Levels of uncertainties - Revision history

S173876: /* Levels of Uncertainty – From Determinism to Indeterminacy */

2023-05-09T14:27:04Z

Levels of Uncertainty – From Determinism to Indeterminacy

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	== Levels of Uncertainty – From Determinism to Indeterminacy ==		== Levels of Uncertainty – From Determinism to Indeterminacy ==
	In the following section, a framework the levels of uncertainty will be introduced, along with a guide on how to define the level of uncertainty a project, program or portfolio may face. This can be applied to activities, to decisions and to managerial practices, etc.		In the following section, a framework for the levels of uncertainty will be introduced, along with a guide on how to define the level of uncertainty a project, program or portfolio may face. This can be applied to activities, to decisions and to managerial practices, etc.

	The levels of uncertainty is a way of describing the probability of uncertainties’ states within a given system (Bevan 2022). Usually, the levels are between determinacy and indeterminacy with intermediate states between the two outer cases (Walker et. al., 2003), but there have been changes to this original description over time, such as describing the levels as a magnitude of uncertainty, e.g. one’s ability to make judgements, qualitative uncertainty, e.g. expert opinions and ambiguities between people (Warmink et. al., 2010).		The levels of uncertainty is a way of describing the probability of uncertainties’ states within a given system (Bevan 2022). Usually, the levels are between determinacy and indeterminacy with intermediate states between the two outer cases (Walker et. al., 2003), but there have been changes to this original description over time, such as describing the levels as a magnitude of uncertainty, e.g. one’s ability to make judgements, qualitative uncertainty, e.g. expert opinions and ambiguities between people (Warmink et. al., 2010).

S173876: /* Levels of Uncertainty – From Determinism to Indeterminacy */

2023-05-09T14:22:30Z

Levels of Uncertainty – From Determinism to Indeterminacy

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	== Levels of Uncertainty – From Determinism to Indeterminacy ==		== Levels of Uncertainty – From Determinism to Indeterminacy ==
	In the following section, a framework ~~for defining~~ levels will be introduced, along with a guide on how to define the level of uncertainty a project, program or portfolio may face. This can be applied to activities, to decisions and to managerial practices.		In the following section, a framework the levels of uncertainty will be introduced, along with a guide on how to define the level of uncertainty a project, program or portfolio may face. This can be applied to activities, to decisions and to managerial practices, etc.

	The levels of uncertainty is a way of describing the probability of uncertainties’ states within a given system (Bevan 2022). Usually, the levels are between determinacy and indeterminacy with intermediate states between the two outer cases (Walker et. al., 2003), but there have been changes to this original description over time, such as describing the levels as a magnitude of uncertainty, e.g. one’s ability to make judgements, qualitative uncertainty, e.g. expert opinions and ambiguities between people (Warmink et. al., 2010).		The levels of uncertainty is a way of describing the probability of uncertainties’ states within a given system (Bevan 2022). Usually, the levels are between determinacy and indeterminacy with intermediate states between the two outer cases (Walker et. al., 2003), but there have been changes to this original description over time, such as describing the levels as a magnitude of uncertainty, e.g. one’s ability to make judgements, qualitative uncertainty, e.g. expert opinions and ambiguities between people (Warmink et. al., 2010).

