The Billion Dollar Solution that Isn’t: How systems modeling in foreign aid could save billions and serve the poor

ABSTRACT Monika Aring and Bobbin Teegarden argue that much of the foreign aid for economic and job growth in poor countries cannot work because the underlying assumption – that outside experts can fix the problem is misguided. They suggest that aid could achieve far better results if donors could distinguish between two opposite problem archetypes – technical and adaptive systems problems. Technical problems are problems for which societies have already developed solutions that work.  Adaptive systems problems are new problems for which a society has not yet developed a sustainable solution. They argue that the lack of jobs to sustain livelihoods is an adaptive systems problem that has to be solved by the system’s stakeholders, supported by outside experts.

KEYWORDS technical problems; adaptive systems problems; experts;
employment; local stakeholders; modelling process


Well-intentioned aid projects: why are they not delivering better results?

There is widespread agreement in the aid community (Ridell, 2007) that much of the approximately US$100 billion spent annually on overseas development assistance by rich countries (OECD) does not produce the results intended by the donors.  Despite well- intentioned efforts on the part of donor institutions, the changes that the aid should produce mostly fall short of the desired outcomes.  Consider one recent aid initiative that was initially intended to create jobs and build the economy of a Middle Eastern country. The donor formulated the project, specifying measurable outcomes, including the number of new jobs created as the result of the donor investment. The donor then put the project out for bid. As required, the contractor worked with leaders from the country’s economic development agency to create a tax-free, special enterprise zone plus various incentives to attract foreign companies to locate in the zone. In this case, a Chinese garment assembly company took advantage of the tax-free zone and brought in its own workers and equipment. The recipient country’s project director proudly asserted that 800 new jobs were created. Upon closer scrutiny, it turned out that most of these jobs were held by Chinese workers who lived and worked in portable trailers within the tax-free zone.1 Or consider the millions of dollars of aid that were poured into accelerating the education outcomes for Egypt’s youth. As a result, there are far more educated youth; according to recent employer surveys, youth lack many critical employ ability and technical skills and remain jobless (El Zanaty and Associates, 2007). Or consider the millions of dollars of aid that were poured into building several state-of-the art vocational training centers  in a tsunami-devastated country not considering that the country lacks the resources to maintain these centers after the aid ended.2 These are common place scenarios. Sadly, it is probable that many, if not most, foreign-aid projects born from donor investments often disappear when the funding and technical assistance go away.


Two problem archetypes: technical and adaptive systems

What is going on? While there are many factors at work, one of them is that most aid tends to diagnose and then treat adaptive problems as if they were technical problems. Ron Heifetz, a leading expert on adaptive leadership at Harvard’s John F. Kennedy School of Government (Heifetz, 1994:1), suggests that there are two basic problem archetypes: technical and adaptive. Technical problems respond well to solutions coming from outside experts, while adaptive systems problems must be solved from the inside out, by those involved in the problem. One community’s battle with a string of illegally set fires underscores the difference in both problem type and response. A house on fire is a technical problem where outside experts in this case firefighters  come in and put the fire out. Arson in a community, on the other hand, can be viewed as an adaptive systems problem where the community’s stakeholders have to do the work of determining the solution. To solve the adaptive systems problem, community members have to do the work to find out who is causing the arson and why, and then work together to change the underlying conditions.

In one particular community, one ethnic group owned the local shops. Youth from another ethnic group got angry because local shopkeepers would not hire them. Community members were able to stop the arson because they got the shopkeepers to agree to hire youth from different ethnic groups  after much arguing, negotiation, and discussions. Seen from another point of view, technical problems are problems where authority figures (experts or consultants) know what to do and do it. To solve adaptive systems problems, stakeholders inside the system have to solve the problem themselves. Outside help can be brought in where needed, but the problem (and its solution) must be generated by the system’s stakeholders (Heifetz,1994).

