Agent-based Modeling System Research Articles

A novel approach for systematically calibrating transport planning model systems

Calibration of a transport planning model system is a complex process. While trial-and-error methods and modelling expertise are still the backbone of calibration of transport models, analytical approaches automating the calibration process can improve the accuracy of the models. Introducing a model to guide modellers in the calibration process of large-scale transport planning model systems is the core of this study, where a systematic model for choosing the most appropriate models and parameters is discussed. The effectiveness of the proposed model is investigated by comparing three scenarios which are built on the Travel/Activity Scheduler for Household Agents model as a large-scale agent-based model system.

Analyzing fine-scale spatiotemporal drivers of wildfire in a forest landscape model

We developed and applied a wildfire simulation package in the Envision agent-based landscape modelling system. The wildfire package combines statistical modelling of fire occurrence with a high-resolution, mechanistic wildfire spread model that can capture fine scale effects of fire feedbacks and fuel management, and replicate restoration strategies at scales that are meaningful to forest managers. We applied the model to a landscape covering 1.2 million ha of fire prone area in central Oregon, USA where wildland fires are increasingly impacting conservation, amenity values and developed areas. We conducted simulations to examine the effect of human versus natural ignitions on future fire regimes under current restoration programs, and whether contemporary fire regimes observed in the past 20 years are likely to change as result of fire feedbacks and management activities. The ignition prediction model revealed non-linear effects of location and time of year, and distinct spatiotemporal patterns for human versus natural ignitions. Fire rotation interval among replicate simulations varied from 78 to 170 years and changed little over the 50-yr simulation, suggesting a stable but highly variable and uncertain future fire regime. Interestingly, the potential for fire-on-fire feedbacks was higher for human versus natural ignitions due to human ignition hotspots within the study area. We compare the methods and findings with other forest landscape simulation model (FLSM) studies and discuss future application of FLSMs to address emerging wildfire management and policy issues on fire frequent forests in the western US.

Deep reinforcement learning of cell movement in the early stage of C.elegans embryogenesis.

Cell movement in the early phase of Caenorhabditis elegans development is regulated by a highly complex process in which a set of rules and connections are formulated at distinct scales. Previous efforts have demonstrated that agent-based, multi-scale modeling systems can integrate physical and biological rules and provide new avenues to study developmental systems. However, the application of these systems to model cell movement is still challenging and requires a comprehensive understanding of regulatory networks at the right scales. Recent developments in deep learning and reinforcement learning provide an unprecedented opportunity to explore cell movement using 3D time-lapse microscopy images. We present a deep reinforcement learning approach within an agent-based modeling system to characterize cell movement in the embryonic development of C.elegans. Our modeling system captures the complexity of cell movement patterns in the embryo and overcomes the local optimization problem encountered by traditional rule-based, agent-based modeling that uses greedy algorithms. We tested our model with two real developmental processes: the anterior movement of the Cpaaa cell via intercalation and the rearrangement of the superficial left-right asymmetry. In the first case, the model results suggested that Cpaaa's intercalation is an active directional cell movement caused by the continuous effects from a longer distance (farther than the length of two adjacent cells), as opposed to a passive movement caused by neighbor cell movements. In the second case, a leader-follower mechanism well explained the collective cell movement pattern in the asymmetry rearrangement. These results showed that our approach to introduce deep reinforcement learning into agent-based modeling can test regulatory mechanisms by exploring cell migration paths in a reverse engineering perspective. This model opens new doors to explore the large datasets generated by live imaging. Source code is available at https://github.com/zwang84/drl4cellmovement. Supplementary data are available at Bioinformatics online.

Bridging the Gap between ABM and MAS: A Disaster-Rescue Simulation Using Jason and NetLogo

An agent is an autonomous computer system situated in an environment to fulfill a design objective. Multi-Agent Systems aim to solve problems in a flexible and robust way by assembling sets of agents interacting in cooperative or competitive ways for the sake of possibly common objectives. Multi-Agent Systems have been applied to several domains ranging from many industrial sectors, e-commerce, health and even entertainment. Agent-Based Modeling, a sort of Multi-Agent Systems, is a technique used to study complex systems in a wide range of domains. A natural or social system can be represented, modeled and explained through a simulation based on agents and interactions. Such a simulation can comprise a variety of agent architectures like reactive and cognitive agents. Despite cognitive agents being highly relevant to simulate social systems due their capability of modelling aspects of human behaviour ranging from individuals to crowds, they still have not been applied extensively. A challenging and socially relevant domain are the Disaster-Rescue simulations that can benefit from using cognitive agents to develop a realistic simulation. In this paper, a Multi-Agent System applied to the Disaster-Rescue domain involving cognitive agents based on the Belief–Desire–Intention architecture is presented. The system aims to bridge the gap in combining Agent-Based Modelling and Multi-Agent Systems approaches by integrating two major platforms in the field of Agent-Based Modeling and Belief-Desire Intention multi-agent systems, namely, NetLogo and Jason.

