MAS-Based Interaction Simulation within Asymmetric Information on Emergency Management of Urban Rainstorm Disaster

The frequent occurrence of urban waterlogging constantly affects resident living and urban construction. Improved adaptive prevention and control strategies are highly requested due to huge economic losses and casualties caused by flood and waterlogging in China. The urban waterlogging may evolve into a serious emergency, generally characterized by high complexity, uncertainty, and time pressure. Coupled with the asymmetric information, waterlogging often exacerbates the impact of urban rainstorm disasters. Through the multi-agent system simulation with given geographic information, government and residents interact under dynamic risk distribution in rainstorm disaster. The results show that the proactive attitude of residents and the government towards disaster relief could have a promoting effect for both, thereby increasing the disaster relief efficiency. Obviously, rapid accurate information collection and analysis facilitate disaster relief to a large extent. Meanwhile, appropriate supply rather than excessive supply may mobilize residents’ self-help and balance replenishment of relief supplies.

[1]  Mehdi Hashemipour,et al.  A disaster multiagent coordination simulation system to evaluate the design of a first‐response team , 2018, Syst. Eng..

[2]  Nezih Altay,et al.  OR/MS research in disaster operations management , 2006, Eur. J. Oper. Res..

[3]  G. Nel,et al.  The link between Internet investor relations and information asymmetry , 2018 .

[4]  Daisuke Satoh,et al.  Reduction of communication demand under disaster congestion using control to change human communication behavior without direct restriction , 2018, Comput. Networks.

[5]  Yong Shi,et al.  Risk analysis of rainstorm waterlogging on residences in Shanghai based on scenario simulation , 2012, Natural Hazards.

[6]  Shaligram Pokharel,et al.  Optimization models in emergency logistics: A literature review , 2012 .

[7]  Miao Wang,et al.  An agent-based model for risk-based flood incident management , 2011 .

[8]  Mohammad Marufuzzaman,et al.  Drones for disaster response and relief operations: A continuous approximation model , 2017 .

[9]  M. P. Scaparra,et al.  Protecting Supply Systems to Mitigate Potential Disaster , 2012 .

[10]  I. García‐Sánchez,et al.  The Causal Link between Sustainable Disclosure and Information Asymmetry: The Moderating Role of the Stakeholder Protection Context , 2016 .

[11]  A. Colman,et al.  The complexity of cooperation: Agent-based models of competition and collaboration , 1998, Complex..

[12]  Akira Namatame,et al.  Contagion of Evacuation Decision Making on Real Map , 2016, Mob. Networks Appl..

[13]  Kincho H. Law,et al.  A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations , 2007, AI & SOCIETY.

[14]  Sharad Sharma,et al.  Modeling human behavior during emergency evacuation using intelligent agents: A multi-agent simulation approach , 2017, Information Systems Frontiers.

[15]  Naim Kapucu,et al.  Information Communication Technology Utilization for Effective Emergency Management Networks , 2016 .

[16]  Charles E. Fritz,et al.  The NORC Studies of Human Behavior in Disaster , 1954 .

[17]  M. Skidmore,et al.  The Effects of Natural Disasters on Social Trust: Evidence from South Korea , 2018, Sustainability.

[18]  J. W. Delleur,et al.  Prediction of Suspended Solids in Urban Sewers by Transfer Function Model , 1994 .

[19]  Wenji Mao,et al.  Social Computing: From Social Informatics to Social Intelligence , 2007, IEEE Intell. Syst..

[20]  Dan Amiram,et al.  Do Information Releases Increase or Decrease Information Asymmetry? New Evidence from Analyst Forecast Announcements , 2016 .

[21]  Beverley Raphael,et al.  Crowds and Other Collectives: Complexities of Human Behaviors in Mass Emergencies , 2005, Psychiatry.

[22]  Shuwen Zhang,et al.  Using auction-based task allocation scheme for simulation optimization of search and rescue in disaster relief , 2018, Simul. Model. Pract. Theory.

[23]  Uta Berger,et al.  Pattern-Oriented Modeling of Agent-Based Complex Systems: Lessons from Ecology , 2005, Science.

[24]  Q. Yang,et al.  MAS-Based Evacuation Simulation of an Urban Community during an Urban Rainstorm Disaster in China , 2020, Sustainability.

[25]  Colin Camerer,et al.  When Does "Economic Man" Dominate Social Behavior? , 2006, Science.

[26]  Li Li,et al.  Steps toward Parallel Intelligence , 2016 .

[27]  Y. Zhang,et al.  Control and remediation methods for eutrophic lakes in the past 30 years. , 2020, Water science and technology : a journal of the International Association on Water Pollution Research.

[28]  Chuanhao Wu,et al.  Application of the stormwater management model to a piedmont city: a case study of Jinan City, China. , 2014, Water science and technology : a journal of the International Association on Water Pollution Research.

[29]  A. Apte,et al.  Stochastic Optimization for Natural Disaster Asset Prepositioning , 2010 .

[30]  Xiaoyu Chen,et al.  A fuzzy comprehensive evaluation model for flood risk based on the combination weight of game theory , 2015, Natural Hazards.

[31]  Boni Su,et al.  Integrated simulation method for waterlogging and traffic congestion under urban rainstorms , 2016, Natural Hazards.

