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Fundamental and Real-World Challenges in Economics


  • Dirk Helbing
  • Stefano Balietti


In the same way as the Hilbert Program was a response to the foundational crisis of mathematics, this article tries to formulate a research program for the socio-economic sciences. The aim of this contribution is to stimulate research in order to close serious knowledge gaps in mainstream economics that the recent financial and economic crisis has revealed. By identifying weak points of conventional approaches in economics, we identify the scientific problems which need to be addressed. We expect that solving these questions will bring scientists in a position to give better decision support and policy advice. We also indicate, what kinds of insights can be contributed by scientists from other research fields such as physics, biology, computer and social science. In order to make a quick progress and gain a systemic understanding of the whole interconnected socio-economic-environmental system, using the data, information and computer systems available today and in the near future, we suggest a multi-disciplinary collaboration as most promising research approach.

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  • Dirk Helbing & Stefano Balietti, 2010. "Fundamental and Real-World Challenges in Economics," Papers 1012.4446,
  • Handle: RePEc:arx:papers:1012.4446

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    References listed on IDEAS

    1. Tim Leung & Ronnie Sircar, 2009. "Accounting For Risk Aversion, Vesting, Job Termination Risk And Multiple Exercises In Valuation Of Employee Stock Options," Mathematical Finance, Wiley Blackwell, vol. 19(1), pages 99-128.
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    Cited by:

    1. Leonardo dos Santos Pinheiro & Flavio Codeco COelho, 2017. "An Agent-based Model of Contagion in Financial Networks," Papers 1703.07513,
    2. Florian Kuhnlenz & Pedro H. J. Nardelli & Santtu Karhinen & Rauli Svento, 2017. "Implementing Flexible Demand: Real-time Price vs. Market Integration," Papers 1709.02667,, revised Feb 2018.
    3. Sitthiyot, Thitithep, 2015. "Macroeconomic and Financial Management in an Uncertain World: What Can We Learn from Complexity Science?," MPRA Paper 73753, University Library of Munich, Germany, revised 11 Dec 2015.

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