Use Of Agent-based Models Research Articles

Understanding the Residential Water Demand Response to Price Changes: Measuring Price Elasticity with Social Simulations

Water pricing is an economic instrument traditionally used to reduce water demand. However, its effective implementation requires knowledge of the extent to which users reduce water consumption with increasing water prices. The price elasticity of water demand has been estimated using econometric regression, which relies on cross-sectional and time-series water data. As an alternative, we propose the use of agent-based modelling, which does not require reliable historical data on water prices and consumption and enables the simulation of multiple scenarios with different consumer profiles, behaviour profiles and water price changes, thereby allowing comprehensive understanding of price elasticity estimates. To illustrate the potential use of agent-based modelling for the estimation of water demand price elasticity, we performed an empirical application to a residential area in Chile. Price elasticity estimates ranged from −0.0159 to −0.1036 (mean −0.0250), indicating that residential water consumption is inelastic to price changes. This result is consistent with previous findings. Agent-based modelling is an alternative for the ex-ante assessment of the potential effectiveness of water pricing policies intended to reduce residential water demand.

A data-driven agent-based model of occupants’ thermal comfort behaviors for the planning of district-scale flexible work arrangements

In a global context of increasing flexibility in the way workplaces and the districts in which they are located are used, there is a need for occupant-driven approaches to plan urban energy systems. Several authors have suggested the use of agent-based models (ABM) of building occupants in urban building energy simulations due to their ability to reproduce emergent behaviors from individual agents’ actions. However, few works in the literature take full advantage of the ABM paradigm, accounting for both occupant presence and energy-relevant behaviors at the district scale. In this work, we propose a methodology to develop a data-driven, agent-based model of building occupants’ activities and thermal comfort in an urban district. Our methodology combines the use of campus-scale Wi-Fi data to derive feasible occupant activity and location plans, along with thermal preference profiles derived from a longitudinal field study where off-the-shelf, non-intrusive sensors were used to capture the right-here-right-now thermal preference of 35 participants in the same case study district. Our model is then used to explore how different district operation strategies could affect building energy performance in the context of increased workspace flexibility. Our results show that by providing a diversity of building operation conditions, with different buildings having different set point temperatures, occupants’ thermal comfort hours could be improved by an average of about 10% with little effect on district energy performance. Meanwhile, a 6%–15% average decrease in space cooling energy use intensity was observed when implementing occupant-driven ventilation and setpoint controls, regardless of location choice scenario.

Navigating the Stock Market: Modeling Wealth Exchange and Network Interaction with Loss Aversion, Disposition Effect and Anchoring and Adjustment Bias

This study aims to integrate meta-analysis into agent-based models to provide foundational insights into biased agent interactions. It delves into how various behavioral biases, such as anchoring, disposition effect, loss aversion, and others, influence market dynamics and investor decisions. By utilizing agent-based models, it offers simulations of market scenarios and investor behaviors, highlighting the impact of individual decisions on market dynamics. The innovative approach of this study lies in its integration of behavioral finance theories with real market data, offering a nuanced analysis of market behaviors. This work contributes a new perspective to behavioral finance and encourages the use of agent-based models to deepen the understanding of market dynamics and investor behaviors, potentially aiding in financial market analysis and policy formulation. This study aims to provide a foundational infrastructure for research that wishes to integrate meta-analysis into agent-based models and enable the examination of biased agent behaviors. The findings of this study have managed to model the interactions of loss aversion, disposition effect, and anchoring and adjustment bias in the closest manner to the real world by considering agents' socio-demographic and psychological factors. The results are highly conducive to more accurately modeling human behaviors in portfolio optimizations and expanding the applications of Generalized Artificial Intelligence in financial markets.

