IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2502.12309.html
   My bibliography  Save this paper

Eigenvalues in microeconomics

Author

Listed:
  • Benjamin Golub

Abstract

Square matrices often arise in microeconomics, particularly in network models addressing applications from opinion dynamics to platform regulation. Spectral theory provides powerful tools for analyzing their properties. We present an accessible overview of several fundamental applications of spectral methods in microeconomics, focusing especially on the Perron-Frobenius Theorem's role and its connection to centrality measures. Applications include social learning, network games, public goods provision, and market intervention under uncertainty. The exposition assumes minimal social science background, using spectral theory as a unifying mathematical thread to introduce interested readers to some exciting current topics in microeconomic theory.

Suggested Citation

  • Benjamin Golub, 2025. "Eigenvalues in microeconomics," Papers 2502.12309, arXiv.org.
  • Handle: RePEc:arx:papers:2502.12309
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2502.12309
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sara B. Heller & Benjamin Jakubowski & Zubin Jelveh & Max Kapustin, 2022. "Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It," NBER Working Papers 30170, National Bureau of Economic Research, Inc.
    2. Michael D. König & Xiaodong Liu & Yves Zenou, 2019. "R&D Networks: Theory, Empirics, and Policy Implications," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 476-491, July.
    3. Matthew Elliott & Benjamin Golub, 2019. "A Network Approach to Public Goods," Journal of Political Economy, University of Chicago Press, vol. 127(2), pages 730-776.
    4. Simone Cerreia-Vioglio & Roberto Corrao & Giacomo Lanzani, 2024. "Dynamic Opinion Aggregation: Long-Run Stability and Disagreement," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(3), pages 1406-1447.
    5. Antoni Calvó-Armengol & Eleonora Patacchini & Yves Zenou, 2009. "Peer Effects and Social Networks in Education," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(4), pages 1239-1267.
    6. Andrea Galeotti & Benjamin Golub & Sanjeev Goyal, 2020. "Targeting Interventions in Networks," Econometrica, Econometric Society, vol. 88(6), pages 2445-2471, November.
    7. Jeong, Daeyoung & Shin, Euncheol, 2024. "Optimal influence design in networks," Journal of Economic Theory, Elsevier, vol. 220(C).
    8. Bindel, David & Kleinberg, Jon & Oren, Sigal, 2015. "How bad is forming your own opinion?," Games and Economic Behavior, Elsevier, vol. 92(C), pages 248-265.
    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. Thomas J. Sargent & John Stachurski, 2022. "Economic Networks: Theory and Computation," Papers 2203.11972, arXiv.org, revised Jul 2022.
    2. Belhaj, Mohamed & Deroïan, Frédéric, 2018. "Targeting the key player: An incentive-based approach," Journal of Mathematical Economics, Elsevier, vol. 79(C), pages 57-64.
    3. Yang Sun & Wei Zhao & Junjie Zhou, 2021. "Structural Interventions in Networks," Papers 2101.12420, arXiv.org, revised Feb 2021.
    4. Ryan Kor & Junjie Zhou, 2021. "Multi-activity Influence and Intervention," Papers 2106.09410, arXiv.org, revised Nov 2022.
    5. Kor, Ryan & Zhou, Junjie, 2023. "Multi-activity influence and intervention," Games and Economic Behavior, Elsevier, vol. 137(C), pages 91-115.
    6. Yang Sun & Wei Zhao & Junjie Zhou, 2023. "Structural Interventions In Networks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(4), pages 1533-1563, November.
    7. Mori, Tomoya & Sakaguchi, Shosei, 2018. "Collaborative knowledge creation: Evidence from Japanese patent data," MPRA Paper 88716, University Library of Munich, Germany.
    8. Chen, Ying-Ju & Zenou, Yves & Zhou, Junjie, 2022. "The impact of network topology and market structure on pricing," Journal of Economic Theory, Elsevier, vol. 204(C).
    9. Jackson, Matthew O. & Zenou, Yves, 2015. "Games on Networks," Handbook of Game Theory with Economic Applications,, Elsevier.
    10. Hahn, Youjin & Islam, Asadul & Patacchini, Eleonora & Zenou, Yves, 2015. "Network Structure and Education Outcomes: Evidence from a Field Experiment in Bangladesh," IZA Discussion Papers 8872, Institute of Labor Economics (IZA).
    11. Cohen-Cole, Ethan & Patacchini, Eleonora & Zenou, Yves, 2015. "Static and dynamic networks in interbank markets," Network Science, Cambridge University Press, vol. 3(1), pages 98-123, March.
    12. Kim, Jun Sung & Patacchini, Eleonora & Picard, Pierre M. & Zenou, Yves, 2017. "Urban Interactions," Working Paper Series 1192, Research Institute of Industrial Economics.
    13. Matthew O. Jackson & Brian W. Rogers & Yves Zenou, 2017. "The Economic Consequences of Social-Network Structure," Journal of Economic Literature, American Economic Association, vol. 55(1), pages 49-95, March.
    14. Allouch, Nizar & King, Maia, 2021. "Welfare targeting in networks," Journal of Mathematical Economics, Elsevier, vol. 96(C).
    15. Ushchev, Philip & Zenou, Yves, 2018. "Price competition in product variety networks," Games and Economic Behavior, Elsevier, vol. 110(C), pages 226-247.
    16. Topa, Giorgio & Zenou, Yves, 2015. "Neighborhood and Network Effects," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 561-624, Elsevier.
    17. Mohanty, Sambit & Rao, K.S. Mallikarjuna & Roy, Jaideep, 2024. "Kantian imperatives in public goods networks," Journal of Economic Behavior & Organization, Elsevier, vol. 224(C), pages 194-214.
    18. Jun Sung Kim & Eleonora Patacchini & Pierre M. Picard & Yves Zenou, 2023. "Spatial interactions," Quantitative Economics, Econometric Society, vol. 14(4), pages 1295-1335, November.
    19. Sun, Yang & Zhao, Wei, 2024. "Relative performance evaluation in spillover networks," Games and Economic Behavior, Elsevier, vol. 145(C), pages 285-311.
    20. Belhaj, Mohamed & Deroïan, Frédéric, 2019. "Group targeting under networked synergies," Games and Economic Behavior, Elsevier, vol. 118(C), pages 29-46.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2502.12309. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.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.