Modeling mountain pine beetle infestation with an agent-based approach at two spatial scales

Extensive outbreaks of tree-killing insects have been occurring in many parts of North America, including the province of British Columbia, raising concerns about the health of pine forest ecosystems. The dynamic phenomenon of mountain pine beetle (MPB), Dendroctonus ponderosae Hopkins, infestation outbreaks is an inherent spatial and temporal complex process. Agent-based modeling (ABM) facilitates simulating spatial interactions that describe the ecological context in which insect populations spread. The main objective of this study was to develop a model of the MPB forest infestation dynamics. This spatially explicit model integrates geographic information systems (GISs) and ABM to simulate MPB outbreaks at the tree and landscape scales, providing spatiotemporal information of annual distribution and patterns of MPB outbreaks. This prototype was implemented with geographic data generated from aerial overview surveys carried out by the B.C. Ministry of Forests and Range, for the study site in Kamloops, Canada. Results show the direct influence that vigorous forest stands and trees have on higher breeding rates, and therefore in the MPB population increment at a tree scale, in a period of 5 years. The simulation results at the landscape level help to determine the most probable locations of future MPB infestations in a time frame of 10 years.     

An agent-based model of public space use

Computational models have been described as exceptionally adept at examining the complex relationships of human and crowd behaviour, with a significant portion dedicated to investigating spatial behaviour in defined environments. Within this context, this paper presents an agent-based model (ABM) for simulating activity in public spaces at the level of the individual user. Although other ABMs of individuals' spatial activity exist, they are often found to simulate specific building-related activities, and fewer still are found to examine activity in public spaces, in a systematic manner. This research provides a generalized formalization of human spatial behaviour incorporating stationary activities and social interaction within a 3D environment, and is presented using a widely accepted framework for describing ABM, the Overview, Design Concepts, and Details (ODD) protocol. A sample study using a synthetic environment is used to demonstrate applicability, and the model is tested extensively to establish robustness. Furthermore, model output is compared to observed activity patterns in other studies of similar spaces, and simulated spatial patterns of activity are found to match those observed in real-world scenarios, providing insight into the dynamics of the processes, and highlighting the potential of this approach for studying the complexities of human spatial behaviour.     

Visualizing the workings of agent-based models: Diagrams as a tool for communication and knowledge acquisition

One of the biggest challenges in developing agent-based models (ABMs) is the acquisition of domain knowledge. Interviews, or other types of face-to-face communication, are one of the ways of doing it but problems might arise if we deal with tacit knowledge and if the interviewer and the interviewee come from a very different cultural and educational background. This research proposes a visualization approach to be used in the interaction between domain and modeling experts that will facilitate communication in these kinds of situations. The approach is based on the findings of knowledge visualization and related fields, as well as on a firm understanding of ABMs. Three important aspects of ABMs that need to be visualized in order to facilitate understanding were identified. In a case study, performed in relation to a project where a spatial ABM was developed, three node-link diagrams were created according to the developed approach. They visualize the conceptual structure, the simulation process, and the data model of the ABM. These diagrams were positively received by the stakeholders of the project and they improved workflow and communication in the project.     

A multi‐resolution multi‐classifier system for speaker verification

This paper describes a Speaker Verification System based on the use of multi resolution classifiers in order to cope with performance degradation due to natural variations of the excitation source and of the vocal tract. The different resolution representations of the speaker are obtained by considering multiple frame lengths in the feature extraction process and from these representations a single Pseudo‐Multi Parallel Branch (P‐MPB) Hidden Markov Model is obtained. In the verification process, different resolution representations of the speech signal are classified by multiple P‐MPB systems: the final decision is obtained by means of different combination techniques. The system based on the Weighted Majority Vote technique considerably outperforms baseline systems: improvements are between 15% and 38%. The execution time of the verification process is also evaluated and it proves to be very acceptable, thus allowing the use of the approach for applications in real time systems.     

Analysis of crime patterns through the integration of an agent-based model and a population microsimulation

In recent years, criminologists have become interested in understanding crime variations at progressively finer spatial scales, right down to individual streets or even houses. To model at these fine spatial scales, and to better account for the dynamics of the crime system, agent-based models of crime are emerging. Generally, these have been more successful in representing the behaviour of criminals than their victims. In this paper it is suggested that individual representations of criminal behaviour can be enhanced by combining them with models of the criminal environment which are specified at a similar scale. In the case of burglary this means the identification of individual households as targets. We will show how this can be achieved using the complementary technique of microsimulation. The work is significant because it allows agent-based models of crime to be refined geographically (to allow, for example, individual households with varying wealth or occupancy measures) and leads to the identification of the characteristics of individual victims.     

