The difference between being flexible and adaptive
Arnaldo Pellini
rick davies
- Scenarios where some probabilities can be assigned - this can be dealt with as a risk
- Scenarios where it is not possible to do so – this has to be dealt with as an uncertainty
Identifiable risks: in scenarios where risks are identifiable, a specific strategy may be appropriate and needed to respond to each different scenario of this kind. These different kinds of scenarios may vary not only in terms of their estimated likelihood but possibly also on other criteria that may have consequences that can be forfended e.g desirability. Developing such specific strategies could be described as being 'adaptive '
Uncertainties: For scenarios are characterised by uncertainty. what is needed are more generalised and robust strategies. Here is a useful idea from Sandra Mitchell’s book Unsimple Truths: Science Complexity and Policy (2009).
Rather than maximise expected utility, Popper, Lempert and Bankes (2002:423) recommend identifying and adopting what they called the most robust strategies. These strategies might not have been the best possible option available as any one outcome but their satisfactory outcomes occur in the largest range of future contingencies. Robustness analysis requires one to consider models take into account what we do know without pretending that we have precise probability assignments for what we don’t know. Rather it analyses a range of diverse but possible scenarios and the ways in which a policy decision today would play out in each of them. As they put it, a key insight from scenario-based planning is that multiple highly differentiated views of the future can capture the information we have about the future better than any single best estimate” p93 [underlining added]
The development of more generalised and robust strategies could be described as being 'flexible '
Regarding the options for identifiable risks and for uncertainties, there may be some science that is relevant here, to do with “bet-hedging” strategies – a particular evolutionary strategy found where there is a high level of environmental unpredictability.
“The difference between adaptive plasticity and bet-hedging is that plastic norms of reaction result in the expression of an optimal phenotype over a range of environments, whereas bet-hedging expresses a single phenotype (that may be a fixed level of diversification) that is neither optimal nor a failure across all environments“(Simons, 2011)
For more, see this Wikipedia article on bet-hedging in biology
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Nilima Gulrajani
Marcus Jenal
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<Honig and Gulrajani .pdf>
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Chris Roche
Best
Chris Roche
Professor of Development Practice,
Director of the Institute for Human Security and Social Change,
Deputy Director (Impact), Developmental Leadership Program.
La Trobe University, Victoria 3086, Australia
Mobile: 0408 617 305
On 2 Jun 2020, at 9:36 pm, Nilima Gulrajani <nilim...@gmail.com> wrote:
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On 2 Jun 2020, at 03:46, rick davies <rick....@gmail.com> wrote:
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<Honig and Gulrajani .pdf>
Charlotte Ornemark
There seems to be a false dichotomy in the way this question is being asked. Surely you need programme flexibility in order to be adaptive. “Rigorous adaptation” has been used as a term to indicate that adaptation is intentional and planned rather than being random, at someone’s (maybe a project manager's or funder’s) whim. All that is good and makes sense. But whose learning (and rigour) is important for locally driven change? Ours, or those with the highest stake in the change processes?
The distinction between “high level of uncertainty = flexible programming”, and “high probability of different risk/mitigation scenarios = intentional adaptation” is helpful and interesting, but it seems to overlook that you may need both.
Take the presumably somewhat less complex (and more 'complicated' in the realm of Snowden) issue of malaria prevention as an example. One initiative we evaluated some time back in Kenya noticed that the distribution of bed nets in one rural district led to a drop in prevalence rates among men, but not as much among women. Why? It turned out that women got up before sunrise to put on the hot water “for my husband’s bath” and to prepare breakfast for the family. The need to go outside and be next to water (where mosquitoes bred) before sunrise increased women's risk. The distributed bed nets did little to change these embedded gender patterns. This was clearly just one of several compounded risk factors. Yet, if women stopped getting up early to serve their families, they feared being beaten by their husbands. One more tangible risk (being beaten) outweighed the seemingly less consequential risk of being bitten by mosquitos (even if it led to malaria).