The decline in the British bank population since 1810 obeys a law of negative compound interest

Two models by Garnett et al. [J. Bus. Hist. 57(1):182-202 (2015)] for the organisational demography of British banks are explored analytically: the exponential model; and the agent-based system (ABM) governed by probabilistic interactions. Exact expressions for ABM expectation values are derived, revealing first that bank creations obey a ‘birth’ process, and second, that one of the ABM hypotheses may be discarded. The expectation values are used to demonstrate that beneath its stochastic implementation, the ABM model is a discrete analogue of the exponential model, meaning that the decline in the British bank population obeys a law of negative compound interest.

Analysis and simulation of collaboration policies among manufacturing industries and its effects on the maritime transportation cost

In this paper, a model is presented to analyse the effects on transportation cost through collaboration policies on maritime transportation. On this way, just the collaboration among manufacturing industries that use maritime transportation for export purposes is considered. The Collaborative Logistics era and Collaborative Transportation Management were researched to validate the proposed study and to obtain data for use in the model. Entrepreneurs and specialists were interviewed. Subsequently, a study was conducted on Agent-Based Modelling and Simulation and System Dynamics. The proposed model with System Dynamics was used to simulate the systemic effects from the collaborative policies of manufacturing industries, which increase bargaining power when the industries are allied to each other and can reduced maritime freight rates. This work helped to elucidate the importance of using simulation to analyse the behaviour of maritime transportation cost coming from the collaboration policies among industries.

Modeling and simulation of forward osmosis process using agent-based model system

Abstract In recent years forward osmosis (FO) process has attracted considerable attention in the field of water treatment and desalination. However, the lack of sufficient information on all possible parameters, affecting the process performance, is a major obstacle that avoids improving the function of this method. In this study, an algorithm based upon a reliable model was developed for osmosis through asymmetric membrane oriented in FO mode to be applied in agent-based model. This model was built in the NetLogo platform, which is a programmable modeling environment for simulating natural phenomena. The feasibility of this modeling was proven by comparing the simulation results and empirical data obtained from literature. The influence of net bulk osmotic pressure difference between the draw solution (DS) and the feed solution (FS) on average water flux was studied for various draw solutes. Furthermore, the effects of process parameters, including temperature, length of module, FS cross-flow velocity, pure water permeability coefficient, and structural parameter of membrane on average water flux were investigated. As a result, it could be concluded that Netlogo agent-based model had the quite well potential to simulate the FO process.

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 model of organizational ambidexterity decisions and strategies in new product development

Organizational ambidexterity, defined as the pursuit of both exploitation and exploration, has become an important topic in the study of organizations, especially in innovation management theory. Previous literature has not focused on the strategic (game-theoretic) aspects of organizational ambidexterity or on its decision-making aspects. Little is known about how or even whether the decision to adopt ambidexterity is competitively advantageous in the presence of diverse strategies that competitors may adopt. This facet of the subject is inherently game-theoretic; the value of a decision by one firm depends in part on decisions made by other firms. This paper initiates systematic investigation of these strategic aspects, including the overall performance of available strategies. Specifically, this study examines questions of ambidexterity-related strategy performance in the context of new product development. The main contributions are (1) to introduce and make available to the research community an agent-based model and decision support system that captures many of the key aspects and tradeoffs, which have been identified in the literature, of the exploration---exploitation dilemma faced by firms in the new product development process, with a focus on organizations' product investment decisions and (2) to report on results obtained from the model, calibrated with available data from the literature, augmented by new data collected from interviews with practitioners.

Complex systems for a complex issue: race in health research.

While agent-based modeling and complex systems approaches are fledgling methodologies in health research, they are well suited to characterizing the myriad influences of race on health.

An agent-based modeling system for travel demand simulation for hurricane evacuation

This paper presents an agent-based travel demand model system for hurricane evacuation simulation, which is capable of generating comprehensive household activity-travel plans. The system implements econometric and statistical models that represent travel and decision-making behavior throughout the evacuation process. The system considers six typical evacuation decisions: evacuate/stay, accommodation type choice, evacuation destination choice, mode choice, vehicle usage choice, and departure time choice. It explicitly captures the shadow evacuation population. In addition, the model system captures pre-evacuation preparation activities using an activity-based approach.A demonstration study that predicts activity-travel patterns using model parameters estimated for the Miami-Dade area for a hypothetical category-4 hurricane is discussed. The simulation results clearly indicate the model system produces a distribution of choice patterns that is consistent with sample observations and existing literature. The model system also identifies the proportion of the shadow evacuation population and their geographical extent. About 23% of the population outside the designated evacuation zone would evacuate. The shadow evacuation demand is mainly located within 5km of the coastline. The output demand of the model system works with agent-based traffic simulation tools and conventional trip-based simulation tools.