Methodology for regional industrial complex management: Architecture of an agent-based model

Industry is the backbone of the economy of developed countries and individual regions. To optimize management processes in such a complex and multi-level sector, specific economic-mathematical models and practical tools have to be developed. The paper discusses the optimal architecture of the regional industrial complex management model on a modern theoretical-methodological and instrumental (program) basis. The classical management theory, optimization theory and game theory constitute the methodology of this study. Among the research methods applied are agent-based and minimax approaches. We substantiate the use of agent-based modelling to simulate administering the regional industrial complex. The paper presents a three-tiered management architecture consisting of federal, regional and company level authorities (united by type of activity). For each level, control agents are identified and a set of indicators formed, which cover the structure of the phase vector, including its attributes, key parameters, control actions used, risks, a model of the parameters’ dynamics, and a model of the data possessed by the object. We build a hierarchical structure of administration and information relationships in the model and, based on the minimax approach, create an algorithm of agents’ efforts to select optimal control actions. The proposed architecture will allow forming a flexible toolkit for assessing industrial development scenarios and producing the best step-by-step management pattern of the regional industrial complex.

AN AGENT BASED MODEL TO IMPROVE TRAFFIC FLOW AND ASSESS THE IMPACT OF SMART TRAFFIC LIGHT

The Traffic in developing country undergoes severs perturbations due to either a misunderstanding of the physical phenomenon or in bad calibration of equipments such as traffic light. Due to this high complexity, mathematical models fail to well represent different scenario. Those issues are mostly due to the high amount of parameter which should be taken into consideration when dealing with some mathematical model and the drivers behaviors. Since agent-based model is a computational simulation that represents individual entities (called agents) and their interactions within a defined environment. It is a powerful tools used to investigate human behavior impact in a mathematical model. Therefore, this paper proposed a novel agent-based approach framework for effectively making traffic light timing dynamic according to traffic density. Moreover, based on different rules such as lane changing, stop and goat the traffic light, we develop an Agent based model to represent the relationships between the traffic changing and its environment. The use of agent-based models for traffic light time optimization offers a more granular, adaptive, and integrative approach that better capture the complexities of real-world traffic scenarios. Finally, the experimental result is applied on synthetic data and compared to other results in almost the same context for both static and dynamic traffic light. The results show that the travel time distribution is less for dynamic traffic light.

Development of an agent-based model incorporating Function–Behavior–Structure framework to enable systems engineering design process evaluation

An important step in the development of any process is evaluation. Evaluation ensures that the process affects effectiveness, efficiency, or other metrics as designed. Without evaluation, processes must stand solely on the theories, axioms, and/or heuristics that formed them. Outcome or objectives-based evaluations are especially useful in seeing if the expected impact is realized. Systems and design engineering present problems for outcome evaluations due to system design efforts being long in duration, expensive, organization/team specific, and environment/context specific. These characteristics make repeating the same effort under the same conditions using different processes difficult, if not impossible. While a comparison of processes for evaluation in a real application may not be feasible, a simulation of the processes with agents that capture system design behaviors may produce findings to support hypotheses. This paper examines the use of agent-based modeling and simulation to compare pseudo-waterfall and pseudo-agile engineering processes for a simple design problem. The Function–Behavior–Structure (FBS) model of design is used along with empirical data from FBS studies to examine the performance of a two-person design team using pseudo-waterfall and pseudo-agile engineering processes. The results of this exercise show possible advantages of agile-like processes in total time to complete the design.

A Framework for Data-Driven Agent-Based Modelling of Agricultural Land Use

Agent-based models (ABMs) are particularly suited for simulating the behaviour of agricultural agents in response to land use (LU) policy. However, there is no evidence of their widespread use by policymakers. Here, we carry out a review of LU ABMs to understand how farmers’ decision-making has been modelled. We found that LU ABMs mainly rely on pre-defined behavioural rules at the individual farmers’ level. They prioritise explanatory over predictive purposes, thus limiting the use of ABM for policy assessment. We explore the use of machine learning (ML) as a data-driven alternative for modelling decisions. Integration of ML with ABMs has never been properly applied to LU modelling, despite the increased availability of remote sensing products and agricultural micro-data. Therefore, we also propose a framework to develop data-driven ABMs for agricultural LU. This framework avoids pre-defined theoretical or heuristic rules and instead resorts to ML algorithms to learn agents’ behavioural rules from data. ML models are not directly interpretable, but their analysis can provide novel insights regarding the response of farmers to policy changes. The integration of ML models can also improve the validation of individual behaviours, which increases the ability of ABMs to predict policy outcomes at the micro-level.

