IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1408.1365.html
   My bibliography  Save this paper

Kinetic Exchange Models in Economics and Sociology

Author

Listed:
  • Sanchari Goswami
  • Anirban Chakraborti

Abstract

In this article, we briefly review the different aspects and applications of kinetic exchange models in economics and sociology. Our main aim is to show in what manner the kinetic exchange models for closed economic systems were inspired by the kinetic theory of gas molecules. The simple yet powerful framework of kinetic theory, first proposed in 1738, led to the successful development of statistical physics of gases towards the end of the 19th century. This framework was successfully adapted to modeling of wealth distributions in the early 2000's. In later times, it was applied to other areas like firm dynamics and opinion formation in the society, as well. We have tried to present the flavour of the several models proposed and their applications, intentionally leaving out the intricate mathematical and technical details.

Suggested Citation

  • Sanchari Goswami & Anirban Chakraborti, 2014. "Kinetic Exchange Models in Economics and Sociology," Papers 1408.1365, arXiv.org.
    • Handle: RePEc:arx:papers:1408.1365
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1408.1365
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. repec:cup:cbooks:9781107013445 is not listed on IDEAS
    2. Frédéric Abergel & Anirban Chakraborti & Hideaki Aoyama & B.K. Chakrabarti & Asim Gosh, 2014. "Econophysics of agent-based models," Post-Print hal-01006419, HAL.
    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. Khaleque, Abdul & Sen, Parongama, 2014. "Damage spreading transition in an opinion dynamics model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 599-608.
    2. Igor Halperin & Ilya Feldshteyn, 2018. "Market Self-Learning of Signals, Impact and Optimal Trading: Invisible Hand Inference with Free Energy," Papers 1805.06126, arXiv.org.
    3. Kittiwat Tangmongkollert & Sujin Suwanna, 2016. "Modeling of price and profit in coupled-ring networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 89(6), pages 1-9, June.

    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:1408.1365. 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.