S173876: /* Introduction: Uncertainties and Risks */

2023-05-09T14:02:02Z

Introduction: Uncertainties and Risks

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	Uncertainty is a multifaceted creature and has been generally defined as “Any departure from the unachievable ideal of complete determinism”. Many frameworks have been developed to explain uncertainty, with different aspects. Most frameworks base themselves on the work of Walker et. al. (2003) (original 3 parts demarcated with *), dealing with 3 facets of uncertainty. Additions to the frameworks have however been made over time (Bevan, 2022), and as such consist of:		Uncertainty is a multifaceted creature and has been generally defined as “Any departure from the unachievable ideal of complete determinism”. Many frameworks have been developed to explain uncertainty, with different aspects. Most frameworks base themselves on the work of Walker et. al. (2003) (original 3 parts demarcated with *), dealing with 3 facets of uncertainty. Additions to the frameworks have however been made over time (Bevan, 2022), and as such consist of:
	# '''Nature''' (epistemic/aleatory)*. ~~Typically distuinguished through measurability~~, ~~Meta-~~uncertainty~~, Nature, reducility~~. We will mainly be dealing with epistemic uncertainty in this article.		# '''Nature''' (epistemic/aleatory)*. Aleatory uncertainty is mainly about natural randomness, where epistemic is uncertainty in parameters. We will mainly be dealing with epistemic uncertainty in this article.
	# '''Level of uncertainty'''*. The level defines how certain we are on a spectrum between determinacy/risk to indeterminacy/ignorance.		# '''Level of uncertainty'''*. The level defines how certain we are on a spectrum between determinacy/risk to indeterminacy/ignorance.
	# '''Division of uncertainty by cognizance''' (or lack thereof). Different types of ignorance/surprises. (See Bevan (2022) for more information).		# '''Division of uncertainty by cognizance''' (or lack thereof). Different types of ignorance/surprises. (See Bevan (2022) for more information).

S173876: /* Introduction: Uncertainties and Risks */

2023-05-09T13:59:14Z

Introduction: Uncertainties and Risks

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	Uncertainty is a multifaceted creature and has been generally defined as “Any departure from the unachievable ideal of complete determinism”. Many frameworks have been developed to explain uncertainty, with different aspects. Most frameworks base themselves on the work of Walker et. al. (2003) (original 3 parts demarcated with *), dealing with 3 facets of uncertainty. Additions to the frameworks have however been made over time (Bevan, 2022), and as such consist of:		Uncertainty is a multifaceted creature and has been generally defined as “Any departure from the unachievable ideal of complete determinism”. Many frameworks have been developed to explain uncertainty, with different aspects. Most frameworks base themselves on the work of Walker et. al. (2003) (original 3 parts demarcated with *), dealing with 3 facets of uncertainty. Additions to the frameworks have however been made over time (Bevan, 2022), and as such consist of:
	# '''Nature''' (epistemic/aleatory)*. Typically distuinguished through measurability, Meta-uncertainty, Nature, reducility. We will mainly be dealing with epistemic uncertainty in this article.		# '''Nature''' (epistemic/aleatory)*. Typically distuinguished through measurability, Meta-uncertainty, Nature, reducility. We will mainly be dealing with epistemic uncertainty in this article.
	# '''Level of uncertainty'''*. ~~Indeterminacy~~/~~ignorance~~ to ~~determinacy~~/~~risk~~.		# '''Level of uncertainty'''*. The level defines how certain we are on a spectrum between determinacy/risk to indeterminacy/ignorance.
	# '''Division of uncertainty by cognizance''' (or lack thereof). Different types of ignorance/surprises. (See Bevan (2022) for more information).		# '''Division of uncertainty by cognizance''' (or lack thereof). Different types of ignorance/surprises. (See Bevan (2022) for more information).
	# '''The location/source'''*. Here, we refer to Oehmen & Kwakkel (2023) where the location/source can be seen in management as organizational, technical or as requirement driven.		# '''The location/source'''*. Here, we refer to Oehmen & Kwakkel (2023) where the location/source can be seen in management as organizational, technical or as requirement driven.

S173876: /* Introduction: Uncertainties and Risks */

2023-05-09T13:58:08Z

Introduction: Uncertainties and Risks

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	== Introduction: Uncertainties and Risks ==		== Introduction: Uncertainties and Risks ==
	Levels of ~~Uncertainty~~ is one of the facets of uncertainty. In the following section the basic framework of uncertainty is introduced.		Levels of uncertainty is one of the facets of uncertainty. In the following section the basic framework of uncertainty is introduced.

