Intelligent judgements over health risks in a spatial agent-based model

© 2018 The Author(s). Background: Millions of people worldwide are exposed to deadly infectious diseases on a regular basis. Breaking news of the Zika outbreak for instance, made it to the main media titles internationally. Perceiving disease risks motivate people to adapt their behavior toward a safer and more protective lifestyle. Computational science is instrumental in exploring patterns of disease spread emerging from many individual decisions and interactions among agents and their environment by means of agent-based models. Yet, current disease models rarely consider simulating dynamics in risk perception and its impact on the adaptive protective behavior. Social sciences offer insights into individual risk perception and corresponding protective actions, while machine learning provides algorithms and methods to capture these learning processes. This article presents an innovative approach to extend agent-based disease models by capturing behavioral aspects of decision-making in a risky context using machine learning techniques. We illustrate it with a case of cholera in Kumasi, Ghana, accounting for spatial and social risk factors that affect intelligent behavior and corresponding disease incidents. The results of computational experiments comparing intelligent with zero-intelligent representations of agents in a spatial disease agent-based model are discussed. Methods: We present a spatial disease agent-based model (ABM) with agents' behavior grounded in Protection Motivation Theory. Spatial and temporal patterns of disease diffusion among zero-intelligent agents are compared to those produced by a population of intelligent agents. Two Bayesian Networks (BNs) designed and coded using R and are further integrated with the NetLogo-based Cholera ABM. The first is a one-tier BN1 (only risk perception), the second is a two-tier BN2 (risk and coping behavior). Results: We run three experiments (zero-intelligent agents, BN1 intelligence and BN2 intelligence) and report the results per experiment in terms of several macro metrics of interest: an epidemic curve, a risk perception curve, and a distribution of different types of coping strategies over time. Conclusions: Our results emphasize the importance of integrating behavioral aspects of decision making under risk into spatial disease ABMs using machine learning algorithms. This is especially relevant when studying cumulative impacts of behavioral changes and possible intervention strategies

Hybrid social force-fuzzy logic evacuation simulation model for multiple exits

One of the most important aspect of evacuation management system, when it comes to organizing a safer large- scale gathering is crowd dynamics. Utilizing evacuation simulation of crowd dynamics during egress, for planning efficient crowd control can minimize crowd disaster to a great extent. Most of the previous studies on evacuation models have been done over a discrete space which have neglected the uncertainty aspect of an agent’s decision making, especially when it comes to panic situations. This study proposes a model for evacuation simulation under uncertainty conditions in a continuous space via computer simulations. It will focus on developing an intelligent simulation model utilizing one of the artificial intelligence techniques which is fuzzy logic. Social Force Model will be taken as the base for basic agent motion. Membership functions such as distance from the exit, familiarity and visibility of the exit, density of crowd around the exit are incorporated in the fuzzy logic system to model the system. From our findings, it can be deduced that factors such as density, distance, and familiarity all considerably affect the time of evacuation of agents from the threat place. Indeed, uncertainty aspect influences agents’ decision making, thus affecting the result of evacuation time

Human social dynamics multi agent system

Current political and economic events are placing an emphasis on energy production and consumption more than ever before. This leads to the necessity for continued research with power distribution systems and factors influencing system operation. The Human Social Dynamics Multi Agent System (HSDMAS) is a project contributing to the study of power distribution networks. By examining power failures as a string of related events while incorporating intelligent learning agents representing human factors, the HSDMAS takes a unique approach towards the understanding and prevention of large scale power failures by coupling a probabilistic model of load-dependent cascading failure, CASCADE, with a dynamic power systems model, OPA. The HSDMAS project focuses on improving and optimizing the performance of the CASCADE and OPA models individually, then develops an interactive multi- layer, multi-agent system modeling power transmission and human factors represented by utility optimization

Analysis of Social Unrest Events using Spatio-Temporal Data Clustering and Agent-Based Modelling

Social unrest such as appeals, protests, conflicts, fights and mass violence can result from a wide ranging of diverse factors making the analysis of causal relationships challenging, with high complexity and uncertainty. Unrest events can result in significant changes in a society ranging from new policies and regulations to regime change. Widespread unrest often arises through a process of feedback and cascading of a collection of past events over time, in regions that are close to each other. Understanding the dynamics of these social events and extrapolating their future growth will enable analysts to detect or forecast major societal events. The study and prediction of social unrest has primarily been done through case-studies and study of social media messaging using various natural language processing techniques. The grouping of related events is often done by subject matter experts that create profiles for countries or locations. We propose two approaches in understanding and modelling social unrest data: (1) spatio-temporal data clustering, and (2) agent-based modelling. We apply the clustering solution to real-world unrest events with socioeconomic and infrastructure factors. We also present a framework of an agent-based model where unrest events act as intelligent agents that continuously study their environment and perform actions. We run simulations of the agent-based model under varying conditions and evaluate the results in comparison to real-world data. Our results show the viability of our proposed solutions. Adviser: Leen-Kiat Soh and Ashok Sama

Human–agent team dynamics: a review and future research opportunities

Humans teaming with intelligent autonomous agents is becoming indispensable in work environments. However, human–agent teams pose significant challenges, as team dynamics are complex arising from the task and social aspects of human–agent interactions. To improve our understanding of human–agent team dynamics, in this article, we conduct a systematic literature review. Drawing on Mathieu et al.’s (2019) teamwork model developed for all-human teams, we map the landscape of research to human–agent team dynamics, including structural features, compositional features, mediating mechanisms, and the interplay of the above features and mechanisms. We reveal that the development of human–agent team dynamics is still nascent, with a particular focus on information sharing, trust development, agents’ human likeness behaviors, shared cognitions, situation awareness, and function allocation. Gaps remain in many areas of team dynamics, such as team processes, adaptability, shared leadership, and team diversity. We offer various interdisciplinary pathways to advance research on human–agent teams

Public debates driven by incomplete scientific data: the cases of evolution theory, global warming and H1N1 pandemic influenza

Public debates driven by incomplete scientific data where nobody can claim absolute certainty, due to current state of scientific knowledge, are studied. The cases of evolution theory, global warming and H1N1 pandemic influenza are investigated. The first two are of controversial impact while the third is more neutral and resolved. To adopt a cautious balanced attitude based on clear but inconclusive data appears to be a lose-out strategy. In contrast overstating arguments with wrong claims which cannot be scientifically refuted appear to be necessary but not sufficient to eventually win a public debate. The underlying key mechanism of these puzzling and unfortunate conclusions are identified using the Galam sequential probabilistic model of opinion dynamics. It reveals that the existence of inflexible agents and their respective proportions are the instrumental parameters to determine the faith of incomplete scientific data public debates. Acting on one's own inflexible proportion modifies the topology of the flow diagram, which in turn can make irrelevant initial supports. On the contrary focusing on open-minded agents may be useless given some topologies. When the evidence is not as strong as claimed, the inflexibles rather than the data are found to drive the opinion of the population. The results shed a new but disturbing light on designing adequate strategies to win a public debate.Comment: 31 pages, 7 figure

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page