RDM - Revision history

S164513: /* The foundation of RDM */

2021-02-28T18:47:48Z

The foundation of RDM

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	The primary theoretical focus lays around uncertainty theories more specifically in the area of [[Decision Making Under Deep Uncertainty (DMDU)]], the reason for this is that the RDM methodology is used for developing strategies under deep uncertainty. As stated in ''The standard for portfolio management'', uncertainties are in general just the result of a lack or limited knowledge about a certain state in time and are almost impossible to avoid when making decisions in our modern world <ref name="Standard2017"> The standard for portfolio management — fourth edition. (2017), ProQuest Ebook Central, Page 91-94, https://ebookcentral-proquest-com.proxy.findit.dtu.dk/lib/DTUDK/detail.action? </ref>. Many books have been written about decisionmaking under uncertainty, but only a very limited amount includes DMDU. Even though the literature about DMDU is limited, however increasing, several tools and different analytical approaches exist. They share the underlying paradigm that they all seeks to reduce strategies vulnerability against the uncertainties associated with the future, which in some literature is defined as [[Assumption Based Planning (ABP)]]<ref name ="ABP"> Dewar J.A., Builder C.H. W., Hix M., Levin M.H. (1993), Assumption-Based Planning: A planning tool for very uncertain times. MR-114-A, RAND, Santa Monica, California. </ref>.		The primary theoretical focus lays around uncertainty theories more specifically in the area of [[Decision Making Under Deep Uncertainty (DMDU)]], the reason for this is that the RDM methodology is used for developing strategies under deep uncertainty. As stated in ''The standard for portfolio management'', uncertainties are in general just the result of a lack or limited knowledge about a certain state in time and are almost impossible to avoid when making decisions in our modern world <ref name="Standard2017"> The standard for portfolio management — fourth edition. (2017), ProQuest Ebook Central, Page 91-94, https://ebookcentral-proquest-com.proxy.findit.dtu.dk/lib/DTUDK/detail.action? </ref>. Many books have been written about decisionmaking under uncertainty, but only a very limited amount includes DMDU. Even though the literature about DMDU is limited, however increasing, several tools and different analytical approaches exist. They share the underlying paradigm that they all seeks to reduce strategies vulnerability against the uncertainties associated with the future, which in some literature is defined as [[Assumption Based Planning (ABP)]]<ref name ="ABP"> Dewar J.A., Builder C.H. W., Hix M., Levin M.H. (1993), Assumption-Based Planning: A planning tool for very uncertain times. MR-114-A, RAND, Santa Monica, California. </ref>.

	RDM is developed based on some already existing methods, paradigms and approaches, the most important ones will be reviewed in the following section.		RDM is developed based on some already existing methods, paradigms and approaches, the most important ones will be reviewed in the following section. The foundation of RDM is visualised in the figure below. It shows that RDM is based on three main concepts, Decision Analysis, Assumption-Based Planning and Scenarios which then are united in the forth concept, Exploratory modelling.

	[[File:Foundation1.png\|500px\|thumb\|center\|The foundation of RDM]]		[[File:Foundation1.png\|500px\|thumb\|center\|The foundation of RDM]]

S164513: /* The foundation of RDM */

2021-02-28T18:42:16Z

The foundation of RDM

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	RDM is developed based on some already existing methods, paradigms and approaches, the most important ones will be reviewed in the following section.		RDM is developed based on some already existing methods, paradigms and approaches, the most important ones will be reviewed in the following section.

	[[File:~~Foundation~~.png\|500px\|thumb\|center\|The foundation of RDM]]		[[File:Foundation1.png\|500px\|thumb\|center\|The foundation of RDM]]

	==== Decision Analysis ====		==== Decision Analysis ====

S164513: /* The foundation of RDM */

2021-02-28T18:40:19Z

The foundation of RDM

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	RDM is developed based on some already existing methods, paradigms and approaches, the most important ones will be reviewed in the following section.		RDM is developed based on some already existing methods, paradigms and approaches, the most important ones will be reviewed in the following section.

	[[File:~~Figur~~.png\|500px\|thumb\|center\|~~Steps in an~~ RDM ~~analysis<ref name="Lempert2019"/>~~]]		[[File:Foundation.png\|500px\|thumb\|center\|The foundation of RDM]]

	==== Decision Analysis ====		==== Decision Analysis ====

S164513: /* The foundation of RDM */

2021-02-28T18:39:00Z

The foundation of RDM

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	RDM is developed based on some already existing methods, paradigms and approaches, the most important ones will be reviewed in the following section.		RDM is developed based on some already existing methods, paradigms and approaches, the most important ones will be reviewed in the following section.