	Uncertainty is a multifaceted creature and has been generally defined as “Any departure from the unachievable ideal of complete determinism”. Many frameworks have been developed to explain uncertainty, with different aspects. Most frameworks base themselves on the work of Walker et. al. (2003) (original 3 parts demarcated with *), dealing with 3 facets of uncertainty. Additions to the frameworks have however been made over time (Bevan, 2022), and as such consist of:		Uncertainty is a multifaceted creature and has been generally defined as “Any departure from the unachievable ideal of complete determinism”. Many frameworks have been developed to explain uncertainty, with different aspects. Most frameworks base themselves on the work of Walker et. al. (2003) (original 3 parts demarcated with *), dealing with 3 facets of uncertainty. Additions to the frameworks have however been made over time (Bevan, 2022), and as such consist of:
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	# '''Difficulties in communication/forming consensus'''. This is seen as incommensurate values (i.e. not agreed upon values between stakeholders), and we will assume that projects, programs and portfolios will contain differences in these values that drive reasoning for projects. We will mainly deal with uncertainty under incommensurate values in this article. (Oehmen & Kwakkel 2023)		# '''Difficulties in communication/forming consensus'''. This is seen as incommensurate values (i.e. not agreed upon values between stakeholders), and we will assume that projects, programs and portfolios will contain differences in these values that drive reasoning for projects. We will mainly deal with uncertainty under incommensurate values in this article. (Oehmen & Kwakkel 2023)
	# '''Incorporating uncertainties with human values/subjectivity, normativity'''. Incommensurate values, similar to point 5.		# '''Incorporating uncertainties with human values/subjectivity, normativity'''. Incommensurate values, similar to point 5.
	In this article, we will put our focus towards the levels of uncertainty.		In this article, we will put our focus towards the levels of uncertainty.

	== Levels of Uncertainty – From Determinism to Indeterminacy ==		== Levels of Uncertainty – From Determinism to Indeterminacy ==
	In the following section, a framework for defining levels will be introduced, along with a guide on how to define the level of uncertainty a project, program or portfolio may face. This can be applied to activities, to decisions and to managerial practices.		In the following section, a framework for defining levels will be introduced, along with a guide on how to define the level of uncertainty a project, program or portfolio may face. This can be applied to activities, to decisions and to managerial practices.

S173876: /* Classification of expressed uncertainty-descriptions */

2023-05-09T13:53:14Z

Classification of expressed uncertainty-descriptions

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	=== Classification of expressed uncertainty-descriptions ===		=== Classification of expressed uncertainty-descriptions ===
	The level of uncertainty in reporting of project, program or portfolio is often not described thoroughly. This can lead to wrong interpretations of findings, leading further to poor decisions. When presented with uncertainties by project workers, managers, experts or similar, it can be very helpful to be able to evaluate if uncertainties and their levels are thoroughly discussed and considered. Failure to do so can have negative consequences on the project, program or portfolio management (Enserink et. al. 2013). Tenney et. al. (2006) proposes a framework for managers to do so:		The level of uncertainty in reporting of project, program or portfolio is often not described thoroughly, or are not understood and the uncertainty level of a project, program or portfolio is interchanged between levels. This can lead to wrong interpretations of findings and analysis, leading further to poor decisions. When presented with uncertainties by project workers, managers, experts or similar, it can be very helpful to be able to evaluate if uncertainties and their levels are thoroughly discussed and considered. Failure to do so can have negative consequences on the project, program or portfolio management (Enserink et. al. 2013). Tenney et. al. (2006) proposes a framework for managers to do so:
	{\| class="wikitable"		{\| class="wikitable"
	\|+ Uncertainty Analysis Degree		\|+ Uncertainty Analysis Degree
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	\| XXX \|\| Uncertainty and their levels are explained and/or discussed to some degree		\| XXX \|\| Uncertainty and their levels are explained and/or discussed to some degree
	\|}		\|}
	With this categorization it is possible for the manager to set requirements for documentation when receiving information prior to making decisions. For example, a manager may require documentation prior to decisions to express uncertainties, at least through statements (such as probable, improbable, etc.) but not necessarily in the wording of the specific levels of uncertainty. This way, decisions are made more transparent and aware, and it is possible for a manager to set a baseline of uncertainty reflection (Tenney et. al. 2006). (Managers can also require multiple perspectives to be highlighted (Enserink et. al., 2013)		With this categorization it is possible for the manager to set requirements for documentation when receiving information prior to making decisions. For example, a manager may require documentation prior to decisions to express uncertainties, at least through statements (such as probable, improbable, etc.) but not necessarily in the wording of the specific levels of uncertainty. This way, decisions are made more transparent and aware, and it is possible for a manager to set a baseline of uncertainty reflection (Tenney et. al. 2006). Managers can also require multiple perspectives to be highlighted (Enserink et. al., 2013).

