Robust Decision Making: better decisions under uncertainty - Revision history

Colombofabio: /* Case study: Application to Water Planning Management */

2023-05-02T10:55:54Z

Case study: Application to Water Planning Management

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	Probability thresholds that, with sufficient certainty, would indicate that a given vulnerability would be likely to occur, and that a consequence management action should be taken, were determined based on statistical analysis and the primary vulnerabilities found. This data was used to create an effective plan for the whole Basin that directs the investment of greater water supply yield and demand reductions. The sample paths (dashed lines) in ~~the~~ Figure 3 illustrate one of many potential implementation routes. If the basin were to follow a trajectory that was consistent with the vulnerability “Below Historical Streamflow During Extreme Drought”, the example paths illustrate how basin managers would provide additional supply. The figure shows decisions taken from Basin managers up to 2030. It then highlights a decision point in 2030 where, by evaluating scenarios, it might be possible that the future will not be consistent with the same vulnerability. If conditions are consistent with the “Severe Declining Supply Scenario”, then the Basing managers should increase the net supply to more than 3.6 maf between 2031-2040. The same decision approach is taken for the following years.		Probability thresholds that, with sufficient certainty, would indicate that a given vulnerability would be likely to occur, and that a consequence management action should be taken, were determined based on statistical analysis and the primary vulnerabilities found. This data was used to create an effective plan for the whole Basin that directs the investment of greater water supply yield and demand reductions. The sample paths (dashed lines) seen in Figure 3 illustrate one of many potential implementation routes. If the basin were to follow a trajectory that was consistent with the vulnerability “Below Historical Streamflow During Extreme Drought”, the example paths illustrate how basin managers would provide additional supply. The figure shows decisions taken from Basin managers up to 2030. It then highlights a decision point in 2030 where, by evaluating scenarios, it might be possible that the future will not be consistent with the same vulnerability. If conditions are consistent with the “Severe Declining Supply Scenario”, then the Basing managers should increase the net supply to more than 3.6 maf between 2031-2040. The same decision approach is taken for the following years.

	[[File:Picture_3_Robust_adaptive_strategies_for_Colorado_River.png‎‎\|600px\|thumb\|center\|Figure 3: Robust adaptive strategy for Colorado River.]]		[[File:Picture_3_Robust_adaptive_strategies_for_Colorado_River.png‎‎\|600px\|thumb\|center\|Figure 3: Robust adaptive strategy for Colorado River.]]

Colombofabio: /* Case study: Application to Water Planning Management */

2023-05-02T10:55:24Z

Case study: Application to Water Planning Management

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	Probability thresholds that, with sufficient certainty, would indicate that a given vulnerability would be likely to occur, and that a consequence management action should be taken, were determined based on statistical analysis and the primary vulnerabilities found. This data was used to create an effective plan for the whole Basin that directs the investment of greater water supply yield and demand reductions. The sample paths (dashed lines) in the ~~picture~~ 3 illustrate one of many potential implementation routes. If the basin were to follow a trajectory that was consistent with the vulnerability “Below Historical Streamflow During Extreme Drought”, the example paths illustrate how basin managers would provide additional supply. The figure shows decisions taken from Basin managers up to 2030. It then highlights a decision point in 2030 where, by evaluating scenarios, it might be possible that the future will not be consistent with the same vulnerability. If conditions are consistent with the “Severe Declining Supply Scenario”, then the Basing managers should increase the net supply to more than 3.6 maf between 2031-2040. The same decision approach is taken for the following years.		Probability thresholds that, with sufficient certainty, would indicate that a given vulnerability would be likely to occur, and that a consequence management action should be taken, were determined based on statistical analysis and the primary vulnerabilities found. This data was used to create an effective plan for the whole Basin that directs the investment of greater water supply yield and demand reductions. The sample paths (dashed lines) in the Figure 3 illustrate one of many potential implementation routes. If the basin were to follow a trajectory that was consistent with the vulnerability “Below Historical Streamflow During Extreme Drought”, the example paths illustrate how basin managers would provide additional supply. The figure shows decisions taken from Basin managers up to 2030. It then highlights a decision point in 2030 where, by evaluating scenarios, it might be possible that the future will not be consistent with the same vulnerability. If conditions are consistent with the “Severe Declining Supply Scenario”, then the Basing managers should increase the net supply to more than 3.6 maf between 2031-2040. The same decision approach is taken for the following years.