	[[File:Figur.png\|500px\|thumb\|~~right~~\|Steps in an RDM analysis<ref name="Lempert2019"/>]]		[[File:Figur.png\|500px\|thumb\|center\|Steps in an RDM analysis<ref name="Lempert2019"/>]]

	==== Decision Analysis ====		==== Decision Analysis ====

S164513: /* The foundation of RDM */

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The foundation of RDM

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	RDM is developed based on some already existing methods, paradigms and approaches, the most important ones will be reviewed in the following section.		RDM is developed based on some already existing methods, paradigms and approaches, the most important ones will be reviewed in the following section.

			[[File:Figur.png\|500px\|thumb\|right\|Steps in an RDM analysis<ref name="Lempert2019"/>]]

	==== Decision Analysis ====		==== Decision Analysis ====

S164513: /* Limitations */

2021-02-28T18:27:00Z

Limitations

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	RDM is not an easy method to follow and it certainly has it's requirements in order to be performed correctly, but as the philosophy behind RDM is ''" that it is better to be roughly right than precisely wrong"'', the models used to perform the RDM is often relatively simple and fast models. The problem is that even if a model solely is simple, problems can occur when it is necessary to run the model thousands or even millions of times, with a large number of different strategies and scenarios, which RDM involves. It sets requirements for the computer power and amount of data which in some cases, e.g. in developing countries, maybe doesn't exist causing it to be almost impossible to perform RDM <ref = name"philosophy"> Bhave A.G., Conway D., Dessai S., Stainforth D.A. (2016), Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world, Climate Risk Management, Volume 14, Pages 1-10, ISSN 2212-0963, </ref>. Besides data and computing requirements, then expert knowledge on the field of concern could also be needed. If the decision-making group does not possess this knowledge, they have to hire external experts which can be costly. It is also worth bearing in mind that even with the involvement of expert knowledge, the execution of RDM is based on subjective assessments, which is influenced by the perceptions of the decision-makers<ref name="econadapt"> Econadapt - Toolbox, Robust Decision Making, Retrieved February 16, (2021). https://econadapt-toolbox.eu/robust-decision-making </ref>. It means that RDM is only as good as the data used for it. However, performing thousands or millions of simulations significantly minimizes the risk but will never be able to eliminate it.		RDM is not an easy method to follow and it certainly has it's requirements in order to be performed correctly, but as the philosophy behind RDM is ''" that it is better to be roughly right than precisely wrong"'', the models used to perform the RDM is often relatively simple and fast models. The problem is that even if a model solely is simple, problems can occur when it is necessary to run the model thousands or even millions of times, with a large number of different strategies and scenarios, which RDM involves. It sets requirements for the computer power and amount of data which in some cases, e.g. in developing countries, maybe doesn't exist causing it to be almost impossible to perform RDM <ref = name"philosophy"> Bhave A.G., Conway D., Dessai S., Stainforth D.A. (2016), Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world, Climate Risk Management, Volume 14, Pages 1-10, ISSN 2212-0963, </ref>. Besides data and computing requirements, then expert knowledge on the field of concern could also be needed. If the decision-making group does not possess this knowledge, they have to hire external experts which can be costly. It is also worth bearing in mind that even with the involvement of expert knowledge, the execution of RDM is based on subjective assessments, which is influenced by the perceptions of the decision-makers<ref name="econadapt"> Econadapt - Toolbox, Robust Decision Making, Retrieved February 16, (2021). https://econadapt-toolbox.eu/robust-decision-making </ref>. It means that RDM is only as good as the data used for it. However, performing thousands or millions of simulations significantly minimizes the risk but will never be able to eliminate it.