Building a Multi-Agent System for Emergency Logistics Collaborative Decision

Currently, the decision making of emergency logistics is faced with increasing challenges caused by deficient information, uncertain requirement and shortest response time. Agent-based modeling and multi-agent system have been proved as a promising ways in this field. Based on the previous work of emergency logistics decision framework, this paper presents a detailed design of agent internal structure of the emergency logistics multi-agent system. Some typical agents, such as logistics entity agent, task distribution agent and ontology visiting agent, are discussed from the composed function modules to the specific implementation. As the illustrative examples, the design of these primary agents can characterizes the basic structure of another agent in the emergency logistics multi-agent system, and it will be considered as the effective reference for system implementation.

Business ecosystem and stakeholders’ role transformation: Evidence from Chinese emerging electric vehicle industry

Nurturing an emerging industry’s business ecosystem always requires stakeholders’ efforts and role transformation. By systematically reviewing and studying the evolution of the Chinese electric vehicle industry, this paper constructs a three-dimensional theoretical framework including stages of business ecosystem lifecycle, stakeholder classification and functional roles, to analyse the transformation both of different stakeholders and their functional roles. The findings show that business ecosystem stakeholders have experienced role transformation following a mechanism defined as the ‘Triple Oscillation’ Model during the evolution of the emerging industry. These findings also help develop a conceptual model of agent-based system for business ecosystem evolution, which could be a starting point for further emerging industry study.

FRED (A Framework for Reconstructing Epidemic Dynamics): an open-source software system for modeling infectious diseases and control strategies using census-based populations

BackgroundMathematical and computational models provide valuable tools that help public health planners to evaluate competing health interventions, especially for novel circumstances that cannot be examined through observational or controlled studies, such as pandemic influenza. The spread of diseases like influenza depends on the mixing patterns within the population, and these mixing patterns depend in part on local factors including the spatial distribution and age structure of the population, the distribution of size and composition of households, employment status and commuting patterns of adults, and the size and age structure of schools. Finally, public health planners must take into account the health behavior patterns of the population, patterns that often vary according to socioeconomic factors such as race, household income, and education levels.ResultsFRED (a Framework for Reconstructing Epidemic Dynamics) is a freely available open-source agent-based modeling system based closely on models used in previously published studies of pandemic influenza. This version of FRED uses open-access census-based synthetic populations that capture the demographic and geographic heterogeneities of the population, including realistic household, school, and workplace social networks. FRED epidemic models are currently available for every state and county in the United States, and for selected international locations.ConclusionsState and county public health planners can use FRED to explore the effects of possible influenza epidemics in specific geographic regions of interest and to help evaluate the effect of interventions such as vaccination programs and school closure policies. FRED is available under a free open source license in order to contribute to the development of better modeling tools and to encourage open discussion of modeling tools being used to evaluate public health policies. We also welcome participation by other researchers in the further development of FRED.

A Sequential Experimental Design Method to Evaluate a Combination of School Closure and Vaccination Policies to Control an H1N1-Like Pandemic

During the 2009 H1N1 pandemic, computational agent-based models (ABMs) were extensively used to evaluate interventions to control the spread of emerging pathogens. However, evaluating different possible combinations of interventions using ABMs can be computationally very expensive and time-consuming. Therefore, most policy studies have examined the impact of a single policy decision. To apply a sequential experimental design method with an ABM to analyze policy alternatives composed of a combination of school closure and vaccination policies to provide a set of promising "optimal" combinations of policies to control an H1N1-type epidemic to policy makers. We used an open-source agent-based modeling system, FRED (A Framework for Reconstructing Epidemiological Dynamic), to simulate the spread of an H1N1 epidemic in Alleghany County, Pennsylvania, with a census-based synthetic population. We used an approach called best subset selection method to evaluate 72 alternative policies consisting of a combination of options for school closure threshold, closure duration, Advisory Committee on Immunization Practices prioritization, and second-dose vaccination prioritization policies. Using the attack rate as a performance measure, best subset selection enabled us to eliminate inferior alternatives and identify a small group of alternative policies that could be further evaluated on the basis of other criteria. Our sequential design approach to evaluate a combination of alternative mitigation policies leads to a savings in computational effort by a factor of 2 when examining combinations of school closure and vaccination policies. Best subset selection demonstrates a substantial reduction in the computational burden of a large-scale ABM in evaluating several alternative policies. Our method also provides policy makers with a set of promising policy combinations for further evaluation based on implementation considerations or other criteria.