	[[File:Picture_3_Robust_adaptive_strategies_for_Colorado_River.png‎‎\|600px\|thumb\|center\|Figure 3: Robust adaptive strategy for Colorado River.]]		[[File:Picture_3_Robust_adaptive_strategies_for_Colorado_River.png‎‎\|600px\|thumb\|center\|Figure 3: Robust adaptive strategy for Colorado River.]]

Colombofabio: /* Notes */

2023-05-02T10:53:05Z

Notes

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	<small>[a]</small> - Maf = million acre-feet. It is a non-SI used in reference to large-scale water sources.		<small>[a]</small> - Maf = million acre-feet. It is a non-SI used in reference to large-scale water sources.

	<small>[b]</small> - Lake Mead ~~is situated at~~ the ~~border~~ of Nevada and Arizona.		<small>[b]</small> - In the Southwest of the United States, the Hoover Dam on the Colorado River creates the reservoir known as Lake Mead. It lies 39 kilometers east of Las Vegas in the states of Nevada and Arizona. In terms of water storage capacity, it is the biggest reservoir in the US. Nearly 20 million people and vast tracts of farmland are fed by Lake Mead's water supply, which also reaches parts of Mexico, Arizona, California, and Nevada.

	<small>[c]</small> - The human emotional response of regret, which is frequently felt while making decisions under uncertainty, may be assessed as the amount of variance between a made option and the best choice, according to decision theory.		<small>[c]</small> - The human emotional response of regret, which is frequently felt while making decisions under uncertainty, may be assessed as the amount of variance between a made option and the best choice, according to decision theory.

Colombofabio: /* Application */

2023-05-02T10:50:42Z

Application

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	'''Step1: Decision Framing.'''		'''Step1: Decision Framing.'''

	The RDM process starts with a decision framing workshop in which stakeholders brainstorm and define the key factors in the analysis. These include ~~decision-makers’~~ goals and criteria, the potential courses of action they may choose to accomplish those goals, the uncertainties that might impact the ~~link between actions and~~ results, and the connections between actions, uncertainties, and goals. Once gathered, this information is put into a framework known as “XLRM” <ref name="Lempert 2003"/> <ref name="Jan H"/>, where:		The RDM process starts with a decision framing workshop in which stakeholders brainstorm and define the key factors in the analysis. These include project managers’ goals and criteria, the potential courses of action they may choose to accomplish those goals, the uncertainties that might impact the results, and the connections between actions, uncertainties, and goals. Once gathered, this information is put into a framework known as “XLRM” <ref name="Lempert 2003"/> <ref name="Jan H"/>, where:
	* “X” stands for exogenous variables (factors not under the control of the decision makers)		* “X” stands for exogenous variables (factors not under the control of the decision makers)
	* “L” stands for policy levers (policies that affect the system to achieve goals)		* “L” stands for policy levers (policies that affect the system to achieve goals)

Colombofabio: /* Application */

2023-05-02T10:47:01Z

Application

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	'''Theoretical framework'''		'''Theoretical framework'''

	RDM is a learning process based on a process that the scholarship defines as "deliberation with analysis" <ref name="NRC 2009"/>. The framework requires that the decision-making parties discuss their goals and alternatives, which are based on assessments provided by analysts who imagine scenarios and policy options. This is especially recommended in situations in which a variety of decision-makers operate in rapidly changing circumstances, and whose objectives may change because of their collaboration with others <ref name="NRC 2009"/>. As illustrated in Figure 1 RDM methodology follows 5 major steps, described in the paragraphs below.		RDM is a learning process based on a process that the scholarship defines as "deliberation with analysis" <ref name="NRC 2009"/>. The framework requires that the decision-making parties discuss their goals and alternatives, which are based on assessments provided by analysts who imagine scenarios and policy options. This is especially recommended in situations in which a variety of decision-makers operate in rapidly changing circumstances, and whose objectives may change because of their collaboration with others <ref name="NRC 2009"/>. As illustrated in Figure 1, RDM methodology follows 5 major steps, described in the paragraphs below.