基于ABM与ABS的暴雨洪涝人口风险动态模拟

暴雨洪涝灾害人口风险评估是一项复杂系统工程,在防洪减灾工程理论与实践中具有重要意义.针对灾害管理需要掌握暴雨洪涝人口灾情动态变化的问题,采用智能体建模(Agent-Based Modeling, ABM)构建了暴雨洪涝人口风险模拟模型.在Netlogo平台上,利用智能体仿真(Agent-Based Simulation, ABS)和构建的模型对淮河流域的暴雨洪涝人口风险动态变化进行仿真.仿真结果表明本文的方法能对暴雨的轻度、中度和重度三种人口风险进行动态分析.研究结果表明基于多智能体的模拟仿真能评估暴雨洪涝全过程的人口风险动态变化.     

Lessons learned on development and application of agent-based models of complex dynamical systems

The field of agent-based modelling (ABM) has gained a significant following in recent years, and it is often marketed as an excellent introduction to modelling for the novice modeller or non-programmer. The typical objective of developing an agent-based model is to either increase our mechanistic understanding of a real-world system, or to predict how the dynamics of the real-world system are likely to be affected by changes to internal or external factors. Although there are some excellent ABMs that have been used in a predictive capacity across a number of domains, we believe that the promotion of ABM as an ‘accessible to all’ approach, could potentially lead to models being published that are flawed and therefore generate inaccurate predictions of real-world systems. The purpose of this article is to use our experiences in modelling complex dynamical systems, to reinforce the view that agent-based models can be useful for answering questions of the real-world domain through predictive modelling, but also to emphasise that all modellers, expert and novice alike, must make a concerted effort to adopt robust methods and techniques for constructing, validating and analysing their models, if the result is to be meaningful and grounded in the system of interest.     

Qualitative Models in Computational Simulative Sciences: Representation,Confirmation, Experimentation

The Epistemology Of Computer Simulation (EOCS) has developed as an epistemological and methodological analysis of simulative sciences using quantitative computational models to represent and predict empirical phenomena of interest. In this paper, Executable Cell Biology (ECB) and Agent-Based Modelling (ABM) are examined to show how one may take advantage of qualitative computational models to evaluate reachability properties of reactive systems. In contrast to the thesis, advanced by EOCS, that computational models are not adequate representations of the simulated empirical systems, it is shown how the representational adequacy of qualitative models is essential to evaluate reachability properties. Justification theory, if not playing an essential role in EOCS, is exhibited to be involved in the process of advancing and corroborating model-based hypotheses about empirical systems in ECB and ABM. Finally, the practice of evaluating model-based hypothesis by testing the simulated systems is shown to constitute an argument in favour of the thesis that computer simulations in ECB and ABM can be put on a par with scientific experiments.

     

Ordered model trees: A normal form for disjunctive deductive databases

Model trees were conceived as a structure-sharing approach to represent information in disjunctive deductive databases. In this paper we introduce the concept ofordered minimal model trees as a normal form for disjunctive deductive databases. These are model trees in which an order is imposed on the elements of the Herbrand base. The properties of ordered minimal model trees are investigated as well as their possible utilization for efficient manipulation of disjunctive deductive databases. Algorithms are presented for constructing and performing operations on ordered model trees. The complexity of ordered model tree processing is addressed. Model forests are presented as an approach to reduce the complexity of ordered model tree construction and processing.This research was supported by the National Science Foundation, under the grant Nr. IRI-89-16059, the Air Force Office of Scientific Research, under the grant Nr. AFOSR-91-0350, and the Fulbright Scholar Program.This work was done while visiting at the University of Maryland Institute for Advanced Computer Studies.     

On the engineering of agent-based simulations of social activities with social networks

ContextModels of how people move around cities play a role in making decisions about urban and land-use planning. Previous models have been based on space and time, and have neglected the social aspect of travel. Recent work on agent-based modelling shows promise as a new approach, especially for models with both social and spatial elements.ObjectiveThis paper demonstrates the design and implementation of an agent-based model of social activity generation and scheduling for experimental purposes to explore the effects of social space in addition to physical space. As a side-effect, the paper discusses the need for and requirements on structured design of agent-based models and simulations.MethodModel design was based on the MASQ meta-model and implemented in Python. The model was then tested against several hypotheses with several initial networks.ResultsThe model allowed us to investigate the effects of social networks. We found that the model was most sensitive to the pair attributes of the network, rather than the global or personal attributes.ConclusionAs demonstrated, a structured approach to model development is important in order to be able to understand and apply the results, and for the model to be extensible in the future. Agent-based modelling approaches allow for inclusion of social elements. For models incorporating social networks, testing the sensitivity to the initial network is important to ensure the model performs as expected.     