Adaptive programming would (I believe) in this example involve women themselves in monitoring gender patterns that might put them at higher risk, and support a facilitated dialogue on ways of distributing risks of doing daily chores more equitably. A flexible program would allow for this type of adaptation in programming to happen – even if what was being suggested by women/men in different communities and contexts would not be a uniform answer to ‘roll out’ everywhere (or ‘at scale’). Rather than aiming for ‘solutions at scale’ (which we really need to stop chasing!), it would allow for ‘localization at scale’ based on adaptive ways of doing programming. Also, it would be at whatever time or pace considered most important for those who experience the problem (not for us as implementers). Our own implementation response (with quarterly ‘pause and reflect sessions’ and what-not-adaptive-measures, rigorously planned) must surely be secondary to the emerging needs for learning on the ground, whenever and however implementation lessons emerge. That seems to combine being both flexible and adaptive, with one facilitating the other.
Very best,
Charlotte
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rick davies
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Silva Ferretti
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Niki Palmer
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Claire Devlin
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John Hoven
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Arnaldo Pellini
Arnaldo Pellini
On Tuesday, June 2, 2020 at 10:13:51 AM UTC+3, Arnaldo Pellini wrote:
Niki Palmer
Devon Ysaguirre
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John Hoven
John Hoven
Nicola Nixon
Hi everyone,
Thanks so much for asking about our adaptive programming approaches in TAF! I’ve followed bits and pieces of this conversation during the week and have greatly enjoyed it. I had been trying to find a little headspace to chime in and now it seems I must get on with it and do so!
Firstly, while the original Strategy Testing approach (to which enormous credit goes to Debra Ladner, also I believe on this list!) described it as a highly flexible approach, that was in a time before everything and anything described itself as ‘flexible, adaptive and iterative’ and, as we know, threatened to oversimplify and dilute a burgeoning new practice within the world of DDD. Within TAF a few years later we see Strategy Testing as a method for Adaptive Programming (not simply flexible approaches to program implementation). Like you all, we have debated the distinction between flexible and adaptive. In our internal guidance document, we define adaptive programming as: ‘An approach to development programming in complex and fluid environments that enables teams to adapt strategies, activities and theories of change so that the program may achieve its overarching goal.’
Like the comment below that highlights adaptation to the context, this distinguishes between adaptive programming and, say, being flexible to the needs and demands of donors. We have also found it useful to clarify a little further and we refer to ‘purposeful and meaningful’ adaptation. Our guidance note developed last year states: “Tools and techniques used across TAF allow for purposeful adaptation – where there are processes around decision-making. Systems are used to carve out sufficient time for reflection and document the resulting insights on a day-to-day basis or at regular intervals of two to three months. Most commonly in TAF, Strategy Testing is used for this purpose. Other tools TAF has at its disposal enable meaningful adaptation – where decision-making is evidence not simply opinion-based. Changes of course draw on evidence of the context such as that derived from action research, stakeholder analyses and political economy analyses (of the 21st century variety).”
Secondly, to your question on how it has worked in practice there is an awful lot that can be said and said better than I, so I have copied in some colleagues who are well into a number of years of practice and may wish to elaborate further. To give you a relatively quick overview:
- Some of our country offices – Philippines, Myanmar, Nepal, Sri Lanka, Timor-Leste, in particular – have been practicing Strategy Testing (or a variation) for quite a few years now in a dedicated fashion. To build learning across TAF from these offices we have an internal community of practice with the tongue-in-cheek name of The Adapters, with membership extending to twelve country offices throughout Asia. We convene every six months to share learning across our teams and, on the last occasion in Kathmandu in January, also with external partners. We aim to hold an online session in June.
- We’ve learnt a lot from these sessions and are continuing to do so (and yes, we should put something out into the world on our reflections! it is on our to-do list, believe me!)
- Structured reflection sessions at regular intervals are extremely useful to build a consensus view on new program directions; can be facilitated to allow for robust discussion on challenges and failures; and most importantly can enable programs to grasp new opportunities that present themselves in the operating environment/political economy and to change course. They can also allow for robust discussion on the program outcomes and theory of change and their ongoing validity – which, for me, is at the heart of an adaptive rather than simply flexible, approach. Attention needs to be paid to who joins the sessions at which intervals to get the most out of them, what information and evidence feeds into them (so discussion relies on more than opinions in the room). Some offices operate with concentric circles of reflection that engage donors and implementing partners in those and build a shared understanding.
