scholarly journals Proposal of a Conceptual Model to Represent Urban-Industrial Systems from the Analysis of Existing Worldwide Experiences

2021 ◽  
Vol 13 (16) ◽  
pp. 9292
Author(s):  
Carmen Ruiz-Puente
Keyword(s):  
Complex Systems ◽  
Conceptual Model ◽  
Environmental Impacts ◽  
Urban Areas ◽  
Industrial Symbiosis ◽  
Agent Based Models ◽  
Industrial Systems ◽  
Agent Based ◽  
Self Organized ◽  
Perspective Analysis

The adoption of Industrial Symbiosis (IS) practices within urban areas is gaining interest due to the environmental impacts entailed by the development of cities. However, there is still a lack of knowledge about how the relationships between industrial and urban areas can be modelled. In this context, this research aimed at posing a conceptual model to understand and represent Urban-Industrial Systems (UIS). To this end, a set of worldwide previous UIS experiences were overviewed to identify the agents, dynamics, and collaboration opportunities that characterize them. The multi-perspective analysis of these cases indicated that UIS are complex systems, which means that they are autonomous, self-organized, responsive, nonlinear, and willing to consolidate their resilience. As such, Agent-Based Models (ABM) were suggested to be the most suitable approach for their representation.

Analytic Versus Computational Cognitive Models: Agent-Based Modeling as a Tool in Cognitive Sciences

2019 ◽  
Vol 28 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Jens Koed Madsen ◽  
Richard Bailey ◽  
Ernesto Carrella ◽  
Philipp Koralus
Keyword(s):  
Complex Systems ◽  
Closed Form ◽  
Model Development ◽  
Cognitive Models ◽  
System Level ◽  
Model Parameters ◽  
Social Psychological ◽  
Agent Based Models ◽  
Agent Based ◽  
Closed Form Solutions

Computational cognitive models typically focus on individual behavior in isolation. Models frequently employ closed-form solutions in which a state of the system can be computed if all parameters and functions are known. However, closed-form models are challenged when used to predict behaviors for dynamic, adaptive, and heterogeneous agents. Such systems are complex and typically cannot be predicted or explained by analytical solutions without application of significant simplifications. In addressing this problem, cognitive and social psychological sciences may profitably use agent-based models, which are widely employed to simulate complex systems. We show that these models can be used to explore how cognitive models scale in social networks to calibrate model parameters, to validate model predictions, and to engender model development. Agent-based models allow for controlled experiments of complex systems and can explore how changes in low-level parameters impact the behavior at a whole-system level. They can test predictions of cognitive models and may function as a bridge between individually and socially oriented models.

scholarly journals Stability of subsystem solutions in agent-based models

2017 ◽  
Author(s):  
Matjaž Perc
Keyword(s):  
Phase Transitions ◽  
Complex Systems ◽  
System Size ◽  
Systems Science ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Solid State Physics ◽  
Agent Based ◽  
Correct Determination ◽  
Predictive Understanding

The fact that relatively simple entities, such as particles or neurons, or even ants or bees or humans, give rise to fascinatingly complex behavior when interacting in large numbers is the hallmark of complex systems science. Agent-based models are frequently employed for modeling and obtaining a predictive understanding of complex systems. Since the sheer number of equations that describe the behavior of an entire agent-based model often makes it impossible to solve such models exactly, Monte Carlo simulation methods must be used for the analysis. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among agents that describe systems in biology, sociology or the humanities often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. This begets the question: When can we be certain that an observed simulation outcome of an agent-based model is actually stable and valid in the large system-size limit? The latter is key for the correct determination of phase transitions between different stable solutions, and for the understanding of the underlying microscopic processes that led to these phase transitions. We show that a satisfactory answer can only be obtained by means of a complete stability analysis of subsystem solutions. A subsystem solution can be formed by any subset of all possible agent states. The winner between two subsystem solutions can be determined by the average moving direction of the invasion front that separates them, yet it is crucial that the competing subsystem solutions are characterized by a proper composition and spatiotemporal structure before the competition starts. We use the spatial public goods game with diverse tolerance as an example, but the approach has relevance for a wide variety of agent-based models.