Towards process validation for complex transport models: A sensitivity analysis of a social network-enhanced activity-travel model

Model validation is a significant issue for the modelling of social network-based transportation models because of the many interacting components (the individuals, the environment, and now the network) in the model.In this paper we focus on a sensitivity analysis for such a model, which is part of a larger validation approach known as process validation. This approach investigates both the structure and behaviour of the model, to evaluate whether the model can be used for prediction.The paper draws on a novel set of experiments with an agent-based model which was developed to explore the effects of social networks on activity and travel behaviour. Several versions of the model were created, beginning with a single day model with no interaction, and then adding in multi-day runs with interactions, in order to demonstrate the validation process.The paper argues that testing the model at different levels of complexity increases confidence in the model and makes it easier to locate components or functionality that require improvement. It concludes by suggesting that this approach to sensitivity testing should be adopted for validation of complex transportation models.     

A framework for modeling payments for ecosystem services with agent-based models,Bayesian belief networks and opinion dynamics models

We present an integrated modeling framework for simulating land-use decision making under the influence of payments for ecosystem services. The model combines agent-based modeling (ABM) with Bayesian belief networks (BBNs) and opinion dynamics models (ODM). The model endows agents with the ability to make land-use decisions at the household and plot levels. The decision-making process is captured with the BBNs that were constructed and calibrated with both qualitative and quantitative information, i.e., knowledge gained from group discussions with stakeholders and empirical survey data. To represent interpersonal interactions within social networks, the decision process is further modulated by the opinion dynamics model. The goals of the model are to improve the ability of ABM to emulate land-use decision making and thus provide a better understanding of the potential impacts of payments for ecosystem services on land use and household livelihoods. Our approach provides three important innovations. First, decision making is represented in a causal directed graph. Second, the model provides a natural framework for combining knowledge from experts and stakeholders with quantitative data. Third, the modular architecture and the software implementation can be customized with modest efforts. The model is therefore a flexible, general platform that can be tailored to other studies by mounting the appropriate case-specific “brain” into the agents. The model was calibrated for the Sloping Land Conversion Program (SLCP) in Yunnan, China using data from participatory mapping, focus group interviews, and a survey of 509 farm households in 17 villages.     

A statistical study employing agent-based modeling to estimate the effects of different warehouse parameters on the distance traveled in warehouses

In manually operated warehouses, the travel distance of the order picker has profound effects on the warehouse cost and efficiency. Estimating this distance is difficult because the warehouse environment is a stochastic one, affected by a great number of parameters. Therefore, we present a comprehensive statistical study to assess how the different warehouse parameters and their interactions affect the travel distance. To estimate the travel distance, we simulate the different designs using agent-based modeling (ABM). Having 324 different designs, ABM has enabled us to build one computer model to simulate all the cases. The study shows that having one cross aisle only and using a class-based storage policy decreases the travel distance. Moreover, the results obtained show that choosing the best routing policy depends on the warehouse layout, which proves the importance of considering the interactions among the different parameters.     

Theoretical foundations of human decision-making in agent-based land use models – A review

Recent reviews stated that the complex and context-dependent nature of human decision-making resulted in ad-hoc representations of human decision in agent-based land use change models (LUCC ABMs) and that these representations are often not explicitly grounded in theory. However, a systematic survey on the characteristics (e.g. uncertainty, adaptation, learning, interactions and heterogeneities of agents) of representing human decision-making in LUCC ABMs is missing. Therefore, the aim of this study is to inform this debate by reviewing 134 LUCC ABM papers. We show that most human decision sub-models are not explicitly based on a specific theory and if so they are mostly based on economic theories, such as the rational actor, and mainly ignoring other relevant disciplines. Consolidating and enlarging the theoretical basis for modelling human decision-making may be achieved by using a structural framework for modellers, re-using published decision models, learning from other disciplines and fostering collaboration with social scientists.     

