STREETS: an agent-based pedestrian model.

8-11 Septembe

How bad Isn't the agent-based model CATCHSCAPE?

Due to mounting human pressure, stakeholders in northern Thailand are facing crucial natural resource management issues. The impact of upstream irrigation management on the downstream agricultural viability is a common source of conflict. It has often both biophysical and social origins. CATCHSCAPE has been developed as an Agent-Based model that enables us to describe the whole catchment: hydrology, farmers' behaviour and water management rules. It is meant to simulate scenarios based on assumptions about value of these features as well as some assumptions about context, such as levels of prices for various commodities or climate. The biophysical modules are made of a hydrological system with its distributed water balance, irrigated schemes management, crop and vegetation dynamics. The social dynamics are described as a set of resource management processes (water, land, cash, labour force). Water management is described according to the actual different levels of control (individual, scheme and catchment). Virtual experiments according to a first defined plan are made with two aims: sensitivity analysis of the model through variation of different parameters and extreme scenarios on one hand; overall behaviour of the basin under various realistic scenarios on the other hand. Both sets are meant to give more insight on the consequences of this very virtual catchment behaviour and improve the collective understanding on the real basin. Simulations show that the model is quite robust from a variation of results point of view and help to identify key factors such as farmers' representation on the expected amount of water for a cropping season or pluri-annual climatic trends. (Résumé d'auteur

An Agent-based Route Choice Model

Travel demand emerges from individual decisions. These decisions, depending on individual objectives, preferences, experiences and spatial knowledge about travel, are both heterogeneous and evolutionary. Research emerging from fields such as road pricing and ATIS requires travel demand models that are able to consider travelers with distinct attributes (value of time (VOT), willingness to pay, travel budgets, etc.) and behavioral preferences (e.g. willingness to switch routes with potential savings) in a differentiated market (by tolls and the level of service). Traditional trip-based models have difficulty in dealing with the aforementioned heterogeneity and issues such as equity. Moreover, the role of spatial information, which has significant influence on decision-making and travel behavior, has not been fully addressed in existing models. To bridge the gap, this paper proposes to explicitly model the formation and spread- ing of spatial knowledge among travelers. An Agent-based Route Choice (ARC) model was developed to track choices of each decision-maker on a road network over time and map individual choices into macroscopic flow pattern. ARC has been applied on both SiouxFalls network and Chicago sketch network. Comparison between ARC and existing models (UE and SUE) on both networks shows ARC is valid and computationally tractable. To be brief, this paper specifically focuses on the route choice behavior, while the proposed model can be extended to other modules of travel demand under an integrated framework.Agent-based model, route choice, traffic assignment, travel demand modeling

Design Guidelines for Agent Based Model Visualization

In the field of agent-based modeling (ABM), visualizations play an important role in identifying, communicating and understanding important behavior of the modeled phenomenon. However, many modelers tend to create ineffective visualizations of Agent Based Models (ABM) due to lack of experience with visual design. This paper provides ABM visualization design guidelines in order to improve visual design with ABM toolkits. These guidelines will assist the modeler in creating clear and understandable ABM visualizations. We begin by introducing a non-hierarchical categorization of ABM visualizations. This categorization serves as a starting point in the creation of an ABM visualization. We go on to present well-known design techniques in the context of ABM visualization. These techniques are based on Gestalt psychology, semiology of graphics, and scientific visualization. They improve the visualization design by facilitating specific tasks, and providing a common language to critique visualizations through the use of visual variables. Subsequently, we discuss the application of these design techniques to simplify, emphasize and explain an ABM visualization. Finally, we illustrate these guidelines using a simple redesign of a NetLogo ABM visualization. These guidelines can be used to inform the development of design tools that assist users in the creation of ABM visualizations.Visualization, Design, Graphics, Guidelines, Communication, Agent-Based Modeling

The fractional volatility model: An agent-based interpretation

Based on criteria of mathematical simplicity and consistency with empirical market data, a model with volatility driven by fractional noise has been constructed which provides a fairly accurate mathematical parametrization of the data. Here, some features of the model are discussed and, using agent-based models, one tries to find which agent strategies and (or) properties of the financial institutions might be responsible for the features of the fractional volatility model.Comment: 23 pages, 11 figure