Challenges toward Evidence-Based Policymaking Using Agent-Based Modeling for Federal Sports Grants: A Self-Reflection from a Transdisciplinary Project

Despite their importance, federal grant systems often need more clarity regarding cost-effectiveness, lack of transparency, and slow feedback cycles. Sports funding systems aimed at improving child health and contributing to sustainable development goals are incredibly challenging due to their heterogeneity in stakeholders and regional aspects. Here, we analyze how we tackled these challenges in a transdisciplinary EU project in Austria, targeting the use of agent-based modeling for evidence-based policymaking in a co-creation process with policy stakeholders in the domain of federal sports grants to improve the health and well-being of children and youth. The initial and executed set of procedures is described, along with lessons learned during the project’s lifetime. These lessons derive a framework that provides an adapted set of processes, supporting methods, and critical decision points for an improved use of transdisciplinarity. In addition, the steps of the developed framework are combined with essential aspects of knowledge integration, following the main phases of the policy cycle and providing suggestions for required skills and competencies for capacity building concerning implementing the developed framework in the public sector. Our results show that the combination of transdisciplinarity, human-centered policymaking, and sports, supported by cutting-edge technologies such as agent-based modeling, can achieve significantly better results than a pure disciplinary approach and generate positive spill-over effects.

Assessing the Perceptibility of Prehistoric Monuments on their Landscape. An Exploratory Approach Using Agent-Based Modelling

The perceptibility of a prehistoric monument (the property of being perceptible from its surrounding landscape) can be quite difficult to analyse by means of traditional static models. Such difficulty lies in the fact that perceptibility depends upon many other factors beyond simple topographical position, such as size, colour, contrast with the surroundings or even the specific circumstances of the audience, many such circumstances being of an immaterial nature. In this paper, we explore the potential use of Agent-Based Modelling for the analysis of archaeological perceptibility.

Application of Agent-Based Modelling for Evaluating Level of Service in Airports

Airports worldwide evaluate Level of service (LOS) to assess performance of the terminal for the current scenario and to compare it to a projected scenario. The International Air Transport Association (IATA) global standard is the widely accepted framework that evaluate airport LOS based on space and time characteristics. The application of simulation tools can be used in this regard to depict the airport LOS scenarios more interactively and in detail. Moreover, this approach also enables modelling future scenarios of technological or infrastructure modifications to cater terminal capacity constraints. The use of Agent-Based Modelling (ABM) in evaluating airport LOS is discussed in this paper. The study utilized field study at the check-in hall of a large-sized airport, the Chennai International airport, India to capture the passenger flow characteristics such as passenger entry rate, walking speed, group size, passenger flow sequence and flight movement frequency. These are used as inputs to a microscopic pedestrian modelling tool, AnyLogic. Three scenarios of airport LOS are modelled to evaluate the passenger density and the dwell times. The results show that the variation in number of service points and flow rate significantly affect the passenger dwell time, queuing length, queuing time and passenger density at the check-in zone. Visual and quantitative representations of the scenarios are also presented, analyzed and compared with the existing standard.

