Robust Decision Making (RDM)
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'''Abstract''': | '''Abstract''': | ||
| − | In some situations, decisions on projects or program management must been made under deep uncertainty. In this case, the classic risk management tools are not well adapted. Robust Decision Making (RDM) is one of the solutions which can be used to correctly consider this high level of uncertainty. In contrary of the classic risk management tools, RDM helps decision makers to choose not the optimal solution, but the solution (of set of solutions) which is the less bad regarding the set of possible future. After having | + | In some situations, decisions on projects or program management must been made under deep uncertainty (https://doi.org/10.1007/978-3-030-05252-2 ). In this case, the classic risk management tools are not well adapted. Robust Decision Making (RDM) is one of the solutions which can be used to correctly consider this high level of uncertainty. In contrary of the classic risk management tools, RDM helps decision makers to choose not the optimal solution, but the solution (of set of solutions) which is the less bad regarding the set of possible future(https://doi.org/10.1007/978-3-030-05252-2 ). After having presented the general idea and main principles of RDM, this article aims to give a guideline so that the readers can implement it to their project. This includes a step-by-step methodology and a very short presentation of the tools needed. The relevance and limitations of RDM are also discussed at the end. In this article if nothing is specified, the notions used are defined according to the PMI standards (https://app-knovel-com.proxy.findit.cvt.dk/web/view/khtml/show.v/rcid:kpSPMAGPMP/cid:kt012LZMP2/viewerType:khtml//root_slug:glossary/url_slug:glossary?b-toc-cid=kpSPMAGPMP&b-toc-title=A%20Guide%20to%20the%20Project%20Management%20Body%20of%20Knowledge%20%28PMBOK%20®%20Guide%29%20–%207th%20Edition%20and%20The%20Standard%20for%20Project%20Management&b-toc-url-slug=glossary&page=3&view=collapsed&zoom=1) |
== Presentation of the RDM == | == Presentation of the RDM == | ||
=== General Idea === | === General Idea === | ||
| − | As defined by | + | As defined by Dr Alistair Hunt , Robust decision making is “ a methodology which aims to identify adaptation options or strategies which can perform well over a wider range of possible futures. “. (https://econadapt-toolbox.eu/robust-decision-making). The particularity of this method compared to the classic risk management tools, is that the goal is not to find the optimal solution to a problem but to create a robust solution that is satisfactory for a high number of potential futures (“Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty”). In other terms, RDM doesn’t consist of trying to find the most plausible future and adapt your strategy to it. Instead, the gist of this method is to consider many potential future situations (see next parts for more details) and to assess strategies regarding all the different scenarios. Then the ones which are most robust are valorized compare to those which are optimal in one specific situation (https://doi.org/10.1007/978-3-030-05252-2 ). This method conducts to choose not best but the less bad strategy. The general foundations and a step by step approach are described later in this article. |
=== When to use it ?=== | === When to use it ?=== | ||
| − | As we mentioned it before, one | + | A situation of uncertainties and/or ambiguity (Project Management: A guide to the Project Management Body of Knowledge (PMBOK guide), 7th Edition (2021) |
| + | implies to have a limited and unclear knowledge about future events. When those uncertainties are deep, anticipate precisely the future become so hard that the classic tools of predictions or risk management cannot been use efficiently. Indeed, a high level of uncertainties implies that it is not possible to put reliable statistics or probabilities distributions on possible futures scenarios. And classic approaches need it according to (https://app-knovel-com.proxy.findit.cvt.dk/web/view/khtml/show.v/rcid:kpSRMPPP01/cid:kt011VVSO1/viewerType:khtml//root_slug:3-framework-for-risk-management-in-portfolio-program-and-project-management/url_slug:framework-risk-management?b-toc-cid=kpSRMPPP01&b-toc-title=Standard%20for%20Risk%20Management%20in%20Portfolios%2C%20Programs%2C%20and%20Projects&b-toc-url-slug=framework-risk-management&page=7&view=collapsed&zoom=1 see page 25 in the bullet points).An alternative is then to consider as many possible futures as possible in the evaluation process in order to reduce the risk of their solutions to be totally not adapted to the actual future which will happen. | ||
| + | As we mentioned it before, one particularity of RDM is to consider a very large number of scenarios in the decision process. Therefore, it is specifically adapted to the context of deep uncertainties. A review of the state-of-the art uses of RDM can show us that almost all the current projects which use it are living in a context of such high level of uncertainties. | ||