	A limitation could also be the necessity of external involvement of experts to perform the modelling, there even though the models are simple, acquires some computer modelling skills to perform~~. RDM is thus not a tool you decide to use without first familiarizing yourself with the requirements the execution of an RDM place on the decision-makers, their knowledge and competencies~~. Another limitation, as the annotated literature on RDM shows, is that the RDM methodology is mostly used to support DMDU for policy challenges. Besides the fact that it has mainly been used in connection with long-term issues. Among other things, this is due to the fact that the uncertainty associated with a given decision is largely related to time. Short-term strategies will only very rarely be associated with a high degree of uncertainty (deep uncertainty), which is why RDM is primarily limited to long-term decisions <ref name="Lempert2019"/><ref name="Lempert2003"/>.		A limitation could also be the necessity of external involvement of experts to perform the modelling, there even though the models are simple, acquires some computer modelling skills to perform. Another limitation, as the annotated literature on RDM shows, is that the RDM methodology is mostly used to support DMDU for policy challenges. Besides the fact that it has mainly been used in connection with long-term issues. Among other things, this is due to the fact that the uncertainty associated with a given decision is largely related to time. Short-term strategies will only very rarely be associated with a high degree of uncertainty (deep uncertainty), which is why RDM is primarily limited to long-term decisions <ref name="Lempert2019"/><ref name="Lempert2003"/>. RDM is thus not a tool you decide to use without first familiarizing yourself with the requirements the execution of an RDM place on the decision-makers, their knowledge and competencies.

	== Annotated bibliography ==		== Annotated bibliography ==

S164513: /* Limitations */

2021-02-28T18:26:30Z

Limitations

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	RDM is not an easy method to follow and it certainly has it's requirements in order to be performed correctly, but as the philosophy behind RDM is ''" that it is better to be roughly right than precisely wrong"'', the models used to perform the RDM is often relatively simple and fast models. The problem is that even if a model solely is simple, problems can occur when it is necessary to run the model thousands or even millions of times, with a large number of different strategies and scenarios, which RDM involves. It sets requirements for the computer power and amount of data which in some cases, e.g. in developing countries, maybe doesn't exist causing it to be almost impossible to perform RDM <ref = name"philosophy"> Bhave A.G., Conway D., Dessai S., Stainforth D.A. (2016), Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world, Climate Risk Management, Volume 14, Pages 1-10, ISSN 2212-0963, </ref>. Besides data and computing requirements, then expert knowledge on the field of concern could also be needed. If the decision-making group does not possess this knowledge, they have to hire external experts which can be costly. It is also worth bearing in mind that even with the involvement of expert knowledge, the execution of RDM is based on subjective assessments, which is influenced by the perceptions of the decision-makers<ref name="econadapt"> Econadapt - Toolbox, Robust Decision Making, Retrieved February 16, (2021). https://econadapt-toolbox.eu/robust-decision-making </ref>. It means that RDM is only as good as the data used for it. However, performing thousands or millions of simulations significantly minimizes the risk but will never be able to eliminate it.		RDM is not an easy method to follow and it certainly has it's requirements in order to be performed correctly, but as the philosophy behind RDM is ''" that it is better to be roughly right than precisely wrong"'', the models used to perform the RDM is often relatively simple and fast models. The problem is that even if a model solely is simple, problems can occur when it is necessary to run the model thousands or even millions of times, with a large number of different strategies and scenarios, which RDM involves. It sets requirements for the computer power and amount of data which in some cases, e.g. in developing countries, maybe doesn't exist causing it to be almost impossible to perform RDM <ref = name"philosophy"> Bhave A.G., Conway D., Dessai S., Stainforth D.A. (2016), Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world, Climate Risk Management, Volume 14, Pages 1-10, ISSN 2212-0963, </ref>. Besides data and computing requirements, then expert knowledge on the field of concern could also be needed. If the decision-making group does not possess this knowledge, they have to hire external experts which can be costly. It is also worth bearing in mind that even with the involvement of expert knowledge, the execution of RDM is based on subjective assessments, which is influenced by the perceptions of the decision-makers<ref name="econadapt"> Econadapt - Toolbox, Robust Decision Making, Retrieved February 16, (2021). https://econadapt-toolbox.eu/robust-decision-making </ref>. It means that RDM is only as good as the data used for it. However, performing thousands or millions of simulations significantly minimizes the risk but will never be able to eliminate it.

