scholarly journals Socio-hydrology from the bottom up: A template for agent-based modeling in irrigation systems

2017 ◽  
Author(s):  
Dimitrios Bouziotas ◽  
Maurits Ertsen
Keyword(s):  
Irrigation Management ◽  
Water System ◽  
Agent Based Modeling ◽  
Proof Of Concept ◽  
Management Game ◽  
Irrigation Systems ◽  
Individual Agent ◽  
Bottom Up ◽  
Agent Based ◽  
Agent Behavior

Abstract. Based on a review of key concepts in agent-based modeling for irrigation systems and coupled human-water systems in general, this study presents a proof of concept of an agent-based model based on the existing Irrigation Management Game. After the modeling philosophy and main characteristics are outlined, a number of pilot applications are presented and evaluated. Following the evaluation of the results, future steps that could be incorporated in the model are discussed. The proposed template offers a bottom-up approach to socio-hydrological modelling design, as individual agent behavior explicitly co-shapes the response of the water system, which allows the discovery of emergent dynamics and the conditions under which these are produced. The concepts explained and modeled at a proof-of-concept level in this work serve as a call to the socio-hydrological community to expand its modeling efforts to the agent level.

Agent-Based Modeling and Simulation on Multi-Stage Supply Chain Operation

2011 ◽  
Vol 291-294 ◽  
pp. 3216-3220 ◽  
Author(s):  
Can Can Zhao ◽  
Xiao Dong Zhang ◽  
Shao Juan Lei ◽  
Jun Jiang Qiu
Keyword(s):  
Supply Chain ◽  
Modeling And Simulation ◽  
Cooperative Behavior ◽  
Agent Based Modeling ◽  
Individual Agent ◽  
Agent Based ◽  
Agent Behavior ◽  
Chain Operation ◽  
Multi Stage ◽  
Supply Chain Operation

Supply chain simulation is a fundamental approach for supply chain prediction, management, evaluation, and improvement. In order to simulate member behavior, organizational strategy, and management strategy of the supply chain, an agent-based modeling and simulation approach on multi-stage supply chain operation was proposed in this paper. First, the model structure of multi-stage supply chain operation was introduced. Then, the individual agent behavior model was emphatically studied, and hierarchical colored Petri-nets were used to describe the agent behavior process and cooperative behavior process. Finally, the model was validated using a series of simulation which focused on the agent inventory strategy.

Agent-based Modeling

2021 ◽  
Keyword(s):  
Complex Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Agent Based Modeling ◽  
Urban Systems ◽  
Dynamic Processes ◽  
Modeling Tools ◽  
Individual Agent ◽  
Bottom Up ◽  
Agent Based

Agent-based modeling (ABM) has become widely accepted as a methodological tool to model and simulate dynamic processes of geographical phenomena. A growing number of ABM studies across a variety of domains and disciplines is partially explained by the development of agent-modeling tools and platforms, the availability of micro-data, and the advancement in computer technology and cyberinfrastructure. In addition to these technical reasons, another key motivation underlying ABM research is to address challenges embedded in conventional modeling approaches being relatively coarse, aggregate, static, normative, and inflexible across scales with a reductionist viewpoint (Batty 2005 cited under Application: Urban Systems.” With complexity science, including complex systems, complex adaptive systems, and artificial life, providing theoretical foundations and rationales, ABM is a computational methodology for simulating dynamic processes of nature and human systems driven by disaggregated, heterogeneous, and autonomous entities, i.e., agents, that interact among themselves and their environments. A key fundamental concept of the ABM framework is that a system emerges from the dynamic individual-level interactions from bottom-up, where the simulated outcome is more than the sum of its components. This bottom-up approach enables ABM to exhibit complex system dynamics, properties of which could include feedback effect, path-dependence, phase shift, non-linearity, adaptation, self-organization, tipping points, and emergence. Three key components of ABM are agents, their environment, and their decision rules. Agents are the crucial component in ABM where each individual agent has its own characteristics and agenda, assesses its surrounded situation, and makes decisions. Agents reside in an environment, which can represent a geographic space in case for spatially explicit agent-based models. Agents’ behavioral decisions and interactions within their environment are defined based on a set of rules, which can alter their status and location over time. The purpose of ABM research can be classified into theoretical exploration and empirical investigation as well as the combination of two. In the latter case, ABM can be used as an artificial laboratory experiment to explore what-if scenarios and to investigate how changes in agents, environments, and/or rules affect the macro-level outcomes. ABM has been applied to represent a wide variety of geographic processes and behaviors including but not limited to urban system, land-use/land-cover change, ecology, transportation, animal/human movement, behavioral geography, spatial cognition, transportation, and disease epidemiology. While the growing interest in ABM as a modeling methodology to simulate complex systems is remarkable, there exist various conceptual, methodological, and technical challenges.

scholarly journals AGENT BASED MODELING: FINE-SCALE SPATIO-TEMPORAL ANALYSIS OF PERTUSSIS

