Crowdsourcing intelligence for improving disaster forecasts

INTRODUCTION.Natural disasters,including floods,storms,and tsunamis,pose a great threat to human societies.A recent study highlighted this concern,revealing that billions of people globally were exposed to flood hazards.1 In 2023,Super Typhoon Doksuri caused devasting floods in Beijing and Hebei areas,resulting in massive casualties and huge economic losses.Therefore,there is a need for a precise understanding of disaster processes,reliable forecasting of disaster effects,and timely warning of risks to prevent and mitigate major disasters.2 Numerical modeling stands as the predominant approach to meet these demands.However,the predictive accuracy of such numerical models could be degraded because of various factors:oversimplification of real processes,computational errors,fluctuations of complex environments(e.g.,terrains,precipitations,buildings,and plants),and the influence of human activities(e.g.,evacuation and rescue)during disasters.

Agent-based modeling of emergency evacuation in a railway station square under sarin terrorist attack

Evacuation modeling is a promising measure to support decision making in scenarios such as flooding,explosion,terrorist attack and other emergency incidents.Given the special attention to the terrorist attack,we build up an agent-based evacuation model in a railway station square under sarin terrorist attack to analyze such incident.Sarin dispersion process is described by Gaussian puff model.Due to sarin’s special properties of being colorless and odorless,we focus more on the modeling of agents’perceiving and reasoning process and use a Belief,Desire,Intention(BDI)architecture to solve the problem.Another contribution of our work is that we put forward a path planning algorithm which not only take distance but also comfort and threat factors into consideration.A series of simulation experiments demonstrate the ability of the proposed model and examine some crucial factors in sarin terrorist attack evacuation.Though far from perfect,the proposed model could serve to support decision making.

Strategy evaluation and optimization with an artificial society toward a Pareto optimum

Strategy evaluation and optimization in response to troubling urban issues has become a challenging issue due to increasing social uncertainty,unreliable predictions,and poor decision-making.To address this problem,we propose a universal computational experiment framework with a fine-grained artificial society that is integrated with data-based models.The purpose of the framework is to evaluate the consequences of various combinations of strategies geared towards reaching a Pareto optimum with regards to efficacy versus costs.As an example,by modeling coronavirus disease 2019 mitigation,we show that Pareto frontier nations could achieve better economic growth and more effective epidemic control through the analysis of real-world data.Our work suggests that a nation’s intervention strategy could be optimized based on the measures adopted by Pareto frontier nations through large-scale computational experiments.Our solution has been validated for epidemic control,and it can be generalized to other urban issues as well.

Toward parallel intelligence: An interdisciplinary solution for complex systems

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling,analysis,management,and control.To meet these demands,the parallel systems method rooted in the artificial systems,computational experiments,and parallel execution(ACP)approach has been developed.The method cultivates a cycle termed parallel intelligence,which iteratively creates data,acquires knowledge,and refines the actual system.Over the past two decades,the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines,offering vers atile interdisciplinary solutions for complex systems across diverse fields.This review explores the origins and fundamental concepts of the parallel systems method,showcasing its accomplishments as a diverse array of parallel technologies and applica-tions while also prognosticating potential challenges.We posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.

Leveraging Human-AI Collaboration in Crowd-Powered Source Search:A Preliminary Study

Source search is an important problem in our society,relating to finding fire sources,gas sources,or signal sources.Particularly,in an unexplored and potentially dangerous environment,an autonomous source search algorithm that employs robotic searchers is usually applied to address the problem.Such environments could be completely unknown and highly complex.Therefore,novel search algorithms have been designed,combining heuristic methods and intelligent optimization,to tackle search problems in large and complex search spaces.However,these intelligent search algorithms were not designed to address completeness and optimality,and therefore commonly suffer from the problems such as local optimums or endless loops.Recent studies have used crowd-powered systems to address the complex problems that cannot be solved by machines on their own.While leveraging human intelligence in an AI system has been shown to be effective in making the system more reliable,whether using the power of the crowd can improve autonomous source search algorithms remains unanswered.To this end,we propose a crowd-powered source search approach enabling human-AI collaboration,which uses human intelligence as external supports to improve existing search algorithms and meanwhile reduces human efforts using AI predictions.Furthermore,we designed a crowd-powered prototype system and carried out an experiment with both experts and non-experts,to complete 200 source search scenarios(704 crowdsourcing tasks).Quantitative and qualitative analysis showed that the sourcing search algorithm enhanced by crowd could achieve both high effectiveness and efficiency.Our work provides valuable insights in human-AI collaborative system design.