	=== Levels of uncertainty in future studies: What can be stated in practice ===		=== Levels of uncertainty in future studies: What can be stated in practice ===
	Almost all projects will use future studies to a degree, be it simulation, modelling, scenario-planning or similar. They are however often wrongly interpreted which can lead to poor management choices. Enserink (2013) summarizes good practice when considering future studies:		Almost all projects will use future studies to a degree, be it simulation, modelling, scenario-planning or similar. They are however often wrongly interpreted which can lead to poor management choices. Enserink (2013) summarizes good practice when considering future studies:

S173876: /* Levels of Uncertainty – From Determinism to Indeterminacy */

2023-05-09T13:50:41Z

Levels of Uncertainty – From Determinism to Indeterminacy

← Older revision		Revision as of 13:50, 9 May 2023
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	The levels of uncertainty is a way of describing the probability of uncertainties’ states within a given system (Bevan 2022). Usually, the levels are between determinacy and indeterminacy with intermediate states between the two outer cases (Walker et. al., 2003), but there have been changes to this original description over time, such as describing the levels as a magnitude of uncertainty, e.g. one’s ability to make judgements, qualitative uncertainty, e.g. expert opinions and ambiguities between people (Warmink et. al., 2010).		The levels of uncertainty is a way of describing the probability of uncertainties’ states within a given system (Bevan 2022). Usually, the levels are between determinacy and indeterminacy with intermediate states between the two outer cases (Walker et. al., 2003), but there have been changes to this original description over time, such as describing the levels as a magnitude of uncertainty, e.g. one’s ability to make judgements, qualitative uncertainty, e.g. expert opinions and ambiguities between people (Warmink et. al., 2010).
	The original distinction between levels of uncertainties was proposed by Knight (1921) between risk (measurable and quantifiable) and uncertainty (unmeasurable~~), but quickly further developed by Keynes (1921) describing the difference as probabilities (numerical, comparable, non-comparable, uncertainty~~).		The original distinction between levels of uncertainties was proposed by Knight (1921) between risk (measurable and quantifiable) and uncertainty (unmeasurable).

	In more recent years, Walker et. al. (2003) developed one of the more used distinctions of the different levels of uncertainties (Bevan 2022), which gained the addition of qualitative uncertainty as proposed by Warmink et. al. (2010). The framework of level of uncertainty is however not completely agreed upon, as other distinctions of the levels are also prevalent (See Kwakkel et. al. (2010) and Oehmen & Kwakkel (2023)). The list below shows the different levels described by Walker et. al. (2003) and Warmink et. al. (2010) and moves from determinacy to total ignorance (the two ends of the spectrum):		In more recent years, Walker et. al. (2003) developed one of the more used distinctions of the different levels of uncertainties (Bevan 2022), which gained the addition of qualitative uncertainty as proposed by Warmink et. al. (2010). The framework of level of uncertainty is however not completely agreed upon, as other distinctions of the levels are also prevalent (See Kwakkel et. al. (2010) and Oehmen & Kwakkel (2023)). The list below shows the different levels described by Walker et. al. (2003) and Warmink et. al. (2010) and moves from determinacy to total ignorance (the two ends of the spectrum):
	[[File:Chooosing Level of Uncertainty.png\|400px\|right\|middle\|thumb\|Figure 1. Derived from Warmink (2010)]]		[[File:Chooosing Level of Uncertainty.png\|400px\|right\|middle\|thumb\|Figure 1. Derived from Warmink (2010)]]
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	By reducing uncertainties like scenario uncertainty or qualitative uncertainty to statistical uncertainty, the predictions and knowledge of the behaviour in the project/program/portfolio system goes from being uncertain to perceived factual. Decisions made on changed levels of uncertainty are often poor. (Enserink et. al., 2013).		By reducing uncertainties like scenario uncertainty or qualitative uncertainty to statistical uncertainty, the predictions and knowledge of the behaviour in the project/program/portfolio system goes from being uncertain to perceived factual. Decisions made on changed levels of uncertainty are often poor. (Enserink et. al., 2013).

