficiency advantages over existing methods but also presents a more realistic representation of a complex environment than most previous attempts. The potential applications of this method span the management of public spaces under 'normality' to exceptional circumstances such as disaster response, marking a significant advancement for real-time agent-based modelling applications.

HIV/AIDS spread depends upon complex patterns of interaction among various subsets emerging at population level. This added complexity makes it difficult to study and model AIDS and its dynamics. AIDS is therefore a natural candidate to be modeled using agent-based modeling, a paradigm well-known for modeling Complex Adaptive Systems (CAS). While agent-based models are well-known to effectively model CAS, often times models can tend to be ambiguous and using only using text-based specifications (such as ODD) making models difficult to be replicated. Previous work has shown how formal specification may be used in conjunction with agent-based modeling to develop models of various CAS. However, to the best of the authors’ knowledge, no such model has been developed in conjunction with AIDS. In this paper, we present a Formal Agent-Based Simulation modeling framework (FABS-AIDS) for an AIDS-based CAS. FABS-AIDS employs the use of a formal specification model in conjunction with an agent-based model to reduce ambiguity as well as improve clarity in the model definition. The proposed model demonstrates the effectiveness of using formal specification in conjunction with agent-based simulation for developing models of CAS in general and, social network-based agent-based models, in particular.

An agent-based modeling approach to collaborative classrooms evacuation process

The aim of this Special Issue is to introduce to the readers a selection of papers from the 3rd Workshop on Applications of Software Agents - WASA 2013 in the area of agent-based modelling and simulation. WASA'2013 was held in Sinaia, Romania, during October 11-13, 2013. The WASA 2013 workshop was organized within the framework of the 17th International Conference on Theory, Control and Computing - ICSTCC'2013. The aim of the WASA series of workshops is to contribute to the advancement of technologies and applications of software agents' by bridging the gap between the theory and practice of software agents. Fifteen papers were accepted for presentation at WASA'2013. Among them, 3 papers were selected, after further extension and additional review, for inclusion in this Special Issue on Modeling and Applications of Software Agents. The article A Supporting the Evaluation of the Operational Effectiveness of Naval Tasks Based on Agent Simulation by Davide Anghinolfi, Alberto Capogrosso, Massimo Paolucci, and Perra Francesco is in the area of agent-based modelling and simulation. The authors propose an agent-based simulation model denoted as denoted as Operational Evaluator Model, for the multi-dimensional analysis of alternative configurations of military naval units to support the design of naval vessels. After the careful consideration of several existing frameworks for agent-based simulation, the authors propose their own framework denoted as ABSF. The paper contains convincing experimental results obtained by simulation of a specific Anti-surface Warfare use case that support the advantages of ABSF over other agent-based simulation frameworks. The article Formal Modelling and of a Multi-Agent Nano-Robotic Drug Delivery System by Marina Ntika, Petros Kefalas, and Ioanna Stamatopoulou is in the area of agent-based modelling and simulation. The authors propose an agent-based simulation model for targeted drug delivery with the help of nano-robots. The simulation model is developed using X-machines, while the simulation itself was achieved using NetLogo. The paper reports interesting experimental results that were obtain by applying the proposed agent-based simulation model, thus supporting the usefulness of formal multi-agent system for the better understanding of collaborative nano-robotic technologies for drug delivery in unhealthy tissues. The article ABVE-Construct: An Agent-Based Virtual Enterprise Model for Civil Engineering by Mihaela Oprea is in the area of agent-based enterprise modelling. This paper proposes a generic framework for virtual enterprises development denoted as VE-Frame. Based on this framework, a new agent-based virtual enterprise model for the civil engineering domain denoted as ABVE-Construct, is formulated. The ABVE-Construct ontology and design model are developed using OWL and Protege, as well as using Prometheus Design Tool. The proposed framework and model are evaluated by considering a specific case study of a residential building construction task. We would also like to thank all the reviewers for their restless reviewing effort and valuable feedback and all the authors who submitted papers to WASA'2013 and to this Special Issue.