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Towards Good Social Science

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Abstract

The paper investigates what is meant by "good science" and "bad science" and how these differ as between the natural (physical and biological) sciences on the one hand and social sciences on the other. We conclude on the basis of historical evidence that the natural science are much more heavily constrained by evidence and observation than by theory while the social sciences are constrained by prior theory and hardly at all by direct evidence. Current examples of the latter proposition are taken from recent issues of leading social science journals. We argue that agent based social simulations can be used as a tool to constrain the development of a new social science by direct (what economists dismiss as anecdotal) evidence and that to do so would make social science relevant to the understanding and influencing of social processes. We argue that such a development is both possible and desirable. We do not argue that it is likely.

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  • Scott Moss & Bruce Edmonds, 2005. "Towards Good Social Science," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-13.
    • Handle: RePEc:jas:jasssj:2005-63-1
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    Cited by:

    1. Breukers, Yorick & de Reuver, Mark & Oey, Michel & Bouwman, Harry, 2015. "Mobile data offloading: an agent-based modelling study on the effectiveness of Wi-Fi offloading," 26th European Regional ITS Conference, Madrid 2015 127129, International Telecommunications Society (ITS).
    2. Shah Jamal Alam & Ruth Meyer & Gina Ziervogel & Scott Moss, 2007. "The Impact of HIV/AIDS in the Context of Socioeconomic Stressors: an Evidence-Driven Approach," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(4), pages 1-7.
    3. Thomas Brenner & Claudia Werker, 2007. "A Taxonomy of Inference in Simulation Models," Computational Economics, Springer;Society for Computational Economics, vol. 30(3), pages 227-244, October.
    4. J. Gareth Polhill & Edoardo Pignotti & Nicholas M. Gotts & Pete Edwards & Alun Preece, 2007. "A Semantic Grid Service for Experimentation with an Agent-Based Model of Land-Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(2), pages 1-2.
    5. Masanori Hirano & Kiyoshi Izumi & Takashi Shimada & Hiroyasu Matsushima & Hiroki Sakaji, 2020. "Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations," JRFM, MDPI, vol. 13(4), pages 1-20, April.
    6. Barnaud, Cécile & Bousquet, François & Trebuil, Guy, 2008. "Multi-agent simulations to explore rules for rural credit in a highland farming community of Northern Thailand," Ecological Economics, Elsevier, vol. 66(4), pages 615-627, July.
    7. Pawel Sobkowicz, 2009. "Modelling Opinion Formation with Physics Tools: Call for Closer Link with Reality," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-11.
    8. Troost, Christian & Huber, Robert & Bell, Andrew R. & van Delden, Hedwig & Filatova, Tatiana & Le, Quang Bao & Lippe, Melvin & Niamir, Leila & Polhill, J. Gareth & Sun, Zhanli & Berger, Thomas, 2023. "How to keep it adequate: A protocol for ensuring validity in agent-based simulation," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 159, pages 1-21.

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