Abstract
In the previous chapter, we saw that subjectivity tends to be stronger than objectivity, which in turn impacted the efficacy of decision-making. The higher the level of uncertainty the higher the level individuals and groups rely on their biases and use of heuristics to make decisions. Here we examine a variety of approaches to counteract biases as well as confronting the challenges of digital disinformation, filter bubbles, and social media influenced echo chambers. Mitigation approaches such as improving media literacy and fact-checking are reviewed to challenge the worst of that behaviour governed by these traits and to mitigate their impact. Reference is made as to how Finland has introduced methods to mitigate the worst influence of targeted disinformation as well as a selection of ideas to reduce individual and group-based cognitive dissonance.
We cannot fully grasp the nature and the implications of what happened in the concentration camps if we shy away from facing the destructive tendencies of man, the aggressive aspect of our animal inheritance which in man has assumed a specifically human and peculiarly destructive form
Bruno Bettelheim—The Informed Heart.
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Notes
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If you require further information of Je Hyun Kim’s work he can be contacted at: 245656@network.rca.ac.uk. He is currently on the Innovation Design Engineering programme a joint course at Imperial College’s Dyson School of Design Engineering and the Royal College of Art (RCA).
References
Bonaccorsi, A., Apreda, R., & Fantoni, G. (2020). Expert biases in technology foresight. Why they are a problem and how to mitigate them. Technological Forecasting & Social Change.
CNN Report. (2019). Finland is winning the war on fake news. What it’s learned may be crucial to Western democracy. E Kiernan
Defense One. (2019, October). https://www.defenseone.com
FactBar EDU. (2018). Elections approach – are you ready? Fact checking for educators and future voters. FactBar EDU.
Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982). Judgment under uncertainty: Heuristics and biases. Cambridge University Press.
Pherson, R. H. (2019). Handbook of analytic tools & technique (5th ed.). Pherson Associates.
Pherson, R. H. (2021). Moving toward constructive solutions: The analytic insider.
Pherson, R. H., & Pyrik, R. (2018). Analyst’s guide to indicators. The Analyst’s Bookshop, Pherson Associates LLC.
Taylor, J. (2013). Cognitive biases are bad for business.
Tetlock, P. E. (2005). Expert political judgement. Princeton University Press.
Wardle, C., & Derakshan, H. (2017, September). Information disorder: Toward an interdisciplinary framework for research and policy making. Council of Europe.
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Garvey, B. (2022). How to Mitigate the Impact of the Behavioural Minefield. In: Uncertainty Deconstructed. Science, Technology and Innovation Studies. Springer, Cham. https://doi.org/10.1007/978-3-031-08007-4_10
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DOI: https://doi.org/10.1007/978-3-031-08007-4_10
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