	~~Another~~ limitation could be the necessity of external involvement of experts to perform the modelling, there even though the models are simple, acquires some computer modelling skills to perform. RDM is thus not a tool you decide to use without first familiarizing yourself with the requirements the execution of an RDM place on the decision-makers, their knowledge and competencies. Another limitation, as the annotated literature on RDM shows, is that the RDM methodology is mostly used to support DMDU for policy challenges. Besides the fact that it has mainly been used in connection with long-term issues. Among other things, this is due to the fact that the uncertainty associated with a given decision is largely related to time. Short-term strategies will only very rarely be associated with a high degree of uncertainty (deep uncertainty), which is why RDM is primarily limited to long-term decisions <ref name="Lempert2019"/><ref name="Lempert2003"/>.		A limitation could also be the necessity of external involvement of experts to perform the modelling, there even though the models are simple, acquires some computer modelling skills to perform. RDM is thus not a tool you decide to use without first familiarizing yourself with the requirements the execution of an RDM place on the decision-makers, their knowledge and competencies. Another limitation, as the annotated literature on RDM shows, is that the RDM methodology is mostly used to support DMDU for policy challenges. Besides the fact that it has mainly been used in connection with long-term issues. Among other things, this is due to the fact that the uncertainty associated with a given decision is largely related to time. Short-term strategies will only very rarely be associated with a high degree of uncertainty (deep uncertainty), which is why RDM is primarily limited to long-term decisions <ref name="Lempert2019"/><ref name="Lempert2003"/>.

	== Annotated bibliography ==		== Annotated bibliography ==

S164513: /* Limitations */

2021-02-28T18:25:44Z

Limitations

← Older revision		Revision as of 18:25, 28 February 2021
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	RDM is not an easy method to follow and it certainly has it's requirements in order to be performed correctly, but as the philosophy behind RDM is ''" that it is better to be roughly right than precisely wrong"'', the models used to perform the RDM is often relatively simple and fast models. The problem is that even if a model solely is simple, problems can occur when it is necessary to run the model thousands or even millions of times, with a large number of different strategies and scenarios, which RDM involves. It sets requirements for the computer power and amount of data which in some cases, e.g. in developing countries, maybe doesn't exist causing it to be almost impossible to perform RDM <ref = name"philosophy"> Bhave A.G., Conway D., Dessai S., Stainforth D.A. (2016), Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world, Climate Risk Management, Volume 14, Pages 1-10, ISSN 2212-0963, </ref>. Besides data and computing requirements, then expert knowledge on the field of concern could also be needed. If the decision-making group does not possess this knowledge, they have to hire external experts which can be costly. It is also worth bearing in mind that even with the involvement of expert knowledge, the execution of RDM is based on subjective assessments, which is influenced by the perceptions of the decision-makers<ref name="econadapt"> Econadapt - Toolbox, Robust Decision Making, Retrieved February 16, (2021). https://econadapt-toolbox.eu/robust-decision-making </ref>. It means that RDM is only as good as the data used for it. However, performing thousands or millions of simulations significantly minimizes the risk but will never be able to eliminate it.		RDM is not an easy method to follow and it certainly has it's requirements in order to be performed correctly, but as the philosophy behind RDM is ''" that it is better to be roughly right than precisely wrong"'', the models used to perform the RDM is often relatively simple and fast models. The problem is that even if a model solely is simple, problems can occur when it is necessary to run the model thousands or even millions of times, with a large number of different strategies and scenarios, which RDM involves. It sets requirements for the computer power and amount of data which in some cases, e.g. in developing countries, maybe doesn't exist causing it to be almost impossible to perform RDM <ref = name"philosophy"> Bhave A.G., Conway D., Dessai S., Stainforth D.A. (2016), Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world, Climate Risk Management, Volume 14, Pages 1-10, ISSN 2212-0963, </ref>. Besides data and computing requirements, then expert knowledge on the field of concern could also be needed. If the decision-making group does not possess this knowledge, they have to hire external experts which can be costly. It is also worth bearing in mind that even with the involvement of expert knowledge, the execution of RDM is based on subjective assessments, which is influenced by the perceptions of the decision-makers<ref name="econadapt"> Econadapt - Toolbox, Robust Decision Making, Retrieved February 16, (2021). https://econadapt-toolbox.eu/robust-decision-making </ref>. It means that RDM is only as good as the data used for it. However, performing thousands or millions of simulations significantly minimizes the risk but will never be able to eliminate it.

