IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/kqfds_v1.html
   My bibliography  Save this paper

Effects of uniform-allocation constraints in networked common-pool resource extraction games

Author

Listed:
  • Schauf, Andrew
  • Oh, Poong

Abstract

Communities that share common-pool resources (CPRs) often coordinate their actions to sustain resource quality more effectively than if they were regulated by some centralized authority. Networked models of CPR extraction suggest that the flexibility of individual agents to selectively allocate extraction effort among multiple resources plays an important role in maximizing their payoffs. However, empirical evidence suggests that real-world CPR appropriators may often de-emphasize issues of allocation, for example by responding to the degradation of a single resource by reducing extraction from multiple resources, rather than by reallocating extraction effort away from the degraded resource. Here, we study the population-level consequences that emerge when individuals are constrained to apply an equal amount of extraction effort to all CPRs that are available to them within an affiliation network linking agents to resources. In systems where all resources have the same capacity, this uniform-allocation constraint leads to reduced collective wealth compared to unconstrained best-response extraction, but it can produce more egalitarian wealth distributions. The differences are more pronounced in networks that have higher degree heterogeneity among resources. In the case that the capacity of each CPR is proportional to its number of appropriators, the uniform-allocation constraint can lead to more efficient collective extraction since it serves to distribute the burden of over-extraction more evenly among the network's CPRs. Our results reinforce the importance of adaptive allocation in self-regulation for populations who share linearly degrading CPRs; although uniform-allocation extraction habits can help to sustain higher resource quality than does unconstrained extraction, in general this does not improve collective benefits for a population in the long term.

Suggested Citation

  • Schauf, Andrew & Oh, Poong, 2022. "Effects of uniform-allocation constraints in networked common-pool resource extraction games," SocArXiv kqfds_v1, Center for Open Science.
  • Handle: RePEc:osf:socarx:kqfds_v1
    DOI: 10.31219/osf.io/kqfds_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/62b13d4b97edfb1be31a3e31/
    Download Restriction: no