- The planning and carrying out of these sessions can be resource intensive, as is the data collection for them (which in some offices involves digitized applications or simpler methods for tracking key events, engagements and policy changes). As a process, those offices who use them have found them to support greater program effectiveness.
- The process of the sessions is insufficient as a basis for adaptive programming. Some of our offices complement strategy testing by building everyday reflection into the team environment, so that it isn’t business-as-usual for three months followed by epiphany! These practices are along the lines of everyday PEA. One office has integrated a more formal PEA into the strategy testing process to strengthen the analytic frame within which they reflect every six months.
- Our last reflection session in January focused on two related themes – building adaptive teams (rather than trying to hire them in) and resourcing the time needed for trust building. In terms of the former, we have been extremely happy to see that in some of our offices where significant time and effort has gone into building team confidence and a great working environment, with processes in place for strategy testing and adaptation, the teams have been in the right mind to creatively adapt their programs to support Covid response quickly, enthusiastically and effectively. I’ve copied our Nepal Country Rep, Meg, above, who is particularly focused on this theme at the moment with her team there.
- And on the last point of trust-building – given the resource intensive nature of some of these activities, combined with the long-term realization that thinking and working politically requires a great deal of engagement with partners (government and non-government), where trust is crucial to fostering long-term sustainable change – we posed ourselves the question of how can we make the argument more strongly (to donors and the community at large) that we need to resource a great deal of stakeholder engagement (or relationship management, for want of a better term) that may or may not lead decisively or immediately to an outcome. Where we continue to exist in a world of governance programming focused on outputs such as policy documents, handbooks or guidelines produced, websites developed, apps created or workshops held, it is difficult to work within a value-for-money frame where trust-building takes up such a lot of time. It is a question we are continue to work on.
Again, thanks for asking! I hope you find some of that helpful or interesting. Happy to engage separately if anyone wants to follow up on any of the above. And as I say, colleagues copied above might enjoy partaking of this conversation as well.
Wishing you all a lovely weekend.
Nicola
Nicola Nixon (Dr.)
Regional Director, Governance
Lakeside Green Building, 5th floor
33 Truc Bach Street, Ba Dinh,
Hanoi, Vietnam
Mob: +84 93 453 9848
Tel: 84 (243) 943-3263 | nicola...@asiafoundation.org
skype: nicola.nixon | www.asiafoundation.org
______________________________________
1. Scenarios where some probabilities can be assigned - this can be dealt with as a risk
2. Scenarios where it is not possible to do so – this has to be dealt with as an uncertainty
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John Hoven
Susanna Campbell
Thank you for this fascinating conversation. In my book - Global Governance and Local Peace: Accountability and Performance in International Peacebuilding - I make the distinction between adaptation and learning. Adaptation refers to changing (either your aim, your program design, or the way it is implemented) without information about whether you are achieving your aim. Learning, on the other hand, refers to "action to align the project/program aims with outcomes" and requires information about the gap between aims and outcomes. You can adapt without learning, but you cannot learn without adapting. I find that better programming (particularly in uncertain and conflict-affected contexts) results from learning, not adaptation.
External Email: Use caution with links and attachments.
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Prof. Susanna P. Campbell
Assistant Professor
School of International Service (SIS)
American University, Washington, DC
(202) 885-1428
@SusannaCampbell
google scholar profile
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Book
Global Governance and Local Peace: Accountability and Performance in International Peacebuilding, Cambridge University Press, 2018.
Recent pieces
Creating an Accountability Framework that Serves the Global Fragility Act's Mission (with D. Honig and S. Rose), Center for Global Development, January 10, 2020.
Foreign Assistance and Accountability in Fragile States (with G. Ingram), Brookings Institution, January 29, 2020.
Book Review Roundtable: A Savage Order, Texas National Security Review, February 5, 2020.
