Government to Government trade – a new development issue, but is it threat or opportunity?

February 18, 2014

Somaliland v Somalia: great new paper on an extraordinary ‘natural experiment’ in aid and governance

February 18, 2014

The Aid trilemma: are complexity, scale and measurability mutually incompatible?

February 18, 2014
empty image
empty image

I’ve been reflecting on Owen Barder’s recent post on the tensions for aid agencies between wanting to go to scale, and acknowledging that lasting development solutions have to emerge from discussions among local actors, based on local context.

Seems to me we have something of an aid trilemma here – I would add in attribution to the mix as a third element. You can have two out of three of the following:

  1. Able to go to scale (reaching millions of people, rather than a couple of hundred)
  2. Compatible with complex systems (inherently unpredictable, discontinuous, shaped by local context)
  3. Measurable and attributable (being able to say change happened and that it was due to a given intervention or action)

Aid trilemma vennTwo out of three gives you three different permutations:

Interventions in complex systems that go to scale, but whose impact is largely unattributable:

  • Working on the ‘enabling environment’ (rule of law, strengthening civil society, access to finance)
  • Improving quality of essential services (see Lant Pritchett)
  • Problem-driven iterative adaptation (Matt Andrews)
  • Convening and brokering relationships/acting as a catalyst/other kinds of leverage rather than trying to do stuff on your own


Interventions that go to scale and are clearly attributable, but are not suited to complex systems

  • Bednets, food aid and other straightforward(ish) service delivery
  • Essential Services – quantity (getting kids into school) rather than quality (teaching them anything – Lant Pritchett again)
  • Simple replicable institutions (football/soccer, Owen thinks postal services)


Interventions in complex systems that are clearly attributable, but cannot go to scale


The question then arises, what (if anything) lies in the sweet spot, namely interventions that manage simultaneously to go to scale, are clearly attributable, and respect the nature of complex systems? Owen reckons a number of CGD pet schemes, such as Cash on Delivery, tick all three boxes, but I’m most dubious on the attribution corner of the trilemma. A government reduces maternal mortality, you reward them with $x thousand dollars per life saved, but how do you know that the offer of that money had anything to do with the outcome? Social franchising might work – a core ‘project in a box’ that allows for plenty of variation according to local circumstance, like Savings for Change. Other suggestions?

What do you think – is the trilemma idea worth pursuing? I’m planning to think this through further with UNDP, Owen and others – keep you posted.


  1. Duncan do you not need another dimension? Part of the debate seems to be about the degree to which these processes are ‘transformational’ or providing short term ‘results’ – see recent Roger Riddell paper on this This is of course the argument about what is defined as ‘works’, and indeed who defines it.

    1. Hmm, first reaction is that short term results are likely to fit into the less complex box, so the distinction is partly subsumed within the existing categories. Agree?

      1. Not really. My point was more about on what basis debates about trade-offs between your trilemma categories are – or could be – made.

  2. Hi Duncan, I can also add another dimension with which I am dealing at the moment: early wins.
    They are often cited as something to look for at the early stages of a programme or project as if they would be always available and identifiable and not just a matter of luck. They may related to attribution/contribution but not always to going to scale or adaptation/learning about context. What do you think?

  3. I baulk at the phrase – ‘not suitable to complex systems’. The world is complex and even with ‘simple’ interventions, the problems happen when we forget that – e.g. giving aid to the Rohingha people in Rakhine has huge political impacts as it is seen as a racially divisive act by the Buddhist Rakhine; building latrines in camps because there is budget for latrines whilst a range of other factors are not addressed. Easy to measure but outcomes are more complex and can have unintended consequences. This seems to be focusing on the nature of the intervention rather than the nature of the place the intervention lands – the context or environment.

    I think it is a mistake to see complex systems as a ‘thing’ or an approach or a method – rather than an ontology, a description of the ‘way the world is’. And that is not to say that we just wring our hands and say all is complex – but it more nuanced.

    I’m also not in agreement that you can’t do something to scale – like savings groups – and yet have a degree of flexibility to allow the approach to be shaped by local needs. And why can’t some measure of success be universal and others be more subtle and be more amenable to qualitative approaches and to spotting unexpected wider outcomes. What you’ve sketched does convey current thinking but may unintentionally cement in that thinking.

