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Archive for the ‘Innovation’ Category

Why Managers Haven’t Embraced Complexity

Posted in Economics, Facilitation, Innovation, Institutions, Knowledge and learning, Leadership, Research, Strategy, Technology on May 10, 2013 | 3 Comments »

This is a cross-post from the HBR written by Richard Straub, and is one of a series of perspectives that will be published leading up to the fifth annual Global Drucker Forum in November 2013 on the theme of Managing Complexity.

Nobody would deny that the world has become more complex during the past decades. With digitization, the interconnectivity between people and things has jumped by leaps and bounds. Dense networks now define the technical, social, and economic landscape.

I remember well when the idea of applying complexity science to management was first being eagerly discussed in the 1990s. By then, for example, scholars at the University of St. Gallen had developed a management model based on systems thinking. Popular literature propagated the ideas of complexity theory — in particular, the notion of the “butterfly effect” by which a small event in a remote part of the world (like the flap of a butterfly’s wings) could trigger a chain of events that would add up to a disruptive change in the larger system (such as a hurricane). Managers’ eyes were opened to the reality that organizations are not just complicated but complex.

Why did this interest and work in complexity not lead to major changes in management practices? There are, I think, a few major reasons that it didn’t — and that also suggest that the overdue change might now finally take place.

Complexity wasn’t a convenient reality given managers’ desire for control.
The promise of applying complexity science to business has undoubtedly been held up by managers’ reluctance to see the world as it is. Where complexity exists, managers have always created models and mechanisms that wish it away. It is much easier to make decisions with fewer variables and a straightforward understanding of cause-and-effect. Here, the shareholder value philosophy, which determines so much of how our corporations operate these days, is the perfect example. Placing a rigid priority on maximizing shareholder returns makes things clear for decision-makers and relieves them of considering difficult tradeoffs. Of course we know that constantly dialing down expenses and investments to boost short-term margins inevitably damages the long-term health of the company. It takes a complexity approach to keep competing values and priorities and the effects of decisions on all of them in view — and not just for management, but equally for investors, analysts, and regulators.

Technology was not yet powerful enough to capture much complexity.
When systems thinkers and theorists turned their attention to economies and organizations in the 1980s and 90s, the tools simply did not exist to model their workings at a level that would yield practical insight. Now, the exponential increase in computing power and the progress in mathematics and statistics have propelled us into a new era. With the ability to draw on data bases and map networks at scales that were unthinkable before, we can hope to understand communication flows through large organizations, and the impact of disturbances and managerial interventions on these flows.

The prospect of non-human decision-making is unnerving.
More recently, with the surge of computer processing power, another nagging concern has formed in some people’s minds. Does the fact that massive computing power is required for systems-level comprehension mean that the interpretation of information, sense-making, and learning will become “extra-human” activities? Will the computer take over the role of the knowledge worker? Will we soon reach a tipping point when human brainpower is obsolete? Some technophiles (many of them inspired by Ray Kurzweil’s ideas) respond to questions like this with a resounding yes. Yet for most of us it is a disturbing thought, because we have seen so many of the models designed to predict the future state of complex systems (from economies to climates) fall short of accuracy, to say the least.

The eager futurists talking about machines taking over evaluation of situations and decision-making have set back their own cause, as others see them ignoring an essential fact: sense-making is always informed by values. The idea that we might look for value judgments from algorithms is just badly flawed. But fortunately, the recognition is growing that, while computers can provide us with enormous extensions of our storage and processing capacity, they must and will remain only inputs to human brains, where the ultimate evaluation and deliberation must continue to take place. Think of the brain as our own “complexity processor” and itself our most complex organ: It helps us to address complex issues and yet come up with seemingly simple solutions. Those are made possible when we unconsciously see through the myriad of information elements that are stored in our brain as raw material to build meaningful patterns, or the famous “big picture” that humans can develop best.

The recognition of complexity is at its core a view of the world that that makes us more humble and more open. It is the awareness that too often our interventions will not achieve what we wanted and we will be shocked by unintended consequences. (The fact that, following the creation of the Cap-and-Trade Carbon Emission Scheme as a clever new artificial market, more coal is being burned in Europe than before is a mind-boggling example.) At the same time, it is the acknowledgement that simplistic “can do” thinking and linear approaches in organizations and markets, which are by definition complex, won’t be sufficient. And it is the prod to us to better understand why.