Viewed from a modelling perspective, adaptive problem solving works because stakeholders from all parts of the community come together to do a lot of hard work, starting with a shared vision, a commitment to achieve it, and choosing actions that align with that commitment. In the case of the community with arson, members generated a vision for a future where shops owned by many different ethnic groups can thrive alongside each other, providing jobs and goods for the community. Once a shared vision had been created, community stakeholders came back to their current reality to gather the facts of their current situation. As participants in the process compare their desired future with their present situation, they experience the gap between where they want to be and where they are now.  Seeing the gap makes it possible to address it, and that process starts as the various participants select strategies they think will work best. As they take actions, participants continuously review and adapt their strategies, a process that lets them respond to changes in their environment as they occur.  By participating in the adaptive process, community leaders and members are in a sense forced to find new ways of working together.  Instead of attempts to come up with quick fixes, such as getting rid of the immigrants in the arson example, the adaptive approach allows for the emergence of new and different solutions, such as convincing the immigrants to change their hiring practices while creating opportunities for minority youth and shopkeepers to develop mutual trust and respect. To work best, the adaptive process should allow new leaders to emerge from groups that may not be a part of the current leadership structure.  Often, it is the new leaders who hold missing pieces of the puzzle.

 

Creating and skills in developing countries is largely an adaptive systems problem

Nearly all the world’s developing countries face daunting challenges, including the need for better governance, institutional reforms, education, and health. Recent events in the Middle East underline that perhaps the most important challenge now is how to provide economic opportunity for the vast numbers of young people living in countries where there are few prospects of sustainable employment. (Goldstone, 2010) This is no small problem: a large majority of the world’s 15^30-year-olds live in developing nations where there are very few opportunities for sustainable livelihoods.3 At the same time, employers report severe skills gaps, especially in employability skills in most developing countries (World Economic Forum, 2010). The stakes are being raised every day as this global demographic bomb (Goldstone, 2010) is starting to explode within many developing nations, most recently Egypt. For example, just to keep pace with population growth, the 22 Arab nations need to create an estimated 100 million jobs in the next two decades.4 Similar statistics can be found throughout South Asia and parts of Africa.  Regardless of the cause, in most countries where youth populations are expanding rapidly, few jobs exist, institutions are weak, education is insufficient, women have lower status than men, and governance is often fragile. In the last five years alone, a number of experts have suggested that a combination of perceived social injustice, lack of access to good education, and lack of livelihoods act as triggers to radicalizing young people (McLean Hilker and Fraser, 2007).

The handful of countries, such as Singapore, South Korea, Malaysia, and Ireland, who have intentionally transformed their economies, required 15 to 25 years to make the necessary changes. The recent youth uprisings in Tunisia and Egypt underscore how many governments in the region do not have the luxury of time to create enough jobs to absorb their exploding youth population.

 

Creating sustainable livelihoods requires leadership, vision,
commitment, and aligned action among stakeholders

Community leaders and citizens in developing countries cannot do anything about the opportunities they do not see. If, on the other hand, they had a way to see previously hidden opportunities, they could imagine a different future and then act in ways that bring about their vision. For example, in Peru’s San Martin region, years of coordinated effort by the UN, the US and Peruvian governments, foreign aid groups, local leaders, and local farmers are now paying dividends, successfully transforming their economy from coca to cocoa
(Clark, 2010).  A key reason for the turnaround ^ listening to local needs, creating a shared vision and synergy among stakeholders, and sticking to market fundamentals  could carry lessons on how other ‘narcostates’ such as Afghanistan and Colombia could shift production to legal high-value crops.  In this case, USAID used an adaptive approach, facilitating local community leaders to identify their concerns, generate priorities, and discover new, unrealized market opportunities for their crops (Clark, 2010).

These examples suggest that creating sustainable economic growth and livelihoods cannot be achieved by donor-funded experts,who at best ‘consult’ with stakeholders but in fact do most of the work that the stakeholders should be doing. Moreover, because of donor-imposed pressure for results in less than five years, the outside experts usually have to operate on virtually impossible time frames. Instead, creating sustainable livelihoods requires the more time- consuming process of envisioning a different future, hard work, and synergistic action among a country’s stakeholder groups, donors, local companies, and multinational firms participating in local supply chains. Transforming the population bomb into a boon calls for adaptive systems approaches such as the one in the Peruvian example.