	Choosing the level of uncertainty can be done following the questions proposed by Warmink et. al. (2010) as seen in figure 1.		Choosing the level of uncertainty can be done following the questions proposed by Warmink et. al. (2010) as seen in figure 1.

S173876 at 13:49, 9 May 2023

2023-05-09T13:49:39Z

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	This wiki article discusses uncertainties and risks and the different levels of uncertainty that exist. Uncertainty is defined as any departure from the ideal of complete determinism, and it has several facets, such as nature, levels, division of one's cognizance, location/source, difficulties in communication, and human values/subjectivity. The levels of uncertainty, or scales of uncertainty, have been proposed by various scholars and are a way of measuring the probability of uncertainty states within a given system. There are several levels of uncertainty, ranging from statistical uncertainty, which can be expressed in statistical terms, to total ignorance, where we do not know what we do not know. The article describes how managers can use the levels of uncertainties to classify uncertainties in projects, programs, and portfolios, and evaluate the predictive capabilities of the information given to them or set about gathering more information. Developing appropriate reactions is key in managing uncertainties and risks, and getting an understanding of the level of uncertainty is a starting point.		This wiki article discusses uncertainties and risks and the different levels of uncertainty that exist. Uncertainty is defined as any departure from the ideal of complete determinism, and it has several facets, such as nature, levels, division of one's cognizance, location/source, difficulties in communication, and human values/subjectivity. The levels of uncertainty, or scales of uncertainty, have been proposed by various scholars and are a way of measuring the probability of uncertainty states within a given system. There are several levels of uncertainty, ranging from statistical uncertainty, which can be expressed in statistical terms, to total ignorance, where we do not know what we do not know. The article describes how managers can use the levels of uncertainties to classify uncertainties in projects, programs, and portfolios, and evaluate the predictive capabilities of the information given to them or set about gathering more information. Developing appropriate reactions is key in managing uncertainties and risks, and getting an understanding of the level of uncertainty is a starting point.

	== Uncertainties and Risks ==		== Introduction: Uncertainties and Risks ==
			Levels of Uncertainty is one of the facets of uncertainty. In the following section the basic framework of uncertainty is introduced.

	Uncertainty is a multifaceted creature and has been generally defined as “Any departure from the unachievable ideal of complete determinism”. Many frameworks have been developed to explain uncertainty, with different aspects. Most frameworks base themselves on the work of Walker et. al. (2003) (original 3 parts demarcated with *), dealing with 3 facets of uncertainty. Additions to the frameworks have however been made over time (Bevan, 2022), and as such consist of:		Uncertainty is a multifaceted creature and has been generally defined as “Any departure from the unachievable ideal of complete determinism”. Many frameworks have been developed to explain uncertainty, with different aspects. Most frameworks base themselves on the work of Walker et. al. (2003) (original 3 parts demarcated with *), dealing with 3 facets of uncertainty. Additions to the frameworks have however been made over time (Bevan, 2022), and as such consist of:
	# '''Nature''' (epistemic/aleatory)*. Typically distuinguished through measurability, Meta-uncertainty, Nature, reducility. We will mainly be dealing with epistemic uncertainty in this article.		# '''Nature''' (epistemic/aleatory)*. Typically distuinguished through measurability, Meta-uncertainty, Nature, reducility. We will mainly be dealing with epistemic uncertainty in this article.
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	# '''Incorporating uncertainties with human values/subjectivity, normativity'''. Incommensurate values, similar to point 5.		# '''Incorporating uncertainties with human values/subjectivity, normativity'''. Incommensurate values, similar to point 5.
	In this article, we will put our focus towards the levels of uncertainty.		In this article, we will put our focus towards the levels of uncertainty.
	==~~= The levels spectrum~~ – ~~from determinism~~ to ~~indeterminacy =~~==		== Levels of Uncertainty – From Determinism to Indeterminacy ==
			In the following section, a framework for defining levels will be introduced, along with a guide on how to define the level of uncertainty a project, program or portfolio may face. This can be applied to activities, to decisions and to managerial practices.