There has been no watershed event to make it true that managers will apply complexity science to their work today, whereas they could not, or would not, yesterday. Rather, there has been a gradual change in mindset, pushed along by the increasingly evident damage of narrow, simplistic thinking. The toolkit that allows us to understand the dynamics of large systems has continued to evolve. And the reassuring truth has been reasserted that, on top of the logic of algorithms, human values and judgment are essential.

Managers, I think, should now get ready to face the full complexity of their organizations and economic environments and, if not control them, learn how to intervene with deliberate, positive effect. Embracing complexity will not make their jobs easier, but it is a recognition of reality, and an idea whose time has come.

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An experiment too far?

Posted in Accountability, Evaluation, Evolution, Innovation, Knowledge and learning, Public Policy, Research on May 3, 2013 | 6 Comments »

This week a breaking story in the UK focused on how unemployed jobseekers are being forced to complete bogus psychometric tests designed by the government’s Behavioural Insights Team (commonly known as the “nudge” unit). The story raises important issues for ethical experimentation which are very pertinent for aid efforts.

The Guardian reported the story as follows:

The test called My Strength… has been exposed by bloggers as a sham with results having no relation to the answers given. Some of the 48 statements on the DWP test include: “I never go out of my way to visit museums,” and: “I have not created anything of beauty in the last year.” People are asked to grade their answers from “very much like me” to “very much unlike me”. When those being tested complete the official online questionnaire, they are assigned a set of five positive “strengths” including “love of learning” and “curiosity” and “originality”. However, those taking the supposed psychological survey have found that by clicking on the same answer repeatedly, users will get the same set of personality results as those entering a completely opposite set of answers.

The Behavioural Insights Team, meanwhile, argue that their intentions were based on sound evidence and good intentions. This includes the finding from randomised controlled trials (RCTs) of the survey that it had led to ‘building psychological resilience and wellbeing for those who are still claiming after 8 weeks through ‘expressive writing’ and strengths identification.’

For many critics, however, any potential positive benefits of the exercise were diminished by the fact that jobseekers were warned that the survey was compulsory and not filling it out would lead to allowances being curtailed. Instead of building wellbeing, this exercise simply gave the unemployed something else to worry about.

Clearly, there are some fundamental ethical problems with the way that this whole effort was designed and implemented. And of course, this is not unique to nudge efforts, but extends to all kinds of social policy interventions. But the experimental approach of nudge does open up a range of ethical quandaries that we need to be looking at more closely.

What the admirable efforts of the UK blogger community highlight for me is that aid recipients in developed countries do have some means for addressing their grievances about such experimental processes – even if (as in this case) they are indirect and work through informal rather than formal channels of accountability.

However, the poor in developing countries have few such channels for voicing their grievances and issues. As one statistician put it back in 2010 in a review of RCTs:

In conducting research with people, the need for guidance and adherence to ethical standards is of the utmost importance. Most areas of research involving human subjects have compulsory or voluntary codes of conduct and ethical rules, and many countries have strict processes in place to ensure that ethical standards
are met by any research involving human experimental units. There seems to be a gap, however, in research that involves human subjects carried out in the context of international development. We do not have a system of checks and balances that ensures adherence to high ethical standards. This may be because the jurisdiction of research committees does not extend
to the areas where some of this research is conducted.
And this, specifically on RCTs:
When RCTs are proposed for impact evaluation, the issue of consent from participants is not discussed. Telling a group of people that they will be included in an experiment, but not implementing a development intervention that might benefit them, is something that most people working in international development would find difficult.
There is a lot of talk about feedback mechanisms at the moment as a means of addressing the long-observed ‘broken feedback loop’ in foreign aid. But without “a system of checks and balances that ensures adherence to high ethical standards” such mechanisms will be prone to the same problems as other mechanisms used in development.

In a study I wrote on innovation in aid with Kim Scriven and Conor Foley a few years back, we argued that there was a need to find safe spaces for experimentation, and establish mechanisms to promote “honourable risk” if we wanted to see a more innovative, and yet still principled, aid system.

Even though other aspects of aid innovation have advanced considerably since that study, especially in terms of resources and policy attention, I am not sure we have really seen much progress on the issue of “honourable risk”. As a result, many of us in development aid run the risk of taking our experiments just a little too far.

In fact, we may be doing so already, and not know about it.

