Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-24T09:13:20.475Z Has data issue: false hasContentIssue false
  • English
  • Français

Mechanistic modeling for the masses

Published online by Cambridge University Press:  10 February 2022

Matthew A. Turner  and
Paul E. Smaldino Open the ORCID record for Paul E. Smaldino [Opens in a new window]
Matthew A. Turner
Affiliation:
Department of Cognitive and Information Sciences, University of California, Merced, Merced, CA95343, USA. mturner8@ucmerced.edu; psmaldino@ucmerced.edu; http://mt.digital, http://smaldino.com
Paul E. Smaldino
Affiliation:
Department of Cognitive and Information Sciences, University of California, Merced, Merced, CA95343, USA. mturner8@ucmerced.edu; psmaldino@ucmerced.edu; http://mt.digital, http://smaldino.com
Get access Rights & Permissions [Opens in a new window]

Abstract

The generalizability crisis is compounded, or even partially caused, by a lack of specificity in psychological theories. Expanding the use of mechanistic models among psychologists is therefore important, but faces numerous hurdles. A cultural evolutionary approach can help guide and evaluate interventions to improve modeling efforts in psychology, such as developing standards and implementing them at the institutional level.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 45 , 2022 , e33
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bergstrom, C. T., Foster, J. G., & Song, Y. (2016). Why scientists chase big problems: Individual strategy and social optimality. arXiv, 1605.05822.Google Scholar
Craver, C. F. (2006). When mechanistic models explain. Synthese, 153(3), 355–376.CrossRefGoogle Scholar
Fried, E. I. (2020). Lack of theory building and testing impedes progress in the factor and network literature. Psychological Inquiry, 31(4), 271–288.CrossRefGoogle Scholar
Gervais, W. M. (2021). Practical methodological reform needs good theory. Perspectives on Psychological Science, 16(4), 827–843.CrossRefGoogle ScholarPubMed
Henrich, J., & Boyd, R. (2008). Division of labor, economic specialization, and the evolution of social stratification. Current Anthropology, 49(4), 715–724.CrossRefGoogle Scholar
Higginson, A. D., & Munafò, M. R. (2016). Current incentives for scientists lead to underpowered studies with erroneous conclusions. PLoS Biology, 14(11), e2000995.CrossRefGoogle ScholarPubMed
Kauffman, S. A. (1971). Articulation of parts explanation in biology and the rational search for them. In Buck, R. C. & Cohen, R. S. (Eds.), PSA 1970 (pp. 257–272). Irvine, CA: Philosophy of Science Association.CrossRefGoogle Scholar
Mayo, D. G. (2018). Statistical inference as severe testing. Cambridge University Press.CrossRefGoogle Scholar
Meehl, P. E. (1990). Why summaries of research on psychological theories are often uninterpretable. Psychological Reports, 66, 195–244.CrossRefGoogle Scholar
O'Connor, C. (2019). The origins of unfairness. Oxford University Press.CrossRefGoogle Scholar
O'Connor, C., & Weatherall, J. O. (2018). Scientific polarization. European Journal for Philosophy of Science, 8(3), 855–875.CrossRefGoogle Scholar
O'Connor, C., & Weatherall, J. O. (2020). False beliefs and the social structure of science: Some models and case studies. In Allen, D. M. & Howell, J. W. (Eds.), Groupthink in science (pp. 37–48). Springer.CrossRefGoogle Scholar
Oberauer, K., & Lewandowsky, S. (2019). Addressing the theory crisis in psychology. Psychonomic Bulletin & Review, 26(5), 1596–1618.CrossRefGoogle ScholarPubMed
Popper, K. (1963). Conjectures and refutations. Routledge.Google Scholar
Rand, W. (2019). Theory-interpretable, data-driven agent-based modeling. In Davis, P. K., O'Mahony, A. & Pfautz, J. (Eds.), Social-behavioral modeling for complex systems (pp. 337–357). Wiley.CrossRefGoogle Scholar
Schank, J. C., May, C. J., & Joshi, S. S. (2014). Models as scaffold for understanding. In Griesemer, J. R., Wimsatt, W. C. & Caporael, L. R. (Eds.), Developing scaffolds in evolution, culture, and cognition (pp. 147–167). MIT Press.Google Scholar
Smaldino, P. (2019). Better methods can't make up for mediocre theory. Nature, 575, 9.CrossRefGoogle ScholarPubMed
Smaldino, P. E. (2016). Not even wrong: Imprecision perpetuates the illusion of understanding at the cost of actual understanding. Behavioral and Brain Sciences, 39, e163.CrossRefGoogle ScholarPubMed
Smaldino, P. E. (2017). Models are stupid, and we need more of them. In Vallacher, R. R., Nowak, A. & Read, S. J. (Eds.), Computational social psychology (pp. 311–331). Routledge.CrossRefGoogle Scholar
Smaldino, P. E. (2020). How to build a strong theoretical foundation. Psychological Inquiry, 31(4), 297–301.CrossRefGoogle Scholar
Smaldino, P. E., Flamson, T. J., & McElreath, R. (2018). The evolution of covert signaling. Scientific Reports, 8, 4905. https://doi.org/10.1038/s41598-018-22926-1.CrossRefGoogle ScholarPubMed
Smaldino, P. E., & O'Connor, C. (2020). Interdisciplinarity can aid the spread of better methods between communities. MetaArXiv. Retrieved from https://osf.io/preprints/metaarxiv/cm5v3/.Google Scholar
Smaldino, P. E., & Turner, M. A. (2020). Covert signaling is an adaptive communication strategy in diverse populations. SocArXiv. Retrieved from https://osf.io/preprints/socarxiv/j9wyn/.Google Scholar
Smaldino, P. E., Turner, M. A., & Contreras Kallens, P. A. (2019). Open science and modified funding lotteries can impede the natural selection of bad science. Royal Society Open Science, 6(8), 191249.CrossRefGoogle ScholarPubMed
Tiokhin, L., Panchanathan, K., Lakens, D., Vazire, S., Morgan, T., & Zollman, K. (2021). Honest signaling in academic publishing. PLoS ONE, 16(2), e0246675.CrossRefGoogle ScholarPubMed
Zollman, K. J. S. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587.CrossRefGoogle Scholar
Zollman, K. J. S. (2013). Network epistemology: Communication in epistemic communities. Philosophy Compass, 8(1), 15–27.CrossRefGoogle Scholar
Zollman, K. J. S. (2010). Social structure and the effects of conformity. Synthese, 172(3), 317–340.CrossRefGoogle Scholar