 

A Path Forward

First, donors must stop misdiagnosing the problem archetype.  Much international aid misdiagnose adaptive systems problems as technical problems.  Rather than facilitating change from within and asking stakeholders to generate their own solutions, most major world donors typically contract with experts to implement the solution that a donor has developed as part of its local country strategy. Typically, contractors are hired to go into a country and conduct a gap analysis, and offer recommendations for a plan of action and resources to close the gap. In almost all cases, what constitutes the ‘gap’ is largely determined by the donors and their expert consultants, and in only limited cases understood and approved by local stakeholders who, in most cases, lack the resources to sustain the donor’s solution after the aid expires. The problem is that by seeing the development problem as a technical problem, donors hold outside aid experts responsible for making the change happen. Much like holding a physician accountable for a person’s lifestyle practices, treating aid as a technical problem holds the expert contractors accountable for producing results and lets the country’s stakeholders off the hook so that they do not have to do the adaptive work that’s required. Not surprisingly, this rarely works. The landscape is littered with aid projects that disappear after the donor funding expires as local stakeholders lack the resources to sustain the donor’s solution after the aid expires.

By contrast, adaptive systems approaches that hold stakeholders responsible for making changes take patience and time. To do this, donors and recipient country governments have to give up significant control over the outcome. Adaptive changes can be derailed if the stakeholders lack the authority to make changes. A way forward might be to use modelling tools where stakeholders in the adaptive change process can see where they are vis a vis their desired outcomes and where they can test the effectiveness of various strategies before they are implemented.

Just as new ‘smart’ adaptive models integrate data about sources and uses of electrical power,5 similar models are now being developed to help stakeholders in a community ‘see’ the future impact of their actions on their economy’s growth, jobs, and skills (Zhdanova, 2008; Santa Fe Institute Video, 2011).  Armed with such tools, community stakeholders can ask donors to support those actions that are most likely to bring about sustainable livelihoods for the estimated 900 million 20^30-year-olds in poor countries who currently face a prospect of little or no economic opportunity. Further development of such modelling tools could help stakeholders to develop their vision, commitments, and aligned actions as they can see the impact of different actions on their desired future.6

 

How can the adaptive systems approach work at the national level?

Because of the levels of complexity involved, adaptive systems approaches have most often been used at local, grass-roots levels. However, adaptive systems work can be used at any level, in local communities, in states, regions, and even countries. Despite Ireland’s recent banking crisis and economic downturn, it is instructive to look at how Ireland used the adaptive systems approach on a national level without sophisticated modelling tools.

 

Ireland: From high unemployment and poverty to technology powerhouse and second wealthiest country in Europe

During the 1980s, Ireland’s economy was hemorrhaging its educated youth who were leaving the country in droves to seek technology jobs in the US, Canada, and Asia. By the early1990s, Ireland’s leaders realized that if they did nothing to reverse the tide of out- migration, there would soon be no more Ireland. Under the leadership of the then President Mary Robinson, Ireland’s leaders embarked on an aggressive economic development and job creation program.  They formed the Irish Development Authority (IDA), a multi stakeholder group that included leaders from education and labour groups, trade unions, government, and employers. Viewed from the adaptive systems perspective, President Robinson, the country’s authority figure, utilized the IDA to develop an adaptive systems response around a new vision: a healthy Ireland full of young people with sustainable livelihoods.7

 

Stakeholders empowered to imagine a different future, act synergistically and adapt

The first goal the IDA set for itself was to develop an economic growth plan that would encourage the return of the country’s young, professional Diaspora. They built their strategy on what they believed to be their primary asset:  a work force that was literate and eager to work. After considerable research, the IDA concluded that multinational IT companies were starting to use call centers and, with aggressive promotion, these companies might be persuaded to locate their call centers in Ireland. IDA staff began an all-out campaign to recruit companies and it paid off.