Agent-based model of self-organized industrial symbiosis

2017 ◽  
Vol 161 ◽  
pp. 452-465 ◽  
Author(s):  
Mohamed Raouf Ghali ◽  
Jean-Marc Frayret ◽  
Chahid Ahabchane
Keyword(s):  
Industrial Symbiosis ◽  
Agent Based Model ◽  
Agent Based ◽  
Self Organized

A Two-level Agent-Based Model for Hurricane Evacuation in New Orleans

2015 ◽  
Vol 12 (2) ◽  
Author(s):  
Wei Liang ◽  
Nina S.-N. Lam ◽  
Xiaojun Qin ◽  
Wenxue Ju
Keyword(s):  
New Orleans ◽  
Urban Areas ◽  
Computational Time ◽  
Agent Based Model ◽  
Agent Based Models ◽  
Hurricane Evacuation ◽  
Agent Based ◽  
The Real ◽  
Evacuation Traffic ◽  
Mass Evacuation

AbstractMass evacuation of urban areas due to hurricanes is a critical problem in emergency management that requires extensive basic and applied research. Previous research uses agent-based models to simulate individual vehicle and driver behavior, and is limited mostly to a small study area due to the complexity of the models and the computational time needed. To better understand evacuation behavior, simulating the evacuation traffic in a larger region is needed. This paper develops a two-level regional disaster evacuation model by coupling two agent-based models. The first model uses each census block centroid, weighted with its corresponding number of vehicles, as an agent to simulate the local road network traffic. The second model, developed on the platform of a commercial software program called VISSIM, treats each vehicle as an agent to simulate the interstate highway traffic. This two-level agent-based model was used to simulate hurricane evacuation traffic in New Orleans. Validation results with the real Hurricane Katrina’s evacuation data confirm that the proposed model performs well in terms of high model accuracy (i.e., close agreement between the real and simulated traffic patterns) and short model running time. The modeling results show that the average root-mean-square error (RMSE) for the three major evacuation directions was 347.58. Under a simultaneous evacuation strategy, and with 240,251 vehicles in 17,744 agents (census blocks), it would take at least 46.3 hours to evacuate all residents from the New Orleans metropolitan area. This two-level modeling approach could serve as a practical tool for evaluating mass evacuation strategies in New Orleans and other similar urban areas.

scholarly journals A New Agent-Based Methodology for the Seismic Vulnerability Assessment of Urban Areas

2019 ◽  
Vol 8 (6) ◽  
pp. 274 ◽  
Author(s):  
Annalisa Greco ◽  
Alessandro Pluchino ◽  
Luca Barbarossa ◽  
Giovanni Barreca ◽  
Ivo Caliò ◽  
...  
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
Seismic Vulnerability ◽  
Data Sets ◽  
Agent Based ◽  
Self Organized ◽  
Earthquake Simulator ◽  
Geological Features ◽  
Self Organized Criticality ◽  
Combine Information

In order to estimate the seismic vulnerability of a densely populated urban area, it would in principle be necessary to evaluate the dynamic behaviour of individual and aggregate buildings. These detailed seismic analyses, however, are extremely cost-intensive and require great processing time and expertise judgment. The aim of the present study is to propose a new methodology able to combine information and tools coming from different scientific fields in order to reproduce the effects of a seismic input in urban areas with known geological features and to estimate the entity of the damages caused on existing buildings. In particular, we present a new software called ABES (Agent-Based Earthquake Simulator), based on a Self-Organized Criticality framework, which allows to evaluate the effects of a sequence of seismic events on a certain large urban area during a given interval of time. The integration of Geographic Information System (GIS) data sets, concerning both geological and urban information about the territory of Avola (Italy), allows performing a parametric study of these effects on a real context as a case study. The proposed new approach could be very useful in estimating the seismic vulnerability and defining planning strategies for seismic risk reduction in large urban areas

A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain

Energy ◽  
2017 ◽  
Vol 137 ◽  
pp. 1219-1230 ◽  
Author(s):  
Jonas Hinker ◽  
Christian Hemkendreis ◽  
Emily Drewing ◽  
Steven März ◽  
Diego I. Hidalgo Rodríguez ◽  
...  
Keyword(s):  
Conceptual Model ◽  
Agent Based Models ◽  
Agent Based ◽  
Energy Domain
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