Academic articles

Agent-based modelling to visualise trustworthiness: a socio-technical framework

This paper describes a socio-technical study based on physical world scenarios of deceptive behaviour occurring in a virtual collaborative environment. An agent-based modelling (ABM) approach was adopted to visualise trustworthiness that can signal deceptive behaviour in virtual communications among social actors. The modelling strategies were guided by attribution theories toward an agent’s perceived trustworthiness.

Modeling the Cycles of Gang and Criminal Behavior: Understanding the Social and Economic Influences

One of the challenges in developing policy for dealing with asocial behavior, such as burglary, vehicle theft, or violent crimes is the seemingly unpredictable rise and fall of activity. In retrospect these cycles in crime are often attributed to changes in factors such the size of a police force, level unemployment, or high school drop-out rate. What causes changes in these factors can some times be external to a local community, such as economic shifts affecting tax revenue, however many are internally linked. For example when crime is high, there is a call for more police and when crime is low, there is a justification for reducing the size of the force. Therefore, understanding how these factors are linked together as a whole may allow for better policies that reduce asocial behavior further and create more stability in the long term.

Simulazione discreta con il software AnyLogic. Caratteristiche generali e applicazione ad unostudio oculistico

AnyLogic è un ambiente di modellazione virtuale di sistemi discreti, continui ed ibridi. Con questo strumento è possibile creare prototipi di sistemi durante le fasi di studio, progettazione o sviluppo, attraverso cui esplorare aspetti e dettagli della progettazione o della implementazione dei relativi sistemi in modo semplice e privo di rischi

An integrated pedestrian behavior model based on extended decision field theory and social force model

The integrated pedestrian simulation model proposed in this paper allows us to develop a more realistic simulation of pedestrian behaviors at a shopping mall. In particular, consideration of vision of each individual allows us to mimic physical and psychological interactions among the people and the environment more realistically. Similarly, consideration of extended Decision Field Theory allowed us to represent human decision deliberation process. In addition, consideration of a rich set of attributes for the environment as well as people has allowed us to mimic a real shopping mall environment closely. The constructed simulation using AnyLogic software was utilized to conduct several experiments on performance of the mall and scalability of the proposed model.

A simulation modeling approach for improving oral health outcomes of older adults

This interdisciplinary research focuses on improving the oral health of older adults as a means of enhancing their overall wellbeing and quality of life. Periodontal disease is a risk factor for other chronic illnesses, notably diabetes and cardiovascular disease. In order to identify policies that improve oral health for older adults, a dynamic modeling approach that considers community and individual level factors is utilized.

Investigating Output Accuracy for a Discrete Event Simulation Model and an Agent Based Simulation Model

In this paper, we investigate output accuracy for a Discrete Event Simulation (DES) model and Agent Based Simulation (ABS) model. The purpose of this investigation is to find out which of these simulation techniques is the best one for modelling human reactive behaviour in the retail sector. In order to study the output accuracy in both models, we have carried out a validation experiment in which we compared the results from our simulation models to the performance of a real system. Our experiment was carried out using a large UK department store as a case study.

Dynamic Learning in Human Decision Behavior for Evacuation Scenarios under BDI Framework

A novel approach to represent learning in human decision behavior for evacuation scenarios is proposed under the context of an extended Belief-Desire-Intention framework. In particular, we focus on how a human adjusts his perception process (involving a Bayesian belief network) in Belief Module dynamically against his performance in predicting the environment as part of his decision planning function. To this end, a Q-learning algorithm (reinforcement learning algorithm) is employed and further developed.