As call centres employing young Irish high school and college graduates began locating in Ireland, the IDA recognized that they could, at best, keep the call centre business for five to seven years before other countries copied their model at lower costs. The IDA used this five- to seven-year window to move up the value chain from call centres to manufacturing the equipment that runs call centres.  Ireland adapted again by pursuing foreign-owned companies aggressively, persuading them to locate their facilities within Ireland. At the same time, they realized that the skills that were sufficient to staff call centres were not enough to manufacture call centre equipment. Now it became time for the country’s community and technical colleges to adapt to teaching manufacturing skills before the first plant opened its doors.

As new manufacturing companies started producing call centre equipment in Ireland, the IDA once again found that they had at best asked another five to seven years. They adapted again, using this window of time to teach their workforce how to write the software that runs the call centres. Once again, they worked with the education system to anticipate skill needs for tomorrow’s jobs while still teaching today’s skills. Again the IDA embarked on an aggressive recruiting campaign, but this time for software companies, using Intel and others to form the core of a growing group of software firms. Now they were in a position to recruit young Diaspora professionals to return to Ireland and help Ireland become the European headquarters for major technology companies.8

Even though Ireland’s success story has been dimmed by financial crises in recent years, their example shows the power and complexity of an adaptive systems approach. A similar approach was also used in a handful of countries, most notably in South Korea, Singapore, Malaysia, and in cities like Shanghai, as well as various regions of China.  However, the fact that it has been accomplished so infrequently points to the difficulties of sustaining an adaptive process that leads to economic growth and job creation. One of the weaknesses of the approach is that it depends almost entirely on the alignment of leadership at multiple levels of society, something not easy to accomplish in most developing countries where trust is a scarce commodity. However, the central elements of forging a shared vision, seeing possibilities that were previously hidden, and mobilizing stakeholders to make the changes, these central elements work because they unleash people’s ability to go beyond their current constraints if they can envision the desired future. And if done with integrity, the process builds trust, the needed social capital.

One of the reasons Ireland was so successful is that its leaders were able to mobilize the country to act like a community. Is there a way that helps countries, states, and other communities to unleash their potential for sustainable economic and job growth? Recent advances in web-based adaptive, collaborative group modelling techniques promise an exciting platform for communities to implement their own mini-Ireland processes.

 

Facilitating adaptive change using visual, interactive modelling

Outside experts could have come to Ireland and identified the solution, but they could never have made it happen. The example of Ireland’s adaptive change shows that the definition of ‘community’ can be as large as a country, or as small as a rural area in Peru or a city in Africa.  Thanks to recent developments in the modelling of complex systems such as the work being done at the Santa Fe Institute, the group specification of vision, goals, and projects can be accomplished iteratively (as did Ireland), and far more effectively, by using computer-based models that visually depict what the community wants to achieve.9 Ireland maintained a constant forward evolution over a number of years, by continually reacting to changes in their conceptual model of a complex, interlinked, and global economy (e.g., outsourcing being taken up by other countries). What Ireland did was to iteratively revisit their ‘model’ of Ireland in a changing world economy. This same process can be achieved in much faster cycles by giving ‘communities’ like Ireland interactive modelling tools that they can use to visualize and test alternative interactions in a changing world economy. They can actually see where they are now, and where they ‘could be’.

There are several characteristics of an interactive, visual community modelling activity:

1. Visualization in the form of a model leads to better communal understanding, not just for the community itself, but also as away of interacting with neighbouring communities and collaborators; the information in the model becomes transparent and available for everyone who participates.

2. Visual collaborative models make it possible for various subgroups (e.g., micro-enterprises) to add their knowledge and concerns.  In this way, traditionally disenfranchised participants become empowered to add value to the process, accelerating sustainable, adaptive change.

3. Visual modelling tools allow participants in the process to see the likely outcome of their emerging plans, both the good and the bad, and tailor them in real time.  Visual models can operate on multiple levels, such as the level of a country like Ireland, or an individual community.