	The levels of uncertainty is a way of describing the probability of uncertainties’ states within a given system (Bevan 2022). Usually, the levels are between determinacy and indeterminacy with intermediate states between the two outer cases (Walker et. al., 2003), but there have been changes to this original description over time, such as describing the levels as a magnitude of uncertainty, e.g. one’s ability to make judgements, qualitative uncertainty, e.g. expert opinions and ambiguities between people (Warmink et. al., 2010).		The levels of uncertainty is a way of describing the probability of uncertainties’ states within a given system (Bevan 2022). Usually, the levels are between determinacy and indeterminacy with intermediate states between the two outer cases (Walker et. al., 2003), but there have been changes to this original description over time, such as describing the levels as a magnitude of uncertainty, e.g. one’s ability to make judgements, qualitative uncertainty, e.g. expert opinions and ambiguities between people (Warmink et. al., 2010).
	The original distinction between levels of uncertainties was proposed by Knight (1921) between risk (measurable and quantifiable) and uncertainty (unmeasurable), but quickly further developed by Keynes (1921) describing the difference as probabilities (numerical, comparable, non-comparable, uncertainty).		The original distinction between levels of uncertainties was proposed by Knight (1921) between risk (measurable and quantifiable) and uncertainty (unmeasurable), but quickly further developed by Keynes (1921) describing the difference as probabilities (numerical, comparable, non-comparable, uncertainty).

S173876: /* Selection of appropriate analytical technique */

2023-05-09T12:09:23Z

Selection of appropriate analytical technique

S173876: /* Selection of appropriate analytical technique */

2023-05-09T12:08:00Z

Selection of appropriate analytical technique

← Older revision		Revision as of 12:08, 9 May 2023
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	The application of levels of uncertainty are within several domains. Below, this article will attempt to cover some of the essential aspects of the application of levels of uncertainties.		The application of levels of uncertainty are within several domains. Below, this article will attempt to cover some of the essential aspects of the application of levels of uncertainties.
	=== Selection of appropriate analytical technique ===		=== Selection of appropriate analytical technique ===
	Selecting an appropriate analysis in uncertain situations is difficult, and often times a wrong approach can lead to faulty decisions. Based on the level of uncertainty project dimensions may be exposed to, the project manager can make better analysis of the parts of the system based on the level of uncertainty attained to that specific part. By applying uncertainty levels to likelihood of events along with ~~outcomes~~, it is possible to decide on an appropriate means of uncertainty analysis (Stirling, 2010) (See figure 2).		Selecting an appropriate analysis in uncertain situations is difficult, and often times a wrong approach can lead to faulty decisions. Based on the level of uncertainty project dimensions may be exposed to, the project manager can make better analysis of the parts of the system based on the level of uncertainty attained to that specific part. Stirling (2010) advices decision makers to move away from a pure risk driven decision making, as this will not allow for surprises to factor in. He also highlights that quantitative and qualitative methods when used to analyze purely risk are too ambiguous and will not present a whole picture of the consequences of actions. Here, the advice is to move beyond risk to uncertainty, ambiguity and ignorance, and use both quantitative and qualitative methods. By applying uncertainty levels to likelihood of events along with possibilities, it is possible to decide on an appropriate means of uncertainty analysis (Stirling, 2010) (See figure 2).