	[[File:Picture_1_XLRM_framework.png\|600px\|thumb\|center\|Figure 1: XLRM framework]]		[[File:Picture_1_XLRM_framework.png\|600px\|thumb\|center\|Figure 1: XLRM framework]]

Colombofabio: /* Application */

2023-05-02T10:46:40Z

Application

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	'''Theoretical framework'''		'''Theoretical framework'''

	RDM is a learning process based on a process that the scholarship defines as "deliberation with analysis" <ref name="NRC 2009"/>. The framework requires that the decision-making parties discuss their goals and alternatives, which are based on assessments provided by analysts who imagine scenarios and policy options. This is especially recommended in situations in which a variety of decision-makers operate in rapidly changing circumstances, and whose objectives may change because of their collaboration with others. <ref name="NRC 2009"/> As illustrated in Figure 1 RDM methodology follows 5 major steps, described in the paragraphs below.		RDM is a learning process based on a process that the scholarship defines as "deliberation with analysis" <ref name="NRC 2009"/>. The framework requires that the decision-making parties discuss their goals and alternatives, which are based on assessments provided by analysts who imagine scenarios and policy options. This is especially recommended in situations in which a variety of decision-makers operate in rapidly changing circumstances, and whose objectives may change because of their collaboration with others <ref name="NRC 2009"/>. As illustrated in Figure 1 RDM methodology follows 5 major steps, described in the paragraphs below.

	[[File:Picture_1_XLRM_framework.png\|600px\|thumb\|center\|Figure 1: XLRM framework]]		[[File:Picture_1_XLRM_framework.png\|600px\|thumb\|center\|Figure 1: XLRM framework]]

Colombofabio: /* Foundations of Robust Decision Making */

2023-05-02T09:56:23Z

Foundations of Robust Decision Making

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	== Foundations of Robust Decision Making ==		== Foundations of Robust Decision Making ==
	RDM combines four crucial concepts - Decision Analysis, Assumption-Based Planning, scenarios, and Exploratory Modelling - to provide evidence-based analysis that informs choices. This approach empowers ~~decision-makers~~ to navigate uncertainty, assess vulnerabilities, and identify robust strategies that can withstand potential futures.		RDM combines four crucial concepts - Decision Analysis, Assumption-Based Planning, scenarios, and Exploratory Modelling - to provide evidence-based analysis that informs choices. This approach empowers project managers to navigate uncertainty, assess vulnerabilities, and identify robust strategies that can withstand potential futures.

	'''''Decision Analysis (DA)'''''		'''''Decision Analysis (DA)'''''
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	'''''Assumption-Based Planning (ABP)'''''		'''''Assumption-Based Planning (ABP)'''''
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	By expanding awareness of how and why things could fail, RDM encapsulates the ideas of stress testing and red teaming to lessen the harmful impacts of overconfidence in current plans and processes. The former idea puts a system through rigorous testing to establish its breaking points, whereas the latter forms an independent group of people (often external parties) to find ways to thwart an organization's plans <ref name="Dewar"/>. This approach was first implemented in the so-called Assumption-Based Planning (ABP) framework. Starting with a written version of an organization's plans, ABP finds the explicit and implicit assumptions made during the formulation of that plan that, if inaccurate, would result in failure. These assumptions are identified by ~~decision-makers~~, who then create backup plans and "hedging" strategies to be used in case of necessity <ref name="DMUDU"/>.		By expanding awareness of how and why things could fail, RDM encapsulates the ideas of stress testing and red teaming to lessen the harmful impacts of overconfidence in current plans and processes. The former idea puts a system through rigorous testing to establish its breaking points, whereas the latter forms an independent group of people (often external parties) to find ways to thwart an organization's plans <ref name="Dewar"/>. This approach was first implemented in the so-called Assumption-Based Planning (ABP) framework. Starting with a written version of an organization's plans, ABP finds the explicit and implicit assumptions made during the formulation of that plan that, if inaccurate, would result in failure. These assumptions are identified by project managers, who then create backup plans and "hedging" strategies to be used in case of necessity <ref name="DMUDU"/>.

