Consensus: group decision-making & community organizing #5

 What about consensus in massive complex government projects?

In complex government projects some innovative consensus methods have helped to reduce non-expert but fully stakeholder conflict with experts by a modified consensus approach that seeks the basic elements of consensus but in a streamlined fashion to reduce the high costs of a longer, classical consensus process. This may not only reduce costs and timelines on the front end, but may very well reduce subsequent resistance and slowdowns from the classical command-and-control approach.

"In this way, collective intelligence across different subgroups of society can be leveraged simultaneously, involving experts and non-experts, called heterogeneous decision-makers in this study" (Singh, Baranwal & Tripathi, 2023, p. 3936).

Researchers into consensus processes, including large organization efforts to achieve a version of consensus in complex policy or project initiatives now incorporate more complex variables into their analysis, including the amount of historical knowledge the moderators or facilitators have about their participants, the ultimate decision-makers, which can make this research more accurate and thus more usable (Liang, Qu & Dai, 2024).

Another permutation of consensus development is the Delphi process in which a large diverse group of experts (by both profession and germane lived experience) meet to develop points in a plan to effectuate some desired change, and successive rounds of polling and explainers gradually generate a prioritized plan acceptable to virtually all (Ahmed, et al., 2025). This has potential to involve geographically dispersed, very differently qualified experts in a large collective decision-making and plan creation. In the research cited by Ahmed and colleagues, the presenting problem was a data-driven conclusion that health care inequities across the UK were not diminishing, and in fact were worsening. Some 76 experts--medical professionals at virtually all levels, victims of health care inequities, researchers, and officials--participating in this Delphi consensus process reached consensus after several rounds of surveys and consideration of reports from each round over two years. This robust process holds strong promise for wiser decisions around addressing persistent public problems.

References

Ahmed, F., Woodhead, C., Hossaini, A., Stanley, N., Ensum, L., Rhead, R., Onwumere, J., Mir, G., Dyer, J., & Hatch, S. L. (2025). Guiding principles for accelerating change through health inequities research and practice: A modified Delphi consensus process. PLoS ONE, 20(7), 1–15. https://doi-org.proxy.lib.pdx.edu/10.1371/journal.pone.0327552

Liang, H., Qu, S., & Dai, Z. (2024). Robust maximum fairness consensus models with aggregation operator based on data-driven method. Journal of Intelligent & Fuzzy Systems, 47(1/2), 111–129. https://doi-org.proxy.lib.pdx.edu/10.3233/JIFS-237153

Singh, M., Baranwal, G., & Tripathi, A. K. (2023). A novel 2-phase consensus with customized feedback based group decision-making involving heterogeneous decision-makers. Journal of Supercomputing, 79(4), 3936–3973. https://doi-org.proxy.lib.pdx.edu/10.1007/s11227-022-04796-7