2017 ◽  
Vol IV-4/W2 ◽  
pp. 37-46
Author(s):  
D. A. Mills
Keyword(s):  
Asymptomatic Carrier ◽  
Temporal Analysis ◽  
Agent Based Modeling ◽  
Fine Scale ◽  
Individual Agent ◽  
Agent Based ◽  
Asymptomatic Carriers ◽  
Temporal Variables ◽  
Spatio Temporal

In epidemiology, spatial and temporal variables are used to compute vaccination efficacy and effectiveness. The chosen resolution and scale of a spatial or spatio-temporal analysis will affect the results. When calculating vaccination efficacy, for example, a simple environment that offers various ideal outcomes is often modeled using coarse scale data aggregated on an annual basis. In contrast to the inadequacy of this aggregated method, this research uses agent based modeling of fine-scale neighborhood data centered around the interactions of infants in daycare and their families to demonstrate an accurate reflection of vaccination capabilities. Despite being able to prevent major symptoms, recent studies suggest that acellular Pertussis does not prevent the colonization and transmission of Bordetella Pertussis bacteria. After vaccination, a treated individual becomes a potential asymptomatic carrier of the Pertussis bacteria, rather than an immune individual. Agent based modeling enables the measurable depiction of asymptomatic carriers that are otherwise unaccounted for when calculating vaccination efficacy and effectiveness. Using empirical data from a Florida Pertussis outbreak case study, the results of this model demonstrate that asymptomatic carriers bias the calculated vaccination efficacy and reveal a need for reconsidering current methods that are widely used for calculating vaccination efficacy and effectiveness.

Spatial Units as Agents: Making the Landscape an Equal Player in Agent-Based Simulations

2002 ◽  
Author(s):  
Paul Box
Keyword(s):  
Cell Structure ◽  
Natural Extension ◽  
Data Gathering ◽  
Agent Based Modeling ◽  
Emergent Behavior ◽  
Modeling Tools ◽  
Bottom Up ◽  
Agent Based ◽  
Landscape Processes ◽  
Dynamic Landscape

Agent-based modeling has generated considerable interest in recent years as a tool for exploring many of the processes that can be modeled as bottom up processes. This has accelerated with the availability of software packages, such as Swarm and StarLogo, that allow for relatively complex simulations to be constructed by researchers with limited computer-programming backgrounds. A typical use of agent-based models is to simulate scenarios where large numbers of individuals are inhabiting a landscape, interacting with their landscape and each other by relatively simple rules, and observing the emergent behavior of the system (population) over time. It has been a natural extension in this sort of a study to create a landscape from a “real world” example, typically imported through a geographic information system (GIS). In most cases, the landscape is represented either as a static object, or a “stage” upon which the agents act (see Briggs et al. , Girnblett et al., and Remm). In some cases, an approximation of a dynamic landscape has been added to the simulation in a way that is completely exogenous to the population being simulated; the dynamic conditions are read from historical records, in effect “playing a tape” of conditions, to which the population reacts through time (such as Dean et al. and Kohler et al. ). There has also been many simulations where dynamic landscape processes have been modeled through “bottom up” processes, where localized processes in landscapes are simulated, and the global emergent processes are observed. Topmodel is a Fortran-based implementation of this concept for hydrologic processes; and PCRaster has used similar software constructs to simulate a variety of landscape processes, with sophisticated visualization and data-gathering tools. In both of these examples, the landscape is represented as a regular lattice or cell structure. There are also many examples of “home grown” tools (simulations created for a specific project), applying cellular automata (CA) rules to landscapes to simulate urban growth, wildfire , lava flows, and groundwater flow. There are also examples of how agent-based modeling tools were employed to model dynamic landscape processes such as forest dynamics, i.e., Arborgames. In these models the landscape was the object of the simulation, and free-roaming agents were not considered as part of the model.

Representing ancient southern Mesopotamia irrigated landscapes in an agent-based model

2021 ◽  
Author(s):  
Dengxiao Lang ◽  
Maurits W. Ertsen
Keyword(s):  
Water Availability ◽  
Irrigation System ◽  
Irrigation Management ◽  
Irrigated Agriculture ◽  
Agent Based Model ◽  
Irrigation Systems ◽  
Agent Based ◽  
Primary Canal ◽  
Winter Crops ◽  
Different Levels