	'''''Scenario Analysis (SA)'''''		'''''Scenario Analysis (SA)'''''
	----		----
	To deal with deep uncertainty, RDM builds upon the idea of SA <ref name="Lempert 2003"/>. Scenarios are defined as collections of potential future occurrences that illustrate various worldviews without explicitly assigning a relative likelihood score <ref name="Wack 1985"/>. They are frequently envisioned in deliberative processes involving stakeholders and probabilities are often not considered. This is because, without considering probabilities, stakeholders are more prone to broaden the range of scenarios taken into consideration and consider their decisions from a larger variety of angles and perspectives, leading to including unexpected outcomes as well. In incorporating analytical techniques borrowed from SA, RDM declines the knowledge about the future into a selected range of potential situations—a technique that assists ~~decision-makers~~ in envisioning future risks and better navigating situation characterised by deep uncertainty.		To deal with deep uncertainty, RDM builds upon the idea of SA <ref name="Lempert 2003"/>. Scenarios are defined as collections of potential future occurrences that illustrate various worldviews without explicitly assigning a relative likelihood score <ref name="Wack 1985"/>. They are frequently envisioned in deliberative processes involving stakeholders and probabilities are often not considered. This is because, without considering probabilities, stakeholders are more prone to broaden the range of scenarios taken into consideration and consider their decisions from a larger variety of angles and perspectives, leading to including unexpected outcomes as well. In incorporating analytical techniques borrowed from SA, RDM declines the knowledge about the future into a selected range of potential situations—a technique that assists project managers in envisioning future risks and better navigating situation characterised by deep uncertainty.

	'''''Exploratory Modeling (EM)'''''		'''''Exploratory Modeling (EM)'''''

Colombofabio: /* Origins and Functions */

2023-05-02T09:51:29Z

Origins and Functions

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	According to former United States Secretary of Defence Donald Rumsfeld, there are different types of knowledge: known knowns, known unknowns, and unknown unknowns. Known knowns refer to things that we know for sure. Known unknowns refer to things that we know we do not know. However, the most challenging category is the unknown unknowns, which refers to things that we do not know we do not know <ref name="Rumsfeld"/> <ref name="Defence"/>. The decision-making process in situations affected by a great level of uncertainty is defined as ''decision making under deep uncertainty'' (DMDU) <ref name="Lempert RDM"/>.		According to former United States Secretary of Defence Donald Rumsfeld, there are different types of knowledge: known knowns, known unknowns, and unknown unknowns. Known knowns refer to things that we know for sure. Known unknowns refer to things that we know we do not know. However, the most challenging category is the unknown unknowns, which refers to things that we do not know we do not know <ref name="Rumsfeld"/> <ref name="Defence"/>. The decision-making process in situations affected by a great level of uncertainty is defined as ''decision making under deep uncertainty'' (DMDU) <ref name="Lempert RDM"/>.