4. Visual models can be used at a grass-roots level by many communities at once, and then combined to get a more holistic picture of the country.

5. A number of community models can be compared and combined, by using a common ‘language’ like the semantic web. (Scientific American, 2009)  The semantic web is a network of knowledge generated by countries around the world, and linked by common concepts and domains, and is available to
everyone.

Instead of outside experts developing plans for improving a country’s economy, imagine people all over the world putting local data and knowledge into this web, forming a visual model of their community assets and resources. For example, one such modelling process might be working with local communities to map their forests and indigenous plants, using hand-held devices to feed data to the model. By mapping and modelling these data, community assets that were previously hidden become visible to the community and can be used to help grow the economy, if desired. The previous example is one instance of a worldwide semantic web activity now going on that puts local data and knowledge on the web. This process is called Linked Open Data (see Figure1)  a system that countries worldwide are contributing to, and can be used to map their resources and model their desired future.10

Figure 1 is an example of away to model a community. The figure looks like a static model, but it is not, because the model gets smarter as more people enter data and draw inferences that would not have been possible before. Community modelling is an iterative activity, and is useful even in the beginning stages as a common structure for action. Initial modelling contains the basic concepts for the vision, and subsequent iterations add more and more detail moving towards implementation. Collaborative modelling tools help multiple communal stakeholders create adaptive systems that evolve the community towards being more and more sustainable.

 

Figure 1: Example of a high level community model, with features that could be linked into worldwide Linked Open Data

 A community that wants to create livelihoods might use a model like the one above to examine the resources (and constraints) in each of the features (energy, people, natural resources, etc.) of their community. For example, the model might reveal that the community has natural resources that can be used for energy (methane from their local cattle herd, with a methane processing unit), and an underutilized workforce that could haul methane material from the fields to the processing unit.They could also see that there is some education needed (in the detail under education for alternate energy from methane). The detail under finances reveals that there are grants for underdeveloped communities for alternative energy (including methane production). The point is that an interactive, visual display of each feature makes it possible for stakeholders to visualize and solve the complexities of the current situation, and iteratively explore and discover new opportunities for business, in this case energy production of methane from cattle waste.

This could then be tested around a handful of modeled assumptions: for example, the market for local energy, distribution sources for electricity, pricing for energy and so on. The modelling process makes it possible for stakeholders, in this case local farmers and a new energy producer, to participate in the design of the solution, instead of leaving the design in the hands of the outside experts. Best of all, a dynamic visual model can allow new possibilities to emerge and be seen by the different stakeholder groups as
it reveals complexities, opportunities, and dangers that might be difficult to see with a static technical analysis. Modelling like this enables the adaptive problem-solving process by revealing what governance issues must be addressed (such as negative policies, transparency issues, corruption, etc.)

There are various levels of modelling, all of which could be useful to a community:
– High-level visual modelling techniques have been used in business for over several decades and can be applied to community modelling.
– Generic models of communities and community characteristics are starting to become prevalent for online collaboration in many areas. (Santa Fe Institute Video, 2011) These could be used to initially prime the development of community models. If you start with a model that lets a community recognize itself, containing ‘typical’ community structures and information, participants in the process will immediately start to play with the model.  As religion customs education local community natural resources Worldwide Communities people workforce finances Linked by Open Data government energy Community Model soon as participants see a model of their community, they ‘get it’, want to fix it, and jump in to improve it and make it their own.

Figure 1: Example of a high-level community model, with features that could be linked into worldwide Linked Open Data (in the actual model,  each item is clickable to access various levels of detail and interactivity)

The important point about this modelling is that the model is not fixed. It continues to emerge over time as stakeholders interact with it. It gets better and better, and moves more and more towards common solutions. This is emergent community design that evolves with participants, as they author their future.

A methodology similar to the adaptive systems approach has been used for business process re-engineering for many years, as a non-linear way to model, solve, and evolve complex business problems. Various communities around the globe can take this same adaptive systems approach to accelerate the development of solutions that can be sustained.