| + | More precisely, these deep uncertainties can appears mainly in three situations (https://doi.org/10.1007/978-3-030-05252-2 ) . #to_change First if the contextual uncertainties are deep. It is for example the case when the prediction of the future include climate modelization (http://eprints.lse.ac.uk/68318/1/Conway_Barriers%20and%20opportunities%20.pdf) . Then uncertainties increase when “the set of policies has more rather than fewer degrees of freedom” (https://doi.org/10.1007/978-3-030-05252-2). It means that the more policies can change and influence your project, the more difficult it is to anticipate future situations. Finally, another source of uncertainties is the disagreement which can exists between different experts on the fields concerned by your projects. Indeed, if there is as strong disagreement, it is then impossible to set only one reliable hypothesis. Then many possible futures must be considered. | ||
| + | The field of application we can find are numerous. Here is a non-exhaustive list (https://millennium-project.org/wp-content/uploads/2020/02/22-Robust-Decisionmaking.pdf) :defense, higher education, insurance, science and technology planning, counter-terrorism…Within the state of the art, RDM is very often used when a project, a program or a portfolio has to include climate change in the decision (http://eprints.lse.ac.uk/68318/1/Conway_Barriers%20and%20opportunities%20.pdf / https://econadapt-toolbox.eu/robust-decision-making for example) | ||
| + | |||
=== RDM fondations === | === RDM fondations === | ||
| − | == | + | As mentioned in(https://openknowledge.worldbank.org/bitstream/handle/10986/15603/WPS6465.pdf?sequence=1&isAllowed=y page 6) RDM don’t use computer models and data as a prediction tool to anticipate the best estimate future. Instead, RDM use the principle of exploratory modelling (https://www.researchgate.net/publication/27346533_Exploratory_modeling_and_analysis_a_promising_method_to_deal_with_deep_uncertainty) . It means that a lot of possible future with many different paths are generated in order to stress proposed strategies. The goal is not to rank the plausible scenarios but to explore as many of them as possible. The confrontations of the pre-decided strategies to a large range of scenarios are then used to create a database of KPI and outcomes. In this data base two main types of data can be found. |
| − | === | + | The first is all the relevant data to assess if one strategy is adapted to one specific future regarding the achievement of pre-defined goals. The data use to say if a strategy is adapted or not to a situation really depends on the situation. It exists a large literature which describes how to choose KPI.Give example of data which can be relevant. RDM can use these results and methodologies to include them in the process. |
| − | + | The second type of data is a description of the path used to generate the range of possible futures. That is to say, all the characteristic of one path which are relevant to differentiate it from another one. Those date are mostly generated by coupling algorithms able to generate scenarios and software which can explore those scenarios (comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty )- see steps-by-steps section for more info. | |
| + | With this database(https://doi.org/10.1007/978-3-030-05252-2 )data scientists can then use analytics to identify what are the common characteristics of the futures which make a strategy miss or reach its goals. They can also put the light on the strengths and weaknesses of the strategy regarding a set of different scenarios. These two first steps can be repeated for a large variety of input strategies and assumptions. Contrary to many classic risk assessments tools, the output of analytics in RDM process is not a predefined solutions but a set of key features which identify under which assumptions, which policies and which scenarios a strategy reach its goals. | ||
| + | When all those features are known, they can be transmitted to the decisions makers who will realize a tradeoff analyses in order to come up with a robust strategy. Strategies can then be modified of combine in order to find one which is robust. That is to say for (https://doi.org/10.1007/978-3-030-05252-2 ) “ones that meet multiple objectives over many scenarios “. The all process described above can be reiterated multiples time to stress again the new strategies and to come up with an even more robust decision. | ||
| + | |||
| + | === Collaboration Human/machine === | ||
| + | One important point of RDM is that it relies on a collaboration human-machine (https://doi.org/10.1007/978-3-030-05252-2 ). What make the strength of this method is to combine the power of human decision making and the computational power of computers. Computers are used to generate and explore many different scenarios which are themselves used to stress strategies imagined by human. When it comes to deep uncertainties with a huge number of assumptions to explore, computational power is absolutely needed to cope with the massive among of data to proceed. On the other hand, the decision part and the creation of potential tradeoff then only relies on decision makers who can use the output of the previous computer-oriented steps as a support. This mix between computer science and human intelligence implies to use a set of different tools. As we can see it through different applications of RDM (insert different links with applications), the RDM method only define which general type of tool you need for each step. The exact tools to use are context dependent and must be defined for every new project. A very quick overview and guidance regarding the appropriate tools to use are detailed under the step-by-step section. | ||