Pedro Prieto Martín
- Forced adaptation, where programmes are adapted on a case by case basis, exceptionally, once it becomes obvious that the original plans are not working at all (this is what happened with so many Covid-19 impacted programmes: finding flexibility where none or very few was planned);
- Flexible programming (aka 'Adaptive Management lite'): a stripped-down version of adaptive management that resembles ad hoc contingency planning more than it does planned ‘learning while doing’. It almost always neglects to develop testable hypotheses as the basis for management actions.
- Passive Adaptive Management uses regular monitoring and reflection activities to detect new challenges and, when needed, adjusts plans to remain on track toward achieving the desired outcomes, with the support from operational functions;
- Active Adaptive Management, finally, recognises the need for systematic experimentation to validate assumptions and plans for a regular upgrading of the programme's strategies; it considers learning and the reduction of uncertainty derived from imperfect knowledge as one of the key objectives of the programme’s management effort.
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Pedro Prieto Martín
This diagram suggests that sensing, reflecting, adapting... needs to happen, normally, at different speeds, corresponding to the main levels of operation of the programme. (To some extent, you could link these different levels with single-loop, double-loop and triple-loop learning).
However, if the level of ignorance/uncertainty is very high, as is the case (as you suggested) during the inception period for a programme or also when changes are happening very quickly in the operating context, you would need to accelerate the rhythm at which you sense-reflect-adapt throughout the levels depicted in the diagram.
Best,
Pedro
On Saturday, June 6, 2020 at 1:26:17 PM UTC+1, John Hoven wrote:
Nicola Nixon says that The Asia Foundation’s guidance note developed last year states: “Tools and techniques used across TAF allow for purposeful adaptation ... on a day-to-day basis or at regular intervals of two to three months. Most commonly in TAF, Strategy Testing is used for this purpose.“Does this mean that the recommended practice now is, that the Theory of Change evolves on a day-to-day basis at the start of a project when so much is still undiscovered, and the project has not yet committed to a specific plan of action; and that later in the life of the project, the pace of learning and adaptation slows down to regular intervals of two to three months?That makes sense, in contrast to the practice reported in the 2015 article: “Approximately 4 months after developing the initial TOC, the team conducts its first ST exercise, which is then repeated approximately every 3 to 4 months.”John Hoven
On Fri, Jun 5, 2020 at 11:29 PM Nicola Nixon <nicol...@asiafoundation.org> wrote:
Hi everyone,
Thanks so much for asking about our adaptive programming approaches in TAF! I’ve followed bits and pieces of this conversation during the week and have greatly enjoyed it. I had been trying to find a little headspace to chime in and now it seems I must get on with it and do so!
Firstly, while the original Strategy Testing approach (to which enormous credit goes to Debra Ladner, also I believe on this list!) described it as a highly flexible approach, that was in a time before everything and anything described itself as ‘flexible, adaptive and iterative’ and, as we know, threatened to oversimplify and dilute a burgeoning new practice within the world of DDD. Within TAF a few years later we see Strategy Testing as a method for Adaptive Programming (not simply flexible approaches to program implementation). Like you all, we have debated the distinction between flexible and adaptive. In our internal guidance document, we define adaptive programming as: ‘An approach to development programming in complex and fluid environments that enables teams to adapt strategies, activities and theories of change so that the program may achieve its overarching goal.’
Like the comment below that highlights adaptation to the context, this distinguishes between adaptive programming and, say, being flexible to the needs and demands of donors. We have also found it useful to clarify a little further and we refer to ‘purposeful and meaningful’ adaptation. Our guidance note developed last year states: “Tools and techniques used across TAF allow for purposeful adaptation – where there are processes around decision-making. Systems are used to carve out sufficient time for reflection and document the resulting insights on a day-to-day basis or at regular intervals of two to three months. Most commonly in TAF, Strategy Testing is used for this purpose. Other tools TAF has at its disposal enable meaningful adaptation – where decision-making is evidence not simply opinion-based. Changes of course draw on evidence of the context such as that derived from action research, stakeholder analyses and political economy analyses (of the 21st century variety).”