Use of agent-based modeling to model intermediate force capabilities in (counter)mobility crowd scenarios

In this paper, we use an agent-based model (ABM) to run (counter)mobility scenarios to explore which characteristics of intermediate force capabilities (IFC) are relevant to these, and how they can affect outcomes in gray zone conflicts. Using an ABM called Map-Aware Non-Uniform Automata (MANA), developed by the New Zealand Defense Technology Agency, we implemented two scenarios where the friendly forces’ mobility was limited by large groups of civilians. Then, we employed data farming and analytics methods to analyze the data and identify key parameters influencing the outcomes. The main parameters appeared to be the IFC Range, Power (a measure of the duration of the effect), and Crowd Density. Future research could include a wide range of mobility scenarios and possibly a more detailed IFC representation.

Framework for Integrated Use of Agent-Based and Ambient-Oriented Modeling

Agent-based modeling (ABM) is a flexible and simulation-friendly modeling approach. Ambient-oriented modeling is effective for systems containing ambient and spatial representations. In this paper we propose a framework for the integrated use of agent-based modeling and ambient-oriented modeling. We analyze both agents and ambient in detail. We also compare both modeling approaches as well and analyze their similarities and differences. The integrated implementation provides a new link between mathematical modeling and simulations. The model developed using this framework has four parts. The first part constitutes the identification, definition, and relations of agents. In this part, we use agent-based modeling along with the concepts of discrete-event simulations and system dynamics. The second part of the model is the mathematical representation of the relations of agents, i.e., the parent and child relation of agents. The third part of the model is the representation of the messages along with relational symbols where we utilize the concepts and symbols of relations and messages from ambient-oriented modeling. The fourth and final part of the model is the simulation, where we describe the rules that govern the processes represented in first two parts. The framework is helpful in overcoming certain limitations of both approaches. Moreover, we provide a scenario of a bus rapid transit system (BRTS) as a proof of concept, and we examine the generic concept of BRTSs using the proposed framework.

Using agent-based models to address non-communicable diseases: a review of models and their application to policy

BackgroundAgent based models are a computational methodology in which systems of simulated heterogeneous agents interact with one another and their environment; they are a research tool with the potential to provide greater understanding of the complex, interdependent, and systemic determinants of population health challenges, particularly when co-produced with the decision makers, practitioners, and public who understand and experience these challenges from a variety of perspectives. Although agent-based modelling is becoming more widely used in health research, this methodology is currently underutilised in non-communicable disease prevention. We aim to highlight the potential role of agent-based modelling in supporting policy and practice decision-making in non-communicable disease prevention, using an obesity example to show how an agent-based model can capture the social influences in diet. MethodsWe identified agent-based models addressing non-communicable disease prevention from PubMed, ScienceDirect, Google Scholar, and Web of Science, using the search terms “agent-based model” and “public health” between Jan 1, 2000, and Dec 31, 2022. Case studies were selected that best illustrated the use of agent-based models for UK public health challenges. Selection criteria also included open access code documentation and models that reported core methodologies. FindingsWe identified three key case studies that best exemplified the use of agent-based models for addressing non-communicable disease prevention. A further three core sociological agent-based model case studies that were seen as core to methodology were also identified. Although we did not find any agent-based models that had been used for policy implementation to date, we did identify a model of obesity spread in networked agents. This model simulated how individuals influence each other with respect to food consumption and physical activity. We deconstructed this model to find that it is a good didactic example of agent-based methodology and shows the potential for agent-based models to be used to inform policy and practice decision-making in the future. InterpretationWell-designed agent-based models that are developed in collaboration with public health stakeholders have the potential to provide new insights into non-communicable disease prevention and provide an opportunity to test policy scenarios in silico before they are applied in the real world. FundingPopulation health Agent-based Simulation nEtwork (PHASE).

