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Use of multi-level self-regulating agents to evaluate the impact of innovation policy for the agro-food sector in the Region of Puglia, Italy

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  • Dimitri Gagliardi
  • Francesco Niglia
  • Cinzia Battistella
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    Abstract

    This work aims at exploring the possibilities offered by agent-base modelling techniques in evaluating the outreaching effects of innovation policy measures. This aim is accomplished by modelling and simulating the organisations’ and systems’ reactions to the introduction of alternative set innovation policy strategies in the Agro-food sector of the Puglia region (Italy). The policy measures selected for this exercise are inspired by the innovation policy documents of the Regional Government in particular the Programme for Rural Development (2007 – 2013) and regard the promotion of organic agriculture and OGM-free cultivar; the introduction of a zero-food-miles strategy and new regulations and controls to prevent food adulterations. In order to validate and showcase our agent-based model as a policy impact assessment tool, the paper presents the results of the simulations of three scenarios of intervention. This research intends to provide researchers and policy makers with an additional tool to inform the decision-making process especially in the phase of setting policy objectives and designing new policy measures and strategies.

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    Bibliographic Info

    Paper provided by Public policies and local development in its series Openloc Working Papers with number 1205.

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    Date of creation: 2012
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    Handle: RePEc:trn:utwpol:1205

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    Related research

    Keywords: complex systems dynamics; agent-based modeling; innovation policy; agro-food sector; BDI; JADEX;

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    1. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth Through Creative Destruction," Scholarly Articles 12490578, Harvard University Department of Economics.
    2. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-51, March.
    3. S. Illeris & G. Akehurst, 2002. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 22(1), pages 1-3, January.
    4. Giuseppe Pennisi & Pasquale L. Scandizzo, 2006. "Economic Evaluation in the age of Uncertainty," CEIS Research Paper 86, Tor Vergata University, CEIS.
    5. Simona Cantono & Gerald Silverberg, 2008. "A percolation model of eco-innovation diffusion: the relationship between diffusion, learning economies and subsidies," MERIT Working Papers 025, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    6. Berger, Thomas, 2001. "Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis," Agricultural Economics, Blackwell, vol. 25(2-3), pages 245-260, September.
    7. Etzkowitz, Henry & Leydesdorff, Loet, 2000. "The dynamics of innovation: from National Systems and "Mode 2" to a Triple Helix of university-industry-government relations," Research Policy, Elsevier, vol. 29(2), pages 109-123, February.
    8. Giorgio Fagiolo & Alessio Moneta & Paul Windrum, 2007. "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems," Computational Economics, Society for Computational Economics, vol. 30(3), pages 195-226, October.
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