Exploring communication media options in an inter-organizational disaster response coordination network using agent-based simulation

Abstract Large-scale disasters require rapid, well-coordinated responses from many organizations. This coordination involves extensive communication about specific needs, resource availability, optimal sizes and packaging of goods, and location and timing of deliveries. Faster, more accurate communication within the disaster response network translates to more effective response at the disaster site. This article presents a modeling project that assessed the effects of distinct communication media on response times within a centralized network. In the United States, the Federal Emergency Management Agency (FEMA) provides support and coordination among numerous responding agencies. An initial agent-based simulation model incorporated data from participant interviews and FEMA documentation about communication processes among response organization representatives. This model tracked effects of meetings, meeting scheduling, and bulletins on time required to complete the processing of requests. Following a second round of interviews and detailed observations during a national disaster exercise, we built a second agent-based simulation model that varied usage of email, phone, and face-to-face communications alongside a centralized information system and tracked the processing of anticipated, standard, and non-standard resource requests. Results show significant effects of different combinations of communication media on response times. Recommendations based on the study include: resist the temptation to schedule more than one meeting per day; use bulletins to disseminate information updates; supplement the centralized information system with direct interpersonal communication media such as email; and for unusual or difficult requests enable representatives to have in-person conversations or the closest alternative such as video teleconferencing with screen-sharing.

[1]  Brian Quinn,et al.  Realtime emergency communication in virtual worlds , 2016 .

[2]  Gitte Lindgaard,et al.  Social Network Analysis and Communication in Emergency Response Simulations , 2014, J. Organ. Comput. Electron. Commer..

[3]  Robert V. Tuohy,et al.  Lessons We Don't Learn: A Study of the Lessons of Disasters, Why We Repeat Them, and How We Can Learn Them , 2006 .

[4]  Kevin Crowston,et al.  The interdisciplinary study of coordination , 1994, CSUR.

[5]  Larry J. Shuman,et al.  Modeling emergency medical response to a mass casualty incident using agent based simulation , 2012 .

[6]  Steve Wheeler,et al.  The influence of communication technologies and approaches to study on transactional distance in blended learning , 2007 .

[7]  Alan R. Dennis,et al.  Testing Media Richness Theory in the New Media: The Effects of Cues, Feedback, and Task Equivocality , 1998, Inf. Syst. Res..

[8]  Jack P. C. Kleijnen,et al.  EUROPEAN JOURNAL OF OPERATIONAL , 1992 .

[9]  Mohamed Ben Ahmed,et al.  Assessing large scale emergency rescue plans: an agent based Approach , 2006 .

[10]  Graham Coates,et al.  Agent-based simulation for large-scale emergency response: A survey of usage and implementation , 2012, CSUR.

[11]  John Fry,et al.  Elementary modelling and behavioural analysis for emergency evacuations using social media , 2016, Eur. J. Oper. Res..

[12]  Oguz Dikenelli,et al.  A generic testing framework for agent-based simulation models , 2011, 2011 Federated Conference on Computer Science and Information Systems (FedCSIS).

[13]  R. Daft,et al.  The Selection of Communication Media as an Executive Skill , 1988 .

[14]  Xiangbin Yan,et al.  Influencing Factors of Emergency Information Spreading in Online Social Networks: A Simulation Approach , 2012 .

[15]  B. Baltes,et al.  Computer-Mediated Communication and Group Decision Making: A Meta-Analysis , 2002 .

[16]  Naim Kapucu,et al.  A longitudinal study of evolving networks in response to natural disaster , 2016, Comput. Math. Organ. Theory.

[17]  Susan G. Straus,et al.  Does the medium matter? The interaction of task type and technology on group performance and member reactions. , 1994, The Journal of applied psychology.

[18]  Shinya Hanaoka,et al.  Production , Manufacturing and Logistics Relief inventory modelling with stochastic lead-time and demand , 2014 .

[19]  L. Comfort,et al.  Coordination in Rapidly Evolving Disaster Response Systems , 2004 .

[20]  Naim Kapucu,et al.  Examining Intergovernmental and Interorganizational Response to Catastrophic Disasters , 2010 .

[21]  Frank Fiedrich,et al.  Agent-based systems for disaster management , 2007, Commun. ACM.

[22]  Richard L. Daft,et al.  Organizational information requirements, media richness and structural design , 1986 .

[23]  Caroline C. Krejci,et al.  Coordination in humanitarian relief chains: Practices, challenges and opportunities , 2010 .

[24]  Steven Cohen,et al.  Catastrophe and the Public Service: A Case Study of the Government Response to the Destruction of the World Trade Center , 2002 .

[25]  T. Rebmann,et al.  Lessons public health professionals learned from past disasters. , 2008, Public health nursing.

[26]  Rajan Batta,et al.  Review of recent developments in OR/MS research in disaster operations management , 2013, Eur. J. Oper. Res..

[27]  Charles M. Macal,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[28]  Kevin Crowston,et al.  A Coordination Theory Approach to Organizational Process Design , 1997 .