scholarly journals Agent-based simulation of collective cooperation: from experiment to model

2020 ◽  
Vol 17 (171) ◽  
pp. 20200396
Author(s):  
Benedikt Kleinmeier ◽  
Gerta Köster ◽  
John Drury
Keyword(s):  
Cognitive Processing ◽  
Simulation Models ◽  
First Aid ◽  
Psychological Model ◽  
Pedestrian Dynamics ◽  
Agent Based ◽  
Safe Area ◽  
Invaluable Tool ◽  
Pass Through ◽  
Dense Crowd

Simulation models of pedestrian dynamics have become an invaluable tool for evacuation planning. Typically, crowds are assumed to stream unidirectionally towards a safe area. Simulated agents avoid collisions through mechanisms that belong to each individual, such as being repelled from each other by imaginary forces. But classic locomotion models fail when collective cooperation is called for, notably when an agent, say a first-aid attendant, needs to forge a path through a densely packed group. We present a controlled experiment to observe what happens when humans pass through a dense static crowd. We formulate and test hypotheses on salient phenomena. We discuss our observations in a psychological framework. We derive a model that incorporates: agents’ perception and cognitive processing of a situation that needs cooperation; selection from a portfolio of behaviours, such as being cooperative; and a suitable action, such as swapping places. Agents’ ability to successfully get through a dense crowd emerges as an effect of the psychological model.

Modeling Passenger Behavior in Nonpayment Areas at Rail Transit Stations

2015 ◽  
Vol 2534 (1) ◽  
pp. 101-108 ◽  
Author(s):  
Mingjun Liao ◽  
Gang Liu
Keyword(s):  
Choice Model ◽  
Transition Probability ◽  
Simulation Models ◽  
Critical Issue ◽  
Microscopic Simulation ◽  
Movement Model ◽  
New Ideas ◽  
Path Navigation ◽  
Pass Through ◽  
Passenger Behavior

The nonpayment area at urban transit stations in China usually becomes extremely crowded during peak hours because many passengers queue to buy tickets and pass through the fare gates. How to evaluate the performance of these activities is a critical issue for the design and management of the nonpayment area. This study used microscopic simulation models to investigate passenger behavior in the nonpayment area. The study developed a queue choice model, a passenger movement model, and a path navigation model. Some new ideas were involved. First, the study introduced the concepts of dynamic queue length and dynamic distance between the current passenger and alternative queues into the queue choice model. Second, a new factor, called direction of goal, was proposed to navigate a passenger through the dynamic end of a queue or other goals. This factor was used to construct the transition probability function of a cellular automata model. Finally, the proposed models were calibrated and verified on the basis of a field survey and sensitivity analysis. The results show that the proposed models can capture passenger behaviors in the nonpayment area and perform well for queue estimation.

Agents in bioinformatics

2005 ◽  
Vol 20 (2) ◽  
pp. 117-125 ◽  
Author(s):  
MICHAEL LUCK ◽  
EMANUELA MERELLI
Keyword(s):  
Data Analysis ◽  
Simulation Models ◽  
Biological Data ◽  
System Modelling ◽  
Agent Based Simulation ◽  
Huge Amount ◽  
Agent Based ◽  
Information Agents ◽  
New Knowledge ◽  
Definition Of

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarize and reflect on the presentations and discussions.

scholarly journals ABWiSE v1.0: Toward and Agent-Based Approach to Simulating Wildfire Spread

2021 ◽  
Author(s):  
Jeffrey Katan ◽  
Liliana Perez
Keyword(s):  
Human Life ◽  
Simulation Models ◽  
Empirical Models ◽  
Physical Models ◽  
Fire Behaviour ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Fire Front ◽  
Wildfire Simulation ◽  
Behaviour Simulation

Abstract. Wildfires are a complex phenomenon emerging from interactions between air, heat, and vegetation, and while they are an important component of many ecosystems’ dynamics, they pose great danger to those ecosystems, and human life and property. Wildfire simulation models are an important research tool that help further our understanding of fire behaviour and can allow experimentation without recourse to live fires. Current fire simulation models fit into two general categories: empirical models and physical models. We present a new modelling approach that uses agent-based modelling to combine the complexity found in physical models with the ease of computation of empirical models. Our model represents the fire front as a set of moving agents that respond to, and interact with, vegetation, wind, and terrain. We calibrate the model using two simulated fires and one real fire, and validate the model against another real fire and the interim behaviour of the real calibration fire. Our model successfully replicates these fires, with a Figure of Merit on par with simulations by the Prometheus simulation model. Our model is a stepping-stone in using agent-based modelling for fire behaviour simulation, as we demonstrate the ability of agent-based modelling to replicate fire behaviour through emergence alone.

