Robert J Lempert
Dados Biográficos
| ID | 6542299 |
|---|---|
| NOME | Robert J Lempert |
| PRENOMES | Robert J |
| SOBRENOME | Lempert |
| ASSINATURA | LEMPERT R J |
| AFILIAÇÕES | RAND Corporation |
| ORCID | 0000-0003-0537-3159 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 16 |
| TOTAL DE CITAÇÕES | 124 |
| TOTAL COMO AUTOR | 16 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2002 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2021 |
| ÍNDICE H | 7 |
Measuring global climate risk
A framework for complex climate change risk assessment
Real-world experience underscores the complexity of interactions among multiple drivers of climate change risk and of how multiple risks compound or cascade. However, a holistic framework for assessing such complex climate change risks has not yet been achieved. Clarity is needed regarding the interactions that generate risk, including the role of adaptation and mitigation responses. In this perspective, we present a framework for three categorie…
Understanding scientists’ computational modeling decisions about climate risk management strategies using values-informed mental models
The effect of near-term policy choices on long-term greenhouse gas transformation pathways
Agreeing on Robust Decisions
Investment decision making is already difficult for any diverse group of actors with different priorities and views. But the presence of deep uncertainties linked to climate change and other future conditions further challenges decision making by questioning the robustness of all purportedly optimal solutions. While decision makers can continue to use the decision metrics they have used in the past (such as net present value), alternative methodo…
Climate Change
Disaster signifies extreme impacts suffered when hazardous physical events interact with vulnerable social conditions to severely alter the normal functioning of a community or a society (high confidence). Social vulnerability and exposure are key determinants of disaster risk and help explain why non-extreme physical events and chronic hazards can also lead to extreme impacts and disasters, while some extreme events do not. Extreme impacts on hu…
Improving the contribution of climate model information to decision making
In this paper, we review the need for, use of, and demands on climate modeling to support so‐called ‘robust’ decision frameworks, in the context of improving the contribution of climate information to effective decision making. Such frameworks seek to identify policy vulnerabilities under deep uncertainty about the future and propose strategies for minimizing regret in the event of broken assumptions. We argue that currently there is a severe und…
The need for and use of socio-economic scenarios for climate change analysis
Thinking inside the box
Methods for Long-Term Environmental Policy Challenges
This article provides a concise overview of methods for analyzing policy choices that have been used in the study of long-term environmental challenges. We open with an overview of the broad classes of methods used for long-term policy analysis, and subsequent sections will describe in depth three particular methods. They are: statistical models, such as employed in the debate on the environmental Kuznets curve, which infer past patterns from dat…
Managing the Risk of Uncertain Threshold Responses
Many commentators have suggested the need for new decision analysis approaches to better manage systems with deeply uncertain, poorly characterized risks. Most notably, policy challenges such as abrupt climate change involve potential nonlinear or threshold responses where both the triggering level and subsequent system response are poorly understood. This study uses a simple computer simulation model to compare several alternative frameworks for…
A new analytic method for finding policy-relevant scenarios
A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios
Robustness is a key criterion for evaluating alternative decisions under conditions of deep uncertainty. However, no systematic, general approach exists for finding robust strategies using the broad range of models and data often available to decision makers. This study demonstrates robust decision making (RDM), an analytic method that helps design robust strategies through an iterative process that first suggests candidate robust strategies, ide…
Shaping the Next One Hundred Years
The checkered history of predicting the future — e.g., “Man will never fly” — has dissuaded policymakers from considering the long-term effects of decisions. New analytic methods, enabled by modern computers, transform our ability to reason about the future. The authors here demonstrate a quantitative approach to long-term policy analysis (LTPA). Robust methods enable decisionmakers to examine a vast range of futures and design adaptive strategie…
Confronting Surprise
Surprise takes many forms, all tending to disrupt plans and planning systems. Reliance by decision makers on formal analytic methodologies can increase susceptibility to surprise as such methods commonly use available information to develop single-point forecasts or probability distributions of future events. In doing so, traditional analyses divert attention from information potentially important to understanding and planning for effects of surp…
Making Computational Social Science Effective
There has been significant recent interest in Agent Based Modeling in many social sciences including economics, sociology, anthropology, political science, and game theory. This article describes three problems that need to be addressed in order for such models to become effective tools for formulating new social theory and informing policy debates and suggests approaches to meeting them. These issues are computational epistemology, research meth…
A new analytic method for finding policy-relevant scenarios
The need for and use of socio-economic scenarios for climate change analysis
Improving the contribution of climate model information to decision making
In this paper, we review the need for, use of, and demands on climate modeling to support so‐called ‘robust’ decision frameworks, in the context of improving the contribution of climate information to effective decision making. Such frameworks seek to identify policy vulnerabilities under deep uncertainty about the future and propose strategies for minimizing regret in the event of broken assumptions. We argue that currently there is a severe und…
Confronting Surprise
Surprise takes many forms, all tending to disrupt plans and planning systems. Reliance by decision makers on formal analytic methodologies can increase susceptibility to surprise as such methods commonly use available information to develop single-point forecasts or probability distributions of future events. In doing so, traditional analyses divert attention from information potentially important to understanding and planning for effects of surp…
