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Modeling Public–Private Partnerships in Disaster Management via Centralized and Decentralized Models

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  • Peiqiu Guan

    (Department of Industrial and System Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260)

  • Jun Zhuang

    (Department of Industrial and System Engineering, University at Buffalo, State University of New York, Buffalo, New York 14260)

Abstract

The objective of this paper is to help both public and private sectors make better decisions in defensive resource allocation through public and private partnerships (PPPs). In this paper, efficient PPPs are studied with regard to disaster preparedness using a decentralized model (sequential game where the public sector is the first mover) and a centralized model. This paper identifies the best public investment policies by evaluating the effectiveness of incentive provisions based on the various private strategic responses. This paper also provides insights into understanding (a) how to construct optimal public and private partnerships and (b) whether, when, and to what extent public and private investments in disaster preparedness could form better PPPs. We study the conditions of the private and public sectors’ allocation strategies when they are strategic complements or substitutes. We find that the private sector that has a higher target valuation or lives in more risky areas invests more and has higher potential to partner with the public sector. We also compare the decentralized model results with the results of the centralized model to study the efficiency of the PPPs and find that the results are similar when the target valuation or the probability of disasters is small.

Suggested Citation

  • Peiqiu Guan & Jun Zhuang, 2015. "Modeling Public–Private Partnerships in Disaster Management via Centralized and Decentralized Models," Decision Analysis, INFORMS, vol. 12(4), pages 173-189, December.
    • Handle: RePEc:inm:ordeca:v:12:y:2015:i:4:p:173-189
    DOI: 10.1287/deca.2015.0319
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    Cited by:

    1. Bei Yan & Feng Mai & Chaojiang Wu & Rui Chen & Xiaolin Li, 2024. "A Computational Framework for Understanding Firm Communication During Disasters," Information Systems Research, INFORMS, vol. 35(2), pages 590-608, June.
    2. Andrew M. Smith & Andrés D. González & Leonardo Dueñas‐Osorio & Raissa M. D'Souza, 2020. "Interdependent Network Recovery Games," Risk Analysis, John Wiley & Sons, vol. 40(1), pages 134-152, January.
    3. Peiqiu Guan & Jing Zhang & Vineet M. Payyappalli & Jun Zhuang, 2018. "Modeling and Validating Public–Private Partnerships in Disaster Management," Decision Analysis, INFORMS, vol. 15(2), pages 55-71, June.
    4. E.R. Akhmetshina & G.T. Guzelbaeva & D.K. Rakhmatullina, 2017. "Special Economic Zone as a Local Area of Public-Private Parthership Implementation," European Research Studies Journal, European Research Studies Journal, vol. 0(2A), pages 346-354.
    5. Elisa F. Long & Gilberto Montibeller & Jun Zhuang, 2022. "Health Decision Analysis: Evolution, Trends, and Emerging Topics," Decision Analysis, INFORMS, vol. 19(4), pages 255-264, December.
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    7. Vicki M. Bier & Yuqun Zhou & Hongru Du, 2020. "Game-theoretic modeling of pre-disaster relocation," The Engineering Economist, Taylor & Francis Journals, vol. 65(2), pages 89-113, April.

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