| + | |||
| + | == Step by Step methodology == | ||
| + | The aim of this section is to give a detailed guideline for the reader to be able to apply RDM to a new project, program, or portfolio management situation. Each sub-section presents a step of the process and briefly discuss the useful tools for this section. The presentation order is chronological. The different steps are based on the methodology described by Robert Lempert (in https://openknowledge.worldbank.org/bitstream/handle/10986/15603/WPS6465.pdf?sequence=1&isAllowed=y) | ||
| + | === a) Define Strategies === | ||
==Limitations == | ==Limitations == | ||
== References == | == References == | ||
Revision as of 17:37, 19 February 2022
Abstract: In some situations, decisions on projects or program management must been made under deep uncertainty (https://doi.org/10.1007/978-3-030-05252-2 ). In this case, the classic risk management tools are not well adapted. Robust Decision Making (RDM) is one of the solutions which can be used to correctly consider this high level of uncertainty. In contrary of the classic risk management tools, RDM helps decision makers to choose not the optimal solution, but the solution (of set of solutions) which is the less bad regarding the set of possible future(https://doi.org/10.1007/978-3-030-05252-2 ). After having presented the general idea and main principles of RDM, this article aims to give a guideline so that the readers can implement it to their project. This includes a step-by-step methodology and a very short presentation of the tools needed. The relevance and limitations of RDM are also discussed at the end. In this article if nothing is specified, the notions used are defined according to the PMI standards (https://app-knovel-com.proxy.findit.cvt.dk/web/view/khtml/show.v/rcid:kpSPMAGPMP/cid:kt012LZMP2/viewerType:khtml//root_slug:glossary/url_slug:glossary?b-toc-cid=kpSPMAGPMP&b-toc-title=A%20Guide%20to%20the%20Project%20Management%20Body%20of%20Knowledge%20%28PMBOK%20®%20Guide%29%20–%207th%20Edition%20and%20The%20Standard%20for%20Project%20Management&b-toc-url-slug=glossary&page=3&view=collapsed&zoom=1)
Contents |
Presentation of the RDM
General Idea
As defined by Dr Alistair Hunt , Robust decision making is “ a methodology which aims to identify adaptation options or strategies which can perform well over a wider range of possible futures. “. (https://econadapt-toolbox.eu/robust-decision-making). The particularity of this method compared to the classic risk management tools, is that the goal is not to find the optimal solution to a problem but to create a robust solution that is satisfactory for a high number of potential futures (“Comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty”). In other terms, RDM doesn’t consist of trying to find the most plausible future and adapt your strategy to it. Instead, the gist of this method is to consider many potential future situations (see next parts for more details) and to assess strategies regarding all the different scenarios. Then the ones which are most robust are valorized compare to those which are optimal in one specific situation (https://doi.org/10.1007/978-3-030-05252-2 ). This method conducts to choose not best but the less bad strategy. The general foundations and a step by step approach are described later in this article.
When to use it ?
A situation of uncertainties and/or ambiguity (Project Management: A guide to the Project Management Body of Knowledge (PMBOK guide), 7th Edition (2021)
implies to have a limited and unclear knowledge about future events. When those uncertainties are deep, anticipate precisely the future become so hard that the classic tools of predictions or risk management cannot been use efficiently. Indeed, a high level of uncertainties implies that it is not possible to put reliable statistics or probabilities distributions on possible futures scenarios. And classic approaches need it according to (https://app-knovel-com.proxy.findit.cvt.dk/web/view/khtml/show.v/rcid:kpSRMPPP01/cid:kt011VVSO1/viewerType:khtml//root_slug:3-framework-for-risk-management-in-portfolio-program-and-project-management/url_slug:framework-risk-management?b-toc-cid=kpSRMPPP01&b-toc-title=Standard%20for%20Risk%20Management%20in%20Portfolios%2C%20Programs%2C%20and%20Projects&b-toc-url-slug=framework-risk-management&page=7&view=collapsed&zoom=1 see page 25 in the bullet points).An alternative is then to consider as many possible futures as possible in the evaluation process in order to reduce the risk of their solutions to be totally not adapted to the actual future which will happen.