Making Computational Social Science Effective
There has been significant recent interest in Agent Based Modeling in many social sciences including economics, sociology, anthropology, political science, and game theory. This article describes three problems that need to be addressed in order for such models to become effective tools for formulating new social theory and informing policy debates and suggests approaches to meeting them. These issues are computational epistemology, research meth…
The effect of near-term policy choices on long-term greenhouse gas transformation pathways
Methods for Long-Term Environmental Policy Challenges
This article provides a concise overview of methods for analyzing policy choices that have been used in the study of long-term environmental challenges. We open with an overview of the broad classes of methods used for long-term policy analysis, and subsequent sections will describe in depth three particular methods. They are: statistical models, such as employed in the debate on the environmental Kuznets curve, which infer past patterns from dat…
Understanding scientists’ computational modeling decisions about climate risk management strategies using values-informed mental models
Confronting Surprise
Surprise takes many forms, all tending to disrupt plans and planning systems. Reliance by decision makers on formal analytic methodologies can increase susceptibility to surprise as such methods commonly use available information to develop single-point forecasts or probability distributions of future events. In doing so, traditional analyses divert attention from information potentially important to understanding and planning for effects of surp…
Making Computational Social Science Effective
There has been significant recent interest in Agent Based Modeling in many social sciences including economics, sociology, anthropology, political science, and game theory. This article describes three problems that need to be addressed in order for such models to become effective tools for formulating new social theory and informing policy debates and suggests approaches to meeting them. These issues are computational epistemology, research meth…
Shaping the Next One Hundred Years
The checkered history of predicting the future — e.g., “Man will never fly” — has dissuaded policymakers from considering the long-term effects of decisions. New analytic methods, enabled by modern computers, transform our ability to reason about the future. The authors here demonstrate a quantitative approach to long-term policy analysis (LTPA). Robust methods enable decisionmakers to examine a vast range of futures and design adaptive strategie…
A General, Analytic Method for Generating Robust Strategies and Narrative Scenarios
Robustness is a key criterion for evaluating alternative decisions under conditions of deep uncertainty. However, no systematic, general approach exists for finding robust strategies using the broad range of models and data often available to decision makers. This study demonstrates robust decision making (RDM), an analytic method that helps design robust strategies through an iterative process that first suggests candidate robust strategies, ide…
Managing the Risk of Uncertain Threshold Responses
Many commentators have suggested the need for new decision analysis approaches to better manage systems with deeply uncertain, poorly characterized risks. Most notably, policy challenges such as abrupt climate change involve potential nonlinear or threshold responses where both the triggering level and subsequent system response are poorly understood. This study uses a simple computer simulation model to compare several alternative frameworks for…
A new analytic method for finding policy-relevant scenarios
Methods for Long-Term Environmental Policy Challenges
This article provides a concise overview of methods for analyzing policy choices that have been used in the study of long-term environmental challenges. We open with an overview of the broad classes of methods used for long-term policy analysis, and subsequent sections will describe in depth three particular methods. They are: statistical models, such as employed in the debate on the environmental Kuznets curve, which infer past patterns from dat…
Thinking inside the box
Climate Change
Disaster signifies extreme impacts suffered when hazardous physical events interact with vulnerable social conditions to severely alter the normal functioning of a community or a society (high confidence). Social vulnerability and exposure are key determinants of disaster risk and help explain why non-extreme physical events and chronic hazards can also lead to extreme impacts and disasters, while some extreme events do not. Extreme impacts on hu…
Improving the contribution of climate model information to decision making
In this paper, we review the need for, use of, and demands on climate modeling to support so‐called ‘robust’ decision frameworks, in the context of improving the contribution of climate information to effective decision making. Such frameworks seek to identify policy vulnerabilities under deep uncertainty about the future and propose strategies for minimizing regret in the event of broken assumptions. We argue that currently there is a severe und…
The need for and use of socio-economic scenarios for climate change analysis
Agreeing on Robust Decisions
Investment decision making is already difficult for any diverse group of actors with different priorities and views. But the presence of deep uncertainties linked to climate change and other future conditions further challenges decision making by questioning the robustness of all purportedly optimal solutions. While decision makers can continue to use the decision metrics they have used in the past (such as net present value), alternative methodo…
The effect of near-term policy choices on long-term greenhouse gas transformation pathways
Understanding scientists’ computational modeling decisions about climate risk management strategies using values-informed mental models
Measuring global climate risk
A framework for complex climate change risk assessment
Real-world experience underscores the complexity of interactions among multiple drivers of climate change risk and of how multiple risks compound or cascade. However, a holistic framework for assessing such complex climate change risks has not yet been achieved. Clarity is needed regarding the interactions that generate risk, including the role of adaptation and mitigation responses. In this perspective, we present a framework for three categorie…
Computer Science (15 obras) · Management science (11 obras) · Economics (10 obras) · Climate Change Policy and Economics (9 obras) · Business (8 obras) · Engineering (7 obras) · Climate change (6 obras) · Futures contract (5 obras) · Geography (5 obras) · Operations research (5 obras)