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The Aid Explorer: Mapping the Hyperspace of International Aid

Posted in Accountability, Evaluation, Facilitation, Innovation, Institutions, Knowledge and learning, Leadership, Networks, Organisations, Public Policy, Reports and Studies, Research, Self organisation on May 2, 2013 | 1 Comment »

The team behind the Atlas of Economic Complexity (see my post on this here) have come up with a fascinating network-based approach for analysing the global aid system.

As they put it:

International development is a complex global goal that faces massive coordination barriers. The difference in income between rich and poor has expanded over the years from a four to one factor to a hundred to one. Where there once were only a handful of development agencies, thousands have now emerged. The system that connects donor agencies, recipient countries and development challenges is extremely complex and should not be managed with a top-down approach… The Aid Explorer was developed as a tool to facilitate better aid coordination. The Aid Explorer enables users to understand what issues face which countries and which aid organizations are aligned to address these issues.

Some specific pointers:

  • The Aid Explorer’s Profile pages enables us to see which issues face which countries and which organisations are best aligned to address them
  • The Network maps can be used to explore how issues, countries, and organizations relate to each other
  • The Rankings presents the findings and the best alignments of countries, issues and organizations

The process of developing the dataset, and how to use it, is described in more detail in the accompanying paper “The Structure and Dynamics of International Development Assistance“, published earlier this year in the Journal of Globalization and Development.

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Can we rely on our current understanding of the economy?

Posted in Economics, Evolution, Financial crisis, Innovation, Knowledge and learning, Networks, Public Policy, Resilience, Self organisation, Traffic on April 25, 2013 | Leave a Comment »

Many would argue that standard economic theory enabled us to analyse and understand the economy as it used to be,
with long stable periods punctuated only by occasional crises. However, the recent evolution of the global economy should drive us to pursue ways of expanding economic theory
such that it encompasses the new structures and organization
emerging as we globalize and network our world.
This is from a great new paper by Dirk Helbing and Alan Kirman, which asks and attempts to answer many questions that are of importance to development, and answer them by drawing on ideas from complexity theory. Well worth a read.

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Is this the ‘Next Big One’?

Posted in Agriculture, Biology, Communications, Evolution, Healthcare, Innovation, Institutions, Knowledge and learning, Leadership, Networks, Organisations, Public Policy, Research, Resilience, Strategy, Technology on April 23, 2013 | Leave a Comment »

As concern grows about H7N9 in China, this post explores the importance of managing such pandemic risks through collaboration, innovation and systemic thinking

In the month of World Health Day (April 8th), the latest outbreak of bird flu in Asia provided a sobering sense of the challenges the international community still faces. To date, H7N9 has killed 21 people, infected 104, shut down poultry markets across Asia, and has led to Chinese shares tumbling.

The WHO originally announced that the likelihood of this latest pathogen is transmittable between people is low, and that the world should not ‘get into a flap’ – as one observer memorably put it. The potential of bird flu to be the source of the “Next Big One” means however that we cannot be complacent. In terms of global catastrophic risks, it is hard to think of one more serious than the 1918 avian influenza epidemic which killed 50-100 million people worldwide – 3-5% of the global population. This was so devastating in part because the virus had acquired mutations that allowed it to cross from birds to humans, and then to ‘go pandemic’. Based on analysis of the mutations in H7N9, scientists fear that this latest variant may have the same potential.

But there is still a lot we don’t know about H7N9. Where has it come from, why, and how? What is its relationship to earlier variants? How might it mutate? What impact might it have in the future? What does it mean for our ongoing, historically loaded, battle with avian flu?

To answer such questions, we need to draw on a variety of disciplines: epidemiology, molecular biology, virology, all of which fit nicely with the current models of public health. The problem is that many of these models set us, humans, apart from nature. Diseases, the standard narrative goes, encroach on our territory and we need to fend them off. The reality is in fact the exact opposite.

It is now widely acknowledged that many – the majority, in fact – diseases are born in the intersection between society, environment and economy. More than 2/3 of all human infectious diseases are zoonotic in origin, meaning that they somehow crossed species boundaries. The terminology likely to be adopted in future Hollywood blockbusters on the topic is simple but evocative: ‘spillover’. Primates, birds, bats, pigs, rats, mice, dogs, insects – any creature we co-exist with can act as sources or carriers of pathogenic lifeforms.

Spillover is driven by a pattern of activity which is becoming all too familiar. Deforestation: 4% increase can lead to 50% increase in malaria rates. Hunting has led to HIV-AIDS, Ebola, all crossing the species boundary. And, to bring it back to bird flu, livestock. Around 70% of the rural poor and 10% of the urban poor are dependent on livestock. Livestock conditions are increasingly creating tremendous opportunities for pathogens to cross from wild birds to caged birds, and onto humans. And the demand for animal-based protein is expected to grow 50% by 2020, much of it in the developing world. This problem is not going to go away any time soon.