    1. Good challenges Jean, as ever. Of course the world is a complex system, but in any given context, complexity may be more or less relevant. Just as relatively applies everywhere, but for a subset of situations, Newtonian physics is a good enough approximation, so in some situations, a linear approach may be a good enough approximation, especially as it facilitates action, provided you keep an eye out for surprises. Infrastructure, vaccinations, bednets, campaigns – sure, you can find examples of complexity in each, but a linear approach often works pretty well.
      Totally agree on scale, which is why I cited Savings for Change as a possible candidate for the sweet spot.

  4. Duncan, while the first couple of comments highlight that any simplification has drawbacks, the trilemma feels a very useful way to focus on some important issues.

    One obvious possibility is to think about where a given sector, or a given donor or INGO, might fall in your diagram. We may not worry if an individual organisation is largely focused on (say) interventions that go to scale and are attributable (but not suited to complex systems); but if an entire sector (UK INGOs?) were to be moving in that direction then we might start to be concerned.

    That said, I think the idea of scale is inherently difficult. It tends to be used to mean something like ‘replicable without great thought’, and can be associated with the sloughing off of technical (and administrative) capacity as a donor agency moves away from policy engagement and towards ‘efficient’ money transfer.

    But my experience is that some of the most important work done by INGOs – ultimately producing results at great scale – would fall in the first instance into your third category of ‘complex, attributable, cannot go to scale’. I’m thinking of the work that Christian Aid have done to support partners working on tax and illicit flow issues. In each case, whether in Tanzania or the Dominican Republic or India, the success of interventions has depended on the understanding of the context (where were the opportunities, from e.g. mining to budgetary accountability; and what style of engagement was likely to build the issue, from parliamentary lobbying to mass campaigning, etc). Each individual case can be seen as distinct and non-scaleable; but together, and in combination with other INGOs (e.g. ActionAid, Oxfam Novib) and donors (e.g. Norway), these interventions have played a role (of perhaps indeterminable scale) in changing the global policy context.

    Back to the trilemma: I guess the implication is that definitional precision, including around time horizons, will be important to keep the clarity that you have in the diagram. But it feels like it could be really useful in thinking about competing directions of travel, not least for INGOs.

  5. Yep, I think this is worth pursuing. The three horns of your trilemma might not be quite the right ones, but it’s very useful to focus on the challenges, trade-offs and decisions that need to be made by people and organisations involved in efforts to promote and support development.

    You suggest that the question that arises is “what’s the sweet spot?” I’d suggest that it’s also useful to consider how best (no doubt, partly dependent on context/issue) to prioritise amongst the 3 (or more) dimensions. If the aim is X, and the context is Y, which dimension should we drop? Scalability? Measurability?

    Have to say, I share Jean Boulton’s unease re how you’ve used “complex systems”. I see complexity science is a way of looking at the world – an ontology – rather than a label that can be applied to some parts of the world.

    And on Cash-on-Delivery aid, I’ve always thought that that was basically accountability without attribution, which might be fine.

    Looking forward to seeing how the discussion goes.

    1. I think Alan is right that COD aid might be a way to get accountability without formal attribution (and that it ‘might be fine’!).

      But you could construct COD aid or DIBs to demonstrate attribution. For example, the Peterborough SIB compares recidivism with a sample of prisoners from other prisons, using Propensity Score Matching. That comes pretty close to a good measure of attribution. That approach might be useful for COD or DIB programmes.

  6. Really interesting thinking and discussion. I guess I’m going to risk repeating much of what Alex says above in that I’d see the concern as being when we get out of an “appropriate balance” of interventions / system changes / adaptations. So question would be how to define what that “appropriate balance” would be – not dissimlar to the old vertical vs horizontal discussion.

    I guess my other point would be that while the model is definitely useful imho, it wouldn’t be as useful if it becomes a discussion about either / or (this maybe fits with Joan’s point). Its more a case of plotting against 3 factors, surely. To take just one example of bednets, are we really saying that bednet distribution from start to finish isn’t complex at a global level? ok so each individual distribution is perhaps less complex – but to get there includes all the research that went into establishing that as an effective instrument of malaria reduction; the often governance / rights dialogue involved in the political push to build resources and backing for distribution at national / regional levels; the upscale in production often in the private sector etc… Maybe not as complex as developing effectiveness of political participation, but as I say, still worth a plot on the graph in the third dimension?

  7. Reply to Duncan – but labelling interventions as ‘simple’ seems to stop engagement with change. I feel there are very many examples of where – if only there had been the ability to join interventions up, to adapt to changing situations, to seize opportunities, to build on positive unexpected outcomes – much more could have happened.