<p>In order to explore possibilities of mimicking the operation of an irrigation system under varied scenarios, the authors have designed the Irrigation-Related Agent-Based Model (IRABM), providing a platform for integrating human and non-human agents (water managers, farmers, barley, river, canals, and gates) together and analyzing the interactions among these agents. IRABM illustrates how barley yields respond to varied irrigation strategies and how patterns of yields vary among the levels of individual farmers, canals, and the whole irrigation system. The model proves how this type of theoretically and empirically informed computer model can be used to develop new insights into studying and simulating interactions between individuals and their environment in an irrigation system. Furthermore, it demonstrates how and why irrigation and yield patterns can emerge from changing actions.</p><p>One of the applications of the model will be for ancient Southern Mesopotamia, the pluvial land between the two rivers Euphrates and Tigris. Our knowledge of irrigation management and irrigated-landscapes in southern Mesopotamia fairly scant due to lack of data, but also because attention for the details of irrigation management has been ignored in archaeological analysis to date. IRABM offers options to synchronize the general features of irrigation systems to the specifics of Mesopotamia. How to represent ancient Mesopotamia in IRABM is the key question we address in this paper.</p><p>Given the low precipitation, the available water in Mesopotamia’s watercourses for cultivation was vital. This prompted the establishment of irrigated agriculture, leading to its sophisticated irrigation systems over time. Management of irrigation activities is both related to water volumes in the different (levels of) water courses, and to the size of a system. Because of the expanding Mesopotamian society, and this its irrigated areas, the unpredictable water availability, and the threat of water scarcity during the crop growing period, coordinating issues were critical.</p><p>How to present ancient Mesopotamian irrigation systems in IRABM and how to fully explore the temporal and spatial coordination issues is our current challenge. Using the standard composition of irrigation systems in the primary canal, secondary canals, and tertiary canals, we can draft sizes of these levels. The cultivated size of agricultural land varied among the different levels of canals. Generally, the primary canal would supply 5 to 6 villages, while the second and tertiary canals might irrigate land in 2 to 3 villages and 1 village, respectively. The main crops were winter crops (barley and wheat). The water regimes of the two rivers are characterized by great, rather unpredictable fluctuations that do not coincide with winter crops.</p><p>This presentation will discuss how the data on ancient Mesopotamian irrigation (including water availability in rivers, canals, and fields, and surface areas of irrigated landscapes) can be meaningfully included in an ABM that allows studying how small/short processes contribute to large-scale patterns and processes occurring in irrigation systems.</p>

scholarly journals An Agent based Modeling for the Gravity Irrigation Management

2013 ◽  
Vol 19 ◽  
pp. 804-813 ◽  
Author(s):  
Salwa Belaqziz ◽  
Aziz El Fazziki ◽  
Sylvain Mangiarotti ◽  
Michel Le Page ◽  
Said Khabba ◽  
...  
Keyword(s):  
Irrigation Management ◽  
Agent Based Modeling ◽  
Agent Based

it takes a(n) (agent-based) village

2005 ◽  
Vol 28 (4) ◽  
pp. 506-507
Author(s):  
teresa satterfield
Keyword(s):  
Learning Approaches ◽  
Proof Of Concept ◽  
Negative Consequences ◽  
Agent Based ◽  
Agent Behavior ◽  
Category Formation

steels & belpaeme (s&b) take technical and conceptual shortcuts that have significant negative consequences on simulation implementation and agent behavior. justifiably, their model represents a proof of concept of the role of culturalism in category formation; nevertheless, the absence of detailed information concerning the embodiment of agents and the superficial implementation of learning approaches make their results less relevant than they could be.

scholarly journals Advances in the agent-based modeling of economic and social behavior

2021 ◽  
Vol 1 (7) ◽  
Author(s):  
Mitja Steinbacher ◽  
Matthias Raddant ◽  
Fariba Karimi ◽  
Eva Camacho Cuena ◽  
Simone Alfarano ◽  
...  
Keyword(s):  
Social Behavior ◽  
Laboratory Experiments ◽  
State Of The Art ◽  
Social Systems ◽  
Agent Based Modeling ◽  
Data Sources ◽  
Future Research ◽  
Economic Networks ◽  
Agent Based ◽  
Agent Behavior

AbstractIn this review we discuss advances in the agent-based modeling of economic and social systems. We show the state of the art of the heuristic design of agents and how behavioral economics and laboratory experiments have improved the modeling of agent behavior. We further discuss how economic networks and social systems can be modeled and we discuss novel methodology and data sources. Lastly, we present an overview of estimation techniques to calibrate and validate agent-based models and show avenues for future research.

scholarly journals Simulating Urban Shrinkage in Detroit via Agent-Based Modeling

2021 ◽  
Vol 13 (4) ◽  
pp. 2283
Author(s):  
Na Jiang ◽  
Andrew Crooks ◽  
Wenjing Wang ◽  
Yichun Xie
Keyword(s):  
Urban Population ◽  
Computational Models ◽  
Housing Markets ◽  
Agent Based Modeling ◽  
Spatially Explicit ◽  
Market Conditions ◽  
Bottom Up ◽  
Agent Based ◽  
Population Loss ◽  
Growing Body

While the world’s total urban population continues to grow, not all cities are witnessing such growth—some are actually shrinking. This shrinkage has caused several problems to emerge, including population loss, economic depression, vacant properties and the contraction of housing markets. Such issues challenge efforts to make cities sustainable. While there is a growing body of work on studying shrinking cities, few explore such a phenomenon from the bottom-up using dynamic computational models. To fill this gap, this paper presents a spatially explicit agent-based model stylized on the Detroit Tri-County area, an area witnessing shrinkage. Specifically, the model demonstrates how the buying and selling of houses can lead to urban shrinkage through a bottom-up approach. The results of the model indicate that, along with the lower level housing transactions being captured, the aggregated level market conditions relating to urban shrinkage are also denoted (i.e., the contraction of housing markets). As such, the paper demonstrates the potential of simulation for exploring urban shrinkage and potentially offers a means to test policies to achieve urban sustainability.

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