Leapfrogging on the success of the human race, trade and transport linkages provide a morbid global transmission network. The rate at which new diseases are emerging and spreading is nothing short of shocking.  Zoonotic diseases have increased in the past 40 years, with at least 43 identified outbreaks since 2004. ILRI estimates that 1.7 million people die each year thanks to spillover diseases. By way of comparison, the highly respected CRED crunch on disaster epidemiology found that the 2001-2010 average annual deaths from natural disasters was 107k  per year.

Ecological and evolutionary principles are vital in understanding these complex system effects on a more solid scientific basis. Experts at the University of Florida made the point in pithy fashion a couple of weeks ago, “If we don’t understand the reservoir and the ecology of the virus, it’s hard to design interventions to protect humans.” But such understanding is – with a few exceptions – still under-utilised in public health.

Of course, every disease is different, every context is unique. But the process by which spillover happens is similar. We can point to ecologies under stress. Life forms under duress. As an excellent briefing by colleagues in the Consortium on Disease Dynamics (CDD) puts it, “The health of people and animals are… interconnected and inextricably linked to the environments both inhabit. Given the complex pathways that lead to spillovers, it is important that prevention and control measures are undertaken with a strategic approach and an understanding of the many interdependencies.”

What does this challenge add up to for the global risk management community? The work by the CDD gives some very useful pointers.

First, multi-disciplinary approaches are vital. The WEF Global Risks report has for some years now been calling for better disciplinary collaboration in order to think about emerging risks. With avian influenza there is a clear need for better collaboration between public health specialists, disease ecologists and evolutionary biologists. Some important work is already happening, under the auspices of entities such as the global One Health initiative, organisations like the EcoHealth Alliance, and initiatives like the USAID-funded Predict, and this work needs to move firmly to the centre of the debate.

Second, anticipation and warning systems – new investments in surveillance are urgently needed to establish and maintain necessary systems at multiple levels – community, national, regional and global. We need multi-stakeholder information platforms, bringing together government, civil society and the private sector in new kinds of networks, in order to establish ‘systemic  surveillance approaches’. This needs to move beyond a focus on specific disease to looking at the whole system, looking at the intersection between disease drivers, disease incidence, and socio-economic factors.

Third, new approaches – especially in the realm of complex adaptive systems – have a lot of relevance for how we think about such outbreaks in the future. Methods such as systems thinking, network analysis, agent-based simulations, and dynamical systems theories can help develop a more precise and accurate understanding of the complex dynamics of disease. Together with the rise in ‘big data’ approaches, there is scope to develop new models and theories of how pandemics unfold which are more appropriate to our ‘hyperconnected world’. We need to be careful however to ground this science in local, community understanding – to support affected communities to become the frontline of defence: adaptive managers of emerging risks.

Fourth, we need changed funding models – funding prevention, not just response, and linking pandemic risks to high-risk development activities, and ensuring that we don’t forget history too quickly. There needs to be attention even when the threats may not be imminent. The private sector, with interests in business continuity, can be key actors here. Done right, such investments can engender what might be seen as positive spillovers. As the CDD work suggests, investments in prevention of avian influenza can provide the basis for such work on other potential pandemics.

In closing, if we want to take a wide-angle lens on the problem of disease outbreaks like HN79, to understand why these diseases are occurring at an increasing rate, we could do worse than taking a lead from Nathan Wolfe. A globally renowned virologist, a couple of weeks ago Wolfe wrote a tub-thumping piece on the WEF blog about the continued risks of unregulated hunting, especially bushmeat, which gave birth to human immunodeficiency virus (HIV). His basic argument was by ignoring the implications of our food production systems, we are running an unacceptably high risk of terrifying global scourges in the future.

Clearly, we all need to start pay much more attention to the intersection of economy, society and the environment if we are serious about proofing ourselves against the Next Big One.

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Perspectives on a Hyperconnected World

Posted in Communications, Conflict and peace building, Economics, Evolution, Financial crisis, Innovation, Networks, Public Policy, Reports and Studies, Research on February 6, 2013 | Leave a Comment »

Many of the grand challenges that confront humanity—problems as diverse as climate change, the stability of markets, the availability of energy and resources, poverty and conflict—often seem to entail impenetrable webs of cause and effect.But these problems are not necessarily impenetrable. Powerful new tools have given scientists a better understanding of complexity. Instead of looking at a system in isolation, complexity scientists step back and look at how the many parts interact to form a coherent whole.

