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Hamed Khalili

PH.D. Student at the chair of Economic and Agricultural Policy

Phone: +49-228-73 2326
E-Mail:

Hello everyone

My name is Hamed Khalili.It is great meeting you here in my webpage. My role within ILR is defined principally within the research project Understanding spatial interactions and structural changes in the dairy production chain . I am born in Iran. I come from a Technical Management studying background in Germany with a B.Sc. degree from Technical University of Clausthal (TUC) and a M.Sc. degree from TUC (In the branch of study "Modeling and Simulation"). My research deals with Learning Models in Economics regardingly implementing of these models in simulation frameworks like Agent Based Models. I believe in continuous learning and enjoy working on my research interests. If you would like to have collaboration on my field of research or you have specific questions in that regard, please contact me via E-mail.

Research Interests

  • Modelling of Multi Agent Systems, Agent Based Simulation.

  • Game theory, Economic Analysis of market Interactions and Theory of the firm.

    Research papers related to the project

  • Outside option and cooperative behavior of learning agents in spatial markets, (Khalili; Heckelei, 2016), presented in EAAE conference, Gaeta, Italy, Draft paper.
  • Abstract: While the magnitude of collusive spatial pricing in raw milk procurement markets is often investigated, Nash equilibria in competitive models doesn’t exist due to discontinuous nature of players’ best response function. We propose a coordinative Nash equilibrium might arise in a broad range of market structures if we understand agents as based on foresight Meta reasoning entities.

  • Rational and convergent Learning in Multi agent spatial market, (Khalili; Heckelei, 2017), Draft paper.
  • Abstract: The question of which pricing forms emerge as equilibrium in spatial markets has not been studied much in the literature. We investigate the issue of two-dimensional spatial price formation between farmers and processors introducing a new Learning Algorithm for rich strategic space.

  • A predictive model of pricing by learning agents in spatial agricultural market, Draft paper.
  • Abstract: We investigate a computational based analysis of competition in a duopsony market with learning milk processors in two-dimensional space. Dynamic programming agents in our model learn to use a memory-based decision procedure based on stored data.