The impact of future cities on commuting patterns: An agent-based approach

2018 ◽  
Vol 46 (6) ◽  
pp. 1079-1096
Author(s):  
Marcello Marini ◽  
Anna P Gawlikowska ◽  
Andrea Rossi ◽  
Ndaona Chokani ◽  
Hubert Klumpner ◽  
...  
Keyword(s):  
Urban Areas ◽  
Simulation Models ◽  
Single Family ◽  
Simulation Framework ◽  
Agent Based ◽  
Commercial Activities ◽  
Transportation Sector ◽  
Commuting Time ◽  
Commuting Patterns ◽  
The Impact

Over the next 35 years, the population of Switzerland is expected to grow by 25%. One possible way to accommodate this larger population is to transform smaller cities in Switzerland through the direct intervention of urban planners. In this work, we integrate agent-based simulation models of people flow, mobility and urban infrastructure with models of the electricity and gas systems to examine the increase of the density of existing residential zones and the creation of new workplaces and commercial activities in these urban areas. This novel simulation framework is used to assess, for the year 2050, two different scenarios of urbanization in a region with small urban areas. It is shown that a densification scenario, with a preference for multi-dwelling buildings, consumes 93% less land than a sprawl scenario, with a preference for single-family houses. The former scenario also accommodates 27% more people than the latter scenario, as there is a higher penetration of battery electric vehicles – and therefore reduced air pollution from the transportation sector – and also a larger shift of commuters to the use of public transport. However, in the former scenario, the commuting time is 20% longer. The outcome of this work demonstrates how this novel simulation framework can be used to support the formulation of policies that can direct the transformation of urban areas.

Scoping the Future with Theory-Driven Models—Where’s the Uncertainty?: Commentary on Lustick and Tetlock 2021

2021 ◽  
Author(s):  
David R. Mandel
Keyword(s):  
National Security ◽  
Policy Decision ◽  
Simulation Models ◽  
Insufficient Data ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Strategic Intelligence ◽  
The Future ◽  
Policy Decision Making ◽  
Rich Data

Lustick and Tetlock outline an intellectually ambitious approach to scoping the future. They are particularly interested in sectors of national security and foreign policy decision-making that require anticipatory strategic intelligence that is difficult to produce because there is insufficient data, even if relevant theories are available. They propose that in these theory-rich/data-impoverished cases, there can be great value in developing agent-based simulation models that incorporate probabilistic rules that cohere with postulates of the theory or theories that are brought to bear on the intelligence challenge. This is the gist of the “simulation manifesto.” The aim of this commentary is to focus on the assessment and representation of key uncertainties in such models and I outline several ways in which uncertainty may arise in the process of simulation model construction.

Behavior Selection Using Utility-Based Reinforcement Learning in Irregular Warfare Simulation Models

2013 ◽  
Vol 4 (3) ◽  
pp. 61-78 ◽  
Author(s):  
Sotiris Papadopoulos ◽  
Francisco Baez ◽  
Jonathan Alt ◽  
Christian Darken
Keyword(s):  
Reinforcement Learning ◽  
Adaptive Learning ◽  
Simulation Models ◽  
The Body ◽  
Irregular Warfare ◽  
Agent Based ◽  
Body Of Knowledge ◽  
Learning Techniques ◽  
Study Results ◽  
The Impact

The Theory of Planned Behavior (TPB) provides a conceptual model for use in assessing behavioral intentions of humans. Agent based social simulations seek to represent the behavior of individuals in societies in order to understand the impact of a variety of interventions on the population in a given area. Previous work has described the implementation of the TPB in agent based social simulation using Bayesian networks. This paper describes the implementation of the TPB using novel learning techniques related to reinforcement learning. This paper provides case study results from an agent based simulation for behavior related to commodity consumption. Initial results demonstrate behavior more closely related to observable human behavior. This work contributes to the body of knowledge on adaptive learning behavior in agent based simulations.

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