Rather than looking at a particular species of fish, for example, they look at how fish interact with other species in its ecosystem. Rather than looking at a financial instrument, they look at how the instrument interacts in the larger scheme of global markets. Rather than think about poverty, they might look at how income relates to conflict, politics and the availability of water. Whatever the object of study happens to be, complexity scientists assemble data, search for patterns and regularities, and build models to understand the dynamics and organization of the system. They step back from the parts and look at the whole.

This kind of thinking is a major departure from traditional science. For centuries, scientists have worked by reducing the object of study down to its constituent components. Complexity science, by contrast, provides a complementary perspective by seeking to understand systems as interacting elements that form, change, and evolve over time.The multiplicity of ideas, concepts, techniques and approaches embodied by the science of complexity can be applied to people, organizations and society as a whole, from economies and companies to epidemics and the environment.

The aim of this paper is to raise awareness about this new science and its ability to bring clarity and insight to many of the complex problems the world faces today.

This is from a new short (8 page) paper from the World Economic Forum Global Agenda Council on Complex Systems.

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Lies, Damned Lies and Big Data

Posted in Accountability, Communications, Evaluation, Innovation, Knowledge and learning, Research on February 1, 2013 | 16 Comments »

This is a guest post by David Hales, a fellow associate of the new complexity think-tank, Synthesis. David specialises in computational social science and here he provides a thought-provoking response to the rise in big data, and some of the more outlandish claims made about it. For a good example of the latter, see Chris Anderson’s piece ‘The Data Deluge Makes the Scientific Method Obsolete‘. In this piece, David makes some very relevant points for development big data initiatives.

david-hales-chicheley-hall

  

Almost everything we do these days leaves some kind of data trace in some computer system somewhere. When such data is aggregated into huge databases it is called “Big Data”. It is claimed social science will be transformed by the application of computer processing and Big Data. The argument is that social science has, historically, been “theory rich” and “data poor” and now we will be able to apply the methods of “real science” to “social science” producing new validated and predictive theories which we can use to improve the world.

What’s wrong with this? On one level nothing. We know so little about the social world that anything is worth a try. Mining these huge databases will almost certainly lead to new ideas and insights. However, before we run headlong into this new world of big data, promoted as it is by corporations such as IBM and the large consultancies, perhaps we might benefit from a little critical reflection.

Firstly what is this “data” we are talking about? In it’s broadest sense it is some representation usually in a symbolic form that is machine readable and processable. And how will this data be processed? Using some form of machine learning or statistical analysis. But what will we find? Regularities or patterns (for a useful discussion of patterns within complex systems, see Greg Fisher’s post, Patterns Amid Complexity). What do such patterns mean? Well that will depend on who is interpreting them.

Given this level of generality, if someone tells you they are working on “big data” it tells you almost nothing. One way to approach the issue if confronted with a “big data” project is to ask the following question based on a thought experiment:

Imagine you had a massive computer database that contained all possible measurements that could ever be made over the entire span of all space and time. You could query it with any question and it would deliver the result instantaneously. All big data is merely a subset of this ‘the biggest data that could ever exist’.  What would your project ask it?”

If no coherent answer can be produced to this question then any such project is at best directionless and at worst not conscious of its aims.

One answer might be “looking for patterns or regularities in the data”. Looking for “patterns or regularities” presupposes a definition of what a pattern is and that presupposes a hypothesis or model, i.e. a theory. Hence big data does not “get us away from theory” but rather requires theory before any project can commence.

What is the problem here? The problem is that a certain kind of approach is being propagated within the “big data” movement that claims to not be a priori committed to any theory or view of the world. The idea is that data is real and theory is not real. That theory should be induced from the data in a “scientific” way.

I think this is wrong and dangerous. Why? Because it is not clear or honest while appearing to be so. Any statistical test or machine learning algorithm expresses a view of what a pattern or regularity is and any data has been collected for a reason based on what is considered appropriate to measure. One algorithm will find one kind of pattern and another will find something else. One data set will evidence some patterns and not others. Selecting an appropriate test depends on what you are looking for. So the question posed by the thought experiment remains “what are you looking for, what is your question, what is your hypothesis?”