    When funding is for longer, allows some adaptation, facilitates more systemic programmes – much more is achieved. It doesn’t have to mean we are immobilised and some of the best examples of aid and development seem to be more holistic and contextual. That’s what experienced practitioners always seem to tell me too. I’m for saying the world is always complex (not keen on the systems word) and changing the framing of everything that is done – which does not have to make it unmeasurable or unprogrammable.

  8. I think that it would be interesting to explore how institutional set up and organization is linked to the issue. Public institutions as well as many INGOs are driven by the thematic areas they cater for (health, education, etc.)and fail to achieve the necessary integration that is necessary to drive changes at the larger scale. What can be facilitated at micro-level (such as a community or a school) with the engagement of decentralized actors can simply not be replicated at larger level as actors are not able or willing to come together. Development is driven by “sectors” or at “levels”; I think it’s the shortcomings at institutional level that is the reason why we find so few things in “the sweet spot”. And yes, I love VSL, but have while we have been able to measure its impact on resilience, we have not been able to create effective links with issues of citizenship & governance for example (or children’s education for the matter).

  9. Duncan – I admire your tenacious focus on complexity. Unfortunately, complexity is somewhat an excuse for the kind of linear/minimalist thinking we see in development. My sense is that it is feasible to unpack the parts (of complex systems) even if they may not behave predictably in different contexts. Understanding the parts, however, allows us to better focus on what to influence and how. The key is to understand that ‘Sweet Spot’ does not equal to ‘one-size fits all’. In other words, change will happen from different starting points. In fact is it not time we gave up on our preoccupation with trying to rigidly control how things turn out? Especially because we often are wrong about which path to take?

    On scaling up, I am of the view that it is best to start at scale then we learn the right lessons. Small scale pilots are never a good pointer for large scale intervention. Moreover, it is erroneous that scaling up automatically means more funds and questions about attribution. For our education reform support, we are tackling the system one system leader at a time while, yes, leveraging their convening authority to do more. How do we know that our support is making a difference? We ask the system leaders. The issue of impact is not then an abstract conversation.

    1. Good points, Cornelius. The issue gets more complicated though if you look at systems that require the cooperation of a range of system leaders – like in the case of child protection systems for example.

  10. One thing that hasn’t been mentioned so far, is that the central intersection you define as the sweet spot, I would describe as ‘models’. This is an overused term, so in work I’ve done with CARE and others in recent years, this has a very specific definition, which I won’t go into full details here, but does require the work to be a unique approach that is recognised by and bought into by a range of actors. Thus a distinction is made between approaches – everyone has approaches – and models, which be definition should be multi-institutional. Thus village savings and loans schemes might be one such model.

    The interesting circle, which has attracted the most comments here, is the complex systems one. I have used Snowdon’s Cynefin framework a lot in recent years in looking at complexity, and being able to differentiate between complex and simple systems etc. In his own work, Snowdon refers to a single event as having different elements, which bears out the points about the world always being complex. It is also of course the problem with models – how adaptable are they really, for if they are not, then they don’t meet the ‘sweet spot’ criteria. One model I have worked with more recently is a participatory, inclusive governance model in Bangladesh, which incorporates complex sets of relationships, and is about building citizen – local government engagement and accountability (as required by the 2010 Union Parishad Act there). There are attempts to scale the model now by extending institutional buy-in, but the point is, the whole process around the work is attempting to achieve the sweet spot as defined in your three circles framework above. And thus it raises questions for each of the circles – is this work adaptable to different local government contexts (in Bangladesh, for a start), can evidence be produced to show its relative (cost-)effectiveness compared with other approaches, and how can the scale and scope of the model be extended whilst retaining quality standards?

    So, in short, the Aid Trilemma diagram does help with the conceptualisation of some extremely relevant current debates!

  11. Duncan, I’m stuck with this niggling feeling in the back of my head that development may be complex, but the current aid architecture is not, and that the two may never meet. Is this a realistic debate? We have Merilee Grindle’s ‘good enough [fill in the blank]’ on the one hand, and then an increasingly complex and complicated discussion around complexity theory/systems/etc on the other. How do these discussions actually fit together? Will we end up with ‘good enough complexity’? ‘Good enough adaptation’? How does this fit discussions about donor incentives, projectization etc? No answers from me, I’m afraid, but a lot of questions…

    1. But that’s OK, isn’t it? The best intervention in a complex system may well be very simple (building firebreaks in forests is simple, even though forest fires are complex). The point is to recognize and understand the complexity of the system, then think about the best way to act upon it.

