In the evolving landscape of risk assessment and policy formulation, the role of expert judgment remains both pivotal and complex. Matthew G. Morgan’s recent work, “Reflections on Obtaining Probabilistic Judgments from Experts for Use in Risk and other Policy Analysis,” published in the International Journal of Disaster Risk Science (2025), offers a profound examination of the intricate process by which experts contribute probabilistic evaluations that influence critical decision-making. This exploration comes at a critical time when governments, industries, and organizations increasingly rely on nuanced risk models to prepare for unpredictable and multidimensional threats.
At the heart of Morgan’s reflection lies the challenge of translating expert insights into quantitative probabilities that can seamlessly integrate into formal risk analyses. Experts, by nature, bring both deep knowledge and cognitive biases shaped by their experience, training, and the context within which their judgments are solicited. Morgan highlights that eliciting accurate probabilistic judgments requires a careful framework designed to mitigate common biases such as overconfidence, anchoring, and availability heuristics. Without such a framework, the risk assessments that guide policy decisions risk being distorted by subjective influences.
Central to Morgan’s discourse is a detailed consideration of uncertainty and how it manifests in expert judgment. Unlike purely empirical data, which can be statistically analyzed and replicated, expert assessments are inherently subjective, contoured by individual perspectives on incomplete or ambiguous information. This epistemic uncertainty demands innovative elicitation methodologies that not only capture the best current estimate but also the uncertainty range perceived by experts. Morgan advocates for techniques that allow experts to express probabilistic distributions rather than point estimates, thereby enriching models with a spectrum of plausible outcomes.
Morgan also addresses the methodological rigor required when aggregating multiple expert judgments. Combining insights from diverse experts necessitates algorithms and protocols that balance individual expert reliability, diversity of opinion, and correlated errors. The paper explores probabilistic aggregation approaches, including Bayesian methods and decision analytic techniques, emphasizing the need for transparency in these processes to ensure trust in the aggregated outcome. He cautions that naive averaging can obscure important nuances, leading to underestimation or overestimation of risks.
The paper navigates through the practical aspects of expert elicitation sessions, focusing on how the design of questions influences the quality of probabilistic judgments. Morgan identifies that the wording of questions, the framing of uncertainty, and the context provided to experts significantly affect their responses. Careful calibration exercises and iterative feedback loops are vital to refine expert inputs and help experts better understand the probabilistic language, ultimately enhancing the utility of their judgments in policy contexts.
Beyond methodological considerations, Morgan places expert elicitation within the broader context of policy analysis and disaster risk management. He argues that probabilistic expert judgments are not mere academic exercises but constitute essential inputs for decision-making frameworks under uncertainty, such as cost-benefit analyses, scenario planning, and resilience assessments. The integration of expert probabilities allows policymakers to weigh trade-offs with greater clarity, making informed choices that can save lives, protect infrastructure, and optimize resource allocation in crisis situations.
Morgan further elucidates the tension between expert elicitation and empirical data collection in risk science. While empirical models benefit from statistically robust datasets, such data can be scarce or nonexistent for emerging risks or unprecedented scenarios. In these circumstances, expert judgment fills critical gaps, providing forward-looking insights grounded in domain expertise. However, he stresses that expert elicitation should complement rather than replace empirical evidence, underscoring the importance of continuous validation and updating of probabilistic estimates as new information becomes available.
The discussion extends to the psychological and social dynamics inherent in expert judgment processes. Group elicitation sessions, for example, introduce complexities related to conformity pressures, dominance of certain voices, and groupthink. Morgan examines methods for structuring group deliberations to safeguard diversity of thought and mitigate undue influence, advocating for structured protocols such as the Delphi method and the use of anonymous submissions to preserve independent assessments while fostering collective insight.
Importantly, the article delves into technological advancements that are transforming expert elicitation. Interactive software tools capable of real-time feedback and statistical visualization are enabling experts to iteratively adjust their probabilistic assessments based on emerging group data and calibration tests. These innovations not only improve accuracy but also enhance expert engagement, a critical factor for the sustained use of elicitation in complex policy environments. Morgan posits that as artificial intelligence and machine learning grow more sophisticated, they will increasingly support expert judgment by integrating heterogeneous data streams and refining probabilistic models.
Morgan’s reflections also engage with ethical considerations, noting that the stakes associated with risk judgments often involve human lives and societal well-being. The transparency of elicitation procedures, the communication of uncertainties to non-expert stakeholders, and the accountability frameworks governing expert involvement are essential for maintaining public trust. He calls for deliberate efforts to document and disclose the assumptions, limitations, and decision criteria underpinning probabilistic judgments, ensuring that policy decisions are not only technically sound but ethically justified.
The paper’s insights resonate strongly in the context of disaster risk reduction efforts. Climate change, pandemics, and technological hazards present unprecedented challenges that defy deterministic prediction. Probabilistic expert judgments represent a powerful tool to navigate this uncertainty, offering adaptable and evidentially grounded forecasts. Morgan highlights case studies where probabilistic elicitation informed national flood risk assessments and pandemic preparedness plans, demonstrating the practical value of these methodologies in shaping resilient policy responses.
Moreover, Morgan stresses the importance of ongoing training and education for experts engaged in elicitation exercises. Developing proficiency in expressing uncertainty, understanding cognitive biases, and interpreting probabilistic outputs is essential for improving the quality of judgments. Institutions must invest in building capacities that transcend technical expertise to encompass the cognitive and communicative skills required for effective participation in risk-informed decision-making processes.
In concluding, Morgan points to future research directions aimed at refining expert elicitation methods and expanding their application domains. He encourages multidisciplinary collaboration among psychologists, statisticians, domain scientists, and policymakers to develop integrated frameworks that balance rigor with practicality. The challenges of climate adaptation, cybersecurity, and emerging infectious diseases demand that expert judgment processes evolve to address the growing complexity and interdependence of global risks.
The article ultimately affirms that while expert judgment is inherently imperfect, when harnessed through robust probabilistic methods and thoughtful design, it becomes an indispensable component of modern risk analysis. Morgan’s reflective synthesis not only advances academic discourse but also offers pragmatic guidance for those tasked with translating uncertainty into actionable knowledge.
As the world confronts continual and multifaceted risks, the lessons distilled from Morgan’s investigation provide a beacon for enhancing our collective capacity to anticipate, prepare for, and mitigate disasters through informed expert insight. This blend of science, psychology, and policy analysis underscores a fundamental truth: in the realm of uncertainty, well-calibrated expert judgment remains humanity’s most vital asset for navigating the unknown.
Subject of Research: Obtaining and utilizing probabilistic judgments from experts for risk assessment and policy analysis.
Article Title: Reflections on Obtaining Probabilistic Judgments from Experts for Use in Risk and other Policy Analysis.
Article References:
Morgan, M.G. Reflections on Obtaining Probabilistic Judgments from Experts for Use in Risk and other Policy Analysis. Int J Disaster Risk Sci (2025). https://doi.org/10.1007/s13753-025-00659-w
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