It seems to me that one must at least try to answer this question if one is to pursue social science. Not just because it is good science but also because it has ethical and political implications.  The view one takes of social phenomena, either consciously or through algorithms and data, frames what is and is not conceivable for past and future social reality. If you doubt the importance of such ideas one should look that the history of the 20th century. Ideas matter. Theory matters. Big data is not a theory-neutral way of circumventing the hard questions. In fact it brings these questions into sharp focus and it’s time we discuss them openly.

Right now we are “data rich” and “theory poor”. We need new theory for the 21st century. That requires critical discussion, reflection, honestly and humility. It is not clear to me that such concerns are prominent within much of the “big data” movement.

Here is a more eloquent and playful take on these issues, by a colleague of mine, in the genre of that wonderful Orwell fable: https://scensci.wordpress.com/2012/12/14/big-data-or-pig-data/

Cross-posted from the Synthesis blog.

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Questioning innovation

Posted in Accountability, Economics, Evaluation, Evolution, Influence, Innovation, Knowledge and learning, Leadership, Networks, Organisations, Public Policy, Reports and Studies, Research, Technology on January 30, 2013 | 5 Comments »

Innovation is popular in aid at the moment, so much so that there is a steady spate of articles which range from trashing its potential contribution to development through to challenging Western, donor, countries’ assumed roles as the ‘providers’ of innovation.

In this post I want to argue that there is a middle  ground between the unthinking mantras that are increasingly peddled by agencies and the growing number of entirely justifiable critiques.

An Economist article made the point succinctly over a decade ago, ‘what precisely constitutes innovation is hard to say, let alone measure.’  Some concluded, as a result, that innovation was a ‘new theology’.

innovation-prayer

However, with a growing chunk of economic growth being driven by industries and products that in fact didn’t exist ten years previously, such dismissals seem increasingly Luddite.

While clarity and precision in thinking about innovation is all-important, it is far from easy. We are not helped by the fact that many innovation stories are in fact apocryphal – retrospectively woven to lend the star protagonists much more agency and awareness than in fact they possessed. This is true of even the best known innovation stories. Take Alexander Fleming’s infamous and much-lauded discovery of penicillin – Fleming himself used to describe the conventional account of his contribution as the ‘Fleming Myth’.

Typically in business the market is the ultimate arbiter of innovation, and as we know, most products fail. In aid, however, the market does not provide an adequate indication of what is successful and what is not. This is largely done by the aid system itself. Bill Easterly made much of the fact that the market could get Harry Potter books anywhere there was a demand, which he compared unfavourably to the inability of the aid system to get simple treatments like vaccines to where they were most needed:

There was no Marshall Plan for Harry Potter, no International financing Facility for books about underage wizards. It is heartbreaking that global society has evolved a highly efficient way to get entertainment to rich adults and children, while it can’t get twelve-cent medicine to dying poor children.”

But as Amartya Sen subsequently argued:

the disparity in the results is indeed heartbreaking… [but] there is a radical difference… between the enterprise of supplying “what is in demand” — which is integrally linked to the buyers’ ability to pay — and that of supplying needed goods and services to people whose income and wealth do not allow a need to be converted into a market demand.

While Sen’s point applies more broadly to aid delivery, it is also relevant to new ideas and innovations within aid.

In any case, whether because of market failure or the wilful self-interest of aid agencies, innovation – which is an ambiguous enough concept in the business realm – becomes very murky territory indeed in development. It is hard to say  what innovation actually is, what it generates, and for whom. Like the famous ruling about pornography, many are of the view that ‘I know it when I see it.’

Such vagueness is the ideal seeding ground for development fads, and indeed, innovation is fast becoming the latest ‘fuzz-word’. Everything is being labelled ‘innovation’: as one blogger memorably put it, we seem to be suffering from Innovation Tourette’s.

Problem

Little wonder that growing numbers of thinkers and writers see the need to beat innovation with a big snarky stick. These criticisms play a vital role in highlighting the risks and downsides of  all shiny new aid agendas – and innovation is no exception.

Having observed such trends in the past, I think there is a danger that between the rise of the fad and the indignant reaction to it, we lose sight and sense of why the issue is question is actually important. Specifically, we risk learning the wrong lessons about what innovation actually is, and the potential it has to add to our work.

What we need is a more precise and accurate way by which to separate the innovation wheat from the faddish chaff. This was in fact one of the key motivations of a study I co-authored with Kim Scriven and Conor Foley while at ALNAP back in 2008-9.