As we mentioned it before, one particularity of RDM is to consider a very large number of scenarios in the decision process. Therefore, it is specifically adapted to the context of deep uncertainties. A review of the state-of-the art uses of RDM can show us that almost all the current projects which use it are living in a context of such high level of uncertainties. More precisely, these deep uncertainties can appears mainly in three situations (https://doi.org/10.1007/978-3-030-05252-2 ) . #to_change First if the contextual uncertainties are deep. It is for example the case when the prediction of the future include climate modelization (http://eprints.lse.ac.uk/68318/1/Conway_Barriers%20and%20opportunities%20.pdf) . Then uncertainties increase when “the set of policies has more rather than fewer degrees of freedom” (https://doi.org/10.1007/978-3-030-05252-2). It means that the more policies can change and influence your project, the more difficult it is to anticipate future situations. Finally, another source of uncertainties is the disagreement which can exists between different experts on the fields concerned by your projects. Indeed, if there is as strong disagreement, it is then impossible to set only one reliable hypothesis. Then many possible futures must be considered. The field of application we can find are numerous. Here is a non-exhaustive list (https://millennium-project.org/wp-content/uploads/2020/02/22-Robust-Decisionmaking.pdf) :defense, higher education, insurance, science and technology planning, counter-terrorism…Within the state of the art, RDM is very often used when a project, a program or a portfolio has to include climate change in the decision (http://eprints.lse.ac.uk/68318/1/Conway_Barriers%20and%20opportunities%20.pdf / https://econadapt-toolbox.eu/robust-decision-making for example)
RDM fondations
As mentioned in(https://openknowledge.worldbank.org/bitstream/handle/10986/15603/WPS6465.pdf?sequence=1&isAllowed=y page 6) RDM don’t use computer models and data as a prediction tool to anticipate the best estimate future. Instead, RDM use the principle of exploratory modelling (https://www.researchgate.net/publication/27346533_Exploratory_modeling_and_analysis_a_promising_method_to_deal_with_deep_uncertainty) . It means that a lot of possible future with many different paths are generated in order to stress proposed strategies. The goal is not to rank the plausible scenarios but to explore as many of them as possible. The confrontations of the pre-decided strategies to a large range of scenarios are then used to create a database of KPI and outcomes. In this data base two main types of data can be found. The first is all the relevant data to assess if one strategy is adapted to one specific future regarding the achievement of pre-defined goals. The data use to say if a strategy is adapted or not to a situation really depends on the situation. It exists a large literature which describes how to choose KPI.Give example of data which can be relevant. RDM can use these results and methodologies to include them in the process. The second type of data is a description of the path used to generate the range of possible futures. That is to say, all the characteristic of one path which are relevant to differentiate it from another one. Those date are mostly generated by coupling algorithms able to generate scenarios and software which can explore those scenarios (comparing Robust Decision-Making and Dynamic Adaptive Policy Pathways for model-based decision support under deep uncertainty )- see steps-by-steps section for more info. With this database(https://doi.org/10.1007/978-3-030-05252-2 )data scientists can then use analytics to identify what are the common characteristics of the futures which make a strategy miss or reach its goals. They can also put the light on the strengths and weaknesses of the strategy regarding a set of different scenarios. These two first steps can be repeated for a large variety of input strategies and assumptions. Contrary to many classic risk assessments tools, the output of analytics in RDM process is not a predefined solutions but a set of key features which identify under which assumptions, which policies and which scenarios a strategy reach its goals. When all those features are known, they can be transmitted to the decisions makers who will realize a tradeoff analyses in order to come up with a robust strategy. Strategies can then be modified of combine in order to find one which is robust. That is to say for (https://doi.org/10.1007/978-3-030-05252-2 ) “ones that meet multiple objectives over many scenarios “. The all process described above can be reiterated multiples time to stress again the new strategies and to come up with an even more robust decision.
Collaboration Human/machine
One important point of RDM is that it relies on a collaboration human-machine (https://doi.org/10.1007/978-3-030-05252-2 ). What make the strength of this method is to combine the power of human decision making and the computational power of computers. Computers are used to generate and explore many different scenarios which are themselves used to stress strategies imagined by human. When it comes to deep uncertainties with a huge number of assumptions to explore, computational power is absolutely needed to cope with the massive among of data to proceed. On the other hand, the decision part and the creation of potential tradeoff then only relies on decision makers who can use the output of the previous computer-oriented steps as a support. This mix between computer science and human intelligence implies to use a set of different tools. As we can see it through different applications of RDM (insert different links with applications), the RDM method only define which general type of tool you need for each step. The exact tools to use are context dependent and must be defined for every new project. A very quick overview and guidance regarding the appropriate tools to use are detailed under the step-by-step section.
Step by Step methodology
The aim of this section is to give a detailed guideline for the reader to be able to apply RDM to a new project, program, or portfolio management situation. Each sub-section presents a step of the process and briefly discuss the useful tools for this section. The presentation order is chronological. The different steps are based on the methodology described by Robert Lempert (in https://openknowledge.worldbank.org/bitstream/handle/10986/15603/WPS6465.pdf?sequence=1&isAllowed=y)
a) Define Strategies
Limitations
References
Sources: https://doi.org/10.1016/j.envsoft.2016.09.017
ISBN 978-3-030-05252-2 (eBook) https://doi.org/10.1007/978-3-030-05252-2
http://eprints.lse.ac.uk/68318/1/Conway_Barriers%20and%20opportunities%20.pdf