Secondly, to your question on how it has worked in practice there is an awful lot that can be said and said better than I, so I have copied in some colleagues who are well into a number of years of practice and may wish to elaborate further. To give you a relatively quick overview:
- Some of our country offices – Philippines, Myanmar, Nepal, Sri Lanka, Timor-Leste, in particular – have been practicing Strategy Testing (or a variation) for quite a few years now in a dedicated fashion. To build learning across TAF from these offices we have an internal community of practice with the tongue-in-cheek name of The Adapters, with membership extending to twelve country offices throughout Asia. We convene every six months to share learning across our teams and, on the last occasion in Kathmandu in January, also with external partners. We aim to hold an online session in June.
- We’ve learnt a lot from these sessions and are continuing to do so (and yes, we should put something out into the world on our reflections! it is on our to-do list, believe me!)
- Structured reflection sessions at regular intervals are extremely useful to build a consensus view on new program directions; can be facilitated to allow for robust discussion on challenges and failures; and most importantly can enable programs to grasp new opportunities that present themselves in the operating environment/political economy and to change course. They can also allow for robust discussion on the program outcomes and theory of change and their ongoing validity – which, for me, is at the heart of an adaptive rather than simply flexible, approach. Attention needs to be paid to who joins the sessions at which intervals to get the most out of them, what information and evidence feeds into them (so discussion relies on more than opinions in the room). Some offices operate with concentric circles of reflection that engage donors and implementing partners in those and build a shared understanding.
- The planning and carrying out of these sessions can be resource intensive, as is the data collection for them (which in some offices involves digitized applications or simpler methods for tracking key events, engagements and policy changes). As a process, those offices who use them have found them to support greater program effectiveness.
- The process of the sessions is insufficient as a basis for adaptive programming. Some of our offices complement strategy testing by building everyday reflection into the team environment, so that it isn’t business-as-usual for three months followed by epiphany! These practices are along the lines of everyday PEA. One office has integrated a more formal PEA into the strategy testing process to strengthen the analytic frame within which they reflect every six months.
- Our last reflection session in January focused on two related themes – building adaptive teams (rather than trying to hire them in) and resourcing the time needed for trust building. In terms of the former, we have been extremely happy to see that in some of our offices where significant time and effort has gone into building team confidence and a great working environment, with processes in place for strategy testing and adaptation, the teams have been in the right mind to creatively adapt their programs to support Covid response quickly, enthusiastically and effectively. I’ve copied our Nepal Country Rep, Meg, above, who is particularly focused on this theme at the moment with her team there.
- And on the last point of trust-building – given the resource intensive nature of some of these activities, combined with the long-term realization that thinking and working politically requires a great deal of engagement with partners (government and non-government), where trust is crucial to fostering long-term sustainable change – we posed ourselves the question of how can we make the argument more strongly (to donors and the community at large) that we need to resource a great deal of stakeholder engagement (or relationship management, for want of a better term) that may or may not lead decisively or immediately to an outcome. Where we continue to exist in a world of governance programming focused on outputs such as policy documents, handbooks or guidelines produced, websites developed, apps created or workshops held, it is difficult to work within a value-for-money frame where trust-building takes up such a lot of time. It is a question we are continue to work on.
Again, thanks for asking! I hope you find some of that helpful or interesting. Happy to engage separately if anyone wants to follow up on any of the above. And as I say, colleagues copied above might enjoy partaking of this conversation as well.
Wishing you all a lovely weekend.
Nicola
Nicola Nixon (Dr.)
Regional Director, Governance
Lakeside Green Building, 5th floor
Mob: +84 93 453 9848
Tel: 84 (243) 943-3263 | nicola.nixon@asiafoundation.org
skype: nicola.nixon | www.asiafoundation.org
______________________________________
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John Hoven
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John Hoven
I’d like to try to pull together some of the ideas in this conversation. I’ll sort them roughly into two categories, which I will discuss in two separate posts. The two categories are “varied contexts” and “changing contexts.” “Varied contexts” relates to differences among contexts, while “changing contexts” relates to changes over time within a particular context.