Using the affordance concept for model design in agent-based simulation

When designing an Agent-Based Simulation Model a central challenge is to formulate the appropriate interactions between agents as well as between agents and their environment. In this contribution we present the idea of capturing agent-environment interactions based on the “affordance” concept. Originating in ecological psychology, affordances represent relations between environmental objects and potential actions that agents may perform using those objects. We assume that explicitly handling affordances based on semantic annotation of entities in simulated space may offer a higher abstraction level for dealing with potential interaction. Our approach has two elements: firstly a methodology for using the affordance concept to identify interactions and secondly a suggestion for integrating affordances into agents’ decision making. We illustrate our approach indicating an agent-based model of after-earthquake behavior.     

Behaviour and space in agent-based modelling: Poverty patterns in East Kalimantan,Indonesia

Applied models of policy interventions are increasingly expected to consider households’ responses to these interventions, which makes agent-based modelling popular in applied policy situations. Implementing an adequate level of agent heterogeneity and mapping it into a spatial environment are critical factors of such applied modelling. However, policy applications demand the characterisation and parameterisation of behavioural response functions of heterogeneous agents and the spatial distribution of heterogeneous agents, which are neither highly transparent nor greatly tested steps in implementing agent-based models. This paper describes an agent-based model of fuel price changes for a case study in East Kalimantan, Indonesia, and specifically: (a) the characterisation and parameterisation approach, (b) resulting agent types for approximating behavioural heterogeneity, and (c) emerging spatial poverty and deforestation patterns. The model highlights the spatial dynamics of poverty dynamics, indicating that the direct impact of deforestation on poverty among forest-dwelling communities is to trigger their migration into peri-urban areas. Overall, the model suggests that poverty increases in response to fuel price reductions.     

Agent-based modeling of ancient societies and their organization structure

Some of the most interesting questions one can ask about early societies, are about people and their relations, and the nature and scale of their organization. In this work, we attempt to answer such questions with approaches introduced by multiagent systems. Specifically, we developed a generic agent-based model (ABM) for simulating ancient societies. Unlike most existing ABMs used in archaeology, our model includes agents that are autonomous and utility-based. Our model can (and does) also incorporate different social organization paradigms and technologies used in ancient societies. Equipped with such paradigms, our model allows us to explore the transition from a simple to a more complex society by focusing on the historical social dynamics—i.e., the flexibility and evolution of power relationships depending on social context and time. As a case study, we employ our model to evaluate the impact of the implemented social and technological paradigms on an artificial Early Bronze Age “Minoan” society located at a particular region of the island of Crete. Model parameter choices are based on archaeological evidence and studies, but are not biased towards any specific assumption. Results over a number of different simulation scenarios demonstrate an impressive sustainability for settlements consisting of and adopting a socio-economic organization model based on self-organization, and which was inspired by a recent framework for modern self-organizing agent organizations. This is the first time a self-organization approach is incorporated in an archaeology ABM system.     

An agent-based modeling approach applied to the spread of cholera

Cholera is an intestinal disease and is characterized by diarrhea and severe dehydration. While cholera has mainly been eliminated in regions that can provide clean water, adequate hygiene and proper sanitation; it remains a constant threat in many parts of Africa and Asia. Within this paper, we develop an agent-based model that explores the spread of cholera in the Dadaab refugee camp in Kenya. Poor sanitation and housing conditions contribute to frequent incidents of cholera outbreaks within this camp. We model the spread of cholera by explicitly representing the interaction between humans and their environment, and the spread of the epidemic using a Susceptible-Exposed-Infected-Recovered model. Results from the model show that the spread of cholera grows radially from contaminated water sources and seasonal rains can cause the emergence of cholera outbreaks. This modeling effort highlights the potential of agent-based modeling to explore the spread of cholera in a humanitarian context.     

An agent-based data-flow testing approach for Web applications

In recent years, Web applications (WAs) have grown so quickly that they have already become crucial to the success of businesses. However, since they are built on Internet and open standard technologies, WAs possess their own unique features, such as dynamic behaviors, heterogeneous representations, and novel data handling mechanisms. These features provide concrete support to the success of WAs, but they bring new challenges to researchers and developers, especially in regard to testing WAs and ensuring their quality. Testing approaches for non-WAs have to be extended to handle these features before they are used in WA testing. This paper presents an agent-based approach to perform data-flow testing of WAs. More precisely, the data-flow testing will be performed by autonomous test agents at the method level, object level, and object cluster level, from low abstraction level to high abstraction level. In the process of the recommended data-flow testing, an agent-based WA testing system (WAT) will automatically generate and coordinate test agents to decompose the task of testing an entire WA into a set of subtasks that can be accomplished by test agents. The test agents, rooted in the Belief–Desire–Intention (BDI) model, cooperate with each other to complete the testing of a WA. An example is used to show the feasibility of the proposed approach.