    File URL: https://libkey.io/10.31219/osf.io/kqfds_v1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Andreas C Drichoutis & Rodolfo M Nayga, 2020. "Economic Rationality under Cognitive Load," The Economic Journal, Royal Economic Society, vol. 130(632), pages 2382-2409.
    2. Marten Scheffer & Steve Carpenter & Jonathan A. Foley & Carl Folke & Brian Walker, 2001. "Catastrophic shifts in ecosystems," Nature, Nature, vol. 413(6856), pages 591-596, October.
    3. Fan, Ruguo & Zhang, Yingqing & Luo, Ming & Zhang, Hongjuan, 2017. "Promotion of cooperation induced by heterogeneity of both investment and payoff allocation in spatial public goods game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 454-463.
    4. Prediger, Sebastian & Vollan, Björn & Frölich, Markus, 2011. "The impact of culture and ecology on cooperation in a common-pool resource experiment," Ecological Economics, Elsevier, vol. 70(9), pages 1599-1608, July.
    5. Francisco C. Santos & Marta D. Santos & Jorge M. Pacheco, 2008. "Social diversity promotes the emergence of cooperation in public goods games," Nature, Nature, vol. 454(7201), pages 213-216, July.
    6. Christoph Hauert & Michael Doebeli, 2004. "Spatial structure often inhibits the evolution of cooperation in the snowdrift game," Nature, Nature, vol. 428(6983), pages 643-646, April.
    7. Lei, Chuang & Wu, Te & Jia, Jian-Yuan & Cong, Rui & Wang, Long, 2010. "Heterogeneity of allocation promotes cooperation in public goods games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4708-4714.
    8. Wang, Qun & Wang, Hanchen & Zhang, Zhuxi & Li, Yumeng & Liu, Yu & Perc, Matjaž, 2018. "Heterogeneous investments promote cooperation in evolutionary public goods games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 570-575.
    9. Li, Jing & Wu, Te & Zeng, Gang & Wang, Long, 2012. "Selective investment promotes cooperation in public goods game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 3924-3929.
    10. Schauf, Andrew & Oh, Poong, 2021. "Myopic reallocation of extraction improves collective outcomes in networked common-pool resource games," SocArXiv w2cxp, Center for Open Science.
    11. Zhang, Haifeng & Shi, Dongmei & Liu, Runran & Wang, Binghong, 2012. "Dynamic allocation of investments promotes cooperation in spatial public goods game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2617-2622.
    12. Deck, Cary & Jahedi, Salar, 2015. "The effect of cognitive load on economic decision making: A survey and new experiments," European Economic Review, Elsevier, vol. 78(C), pages 97-119.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Schauf, Andrew & Oh, Poong, 2021. "Adaptation strategies and collective dynamics of extraction in networked commons of bistable resources," SocArXiv wmtqk, Center for Open Science.
    2. Schauf, Andrew & Oh, Poong, 2021. "Adaptation strategies and collective dynamics of extraction in networked commons of bistable resources," SocArXiv wmtqk_v1, Center for Open Science.
    3. Chuanyun Li & Xia Cao & Ming Chi, 2020. "Research on an evolutionary game model and simulation of a cluster innovation network based on fairness preference," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-17, January.
    4. Fan, Ruguo & Zhang, Yingqing & Luo, Ming & Zhang, Hongjuan, 2017. "Promotion of cooperation induced by heterogeneity of both investment and payoff allocation in spatial public goods game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 454-463.
    5. Wang, Hanchen & Sun, Yichun & Zheng, Lei & Du, Wenbo & Li, Yumeng, 2018. "The public goods game on scale-free networks with heterogeneous investment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 396-404.
    6. Wang, Qun & Wang, Hanchen & Zhang, Zhuxi & Li, Yumeng & Liu, Yu & Perc, Matjaž, 2018. "Heterogeneous investments promote cooperation in evolutionary public goods games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 570-575.
    7. Huang, Yongchao & Wan, Siyi & Zheng, Junjun & Liu, Wenyi, 2023. "Evolution of cooperation in spatial public goods game with interactive diversity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 621(C).
    8. Jorge Peña & Yannick Rochat, 2012. "Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    9. Lv, Shaojie & Wang, Xianjia, 2020. "The impact of heterogeneous investments on the evolution of cooperation in public goods game with exclusion," Applied Mathematics and Computation, Elsevier, vol. 372(C).
    10. Wang, Mie & Kang, HongWei & Shen, Yong & Sun, XingPing & Chen, QingYi, 2021. "The role of alliance cooperation in spatial public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    11. Chen, Qiao & Chen, Tong & Wang, Yongjie, 2017. "Publishing the donation list incompletely promotes the emergence of cooperation in public goods game," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 48-56.
    12. Chen, Qiao & Chen, Tong & Wang, Yongjie, 2019. "Cleverly handling the donation information can promote cooperation in public goods game," Applied Mathematics and Computation, Elsevier, vol. 346(C), pages 363-373.
    13. Li, Jing & Wang, Jiang, 2018. "Locality based wealth rule favors cooperation in costly public goods games," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 1-7.
    14. Ma, Xiaojian & Quan, Ji & Wang, Xianjia, 2021. "Effect of reputation-based heterogeneous investment on cooperation in spatial public goods game," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    15. Yang, Han-Xin & Yang, Jing, 2019. "Reputation-based investment strategy promotes cooperation in public goods games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 886-893.
    16. Wang, Chaoqian & Szolnoki, Attila, 2022. "Involution game with spatio-temporal heterogeneity of social resources," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    17. Lu, Peng, 2015. "Imitating winner or sympathizing loser? Quadratic effects on cooperative behavior in prisoners’ dilemma games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 327-337.
    18. Szolnoki, Attila & Chen, Xiaojie, 2020. "Blocking defector invasion by focusing on the most successful partner," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    19. Zhang, Lan & Xie, Yuan & Huang, Changwei & Li, Haihong & Dai, Qionglin, 2020. "Heterogeneous investments induced by historical payoffs promote cooperation in spatial public goods games," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    20. Zou, Kuan & Han, Wenchen & Zhang, Lan & Huang, Changwei, 2024. "The spatial public goods game on hypergraphs with heterogeneous investment," Applied Mathematics and Computation, Elsevier, vol. 466(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:osf:socarx:kqfds_v1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: OSF (email available below). General contact details of provider: https://arabixiv.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.