	[[File:Uncertainty_matrix.png\|400px\|right\|middle\|thumb\|Figure 2. Adapted from Stirling 2010, “Keep it Complex”, originally published in Nature]]		[[File:Uncertainty_matrix.png\|400px\|right\|middle\|thumb\|Figure 2. Adapted from Stirling 2010, “Keep it Complex”, originally published in Nature]]
	The ~~degree of unproblematic to problematic knowledge~~ can be ~~translated into determinism to indeterminism – i.e.~~ the level of uncertainty. By mapping the uncertainty level onto the uncertainty matrix, it is possible to translate the level of uncertainty a project, program or portfolio may face into actionable approaches (See figure 2). This will make decisions and management processes much more robust against epistemological uncertainties ~~through highlighting and making transparent what exactly we are uncertain~~ about ~~(Stirling, 2010). Using the categories described earlier (Statistical, scenario,~~ and qualitative uncertainty, along with partial ignorance and recognized ignorance) may prove difficult, as the rank-order is only a helpful metaphor (Bevan, 2022). The decision of what category a project may fall into is up to the individual manager. That said, if above qualitative uncertainty, managers could generally consider knowledge as problematic.		The matrix can be used in the following way:
	~~Stirling (2010) advices decision makers to move away from a pure risk driven decision making, as this will not allow for surprises to factor in. He also highlights that quantitative and qualitative~~ methods ~~when used~~ to ~~analyse only risk are too ambiguous and will not present a whole picture of the consequences of actions. Here,~~ the ~~advice is to move beyond risk to~~ uncertainty~~, ambiguity and ignorance, and use both quantitative and qualitative methods.~~		# Map uncertainties about possibilities and probabilities onto matrix
			# Decide on quadrant
			# Choose at minimum 2 methods to make the uncertainty more transparent

	Managers can equally apply the uncertainty, ambiguity and ignorance methods in projects, programs and portfolio management when there is not consensus internally over the framing of possible options, contexts, outcomes, benefits or harms. When this occurs, review the degree of uncertainty on the knowledge of outcomes and possibilities, and choose both quantitative and qualitative methods to review possible actions/decisions. If the degree of uncertainty is complex due to the system the project, program or portfolio is part of, consider using several methods from both uncertainty, ambiguity and ignorance. Managers should move away from singular guidance on decisions to plural and conditional guidance, which should follow the amount of uncertainty there is faced in the project. (Stirling, 2010)		Managers can equally apply the uncertainty, ambiguity and ignorance methods in projects, programs and portfolio management when there is not consensus internally over the framing of possible options, contexts, outcomes, benefits or harms. When this occurs, review the degree of uncertainty on the knowledge of outcomes and possibilities, and choose both quantitative and qualitative methods to review possible actions/decisions. If the degree of uncertainty is complex due to the system the project, program or portfolio is part of, consider using several methods from both uncertainty, ambiguity and ignorance. Managers should move away from singular guidance on decisions to plural and conditional guidance, which should follow the amount of uncertainty there is faced in the project. (Stirling, 2010)

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	Managers can equally apply the uncertainty, ambiguity and ignorance methods in projects, programs and portfolio management when there is not consensus internally over the framing of possible options, contexts, outcomes, benefits or harms. When this occurs, review the degree of uncertainty on the knowledge of outcomes and possibilities, and choose both quantitative and qualitative methods to review possible actions/decisions. If the degree of uncertainty is complex due to the system the project, program or portfolio is part of, consider using several methods from both uncertainty, ambiguity and ignorance. Managers should move away from singular guidance on decisions to plural and conditional guidance, which should follow the amount of uncertainty there is faced in the project. (Stirling, 2010)		Managers can equally apply the uncertainty, ambiguity and ignorance methods in projects, programs and portfolio management when there is not consensus internally over the framing of possible options, contexts, outcomes, benefits or harms. When this occurs, review the degree of uncertainty on the knowledge of outcomes and possibilities, and choose both quantitative and qualitative methods to review possible actions/decisions. If the degree of uncertainty is complex due to the system the project, program or portfolio is part of, consider using several methods from both uncertainty, ambiguity and ignorance. Managers should move away from singular guidance on decisions to plural and conditional guidance, which should follow the amount of uncertainty there is faced in the project. (Stirling, 2010)

