Adversarial collaboration
In science, adversarial collaboration is a modality of collaboration wherein opposing views work together in order to jointly advance knowledge of the area under dispute. This can take the form of a scientific experiment conducted by two groups of experimenters with competing hypotheses, with the aim of constructing and implementing an experimental design in a way that satisfies both groups that there are no obvious biases or weaknesses in the experimental design.[1] Adversarial collaboration can involve a neutral moderator[2] and lead to a co-designed experiment and joint publishing of findings in order to resolve differences.[3] With its emphasis on transparency throughout the research process, adversarial collaboration has been described as sitting within the open science framework.[4]
History
[edit]One of the earliest modern examples of adversarial collaboration was a 1988 collaboration between Erez and Latham with Edwin Locke working as a neutral third party. This collaboration came about as the result of a disagreement from the field of Goal-Setting research between Erez and Latham on an aspect of goal-setting research around the effect of participation on goal commitment and performance. Latham and Erez designed four experiments which explained the differences between their individual findings, but did not coin the term adversarial collaboration.[2] Independently, to Erez, Locke and Latham whose work he was unaware of,[5] Daniel Kahneman developed a similar protocol for adversarial collaboration around ten years later and may have been the first to use the term adversarial collaboration.[6] More recently, Clark and Tetlock have proposed adversarial collaboration as a vehicle for improving how science can self-correct through exploring rival hypotheses which will ultimately expose false claims.[7] Their work has led to the University of Pennsylvania School of Arts & Sciences creating the Adversarial Collaboration Project[8] which seeks to encourage the use of adversarial collaboration as a research approach to address a variety of research questions.[9]
Benefits
[edit]Adversarial collaboration has been recommended by Daniel Kahneman[10] and others as a way of reducing the distorting impact of cognitive-motivational biases on human reasoning[11] and resolving contentious issues in fringe science.[12] It has also been recommended as a potential solution for improving academic commentaries.[13]
Philip Tetlock and Gregory Mitchell have discussed it in various articles. They argue:
Adversarial collaboration is most feasible when least needed: when the clashing camps have advanced testable theories, subscribe to common canons for testing those theories, and disagreements are robust but respectful. And adversarial collaboration is least feasible when most needed: when the scientific community lacks clear criteria for falsifying points of view, disagrees on key methodological issues, relies on second- or third-best substitute methods for testing causality, and is fractured into opposing camps that engage in ad hominem posturing and that have intimate ties to political actors who see any concession as weakness.[14]
References
[edit]- ^ Arts and Sciences, Penn. "Adversarial Collaboration Project". Adversarial Collaboration Project. Archived from the original on 2021-09-15. Retrieved 7 Jan 2022.
- ^ a b Latham, Gary P.; Erez, Miriam; Locke, Edwin A. (1988). "Resolving scientific disputes by the joint design of crucial experiments by the antagonists: Application to the Erez–Latham dispute regarding participation in goal setting". Journal of Applied Psychology. 73 (4): 753–772. doi:10.1037/0021-9010.73.4.753. ISSN 1939-1854.
- ^ Locke, Edwin A.; Latham, Gary P.; Erez, Miriam (1988). "The Determinants of Goal Commitment". The Academy of Management Review. 13 (1): 23. doi:10.2307/258352. JSTOR 258352.
- ^ Rakow, Tim (2022), O'Donohue, William; Masuda, Akihiko; Lilienfeld, Scott (eds.), "Adversarial Collaboration", Avoiding Questionable Research Practices in Applied Psychology, Cham: Springer International Publishing, pp. 359–377, doi:10.1007/978-3-031-04968-2_16, ISBN 978-3-031-04968-2, retrieved 2023-06-20
- ^ "Adversarial Collaboration: An EDGE Lecture by Daniel Kahneman | Edge.org". www.edge.org. Retrieved 2023-06-20.
- ^ Berger, Michele W.; Pennsylvania, University of. "In the pursuit of scientific truth, working with adversaries can pay off". phys.org. Retrieved 2023-06-20.
- ^ Clark, Cory J.; Costello, Thomas; Mitchell, Gregory; Tetlock, Philip E. (March 2022). "Keep your enemies close: Adversarial collaborations will improve behavioral science". Journal of Applied Research in Memory and Cognition. 11 (1): 1–18. doi:10.1037/mac0000004. ISSN 2211-369X. S2CID 248441364.
- ^ "In the pursuit of scientific truth, working with adversaries can pay off". Penn Today. 2022-07-07. Retrieved 2023-06-20.
- ^ "Research | Adversarial Collaboration Project". web.sas.upenn.edu. Retrieved 2023-06-20.
- ^ Kahneman, Daniel; Klein, Gary. Conditions for intuitive expertise: A failure to disagree. American Psychologist, Vol 64(6), Sep 2009, 515-526. doi: 10.1037/a0016755
- ^ Clark, C. J.; Tetlock, P. E. (2022). Adversarial collaboration The next science reform. New York: New York: Springer. pp. 2–3.
- ^ Wagenmakers, E.-J., Wetzels, R., Borsboom, D., & van der Maas, H. L. J. (2010). Why psychologists must change the way they analyze their data: The case of psi. Archived 2011-01-20 at the Wayback Machine
- ^ Heyman, Tom; Moors, Pieter; Rabagliati, Hugh (2020). "The benefits of adversarial collaboration for commentaries". Nature Human Behaviour. 4 (12): 1217. doi:10.1038/s41562-020-00978-6. hdl:1887/3188822. ISSN 2397-3374. PMID 33106628. S2CID 225083325.
- ^ Tetlock, Philip & Gregory Mitchell. 2009. "Implicit Bias and Accountability Systems: What Must Organizations Do to Prevent Discrimination?" Research in Organizational Behavior 29:3-38. Earlier version at [1]
External links
[edit]- The Adversarial Collaboration Project at UPenn studies and supports researchers with differing views
- AI Magazine's AI Bookies column supports an adversarial approach to AI Research
- Lesswrong Has examples of adversarial collaboration