[32]  Jessica L. Heier Stamm,et al.  Improving Humanitarian Operations through Technology‐Enabled Collaboration , 2014 .

[33]  Ali Asghar Alesheikh,et al.  Agent-based task allocation under uncertainties in disaster environments: An approach to interval uncertainty , 2017 .

[34]  Amiya K. Chakravarty,et al.  A contingent plan for disaster response , 2011 .

[35]  Jun Zhao,et al.  Supply contracting and process innovation in a dynamic supply chain with information asymmetry , 2021, Eur. J. Oper. Res..

[36]  Yoshito Takasaki,et al.  Natural Disaster, Poverty, and Development: An Introduction , 2017 .

[37]  Anthony M. Cresswell,et al.  Advances in multi-agency disaster management: Key elements in disaster research , 2010, Inf. Syst. Frontiers.

[38]  Ilan Noy.  The macroeconomic consequences of disasters , 2009 .

[39]  Birgit Müller,et al.  A standard protocol for describing individual-based and agent-based models , 2006 .

[40]  Guijun Li,et al.  A Real Options‐Based Decision‐Making Model for Infrastructure Investment to Prevent Rainstorm Disasters , 2019, Production and Operations Management.

[41]  Alison J. Heppenstall,et al.  Crime reduction through simulation: An agent-based model of burglary , 2010, Comput. Environ. Urban Syst..

[42]  C. Chatterjee,et al.  Modeling urban floods and drainage using SWMM and MIKE URBAN: a case study , 2016, Natural Hazards.

[43]  Masoud Mahootchi,et al.  Developing a new stochastic model considering bi-directional relations in a natural disaster: a possible earthquake in Tehran (the Capital of Islamic Republic of Iran) , 2018, Ann. Oper. Res..

[44]  Dapeng Yu,et al.  An evaluation of the impacts of land surface modification, storm sewer development, and rainfall variation on waterlogging risk in Shanghai , 2012, Natural Hazards.

[45]  L. Pearce.  Disaster Management and Community Planning, and Public Participation: How to Achieve Sustainable Hazard Mitigation , 2003 .

[46]  Yang Liu,et al.  A relief supplies purchasing model via option contracts , 2019, Comput. Ind. Eng..

[47]  Ruisong Quan,et al.  Rainstorm waterlogging risk assessment in central urban area of Shanghai based on multiple scenario simulation , 2014, Natural Hazards.

[48]  Samarth Swarup,et al.  A comparison of multiple behavior models in a simulation of the aftermath of an improvised nuclear detonation , 2016, Autonomous Agents and Multi-Agent Systems.

[49]  Hyun-Soo Lee,et al.  Distributed and interoperable simulation for comprehensive disaster response management in facilities , 2018, Automation in Construction.

[50]  Patrick Hirsch,et al.  Agent-based simulation optimization for dynamic disaster relief distribution , 2018, Central Eur. J. Oper. Res..

[51]  Veronica Strandh,et al.  Emergent Groups in Disaster Research: Varieties of Scientific Observation Over Time and Across Studies of Nine Natural Disasters , 2018 .

[52]  Michael J. North,et al.  Experiences creating three implementations of the repast agent modeling toolkit , 2006, TOMC.

[53]  Bruce K. Sawhill,et al.  Market feedback replaces regulation: adaptation in the electric power industry , 1998 .

[54]  J. Buckland,et al.  Community-based disaster management during the 1997 Red River Flood in Canada. , 1999, Disasters.

[55]  Michael J. North,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[56]  S. Perkins-Kirkpatrick,et al.  Disaster declarations associated with bushfires, floods and storms in New South Wales, Australia between 2004 and 2014 , 2016, Scientific reports.

[57]  Xu Sun,et al.  Multi-Agent Simulation of Individuals’ Escape in the Urban Rainstorm Context Based on Dynamic Recognition-Primed Decision Model , 2020 .

[58]  Youpeng Xu,et al.  Urban Rainwater Utilization and its Role in Mitigating Urban Waterlogging Problems—A Case Study in Nanjing, China , 2012, Water Resources Management.

[59]  Kaoru Takara,et al.  Historical assessment and future sustainability challenges of Egyptian water resources management , 2020 .

[60]  Xin He,et al.  Application of the hidden Markov model in a dynamic risk assessment of rainstorms in Dalian, China , 2018, Stochastic Environmental Research and Risk Assessment.

[61]  Johan Rockström,et al.  Reducing hazard vulnerability: towards a common approach between disaster risk reduction and climate adaptation. , 2006, Disasters.

[62]  Grigory Belyavsky,et al.  A Markovian Mechanism of Proportional Resource Allocation in the Incentive Model as a Dynamic Stochastic Inverse Stackelberg Game , 2018, Mathematics.

[63]  Jian Tang,et al.  Using a combinatorial auction-based approach for simulation of cooperative rescue operations in disaster relief , 2018, Int. J. Model. Simul. Sci. Comput..

[64]  Shanzhong Qi,et al.  Questionable frequent occurrence of urban flood hazards in modern cities of China , 2012, Natural Hazards.

[65]  Common-value group contests with asymmetric information , 2020, Economics Letters.

[66]  R. Burby.  Hurricane Katrina and the Paradoxes of Government Disaster Policy: Bringing About Wise Governmental Decisions for Hazardous Areas , 2006 .