	Oehmen & Kwakkel (2023) goes into further detail about processes to perform under uncertainties are under incommensurate values, meaning that there is not a consensus on a definitive objective to improve upon.		Oehmen & Kwakkel (2023) goes into further detail about processes to perform under uncertainties are under incommensurate values, meaning that there is not a consensus on a definitive objective to improve upon.
	~~====~~ Risk Approach ~~====~~
			'''Risk Approach'''

	When uncertainties are at a risk level, it is especially important to consider the following three approaches:		When uncertainties are at a risk level, it is especially important to consider the following three approaches:
	# '''Risk related public engagement''' – Public engagement can leverage collective knowledge, create buy-in and ownership, but can create anger and mistrust if not meeting quality standards and expectations.		# '''Risk related public engagement''' – Public engagement can leverage collective knowledge, create buy-in and ownership, but can create anger and mistrust if not meeting quality standards and expectations.
	# '''Risk communication''' – Responsible risk communication must both address the subject at hand and improve discourse by using appropriate communication methods. For factors influencing this, see Oehmen & Kwakkel (2023) and guidance see section “Communicating Uncertainties”.		# '''Risk communication''' – Responsible risk communication must both address the subject at hand and improve discourse by using appropriate communication methods. For factors influencing this, see Oehmen & Kwakkel (2023) and guidance see section “Communicating Uncertainties”.
	# '''Social movement theory''' – A theory to explain when and why people move from being complacent to taking action.		# '''Social movement theory''' – A theory to explain when and why people move from being complacent to taking action.
	~~====~~ Uncertainty approach ~~====~~
			'''Uncertainty approach'''

	Approaches to robust uncertainty management practices generally follow 3 steps according to Oehmen & Kwakkel (2023):		Approaches to robust uncertainty management practices generally follow 3 steps according to Oehmen & Kwakkel (2023):
	# '''Exploratory scenario thinking''' – explore multiple scenarios and their consequences to the system		# '''Exploratory scenario thinking''' – explore multiple scenarios and their consequences to the system
	# '''Adaptive planning''' – plans are adaptable from the outset of the project, program or portfolio in response to future changes.		# '''Adaptive planning''' – plans are adaptable from the outset of the project, program or portfolio in response to future changes.
	# '''Decision aiding''' – Having multiple stakeholders agree requires facilitation and a learning process.		# '''Decision aiding''' – Having multiple stakeholders agree requires facilitation and a learning process.
	~~====~~ Ignorance approach ~~====~~
			'''Ignorance approach'''

	Addressing ignorance is especially important at the program and portfolio level, as future operating scenarios, technical limitations or similar may not be predictable. A main strategy for addressing ignorance is creating resilience (a systems capability of managing unforeseen changes) within the projects, programs and portfolios. There are two parts to resilience (as described by Oehmen & Kwakkel (2023)):		Addressing ignorance is especially important at the program and portfolio level, as future operating scenarios, technical limitations or similar may not be predictable. A main strategy for addressing ignorance is creating resilience (a systems capability of managing unforeseen changes) within the projects, programs and portfolios. There are two parts to resilience (as described by Oehmen & Kwakkel (2023)):
	# '''Socio-organizational resilience''' – This can be further deconstructed as; (1) Individual and team resilience; (2) Project and organisational resilience; and (3) Supply chain and industry resilience.		# '''Socio-organizational resilience''' – This can be further deconstructed as; (1) Individual and team resilience; (2) Project and organisational resilience; and (3) Supply chain and industry resilience.