An Autonomous Distal Reward Learning Architecture for Embodied Agents

Distal reward refers to a class of problems where reward is temporally distal from actions that lead to reward. The difficulty for any biological neural system is that the neural activations that caused an agent to achieve reward may no longer be present when the reward is experienced. Therefore in addition to the usual reward assignment problem, there is the additional complexity of rewarding through time based on neural activations that may no longer be present. Although this problem has been thoroughly studied over the years using methods such as reinforcement learning, we are interested in a more biologically motivated neural architectural approach. This paper introduces one such architecture that exhibits rudimentary distal reward learning based on associations of bottom-up visual sensory sequences with bottom-up proprioceptive motor sequences while an agent explores an environment. After sufficient learning, the agent is able to locate the reward through chaining together of top-down motor command sequences. This paper will briefly discuss the details of the neural architecture, the agent-based modeling system in which it is embodied, a virtual Morris water maze environment used for training and evaluation, and a sampling of numerical experiments characterizing its learning properties.

Knowledge-based image segmentation using swarm intelligence techniques

Intelligent techniques such as swarm intelligence techniques rarely have been used for image segmentation or boundary detection. The limited increasing number of agents in the environment and how to find efficiently the right threshold in an image, develop a flexible design, and fully autonomous system that supports different platforms makes the task challenging. Considering challenges this paper presents a swarm-based intelligent technique for image segmentation that is based on a fully agent-based model system, called swarm intelligence-based image segmentation (SIBIS). SIBIS adopts a cellular automata technique where the swarm of agents navigate through the image and operate on their pixels and local regions. Three features such as swarm intelligence, agent-based modelling and cellular automata are integrated to make SIBIS efficient. SIBIS system can find the image segmentation threshold automatically without changing the background or the texture of the image.

Biodiversity and the mitigation of climate change through bioenergy: impacts of increased maize cultivation on farmland wildlife

The public promotion of renewable energies is expected to increase the number of biogas plants and stimulate energy crops cultivation (e.g. maize) in Germany. In order to assess the indirect effects of the resulting land-use changes on biodiversity, we developed six land-use scenarios and simulated the responses of six farmland wildlife species with the spatially explicit agent-based model system ALMaSS. The scenarios differed in composition and spatial configuration of arable crops. We implemented scenarios where maize for energy production replaced 15% and 30% of the area covered by other cash crops. Biogas maize farms were either randomly distributed or located within small or large aggregation clusters. The animal species investigated were skylark (Alauda arvensis), grey partridge (Perdix perdix), European brown hare (Lepus europaeus), field vole (Microtus agrestis), a linyphiid spider (Erigone atra) and a carabid beetle (Bembidion lampros). The changes in crop composition had a negative effect on the population sizes of skylark, partridge and hare and a positive effect on the population sizes of spider and beetle and no effect on the population size of vole. An aggregated cultivation of maize amplified these effects for skylark. Species responses to changes in the crop composition were consistent across three differently structured landscapes. Our work suggests that with the compliance to some recommendations, negative effects of biogas-related land-use change on the populations of the six representative farmland species can largely be avoided.

Hybrid simulation models – When, Why, How?

Agent-Based Modeling and Simulation (ABMS) and System Dynamics (SD) are two popular simulation paradigms. Despite their common goal, these simulation methods are rarely combined and there has been a very low amount of joint research in these fields. However, it seems to be advantageous to combine them to create more accurate hybrid models. In this research, the possible ways to combine these methods are studied. The authors have found five different situations where it will be useful to combine these methods. All of them have already been used in earlier studies, so modelers should use them as possible interfaces to combine the methodologies. By using hybrid simulation models it is possible to create more accurate and reliable Expert Systems (ES).

An Object-Process-Based Modeling Language for Multiagent Systems

While a number of modeling languages for constructing multiagent systems (MASs) have been suggested, none of them is widely accepted and used. A prominent reason for this is the gap that exists between agent-oriented modeling languages and the agent-based system modeling needs, including accessibility, flexibility, and expressiveness. This paper addresses the need for such a language by proposing object-process methodology (OPM)/MAS, an agent modeling language (AML) that extends OPM with an intermediate metamodel of the MAS domain. Three case studies and a comparison to contemporary AMLs demonstrate the novelty and benefits of OPM/MAS.