What kind of prediction? Evaluating different facets of prediction in agent-based social simulation

ABSTRACT Researchers have become increasingly interested in the potential use of agent-based modelling for the prediction of social phenomena, motivated by the desire, first, to further cement the method’s scientific status and, second, to participate in other scenarios, particularly in the aid of decision-making. This article contributes to the current discussion on prediction from the perspective of the disciplinary organisation of agent-based social simulation. It addresses conceptual and practical challenges pertaining to the community of practitioners, rather than individual instances of modelling. As such, it provides recommendations that invite both collective critical discussion and cooperation. The first two sections review conceptual challenges associated with the concept of prediction and its instantiation in the computational modelling of complex social phenomena. They identify methodological gaps and disagreements that warrant further analysis. The second two sections consider practical challenges related to the lack of a prediction framework that, on one hand, gives meaning and accommodates everyday prediction practices and, on the other hand, establishes more clearly the connection between prediction and other epistemic goals. This coordination at the practical level, it is claimed, might help to better position prediction with agent-based modelling within the larger social science’s methodological landscape.

Agent-Based Modelling in Visitor Management of Protected Areas

The research was motivated by the growing importance of visitor management in protected areas, which can be based on knowledge management, system modelling of processes and phenomena, and a deeper knowledge of the experience of visitors in connection with the concept of psychological carrying capacity. The work builds on previous publications and research by the authors, focused on the optimization of tourism impacts, visitor management and the development of the theory and applicability of the concept of carrying capacity. It emphasizes the overview analysis of the possibilities of using agent-based modelling and visualization of visitor flows in protected areas. The analysis of suitable sources was based on the PRISMA method, which showed the main research directions for the use of the agent-based approach in visitor management. For the practical application of modelling, the NetLogo environment was chosen, in which the visitor flows of the model area were simulated. The visitor attendance was evaluated in relation to the psychological carrying capacity. Subsequently, visitor management measures were implemented in the model, and repeated simulations of visitor attendance, based on monitored flows, were run for a specific location around Oheb Castle (the Železné hory/Iron Mountains, Bohemia). The main result is the innovative use of agent-based modelling in visitor management in the context of visitor experience, visitor satisfaction and psychological carrying capacity. The contribution of the presented research is also the proposal of future research directions for more accurate use of psychological carrying capacity in visitor management.

Agent-based Modeling and Disaster Management

Social simulation is usually used to analyze issues in social science and to study behaviors of people inspecific events. Unlike scientific experiments in other hard sciences, which can be tested in a closed environmentor in a laboratory, social simulation applies computation methods to examine social phenomena.In urban planning, understanding stakeholders—which include residents, businesses, factories, and localgovernments—is one of the important factorsin a successful project. In many cases, these stakeholders areheterogeneous individuals who may have different behaviors. Thus, to effectively solve issues in urban planning,planners need to understand stakeholders. Agent-based modeling (ABM) is widely used to analyze behaviors ofstakeholders under implementation of urban policies, especially in the events of natural disasters, which areconsidered as complex systems. In these analyses, spatial structures of affected areason which stakeholdersare located and interact are a crucial ground of ABM. Together with the development of geographic informationsystems (GIS), the database systems and analyses of ABM become more accurate and reliable, especially onphenomena with the complexity of spatial structures.This review article explains the development and definition of ABM and introduces software and toolkitsfor building an agent-based model, as well as reviews articles and research that use ABM to analyze the issuesin theoretical testing and urban planning. Schelling’s Segregation and Hotelling’s Law models are discussed asexamples of theoretical testing while robbery and driving behavior models are selected as the implications ofABM in urban planning. This article also focuses on the use of ABM on natural disaster policies and managementusing case studies of Japan and the United Kingdom.

Towards real-time predictions using emulators of agent-based models

ABSTRACT The use of Agent-Based Models (ABMs) to make predictions in real-time is hindered by their high computation cost and the lack of detailed individual data. This paper proposes a new framework to enable the use of emulators, also referred to as surrogate models or meta-models, coupled with ABMs, to allow for real-time predictions of the behaviour of a complex system. The case study is that of pedestrian movements through an environment. We evaluate two different types of emulators: a regression emulator based on a Random Forest and a time-series emulator using a Long Short-Term Memory neural network. Both emulators perform well, but the time-series emulator proves to generalise better to cases where the number of agents in the system is not known a priori. The results have implications for the real-time modelling of human crowds, suggesting that emulation is a feasible approach to modelling crowds in real-time, where computational complexity prohibits the use of an ABM directly.

