I see the most relevant application areas of this methodology in:
Biorisk
Economics
Improving Institutional Decision-Making
Climate Change
Nuclear War
AI Governance
Longtermism
Are there any papers, writeups, blog posts etc I can check out to see how these techniques are applied “in the wild”? I’d be especially interested in writeups that show how simpler methods yield results deficient in some way, and how this methodology’s results don’t have those deficiencies.
Great question! There are indeed plenty of papers using systems modeling and there are various papers using decision-making under deep uncertainty. It’s not always a combination of both though.
With respect to systems modeling, it might be interesting to look into economics in particular. Traditional economic modeling techniques, such as equilibrium models have certain limitations when it comes to addressing complex economic problems. These limitations arise from the assumptions and simplifications that these models make, which can be inadequate for representing the intricacies of real-world economic systems. Some are mentioned in the post above (rationality and perfect information, homogeneity, static equilibrium, linearity, and additivity. Agent-based modeling offers a better fit for analyzing complex economic problems because it addresses many of the limitations of traditional models. In simple terms, in traditional economic modeling (equilibrium models and standard game theory), it is inherently impossible to account for actual market phenomena (e.g. the emergence of market psychology, price bubbles, let alone market crashes). The Santa Fe Institute has produced some very valuable work on this. I would recommend reading Foundations of complexity economics by W. Arthur Brian. Other books and papers of his are excellent as well.
Some papers using the methodology of decision-making under deep uncertainty (DMDU):
Climate Change Mitigation and Adaptation: DMDU is commonly used in climate change policy-making, where the long-term consequences of policy decisions are uncertain and complex.
Healthcare Resource Allocation: In healthcare, DMDU is used to make decisions on resource allocation, such as the allocation of funding for disease research and the development of new treatments.
Disaster Response Planning: DMDU is also used in disaster response planning, where decision-makers must anticipate and prepare for multiple potential outcomes in the face of extreme uncertainty.
Environmental Protection: Environmental policy-making often involves DMDU, such as decisions on the conservation and management of endangered species, and management of ecosystems and biodiversity.
You mention that
Are there any papers, writeups, blog posts etc I can check out to see how these techniques are applied “in the wild”? I’d be especially interested in writeups that show how simpler methods yield results deficient in some way, and how this methodology’s results don’t have those deficiencies.
Great question! There are indeed plenty of papers using systems modeling and there are various papers using decision-making under deep uncertainty. It’s not always a combination of both though.
With respect to systems modeling, it might be interesting to look into economics in particular. Traditional economic modeling techniques, such as equilibrium models have certain limitations when it comes to addressing complex economic problems. These limitations arise from the assumptions and simplifications that these models make, which can be inadequate for representing the intricacies of real-world economic systems. Some are mentioned in the post above (rationality and perfect information, homogeneity, static equilibrium, linearity, and additivity. Agent-based modeling offers a better fit for analyzing complex economic problems because it addresses many of the limitations of traditional models. In simple terms, in traditional economic modeling (equilibrium models and standard game theory), it is inherently impossible to account for actual market phenomena (e.g. the emergence of market psychology, price bubbles, let alone market crashes). The Santa Fe Institute has produced some very valuable work on this. I would recommend reading Foundations of complexity economics by W. Arthur Brian. Other books and papers of his are excellent as well.
Some papers using the methodology of decision-making under deep uncertainty (DMDU):
Climate Change Mitigation and Adaptation: DMDU is commonly used in climate change policy-making, where the long-term consequences of policy decisions are uncertain and complex.
Climate Change and Decision-Making Under Uncertainty
Climate action with revenue recycling has benefits for poverty, inequality and well-being
Utilitarian benchmarks for emissions and pledges promote equity, climate and development
Robust abatement pathways to tolerable climate futures require immediate global action
Adaptive mitigation strategies hedge against extreme climate futures
Investment Decision Making Under Deep Uncertainty—Application to Climate Change
Healthcare Resource Allocation: In healthcare, DMDU is used to make decisions on resource allocation, such as the allocation of funding for disease research and the development of new treatments.
Dealing With Uncertainty in Early Health Technology Assessment: An Exploration of Methods for Decision Making Under Deep Uncertainty
Designing robust policies under deep uncertainty for mitigating epidemics
Multi-attribute COVID-19 policy evaluation under deep uncertainty
Don’t try to predict COVID-19. If you must, use deep uncertainty methods
Disaster Response Planning: DMDU is also used in disaster response planning, where decision-makers must anticipate and prepare for multiple potential outcomes in the face of extreme uncertainty.
Proposing DAPP-MR as a disaster risk management pathways framework for complex, dynamic multi-risk
Deep uncertainty in humanitarian logistics operations: decision-making challenges in responding to large-scale natural disasters
Deep uncertainty in long-term hurricane risk: Scenario generation and implications for future climate experiments
Resilience by design: A deep uncertainty approach for water systems in a changing world
Planning to adapt: identifying key decision drivers in disaster response planning
Environmental Protection: Environmental policy-making often involves DMDU, such as decisions on the conservation and management of endangered species, and management of ecosystems and biodiversity.
Decision making under deep uncertainty for adapting urban drainage systems to change
Deep Uncertainty, Public Reason, the Conservation of Biodiversity and the Regulation of Markets for Lion Skeletons
Economic Evaluation of Adaptation Pathways for an Urban Drainage System Experiencing Deep Uncertainty
Resilience by design: A deep uncertainty approach for water systems in a changing world
I hope that these pointers help a bit!
Strongly upvoted for such a comprehensive answer, thank you Max! You’ve given me a lot to chew on.