  12. I suspect that there are lessons from other sectors that will be instructive. In epidemiology, for example, cause-and-effect is integral to the whole field, but it is clear that there are some issues that are amenable to linear representations. For example, germ theory is pretty reductionist, and underpins many approaches which suggest we have to either prevent germs from infecting us, or zap them once they have, using appropriate treatments. Of course, even in such situations, you can’t shut out complexity indefinitely: the rise of antimicrobial resistance being a point in case (and proving Jean’s earlier argument).

    And there are some problems – HIV-AIDS, obesity, schizophrenia, Alzheimers, and any number of chronic problems – where linearity is far from helpful, and may make things worse – where positive outcomes can be measured, but is very hard to pin down to any single causative factor.

    This doesn’t mean the latter class of problems get dismissed. In most public health systems in the West, there is a general awareness complex health problems cannot be ignored, nor can they be – on the whole – zapped out of existence by linear approaches (nudge-style approaches to tackling obesity, etc, etc, notwithstanding).

    So there is a general acknowledgment of the need to experiment with new approaches to dealing with complex problems, and with new ways of testing the efficacy or otherwise of our approaches. This is far from an abstract problem. If you start to add up the costs of preventing and treating ‘complex’ and ‘simple’ health problems respectively, the complexity of causation – and what we do about it – quickly becomes a incredibly practical policy issue!

  13. Thanks for sharing! I found this to be very thought-provoking.

    One question I have is about the origins of the elements, and therefore their legitimacy.

    It seems to me that ‘works in complex systems’ and ‘goes to scale’ are elements that are derived from the what the world needs development interventions to be. Simply put, for “development” to happen, interventions need to reach scale in complex systems. On this, I agree.

    ‘Measurable and attributable’, however, is an element that I see more as coming from the limitations the development sector has put upon itself. That is, donors’ desire to see the causal links between the dollars spent and the impact achieved. This to me seems a more artificial element as I view it as coming more from the architecture of the development sector.

    From this view, I would ask whether the third element, ‘Measurable and attributable’ actually belongs in the same way as the first two. As a sector, do we really want to reach the trifecta? Or in the long-term, are we more interested in working the third element out of the equation? What impact does it have if, in the short-term, we actively seek to meet the demand for measurability and attribution, if in the long-term we might seek to eliminate that demand?

  14. By the way, on your last point, Duncan, I would agree that simple solutions to complex problems are important, but I think it may need some nuance. Take the forest fire example: there is a need for to understand of the contextual complexity to come up with the right, relevant, simple solution – one that helps navigate and not simply ignore or reduce down the complexity. The more obvious simple solution applied in US forests was to try and put out all fires, without understanding that some degree of regular, small fires were in fact essential to maintain natural firebreaks. In the words of Wendell Holmes, it is the ‘simplicity on the far side of complexity’ that we need to aim for.

    1. Couldn’t agree more Ben – first, deep observation of the system, then come up with responses, whether simple or not

  15. This wonderful paper (“The Dog and the Frisbee”) by Andy Haldane and Vasileios Madouros, both from the Bank of England, talks about the idea of simple rules as the right response to complex problems:

    In it, they explain that while predicting the flight of a frisbee is a complex problem, a dog can catch a frisbee not because it calculates the non-linear dynamics in real time but because it follows a simple rule (“run at a speed so that the angle of gaze to the frisbee remains roughly constant”). A dog solves this problem better than a physicist.

    This is an example of a simple heuristic ‘on the far side of complexity’ that Ben talks about (quoting Wendell Holmes.)

    The interesting question is: how do we find the simple rules on the far side of complexity?

    Sometimes the response to complexity is: ‘we need to do more analysis and research so that we truly understand’. (This is often what people paid to do research like to conclude.) I think it is mistake to think that we can wade through the complexity and come up on the other side with a simple heuristic, based on fiendishly clever analysis and research. We have to find the heuristics the same way the dog does: by trial and error. And the measurement part is important not because it provides answers to donors, but because it helps accelerate the process of discovery.