	Robustness is a crucial aspect of effective DMDU <ref name="Rosenhead 1972"/> <ref name="Metz 2001"/>. Conventional decision-analytic techniques for risk and decision analysis are designed to identify optimal strategies based on a characterization of uncertainty that follows the axioms of probability theory <ref name="Morgan 1990"/>. However, in scenarios where there is uncertainty about the system model or the distributions of its inputs, traditional decision-analytic approaches often utilize sensitivity analyses to assess the dependence of the optimum strategy on the specification of model and distributions <ref name="Saltelli 2000"/>. While this approach may be suitable when the optimum strategy is relatively insensitive to these key assumptions, it can pose both conceptual and practical challenges when this is not the case. RDM is part of a new breed of computational, multi-scenario simulation approaches that integrates ideas from scenario-based planning into a quantitative framework <ref name="Morgan et al. 1999"/> <ref name="van Asselt 2000"/> <ref name="Metz 2001"/> <ref name=" Nakicenovic 2000"/>. It inverts traditional sensitivity analysis by seeking optimization strategies which good performance is insensitive to the most significant uncertainties. Beginning with one or more system models that link optimization strategies to outcomes and a collection of several plausible probability distributions over the uncertain input parameters to these models, RDM describes uncertainty with various, plausible perspectives of the future <ref name=" Lempert et al. 2006"/>. RDM suggests robust strategies, identifies vulnerabilities, and ~~suggests~~ new or modified strategies.		Robustness is a crucial aspect of effective DMDU <ref name="Rosenhead 1972"/> <ref name="Metz 2001"/>. Conventional decision-analytic techniques for risk and decision analysis are designed to identify optimal strategies based on a characterization of uncertainty that follows the axioms of probability theory <ref name="Morgan 1990"/>. However, in scenarios where there is uncertainty about the system model or the distributions of its inputs, traditional decision-analytic approaches often utilize sensitivity analyses to assess the dependence of the optimum strategy on the specification of model and distributions <ref name="Saltelli 2000"/>. While this approach may be suitable when the optimum strategy is relatively insensitive to these key assumptions, it can pose both conceptual and practical challenges when this is not the case. RDM is part of a new breed of computational, multi-scenario simulation approaches that integrates ideas from scenario-based planning into a quantitative framework <ref name="Morgan et al. 1999"/> <ref name="van Asselt 2000"/> <ref name="Metz 2001"/> <ref name=" Nakicenovic 2000"/>. It inverts traditional sensitivity analysis by seeking optimization strategies which good performance is insensitive to the most significant uncertainties. Beginning with one or more system models that link optimization strategies to outcomes and a collection of several plausible probability distributions over the uncertain input parameters to these models, RDM describes uncertainty with various, plausible perspectives of the future <ref name=" Lempert et al. 2006"/>. RDM suggests robust strategies, identifies vulnerabilities, and proposes new or modified strategies.

	== Foundations of Robust Decision Making ==		== Foundations of Robust Decision Making ==

Colombofabio: /* Case study: Application to Water Planning Management */

2023-05-02T09:41:42Z

Case study: Application to Water Planning Management

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	Robustness was defined as the strategy that minimizes regret under a plethora of conceivable future scenarios. In this case, regret is defined as the extra total yearly supply (volume of water, in maf) required to keep Lake Mead at 1000 feet during the experiment. The more the supply, the more the regret. It is worth noting that regret is not eliminated. In fact, throughout the simulation, it was observed that some strategies pursued big investments in scenarios where annual precipitations are higher and little to no investments in the ones in which these are lower.		Robustness was defined as the strategy that minimizes regret<sup>[[#Notes\|[c] ]]</sup> under a plethora of conceivable future scenarios. In this case, regret is defined as the extra total yearly supply (volume of water, in maf) required to keep Lake Mead at 1000 feet during the experiment. The more the supply, the more the regret. It is worth noting that regret is not eliminated. In fact, throughout the simulation, it was observed that some strategies pursued big investments in scenarios where annual precipitations are higher and little to no investments in the ones in which these are lower.

	'''Trade-off analysis.'''		'''Trade-off analysis.'''

Colombofabio: /* Notes */

2023-05-02T09:40:44Z

Notes

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	==Notes==		==Notes==

	<small>[a]</small> - Maf = million acre-feet. It is a non-SI used in reference to large-scale water sources.		<small>[a]</small> - Maf = million acre-feet. It is a non-SI used in reference to large-scale water sources.


	<small>[b]</small> - Lake Mead is situated at the border of Nevada and Arizona.		<small>[b]</small> - Lake Mead is situated at the border of Nevada and Arizona.


	<small>[c]</small> - The human emotional response of regret, which is frequently felt while making decisions under uncertainty, may be assessed as the amount of variance between a made option and the best choice, according to decision theory.		<small>[c]</small> - The human emotional response of regret, which is frequently felt while making decisions under uncertainty, may be assessed as the amount of variance between a made option and the best choice, according to decision theory.