I’ll start with “varied contexts.” It helps to sort this category into two subcategories:
- Statistical context (e.g., villages or market systems that can be adequately described with a quantitative database)
- Unique context (e.g., villages or market systems characterized by uniquely human desires, capabilities, and relationships)
This is an important distinction, because they are analyzed very differently. Statistical contexts can be studied with statistical models and controlled scientific experiments. Unique contexts cannot, because there is no control group for comparison. Qualitative methods are designed specifically for these contexts. One branch of qualitative analysis focuses on testing hypotheses of cause-and-effect in a one-of-a-kind situation. That is especially relevant for localized development and peacebuilding projects.
I see two principal strategies for adapting to a unique context:
- Transfer responsibility to local stakeholders (“locally led” development and peacebuilding).
- Learn and analyze context-specific desires, capabilities, and relationships at the start of a project, and use this to identify problems and opportunities that can be addressed.
The first of these two strategies is fairly common. The second one is rare, but some comments in this conversation suggest that people are beginning to think about it. (Debra Ladner: “The initial TOC sets out the team’s ‘best guess’ about the most likely path to change. Since this first TOC is based on the team’s initial understanding of the problem and its context, they recognize that the TOC is likely incomplete and will evolve over time as the team builds relationships, gathers new information, experiments, and, most importantly, reflects on what is working and what is not.” Pedro Prieto Martin: “if the level of ignorance/uncertainty is very high, as is the case … during the inception period for a programme ... you would need to accelerate the rhythm at which you sense-reflect-adapt...”)
John Hoven
John Hoven
This posting relates to “changing contexts.” It helps to sort this category into two subcategories:
- Predictably changing contexts (e.g., due to the change of seasons, frequent droughts or floods, or the end of a development project)
- Unpredictably changing contexts (e.g., a hurricane or violent conflict)
The attributes of predictably changing contexts can be learned at the start of a project. The appropriate methods will depend on whether this is a “statistical context” or “unique context” – but, in either case, the project can be designed appropriately at the start, rather than over time as the predictable changes occur. (Susanna Campbell: “Learning … refers to "action to align the project/program aims with outcomes" and requires information about the gap between aims and outcomes… I find that better programming (particularly in uncertain and conflict-affected contexts) results from learning, not adaptation.”)
Resilience for unpredictably changing contexts may be a project goal. One strategy is to build on local capabilities that have evolved to cope with these uncertainties. Another is to strengthen market systems, because profit-seeking makes them inherently resilient. A third (suggested by Rick Davies) is to identify a range of possible scenarios, and build local capabilities that yield satisfactory outcomes in the largest range of future contingencies. Marcus Jenal adds, “There are three aspects to resilience: persistence, adaptation, and transformation.”
Another source of unpredictability arises from a project’s failure to understand the context at the start of the project, or later, and instead just muddle through. This is not the result of an unpredictably changing context. Pedro Prieto Martin suggests the following levels of ‘adaptiveness’ in development programs:
- Forced adaptation, where programmes are adapted … once it becomes obvious that the original plans are not working at all.
- Flexible programming … resembles ad hoc contingency planning more than it does planned ‘learning while doing’.
- Passive Adaptive Management uses regular monitoring and reflection activities to detect new challenges and, when needed, adjusts plans to remain on track toward achieving the desired outcomes, with the support from operational functions;
- Active Adaptive Management, finally, recognizes the need for systematic experimentation to validate assumptions and plans for a regular upgrading of the programme’s strategies; it considers learning and the reduction of uncertainty derived from imperfect knowledge as one of the key objectives of the programme’s management effort.
John Hoven
Philip Smith
On Friday, 12 June 2020 01:07:46 UTC+2, John Hoven wrote:
I’d like to try to pull together some of the ideas in this conversation. I’ll sort them roughly into two categories, which I will discuss in two separate posts. The two categories are “varied contexts” and “changing contexts.” “Varied contexts” relates to differences among contexts, while “changing contexts” relates to changes over time within a particular context.
I’ll start with “varied contexts.” It helps to sort this category into two subcategories:
- Statistical context (e.g., villages or market systems that can be adequately described with a quantitative database)
- Unique context (e.g., villages or market systems characterized by uniquely human desires, capabilities, and relationships)
This is an important distinction, because they are analyzed very differently. Statistical contexts can be studied with statistical models and controlled scientific experiments. Unique contexts cannot, because there is no control group for comparison. Qualitative methods are designed specifically for these contexts. One branch of qualitative analysis focuses on testing hypotheses of cause-and-effect in a one-of-a-kind situation. That is especially relevant for localized development and peacebuilding projects.