	Another limitation could be the necessity of external involvement of experts to perform the modelling, there even though the models are simple, acquires some computer modelling skills to perform. RDM is thus not ~~just~~ a tool you decide to use without first familiarizing yourself with the requirements the execution of an RDM place on decision-makers, their knowledge and competencies. Another limitation, as the annotated literature on RDM shows, is that the RDM methodology is mostly used to support DMDU for policy challenges. Besides the fact that it has mainly been used in connection with long-term . Among other things, this is due to the fact that the uncertainty associated with a given decision is largely related to time. Short-term strategies will only very rarely be associated with a high degree of uncertainty (deep uncertainty), which is why RDM is primarily limited to long-term decisions<ref name="Lempert2019"/><ref name="Lempert2003"/>.		Another limitation could be the necessity of external involvement of experts to perform the modelling, there even though the models are simple, acquires some computer modelling skills to perform. RDM is thus not a tool you decide to use without first familiarizing yourself with the requirements the execution of an RDM place on the decision-makers, their knowledge and competencies. Another limitation, as the annotated literature on RDM shows, is that the RDM methodology is mostly used to support DMDU for policy challenges. Besides the fact that it has mainly been used in connection with long-term issues. Among other things, this is due to the fact that the uncertainty associated with a given decision is largely related to time. Short-term strategies will only very rarely be associated with a high degree of uncertainty (deep uncertainty), which is why RDM is primarily limited to long-term decisions <ref name="Lempert2019"/><ref name="Lempert2003"/>.

	== Annotated bibliography ==		== Annotated bibliography ==

S164513: /* Limitations */

2021-02-28T18:19:42Z

Limitations

← Older revision		Revision as of 18:19, 28 February 2021
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	=== Limitations ===		=== Limitations ===
	RDM is not an easy method to follow and it certainly has it's requirements in order to be performed correctly, but as the philosophy behind RDM is ''" that it is better to be roughly right than precisely wrong"'', the models used to perform the RDM is often relatively simple and fast models. The problem is that even if a model solely is simple, problems can occur when it is necessary to run the model thousands or even millions of times, with a large number of different strategies and scenarios, which RDM involves. It sets requirements for the amount of data which in some cases, e.g. in developing countries, maybe doesn't exist causing it to be almost impossible to perform RDM <ref = name"philosophy"> Bhave A.G., Conway D., Dessai S., Stainforth D.A. (2016), Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world, Climate Risk Management, Volume 14, Pages 1-10, ISSN 2212-0963, </ref>. Besides data and computing requirements, then expert knowledge on the field of concern could also be needed. If the decision-making group does not possess this knowledge, they have to hire external experts which can be costly. It is also worth bearing in mind that even with the involvement of expert knowledge, the execution of RDM is based on subjective assessments, which is influenced by the perceptions of the decision-makers<ref name="econadapt"> Econadapt - Toolbox, Robust Decision Making, Retrieved February 16, (2021). https://econadapt-toolbox.eu/robust-decision-making </ref>. It means that RDM is only as good as the data used for it. However, performing thousands or millions of simulations significantly minimizes the risk but will never be able to eliminate it.		RDM is not an easy method to follow and it certainly has it's requirements in order to be performed correctly, but as the philosophy behind RDM is ''" that it is better to be roughly right than precisely wrong"'', the models used to perform the RDM is often relatively simple and fast models. The problem is that even if a model solely is simple, problems can occur when it is necessary to run the model thousands or even millions of times, with a large number of different strategies and scenarios, which RDM involves. It sets requirements for the computer power and amount of data which in some cases, e.g. in developing countries, maybe doesn't exist causing it to be almost impossible to perform RDM <ref = name"philosophy"> Bhave A.G., Conway D., Dessai S., Stainforth D.A. (2016), Barriers and opportunities for robust decision making approaches to support climate change adaptation in the developing world, Climate Risk Management, Volume 14, Pages 1-10, ISSN 2212-0963, </ref>. Besides data and computing requirements, then expert knowledge on the field of concern could also be needed. If the decision-making group does not possess this knowledge, they have to hire external experts which can be costly. It is also worth bearing in mind that even with the involvement of expert knowledge, the execution of RDM is based on subjective assessments, which is influenced by the perceptions of the decision-makers<ref name="econadapt"> Econadapt - Toolbox, Robust Decision Making, Retrieved February 16, (2021). https://econadapt-toolbox.eu/robust-decision-making </ref>. It means that RDM is only as good as the data used for it. However, performing thousands or millions of simulations significantly minimizes the risk but will never be able to eliminate it.
	Another ~~cost to~~ external involvement ~~could be~~ experts to perform the modelling, there even though the models are simple, acquires some computer modelling skills to perform. Another limitation, as the annotated literature on RDM shows, is that the RDM methodology is mostly used to support DMDU for policy challenges. Besides the fact that it has mainly been used in connection with long-term ~~policy challenges~~. Among other things, this is due to the fact that the uncertainty associated with a given decision is largely related to time. Short-term strategies will only very rarely be associated with a high degree of uncertainty (deep uncertainty), which is why RDM is primarily limited to long-term decisions<ref name="Lempert2019"/><ref name="Lempert2003"/>.
			Another limitation could be the necessity of external involvement of experts to perform the modelling, there even though the models are simple, acquires some computer modelling skills to perform. RDM is thus not just a tool you decide to use without first familiarizing yourself with the requirements the execution of an RDM place on decision-makers, their knowledge and competencies. Another limitation, as the annotated literature on RDM shows, is that the RDM methodology is mostly used to support DMDU for policy challenges. Besides the fact that it has mainly been used in connection with long-term . Among other things, this is due to the fact that the uncertainty associated with a given decision is largely related to time. Short-term strategies will only very rarely be associated with a high degree of uncertainty (deep uncertainty), which is why RDM is primarily limited to long-term decisions<ref name="Lempert2019"/><ref name="Lempert2003"/>.