 

How to implement the adaptive systems approach at the country and/or community level

Much of the technology needed for the modelling process is starting to become available for the non-computer-literate user, enabled via a standard web browser or even via a remote cell phone.  The Linked Open Data site shows how much of the world’s information you can already see for yourself, as part of the ‘world’ community.  The minimal technology required for community modelling is surprisingly simple, and could even be accessed on cell phones via satellite, as a similar project is doing successfully by the Change Organization at the University of Washington, 11 where a group of mentors taught local people to use cell phone models of their environment to collect and transmit their local medicinal knowledge along with samples of their flora and fauna. This led to a better understanding of a complex environment than had never been seen before, resulting in planning that drew on local knowledge embedded in the community.

 

Summary

Under pressure for greater accountability, results, and return on investment, most donors diagnose and treat adaptive systems problems as if they were purely technical. Instead of requiring community stakeholders to do the tough, adaptive work of changing what they do, as in the case of Ireland or the rural community in Peru, donors’ technical staff usually diagnose development problems as technical and then hire contractor firms to implement a solution. This approach sustains an aid industry of consultants and can show results in the required time frame. The problem is that the results usually cannot be sustained after the donor stops investing and the consultants disappear. This is not surprising. When there is arson in a community, it is tempting to send in firefighters.  Solving the problem of arson in a community, however, is much more difficult.  Precisely because arson is an adaptive problem in which a community has no prior experience, community stakeholders must adapt their way through to a solution that can be sustained over time.  Such adaptive problem solving is hard; it often requires trusting in the face of no evidence and giving up cherished privileges or beliefs; adaptive work also tends to take longer than allowed for in the four- or five-year time horizons demanded by most donors.

The authors suggest that donors can help solve adaptive systems problems by bringing in experts who know how to mobilize stakeholder engagement and then use a modelling approach to help stakeholders make the changes they envision.  Fortunately, technology and systems thinking have evolved enough to produce dynamic community modelling tools that let community stakeholders model their community as an interdependent web of knowledge, resources, constraints, and opportunities, all in accelerated time. Once stakeholders see their community as a whole linked system, they can develop and test different scenarios, all the while using feedback from the World Wide Web and their environment to see the results of their choices and determine what changes would be required.  We suggest that community modelling can create a context where leaders, their stakeholders, and donors naturally find themselves doing the required adaptive work as they test alternative solutions. Using the example in the beginning of this article, a dynamic model approach would have made it possible for community stakeholders and their donors to see that locating containers of Chinese workers in a trade-free zone does create new jobs, but that these jobs do not benefit the community. Once this becomes apparent to all, the hard work of making real changes can begin and reveal which policies and practices now constrain better jobs and livelihoods.

If we can teach people to fish, we could teach the same people to model their community’s economy, knowledge, and skills so that they can develop the solutions that work best for them. Dynamic modelling tools can help stakeholders and donors look in the mirror to see whether fishing is an opportunity, determine what kind of fishing might be most productive, or whether they should stop fishing and start developing alternative sources of livelihoods and skills for a future they can sustain without ongoing aid.

View the pdf version

Development, 2012, 55(1), (71–80) r 2012 Society for International
Development 1011-6370/12 www.sidint.net/development/
Development (2012) 55(1), 71–80. doi:10.1057/dev.2011.112