  16. I agree with Ben’s last point! And going back to Ben’s previous point, I think we need to distinguish between causal links at the microscopic level and linear relationships at the macroscopic/systems level. Linear patterns of relationships in ecologies and societies emerge if things are stable but are ‘impermanent’ – will change when conditions change. This needs more said but it is an important issue that gets lost.

    I also very much agree with the comments made about this issue of institutions – the structure and processes within the aid sector can be very reductive and mechanical (although there are good examples). There is often a disconnect between the processes of funding/management/evaluation and the issues dealing with complex, contexted, emergent, relational real-life situations. Facing this disconnect feels like a central issue.

  17. Responding to Owen’s point, I agree that simple rules emerge out of complexity – and they work providing situations are stable. They are little use in dealing with catastrophic change where all changes. And many situations faced by in the field are about catastrophic change – drought, war, rentierism, the unexpected. The concept of simple rules is, I think, useful in looking for the social patterns that have emerged in societies over centuries (eg how fishermen share boats and fish) and can get trampled on in an attempt to modernise or re-build after catastrophe.

    I do agree learning comes from doing more than over-analysing..

  18. While I agree that at the extremes too much analysis can be a bad thing, this is far from the situation we find ourselves in many development situations, and we also need to be careful that we don’t make ‘understanding’ and ‘experimentation’ seem like an “either / or” choice.

    The dog wants to catch the frisbee – which is a pretty straightforward goal, and the focus of trial-and-error experimentation is pretty obvious. But what about problems that aren’t so straightforward? Say, improving education? The frisbees for the international system have been enrolment and completion rates, but focusing on these has been to the detriment of learning outcomes (as Lant P has argued).

    We may need to start experiment in multiple places in the educational system, catching multiple frisbees, all going in different directions. Faced with this, at a minimum, we need a good enough understanding of the problem we face, and the potential places for experimentation AND we need to be able to track how the problem is evolving in response to our portfolio of ‘doing’ experiments.

    A good enough systemic understanding the problem, in other words, is vital to be able to design and implement effective experiments, and also to undertake the measurements that are so essential to the process of discovery.

    The evidence from a range of case studies suggests that a robust understanding of evolving problems and trial-and-error approaches are not at odds with each other – they are both essential.

  19. Hi Duncan
    I read this post just after i had read your one about Somalia and Somaliland (tomorrows post). I know this is the wrong order, but i’d love to know if you see any connections between the two sets of ideas, especially given Barder and co’s idea in the original post you were reading about “the struggle”.

    1. Well broadly, both argue for the greater viability of institutions that emerge from local realities, hybrids etc, rather than blueprints imposed from outside

  20. Thanks Duncan for generating a quite lively exchange. I am worried though that we seem to have settled on a simple versus complex dichotomy. Simplicity comes with understanding which itself is a function of how information is assimilated and then acted on. So the fire-breaker solution seems straightforward only because the executor of the fire-breaking task is predisposed to act almost intuitively. Perhaps attempting to cross fertilize disciplines, Robert Cialdini’s book on persuasion and marketing, Influence: The Psychology of Persuasion, offers a helpful perspective in terms of how we short-cut our way to decisions. My point being that if we can get to the bottom of ‘simplicity’, it may help us to unpack how we get to that state (from complexity). For example, one thing that is clear for me is that new information, that is obtained in a neutral, really experiential and non-threatening (unlike expert knowledge) way has a potential to generate change and new institutional arrangements.

  21. I think it would be useful to distinguish between “measurable” and “attributable”. While I agree that complexity, scale and attribution constitute a challenging (likely intractable) “trilemma”, I am much more optimistic about the feasibility of measuring change in the face of complexity and scale. (Besides, how can we discuss scale in the absence of measurement?)

  22. Scalabiity depends with nature of the project and target population; reaching entire clan of a village, province or country.

  23. Dear Duncan,

    I think this is a trilemma that certainly needs to be addressed, and I would like to read more about it.

    I am very much with Owen, where he advocates trial and error, crossing the river feeling the stones, as a realistic way to address complexity. The Bayesian approach (which I missed somewhat in the Book). The approach of total analysis has brought us some noteworthy disasters where only with hindsight some aspect was overlooked, while an evolutionary approach with short feedback loops should have caught the problem.

    I wrote something about this trilemma – or rather the dilemma between measurable and scalable and complex systems earlier (

    I fear we should embrace the “complexity of the trilemma” instead of trying to solve it.

