An agent-based simulation model for analyzing the governance of the Brazilian Financial System

Regulation can play an important role in effectively managing systemic risk while providing accountability to all affected governments. IMF points out weak governance structures as one of the main causes for financial/economical crisis. However, research in this area is still limited. One of the reasons is the inherent complexity of the public sector governance notion. In this research, the regulatory governance of the financial sector is conceived as a complex system, in which governance is perceived as a phenomenon resulting from the interactions among all the actors that influence or are influenced by regulatory activities within the financial sector. An agent-based simulation was developed to analyze and evaluate the emergent behaviors from the governance in the Brazilian finance sector under different macroeconomics variables and different attitudes, perceptions and desires of economic and political actors. The agent-based model is combined with an econometric model, which is intended to characterize the macroeconomic environment. The regulatory environment is modeled by computational agents using BDI (beliefs–desires–intentions) architecture. The agents have beliefs about their environment and desires they want to satisfy, thus leading them to create intentions to act. The agents’ behavior was modeled using fuzzy rules built by means of content analysis of newspapers and in-depth interviews with experts from the financial area. Computational experiments demonstrate the potential of the agent-based model simulation in the study of complex environments involving regulatory governance.     

An agent-based simulation model of enterprises financial distress for the enterprise of different life cycle stage

This paper proposes a new agent-based simulation model to simulate the causes and processes of enterprise financial distress. The general framework of the model including four agents, which are enterprise, product, bank and macro environment is described. By investigating the different causes of financial distress in enterprise’s different life cycle stages, we implement the simulation model to four specific cases which belong to start-up, growth, mature and decline life cycle stages respectively. Then a comparative analysis between our simulation results and real situation in four proposed specific cases is conducted, demonstrating that the proposed simulation model is a promising tool for comprehensively analyzing the causes and processes of financial distress.     

Modeling the progression of Alzheimer’s disease for cognitive assistance in smart homes

Smart homes provide support to cognitively impaired people (such as those suffering from Alzheimer’s disease) so that they can remain at home in an autonomous and safe way. Models of this impaired population should benefit the cognitive assistance’s efficiency and responsiveness. This paper presents a way to model and simulate the progression of dementia of the Alzheimer’s type by evaluating performance in the execution of an activity of daily living (ADL). This model satisfies three objectives: first, it models an activity of daily living; second, it simulates the progression of the dementia and the errors potentially made by people suffering from it, and, finally, it simulates the support needed by the impaired person. To develop this model, we chose the ACT-R cognitive architecture, which uses symbolic and subsymbolic representations. The simulated results of 100 people suffering from Alzheimer’s disease closely resemble the results obtained by 106 people on an occupational assessment (the Kitchen Task Assessment).     

A spatially explicit agent-based simulation platform for investigating effects of shared pollination service on ecological communities

The alarming reports from around the world on pollinator population declines made the understanding of the effects of shared pollination service on biodiversity into one of the most urgent goals in nature conservation, both for the scientists and managers. The classic field-based methodology which is commonly used in such studies, has three major problems which limit the researchers’ ability to further understand the nature of plant-pollinator dynamics: (1) Natural systems do not allow for a full factorial controlled studies of specific characteristics and traits of both plants and pollinators, because of many confounding effects which are usually unknown. Furthermore, (2) Many of the interactions between plants and pollinators are indirect, via their reciprocal effect on shared pollination services and therefore difficult to detect in the field. Finally, and (3) though plant composition and abundance may be manipulated in the field, it is almost impossible to manipulate pollinator populations, strongly restricting researchers’ ability to thoroughly understand the specific pollinator characteristics which created the observed effects. Therefore, simulation tools are needed that can address this complexity on one hand, and allow to identify potential research directions for targeted experiments on the other hand. Here, we present EcoSimInGrid, a spatially explicit agent-based simulator for investigating effects of shared pollination services on plant communities. EcoSimInGrid can be used to represent complex spatio-temporal interactions among ecological entities of different trophic levels, to investigate effects of plant traits, spatial distribution and pollinator behavior on shared pollination services and to analyze the relative effects of shared pollination and habitat productivity in shaping community diversity. Features like capability to model large ecosystems with tens of thousands of plants and pollinators, graphical user interface, flexible parameter configuration, comprehensive data output and fast speed parallel computing make EcoSimInGrid a welcome addition to the ecological modeling world.     

驾驶员预估效应对交通流合作驾驶格子模型优化状态的影响

基于合作驾驶格子模型, 考虑驾驶员对交通流量的预估效应, 提出一个扩展的交通流合作驾驶格子模型。通过线性稳定性分析和非线性分析探讨驾驶员预估效应对合作驾驶格子模型优化状态的影响。结果表明, 驾驶员预估效应能够进一步增强合作驾驶格子模型优化状态下的稳定性。