Notes
1 Author’s conversationwith director of aTax-free Zone in Jordan 2005.
2 Author’s Reviewof USAID investment inworkforce development, Sri Lanka. For EDC Inc. 2010.
3 ILO website, Highest Youth Unemployment Ever: An interview with ILO Economist Sara Elder, http://www .ilo.org/employment/areas/youth employment/lang–en/index.htm.
4 Silatech ^ A brief. P1http://www.silatech.com/Arabic/medi /pdf/Silatech%20Bief.pdf.
5 http://www.youtube.com/watch?v=sfEbMV295Kk.
6 Santa Fe Institute, ‘Modeling Organizational Complexity’, Topical Meeting, Santa Fe Institute at Intel, Santa Clara,19 July 2010; social and community behaviour research at Santa Fe Institute.Website: http://www
.santafe.edu/.
7 Author’s conversationwith IDA member, 2006.
8 Digital Hub Development Agency website: available online at http://www.thedigitalhub.com/article.php?id¼55.
9 Santa Fe Institute.
10 Linked Open Data Project, Linked Data ^ Connect Distributed Data across the web http://linkeddata.org/; Community-Driven Ontology Management: DERI Case Study, Zhdanova (2008); also Google: community-driven
ontologies; also Linking Open DataW3C SWEO Community Project http://www.w3.org/wiki/SweoIG/TaskForces/ community Projects/Linking Open Data.
11 University of Washington Project: Open Data Kit, available online at http://www.washington.edu/news/imageslibrary/ open_data_kit.jpg/view.

References
Clark, Matthew (2010) ‘Peru Farmers Drop Cocaine in Favor of Cocoa’,   Christian ScienceMonitor,1February.
El Zanaty and Associates (2007) ‘School-to-work Transition: Evidence from Egypt’, Employment Policy Papers. Available online at: http://www.ilo.org/emppolicy/pubs/WCMS_113893/lang…/index.htm.
Goldstone, Jack A. (2010) ‘The New Population Bomb’, Foreign Affairs 89(1): 31 to 43.
Heifetz, Ronald A. (1994) ‘Leadership Without Easy Answers’,The Belknap Press of Harvard University Press.  Available online at: http://www.audubon-area.org/NewFiles/lead-woa.pdf,    accessed 21August 2011.
McLean Hilker, Lyndsay and Erika Fraser (2007) ‘Youth Exclusion, Violence, Conflict and Fragile States’, Report prepared for DFID’s Equity and Rights Team. Final Report,30 April.
Ridell, Roger C. (2007) Foreign Aid Really Work? Oxford: Oxford University Press.
Scientific American (2009) ‘The Semantic Web in Action’,19 January.
Available online at: http://www.scientificamerican.com/article.cfm?id=semantic-web-in-action.
World Economic Forum (2010) ‘Stimulating Economies Through Fostering Talent Mobility’, Geneva, Switzerland.  Available online at: http://www.weforum.org/s?s¼Stimulating+Economies+Through+Fostering.
Zhdanova, AnnaV. (2008) ‘Community-driven Ontology Construction in Social Networking Portals’,Web Intelligence and Agent Systems: An International Journal (6):1^29.
Santa Fe Institute Video (2011) ‘Modeling the Economy From the Ground Up’, 16 September. Available online at: http://www.santafe.edu/news/item/farmer-INET-agent-based-economic-model/.

Other Relevant Information
Linked Data  Connect Distributed Data across the Web  Linked Open Data Project, http://linkeddata.org/. Linking
Open DataW3C SWEO Community Project: http://www.w3.org/wiki/SweoIG/TaskForces/comunityProjects/ Linking Open Data.
‘Community Model’, Free Encyclopedia of E-commerce Website, 24 August 2011, http://ecommerce.hostip.info/ pages/231/Community-Model.html and the Concept Map of Community Innovation, http://www.dubberly.com/ concept-maps/innovation.html.
Community Structure in Social and Biological Networks, Girvan et al. Santa Fe Institute, http://www.santafe.edu/ media/working papers/01-12-077.pdf. Gelernter, David (1992) Mirror Worlds, Oxford University Press.
Johnson, Stephen (1998) Emergence:  The Connected Lives of Ants, Brains Cities and Software USA, Hardcover.
Holland, John, Emergence From Chaos to Order, Oxford University Press.
Change, ODK Collect v1.1Released.Website: http://change.washington.edu/projects/odk.

  • Monika – This is a very interesting article. I really get that there is a lot of money wasted on solutions that don’t stick, regardless of all the resources and good intentions. I am glad that you are working toward solutions that will make a lasting difference. What are the top 3 actions that you recommend for donors that will help make this difference?