	== Annotated bibliography ==		== Annotated bibliography ==

S164513: /* Steps in RDM */

2021-02-28T17:35:53Z

Steps in RDM

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	====Step 2 Evaluate Strategy Across Futures====		====Step 2 Evaluate Strategy Across Futures====

	~~Next~~ step is to use an "agree-on-decision" approach"<ref name="Lempert2019"/>. To do so, RDM uses simulations of the strategies found in Step 1, against a variety of plausible scenarios of the future. These simulations generate a large number of results, which is merged into a large database and the results are evaluated across the different futures <ref name="Lempert2003"/>.		The next step is to use an "agree-on-decision" approach"<ref name="Lempert2019"/>. To do so, RDM uses simulations of the strategies found in Step 1, against a variety of plausible scenarios of the future. These simulations generate a large number of results, which is merged into a large database and the results are evaluated across the different futures <ref name="Lempert2003"/>.


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	====Step 5 New Futures and Strategies====		====Step 5 New Futures and Strategies====

	Finally, the decision-making team can then use the findings from step 4 to evaluate if they have found any robust strategies, with better tradeoffs, compared to existing strategies <ref name="Lempert2003"/>. The final strategies from the RDM will often be what you call, [[Adaptive Decision Strategies]], which are strategies that adapt depending on how the future develops. The adaptive strategies consist of a short-term strategy with actions to be taken in the near future and so-called signposts which are predesignated signals that when observed, certain actions will be taken. These adaptive strategies can either be established from a jury of experts <ref name ="Lempert2003"> Bankes S.C., Lempert R.J., Popper S.W., (2003), Shaping the next one hundred years: New methods for quantitative, long-term policy analysis, Santa Monica, CA, The RAND Corporation, Technological Forecasting and Social Change — 2004, Volume 71, Issue 3, Page 305-307 </ref>, or the most suitable combination of near-term actions, signposts and actions to be taken if the signals are observed, can be determined with the help of optimization algorithms<ref name="Lempert2007"> Collins M., Lempert R.J., (2007), Managing the risk of uncertain threshold responses: Comparison of robust, optimum, and precautionary approaches. Risk Analysis, Volume 27, Issue 4, Page 1009–1026 </ref>.		Finally, the decision-making team can then use the findings from step 4 to evaluate if they have found any robust strategies, with better tradeoffs, compared to existing strategies <ref name="Lempert2003"/>. The final strategies from the RDM will often be what you call [[Adaptive Decision Strategies]], which are strategies that adapt depending on how the future develops. The adaptive strategies consist of a short-term strategy with actions to be taken in the near future and so-called signposts which are predesignated signals that when observed, certain actions will be taken. These adaptive strategies can either be established from a jury of experts <ref name ="Lempert2003"> Bankes S.C., Lempert R.J., Popper S.W., (2003), Shaping the next one hundred years: New methods for quantitative, long-term policy analysis, Santa Monica, CA, The RAND Corporation, Technological Forecasting and Social Change — 2004, Volume 71, Issue 3, Page 305-307 </ref>, or the most suitable combination of near-term actions, signposts and actions to be taken if the signals are observed, can be determined with the help of optimization algorithms<ref name="Lempert2007"> Collins M., Lempert R.J., (2007), Managing the risk of uncertain threshold responses: Comparison of robust, optimum, and precautionary approaches. Risk Analysis, Volume 27, Issue 4, Page 1009–1026 </ref>.

	== Limitations of the RDM Framework ==		== Limitations of the RDM Framework ==