So what did our study suggest in terms of getting more precision in innovation? We found it useful to ask some key questions to identify whether a particular idea or approach was in fact innovative.

  • Q1. Is the idea being proposed a new Product, a new Process or service, a new way of Positioning aid, or a new Paradigm or mental model? Or is it some combination of the four? (see more here)
  • Q2. What are the origins of the idea, and what does it aim to do differently to what is already out there? Where, exactly, is the novelty – is it a whole new thing, or is it new combinations of existing things? What, in partciular, are the implications for relationships with aid recpients? Did the idea involve re-thinking that age-old and much-critiqued relationship?
  • Q3. How disruptive is the innovation? Is it transactional, in that it enables existing efforts to work; incremental in that improves these efforts, or transformational in that it radically changes these efforts?
  • Q4. What precisely are the expected benefits the idea should confer? Can these benefits be framed in terms of existing evaluation criteria of enhanced relevance, efficiency, effectiveness, impact or sustainability of aid? Or are there other, newer, criteria that matter? How can the benefits be measured – qualitatively, quantitatively, or some blend thereof?
  • Q5. What are the potential risks and downsides of the idea for all parties – especially aid recipients – and how will these be mitigated against?
  • Q6. Where can the idea be located in an overarching innovation process? Is it at the early stages of recognition and invention, is it in need of development and implementation, or has it been tested and is now ready for wider diffusion?  (see more here)
  • Q7. What are the networks and relationships that will support and facilitate the innovation process? What capacities and competencies are necessary? Are these in place? How can they be built?
  • Q8. What is the potential scope of the innovation in terms of wider diffusion? Who might benefit, and in what ways? What is the route to scale, and who needs to be engaged to get there?

There are no doubt many more questions that could be asked, but the above provide a good starting point for what might be termed ‘innovation due diligence’. The key, in my opinion, is to use these and others questions to develop more honest, rigorous stories about ongoing and historical  innovations: about how they came about, why, and with what benefits. Such questions are useful because they help us look at innovation from a more systemic perspective: looking not just a idea, but the overall social, technological and institutional context from which it has emerged.

These questions should be relevant whether you are a donor bombarded with new proposals and ideas, an operational aid worker seeking to get funding for your exciting new idea, a blogger wanting to shine a light on the depressing excesses of innovation-speak, or a researcher wanting to investigate an supposed innovation in a systematic fashion (in fact I think we need far more of the last category, but that’s another story).

We will need to keep wielding the big stick as necessary, to curb against such excesses of aid ‘innovation-speak’. But we may also at times need a magnifying glass and ruler – metaphorically speaking – and asking these kinds of questions could help with this. My $0.02 is that if a would-be innovator can’t take a reasonable stab at these questions, they aren’t working hard enough, or they are over-selling something. A lot is spoken about creativity in innovation, but recent work suggests – in echo of the old 1% inspiration, 99% perspiration line – that the larger part of innovation lies in the proper execution of the idea.

I think there is a special role for the aid blogging community in asking such questions and demanding answers. We have seen in the past few years how bloggers have mobilised in a largely self-organised fashion to push back against various poorly considered ideas.

I know there are many bloggers who want to engage with innovation in a serious fashion, and who are dismayed by the current hype surrounding it. We should be able to highlight the good and bad of what we see emerging from the aid innovation agenda. And aid agencies should be willing to open their ideas up to the views and scrutiny of this emerging, globally networked, community of thinkers and analysts. This kind of effort has, in other distributed sectors, developed into new crowd-sourced marketplaces for innovation such as Innocentive. There’s no reason why the same couldn’t happen in our sector.

Our ultimate goal, I’d argue, should be to work to bringing the perspectives of aid recipients into the mix as part of our standard operating procedures. Now that in itself could be seen as a real innovation.

The need for such engagement goes beyond mere niceties. The most effective ideas we uncovered in our 2009 study were precisely those that re-thought and re-formulated this core aid relationship: cash, community approaches to malnutrition, transitional shelter. Put simply, these were the innovations that we found to be most worthy of the term.

Gamechanger

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Mapping politics (and the politics of maps)

Posted in Campaigns, Communications, Innovation, Public Policy, Research on November 7, 2012 | 25 Comments »

So after billions of dollars and several years of hard campaigning, the US elections are finally over. The typical map of the 2012 US election results looks like this:

Which is clearly not a million miles away from the 2008 equivalent.

