Enabling the Reuse of Platform-Dependent Agents in Heterogeneous Agent-Based Applications

2008 ◽  
pp. 209-224
Author(s):  
Giancarlo Fortino ◽  
Alfredo Garro ◽  
Wilma Russo
Keyword(s):  
Heterogeneous Agent ◽  
Agent Based ◽  
Dependent Agents

scholarly journals MARKET STATISTICS OF A PSYCHOLOGY-BASED HETEROGENEOUS AGENT MODEL

2008 ◽  
Vol 11 (07) ◽  
pp. 717-737 ◽  
Author(s):  
HARBIR LAMBA ◽  
TIM SEAMAN
Keyword(s):  
Heterogeneous Agent ◽  
Agent Based ◽  
Agent Model ◽  
Fundamental Parameters ◽  
Market Participants ◽  
Information Stream ◽  
Momentum Trading ◽  
Real Market ◽  
Available Information ◽  
Parameter Values

We continue an investigation into a class of agent-based market models that are motivated by a psychologically-plausible form of bounded rationality. Some of the agents in an otherwise efficient hypothetical market are endowed with differing tolerances to the tension caused by being in the minority. This herding tendency may be due to purely psychological effects, momentum-trading strategies, or the rational response to perverse marketplace incentives. The resulting model has the important properties of being both very simple and insensitive to its small number of fundamental parameters. While it is most certainly a caricature market, with only boundedly rational traders and the globally available information stream being modeled directly, other market participants and effects are indirectly replicated. We show that all of the most important "stylized facts" of real market statistics are reproduced by this model. Another useful aspect of the model is that, for certain parameter values, it reduces to a standard efficient-market system. This allows us to isolate and observe the effects of particular kinds of non-rationality. To this end, we consider the effects of different asymmetries in agent behavior and show that one in particular leads to skew statistics consistent with those seen in some real financial markets.

scholarly journals Agent-based simulation for reconstructing social structure by observing collective movements with special reference to single-file movement

2020 ◽  
Author(s):  
Hiroki Koda ◽  
Zin Arai ◽  
Ikki Matsuda
Keyword(s):  
Nearest Neighbor ◽  
Social Structures ◽  
Spatial Distributions ◽  
Simple Assumption ◽  
Agent Based ◽  
Movement Data ◽  
Single File ◽  
Field Investigations ◽  
Collective Movements ◽  
Dependent Agents

AbstractUnderstanding social organization is fundamental for the analysis of animal societies. In this study, animal single-file movement data ‒serialized order movements generated by simple bottom-up rules of collective movements— are informative and effective observations for the reconstruction of animal social structures using agent-based models. For simulation, artificial 2-dimensional spatial distributions were prepared with the simple assumption of clustered structures of a group. Animals in the group are either independent or dependent agents. Independent agents distribute spatially independently each one another, while dependent agents distribute depending on the distribution of independent agents. Artificial agent spatial distributions aim to represent clustered structures of agent locations ‒a coupling of “core” or “keystone” subjects and “subordinate” or “follower” subjects. Collective movements were simulated following two simple rules, 1) initiators of the movement are randomly chosen, and 2) the next moving agent is always the nearest neighbor of the last moving agents, generating “single-file movement” data. Finally, social networks were visualized, and clustered structures reconstructed using a recent major social network analysis (SNA) algorithm, the Louvain algorithm, for rapid unfolding of communities in large networks. Simulations revealed possible reconstruction of clustered social structures using relatively minor observations of single-file movement, suggesting possible application of single-file movement observations for SNA use in field investigations of wild animals.

scholarly journals Agent-based simulation for reconstructing social structure by observing collective movements with special reference to single-file movement

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243173
Author(s):  
Hiroki Koda ◽  
Zin Arai ◽  
Ikki Matsuda
Keyword(s):  
Nearest Neighbor ◽  
Social Structures ◽  
Spatial Distributions ◽  
Simple Assumption ◽  
Agent Based ◽  
Movement Data ◽  
Single File ◽  
Field Investigations ◽  
Collective Movements ◽  
Dependent Agents

Understanding social organization is fundamental for the analysis of animal societies. In this study, animal single-file movement data—serialized order movements generated by simple bottom-up rules of collective movements—are informative and effective observations for the reconstruction of animal social structures using agent-based models. For simulation, artificial 2-dimensional spatial distributions were prepared with the simple assumption of clustered structures of a group. Animals in the group are either independent or dependent agents. Independent agents distribute spatially independently each one another, while dependent agents distribute depending on the distribution of independent agents. Artificial agent spatial distributions aim to represent clustered structures of agent locations—a coupling of “core” or “keystone” subjects and “subordinate” or “follower” subjects. Collective movements were simulated following two simple rules, 1) initiators of the movement are randomly chosen, and 2) the next moving agent is always the nearest neighbor of the last moving agents, generating “single-file movement” data. Finally, social networks were visualized, and clustered structures reconstructed using a recent major social network analysis (SNA) algorithm, the Louvain algorithm, for rapid unfolding of communities in large networks. Simulations revealed possible reconstruction of clustered social structures using relatively minor observations of single-file movement, suggesting possible application of single-file movement observations for SNA use in field investigations of wild animals.

scholarly journals A population data-driven workflow for COVID-19 modeling and learning

2021 ◽  
Vol 35 (5) ◽  
pp. 483-499
Author(s):  
Jonathan Ozik ◽  
Justin M Wozniak ◽  
Nicholson Collier ◽  
Charles M Macal ◽  
Mickaël Binois
Keyword(s):  
Social Interactions ◽  
High Performance ◽  
Large Scale ◽  
Population Data ◽  
Crisis Response ◽  
Protective Behaviors ◽  
Activity Schedules ◽  
Heterogeneous Agent ◽  
Agent Based ◽  
Disease States

CityCOVID is a detailed agent-based model that represents the behaviors and social interactions of 2.7 million residents of Chicago as they move between and colocate in 1.2 million distinct places, including households, schools, workplaces, and hospitals, as determined by individual hourly activity schedules and dynamic behaviors such as isolating because of symptom onset. Disease progression dynamics incorporated within each agent track transitions between possible COVID-19 disease states, based on heterogeneous agent attributes, exposure through colocation, and effects of protective behaviors of individuals on viral transmissibility. Throughout the COVID-19 epidemic, CityCOVID model outputs have been provided to city, county, and state stakeholders in response to evolving decision-making priorities, while incorporating emerging information on SARS-CoV-2 epidemiology. Here we demonstrate our efforts in integrating our high-performance epidemiological simulation model with large-scale machine learning to develop a generalizable, flexible, and performant analytical platform for planning and crisis response.

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