I see two principal strategies for adapting to a unique context:
- Transfer responsibility to local stakeholders (“locally led” development and peacebuilding).
- Learn and analyze context-specific desires, capabilities, and relationships at the start of a project, and use this to identify problems and opportunities that can be addressed.
The first of these two strategies is fairly common. The second one is rare, but some comments in this conversation suggest that people are beginning to think about it. (Debra Ladner: “The initial TOC sets out the team’s ‘best guess’ about the most likely path to change. Since this first TOC is based on the team’s initial understanding of the problem and its context, they recognize that the TOC is likely incomplete and will evolve over time as the team builds relationships, gathers new information, experiments, and, most importantly, reflects on what is working and what is not.” Pedro Prieto Martin: “if the level of ignorance/uncertainty is very high, as is the case … during the inception period for a programme ... you would need to accelerate the rhythm at which you sense-reflect-adapt...”)
John Hoven
On Mon, Jun 8, 2020 at 1:09 PM John Hoven <jho...@gmail.com> wrote:
Pedro, your article confirms the problem that motivated my question about the pace of learning — that is, operational plans are predetermined (based on assumptions, research, and lessons learned from other contexts), and operational teams try to adapt within that strait-jacket:“Adaptive delivery refers to the day-to-day operation of the programmes ... while continuously questioning whether or not the plans still work in the current situation, and what adaptations may be required.... Adaptive delivery is not always informing adaptive programming — processes of reflection and adaptation at higher levels are slow and can be cumbersome.” ("Adaptive management in SDC" pp. 32,37)You say, “if the level of ignorance/uncertainty is very high, as is the case (as you suggested) during the inception period for a programme ... you would need to accelerate the rhythm at which you sense-reflect-adapt throughout the levels depicted in the diagram.”I agree. But that triggers the question, How rapidly can a Theory of Change evolve throughout the levels depicted in the diagram? That diagram suggests learning measured in months rather than days.I would also replace “sense-reflect-adapt” with “articulate ToC - focus on key causal links - gather information - confirm or reject causal links - revise ToC.” The latter sequence is more firmly grounded in evidence and analysis, and it can be measured in days rather than months.John Hoven
Tel: 84 (243) 943-3263 | nicola.nixon@asiafoundation.org
skype: nicola.nixon | www.asiafoundation.org
______________________________________
Christine Kalume
Tel: 84 (243) 943-3263 | nicola...@asiafoundation.org
skype: nicola.nixon | www.asiafoundation.org
______________________________________
<blockquote style="border-top:none;border-right:none;border-bottom:none;border-left:1pt solid
John Hoven
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Christine Kalume
Philip Smith
John Hoven
Pedro Prieto Martín
On Monday, June 8, 2020 at 6:09:59 PM UTC+1, John Hoven wrote:
Pedro, your article confirms the problem that motivated my question about the pace of learning — that is, operational plans are predetermined (based on assumptions, research, and lessons learned from other contexts), and operational teams try to adapt within that strait-jacket:
“Adaptive delivery refers to the day-to-day operation of the programmes ... while continuously questioning whether or not the plans still work in the current situation, and what adaptations may be required.... Adaptive delivery is not always informing adaptive programming — processes of reflection and adaptation at higher levels are slow and can be cumbersome.” ("Adaptive management in SDC" pp. 32,37)
You say, “if the level of ignorance/uncertainty is very high, as is the case (as you suggested) during the inception period for a programme ... you would need to accelerate the rhythm at which you sense-reflect-adapt throughout the levels depicted in the diagram.”I agree. But that triggers the question, How rapidly can a Theory of Change evolve throughout the levels depicted in the diagram? That diagram suggests learning measured in months rather than days.
I would also replace “sense-reflect-adapt” with “articulate ToC - focus on key causal links - gather information - confirm or reject causal links - revise ToC.”
The latter sequence is more firmly grounded in evidence and analysis, and it can be measured in days rather than months.
John Hoven