In these maps, of which there are thousands online, on TV shows and in newspaper reports, the US states are coloured red or blue according to whether the majority of their voters were Republican or Democrat.

These maps, are, of course an illusion. They suggests that the ‘reds’ might have won because there is more red on the map, and that the reds and blues are sharply divided. Typical comments about such maps run along the lines of “what a huge sea of red”, “there you go, the liberal-conservative divide”, “it really is two different countries, isn’t it?”, and so on.

However, these maps fail to take account of some basic realities. First of all, there is no representation of population. The reality is that the population of the red states is on average significantly lower than that of the blue ones. So while the blue are small in area, they represent large numbers of voters. Second, more importantly for the results of elections, the maps take no account of the distribution of electoral college votes. Third, they take no account of the often fine-grained distribution of voter preferences within states.

Mark Newman, a noted complexity researcher, has done a lot of work on how we can get more realistic, less simplistic maps of complex, real-world phenomena. By drawing such cartograms, which enable maps to be re-scaled according to key variables like population, maps of the electoral spread can be made more realistic and detailed. They can also tell different, more subtle, stories about political allegiance.

By the sounds of things, he is busy working right now on maps of the 2012 election. Here is his depiction of the 2008 election using a population cartogram.

In this, the states have been squashed and stretched to give relative sizes while preserving the overall US structure. A similar thing can be done with the electoral college results. In the map below, the map scales the sizes of states to be proportional to their number of electoral votes in 2008.

As Newman writes:

The areas of red and blue on the cartogram are now proportional to the actual numbers of electoral votes won by each candidate. Thus this map shows at a glance both which states went to which candidate and which candidate won more electoral college votes – something that you cannot tell easily from the normal election-night red and blue map.

Newman and his colleagues went further to map the election results by county, the resulting images are even more striking. This is the equivalent of the first map above, with each county coloured red or blue according to the majority vote in 2008.

Again, the red appears to be in the majority. Using a cartogram of population gives this:

All of these maps are however also somewhat fictional as they pay no attention to the fact that no single state is in fact a sea of red or blue. Instead, as this election showed, every county and state contains quite closely balanced numbers of Republican and Democratic supporters. By using only two colours we lose any sense of this balance, and feed the myth of red states and blue states, and of sharp country-wide divides.

Newman and his colleagues have got around this by using red, blue, and shades of purple in between to indicate the nuance in voting patterns: different shades of purple indicate different splits of votes.

This is the county level map with this applied:

And this is the population cartogram:

As Newman explains:

As this map makes clear, large portions of the country are quite evenly divided, appearing in various shades of purple, although a number of strongly Democratic (blue) areas are visible too, mostly in the larger cities. There are also some strongly Republican areas, but most of them have relatively small populations and hence appear quite small on this map.

What I love about this work is that it clearly demonstrates the power of maps and visualisations to shape our thinking. These depictions pose direct and clear challenges to those lazy, pervasive but ultimately unhelpful narratives (“sea of red”, “lib-con divides”, “country of two parts”, etc, etc).

I think that these more realistic, sophisticated  representations should become much more commonplace in politics and indeed in development. Mark Newman set up the World Mapper project back in 2006, which has a whole host of similar maps, many of which have been widely used in presentations and reports.

Much of this work owes a debt – of sorts – to the infamous and controversial Gall-Peters projection, which provided a new visualisation of the earth using a more egalitarian and precise calculation of the relative landmass of developing countries.

Along broadly similar lines, a recent guest post on this blog looked at how we might use tools like fitness landscapes to more accurately represent non-linear development progress.

Perhaps such tools could slowly help change the way we think about a whole range of complex, routinely over-simplified, phenomena.

Who knows, one day they may even help inform some less divisive narratives about the US political landscape. As President Obama put it this morning in his acceptance speech:

We are not as divided as our politics suggest. We remain more than a collection of red states and blue states.”

Too right.

Postscript on 8th November 2012: the 2012 election maps are now done, and here is the 2012 county cartogram.

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Technophobia, Technojoy

Posted in Evolution, Innovation, Knowledge and learning, Technology on September 26, 2012 | Leave a Comment »

I’ve come up with a set of rules that describe our reactions to technologies:

1.  Anything that is in the world when you’re born is normal and ordinary and is just a natural part of the way the world works.

2.  Anything that’s invented between when you’re fifteen and thirty-five is new and exciting and revolutionary and you can probably get a career in it.

3.  Anything invented after you’re thirty-five is against the natural order of things.”

Douglas Adams, 2002

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