    Like in an investment portfolio, you should diversify: take on risk with a potential high return (no attribution, uncertain outcomes, no evidence base). Have some big institutional values (Government bonds, nobody ever got fired for buying Microsoft, or was it IBM, Google?). These should be the evidence based scalable interventions. Broadly speaking, a lot of health and infrastructure interventions can be found here. In a normal investment portfolio, the bulk of investments belong to this category. Moreover, in a well diversified portfolio, you might do some small scale investments of which you know it will work well, but it will never be a big company. Investing in the chef you know that wants to start his own restaurant.

    So we should not be looking for the magical triangle, we should learn to recognise and live with the diversity of our portfolio.

    Another point is the binary thinking. Scalable or not, attributable or not, complex systems compliant or not. It is a very useful approach for getting clarity of thinking, but it is just not doing justice to the situation at hand. With a potential very high return (legislation process) “somewhat attributable” or “from meta-evaluations likely attributable” might be good enough. With health interventions, “somewhat respectful for complexity” might get a long way.

  24. Interesting discussions, folks. Agree with Jean about the value of the complexity term lying in its ability to help us see the world – presenting it as a ‘feature’ seeds confusion. More so because it is not static. What seems as a complex system now, when acted upon and tracked, has parts that can become more knowable and we move into the ‘simplicity on the other side of complexity’ territory. Gigerenzer in the late 1990s researched the notion of ‘simple heuristics that make us smart’ and the cognitive science behind it.

    Chris Roche’s point is important about which values dominate for agencies and if this skews the trilemma in terms of what is non-negotiable (measurable/attributable) and the others that might become more aspirational. I experience an ‘aid system’ that does not view this trilemma as the terrain of equals but favours narrow definitions of performance/quality and the disconnect with transforming complex systems. Incentives encourage funding the known or knowable, safe bets, the work that can be attributed in simple ways. ‘Trial and error’ – all for it, but I bump into many a results measurement system that does not reward that way of working. Faddism among funding agencies can also lead to cutting short the lengthy incubation time needed for systems change. Politics intervenes and is not a huge fan of the implications of recognising the complexity of structural change.

    I’d be interested to see UNDP look at how it works and the way this affects opportunities to come to the magic middle.

  25. It is tempting to suggest that the primary solution to finding approaches that work in a complex system is simply more of Bill Easterly’s searchers. Demonstrating measurable impacts and going to scale should then just be an iterative process from there. (Not necessarily an easy one, of course!) This can apply even to macro-level interventions such as policy work so long as you remember to throw away the best practice blue prints at the start and instead work iteratively from the present context.

    As for COD aid not being attributable, isn’t the whole point that the mechanism does not care. Since money is fungible the issue of attribution for larger scale aid efforts has always struck me as partly an exercise in self-delusion anyway.

  26. Many thanks Duncan, and thanks to all for the lively debate. For fulfilling these three criteria, what about improvement collaborative-type interventions which facilitate specific groups (e.g. health workers, CHWs, community groups) to identify problems and develop solutions? If properly facilitated, this approach takes into account the complexity and nuances of local systems/realities, is measurable and attributable through evaluation with a control, and I know of at least one such intervention going to scale at the moment, focusing on quality improvement in health care in Ghana (IHI and Ghana Health Services).

    Emily Cummings

  27. Duncan, super post, as always! Looking at the 3 areas, I feel that the biggest shift we’re likely to see is in the ‘measurable and attributable’ area for few reasons:

    – More I read on the topic, the more I am convinced that we as development workers need to spend far more time understanding the context we work in, observing the system we’re a part of, and connecting with those who are already doing something on the issue we’re interested in, in order to start having an idea about how we could start nudging that system with few small scale interventions in order to better understand it, get more data on what may or may not work, and go back to the drawing board (for more observing). As a corollary to Owen’s post, this would go toward identifying the ‘struggle’ going on and creating a safe space for it to take place…

    – I know this is going to sound a bit politically naïve, but whether or not a result can or should be attributable to this or that outfit is of less concern to me as is a method of observing the system itself. Arguably if something tends to gel with a number of people from different background it may be more likely to evolve as opposed to being supported by a mass of people who come from a same sector of perspective- how do you account for that in your decision making on what to support or scale?

    – One more aspect that’s filtering up from a discussion on Giulio’s and mine blog ( can we design criteria for better decision making on what ‘struggles’ to support or how to get to a sweet spot that is value free?

    Really looking forward to this discussion!! Millie

Leave a comment

Translate »