Data assimilation with agent-based models using Markov chain sampling

Every day, weather forecasting centres around the world make use of noisy, incomplete observations of the atmosphere to update their weather forecasts. This process is known as data assimilation, data fusion or state estimation and is best expressed as Bayesian inference: given a set of observations, some prior beliefs and a model of the target system, what is the probability distribution of some set of unobserved quantities or latent variables at some time, possibly in the future? While data assimilation has developed rapidly in some areas, relatively little progress has been made in performing data assimilation with agent-based models. This has hampered the use of agent-based models to make quantitative claims about real-world systems. Here we present an algorithm that uses Markov-Chain-Monte-Carlo (MCMC) methods to generate samples of the parameters and trajectories of an agent-based model over a window of time given a set of possibly noisy, aggregated and incomplete observations of the system. This can be used as-is, or as part of a data assimilation cycle or sequential-MCMC algorithm. Our algorithm is applicable to time-stepping, agent-based models whose agents have a finite set of states and a finite number of ways of acting on the world. As presented, the algorithm is only practical for agents with a few bytes of internal state although we discuss ways of removing this restriction. We demonstrate the algorithm by performing data assimilation with an agent-based, spatial predator-prey model.

A Multi-Scale Suitability Analysis of Home-Improvement Retail-Store Site Selection for Ontario, Canada

A multi-scale suitability analysis using big data (4.7 million suitability scores) is presented across a large spatial extent (1.076 million km2) to identify potential locations for new home-improvement retail stores. Suitability scores were generated for individual property parcels using criteria weights derived from surveyed retail-industry experts. To increase capacity for site selection, distributions of suitability scores were generated at census dissemination areas (populations 500-700; n = 19,963) and census metropolitan and agglomeration areas (core populations >10,000; n = 43). Analogues among metropolitan and agglomeration areas were generated and spatial clustering was used to identify groups of highly-suitable parcels within urban areas. Lastly, individual parcels can be interrogated for overall suitability or individual criteria scores. Our approach combines retail methods typically used in isolation (e.g. location quotient, Huff’s model, network analysis) and demonstrates how a simple survey can be used to weight criteria. Results show that survey respondents were in general agreement and that top-line revenues were more critical to perceived location success than development and operational costs. Analysis of suitability scores found analogues and clusters of census metropolitan areas that coincide with store sales as well as clusters of highly suitable parcels predominantly located around major highways. In addition to identifying challenges and solutions to the presented research, we also describe future research directions that extend the presented static analysis to include processes like store closure and openings, competition, and land use change through the use of agent-based modelling.

A systematic review of agent-based model for flood risk management and assessment using the ODD protocol

Recently, applications of agent-based model (ABM) have been used to understand the interaction between social and hydrological systems. These systems are dynamic and co-evolving, which can be captured through different decision-making entities in an ABM simulation. Therefore, this review aims to better understand the use of ABM for flood risk management and assessment (FRMA). The review comprises a systematic selection of literature using the PRISMA method, which is then assessed using an adapted version of the overview, design, and detail (ODD) protocol to better understand the ABM model development process for FRMA. The review finds that the use of the ODD protocol was only seen in 25% of the studies. The studies which did not explicitly use the ODD had a comprehensive description of the models, albeit done in a non-standardised way. Modellers continue to face the dilemma between parsimony and the breadth of the model as identified from the design component of the ODD. The hydrological component is mainly captured in the sub-model process of the ODD, however, improvements in the definition of the sub-model component may warrant a more comprehensive description of the processes and facilitate comparison across studies. The applications of ABM have shown promise to understand long term flood risks, test the efficacy of policies and better understand the factors that affect warning response during the flood evacuation process. ODD adopted for this review may consequently allow for the adoption and more coherent use of the protocol to document models in FRMA.