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Optimal Administrative Response to Selfish Behaviors in Urban Public Management: The Role of Zero-Determinant Strategies

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  • Ai Zhong Shen
  • Xiang Gao
  • Xiao Ping Wang
  • Shaojian Qu

Abstract

City management involves complex interactions between the manager (administrator), who supervises urban appearance and environmental sanitation, and the managed (speculator), who works in urban areas and is subject to management ordinances. This article provides an iterated game framework for analyzing the extent to which zero-determinant strategies can be used to optimize the intensity decision of supervisory action against municipal code violations, thus enhancing administrative efficiency. To account for characteristics of the public affairs context, it is assumed that each player in our model chooses from a finite set of discrete and random courses of game strategy. As our model constitutes a major extension to the seminal Press and Dyson (2012) model, we resort to the theory of stochastic process to prove the existence of multiple zero-determinant strategies when players can adopt many strategies in the iterated game. Various numerical examples are presented to validate such strategies’ optimality. Our finding is that, given the probability of adopting a particular strategy, an urban administrator can unilaterally (i) set the speculators’ expected payoff to a level equaling to the opportunity cost of abiding by the law and (ii) let their own expected surplus payoff exceed the speculators. Finally, important policy implications can be derived based on these analyses and conclusions.

Suggested Citation

  • Ai Zhong Shen & Xiang Gao & Xiao Ping Wang & Shaojian Qu, 2021. "Optimal Administrative Response to Selfish Behaviors in Urban Public Management: The Role of Zero-Determinant Strategies," Journal of Mathematics, Hindawi, vol. 2021, pages 1-10, October.
  • Handle: RePEc:hin:jjmath:1891679
    DOI: 10.1155/2021/1891679
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    Cited by:

    1. Shen, Aizhong & Gao, Zili & Cui, Dan & Gu, Chen, 2024. "Extortion evolutionary game on scale-free networks with tunable clustering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).

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