Exercising Our Freedom and Intelligence: Part 8

teamsI am not an optimist, but a great believer of hope. ― Nelson Mandela

The future is uncertain. There is little doubt about that.  Although scientific infrastructures, explanatory scientific models, open data platforms, and methods of predicting the future are constantly evolving, our ability to predict and plan for the future remains limited.  But there is one certainty, at least:  we will worry about our future.  Our worry is understandable.  We want to know what the scenario for our future is.  How will the story play out for us, our children, our grandchildren, and all our many relatives?  And if we have a tendency to think big, we might ask, how will the story of life on Earth unfold?

Of course, we’re all related.  Our story is part of a bigger story―the story of life on Earth, the story of life evolving.  We only figured out that we’re all related fairly recently in the history of our cultural evolution.  Evolutionary science, like many other branches of science, is relatively new on the scene.  Evolutionary Science is truly a revelation. There’s a wonderful quote from Christian De Duve demonstrating this in his book, Life Evolving: molecules, mind, and meaning.

“All the known living beings that subsist, grow, and reproduce on this planet – the trees and the flowers, the fungi and the mushrooms, the extraordinary richness of animal life, in the waters, in the air, and on land, including human beings, together with the immensely varied world of invisible bacteria and protists―all maintain and propagate themselves by the same mechanisms, no doubt inherited from a common ancestral form.  The revelation is awe-inspiring.  So is the realization that the unrelenting human urge to understand has, just in our times, disclosed life’s secrets for us.”

Life is one. According to Christen De Duve, simple awareness of this fact can produce awe sufficient enough for a spiritual awakening.  Some commentators who have embraced a broad, evolutionary perspective have experienced such spiritual insights (Gebser, 1985).  But the biological fact remains: everything that lives is made of one or more cells, and every living cell evolved from cells that lived on our planet some 3.5 billion years ago (De Duve, 2002).

We go way back―you, me, and everyone else.  Thus, although the future is uncertain, the scenario for ‘our future’ entails a big OUR.  For better or for worse, we’re in it together, like one big family.

Given the inevitable uncertainty, thinking about our future allows us to conceive of various different future scenarios.  If the evidence of the past in anything to go on, we’re very imaginative when it comes to conceiving of our future. We might conceive of, and be inspired by, a future where democracy is adopted as a model of governance by every nation state on Earth―and further still, by every school and organization across every nation.  It’s not that difficult to conceive.  We might anticipate that developments in the practice of democracy will shape interpersonal behaviors within the population, with more and more people listening carefully to one another as they talk and think and solve problems and work together on various projects.  Thus, we might imagine a future where democracy truly works well for every nation and every community on Earth.  Or we might conceive of something different: we might imagine a future where increasingly intelligent robots and computers take over decision-making activities from humans and perhaps even take over the running of the world!  Indeed, we wouldn’t be the first to imagine such a scenario.

Notably, these scenarios act as hypotheses, which allow us to ask questions about our future and test the implications of various proposed actions. Although the scenarios we conceive of are often relatively simple-minded, and although we often think through the implications of only a few scenario actions at a time, we can nevertheless become very enthusiastic and even inspired at the prospect of some specific predicted implications of our actions.  Indeed, at a population level, we’re relatively easily inspired―although this may change with our cultural evolution.

One way or the other, the scenarios we envisage, and the actions we see as most pertinent to the success of any given scenario, can inspire us and drive our action.  Like Karl Marx we might conceive ‘the riddle of history solved’ by virtue of our scenario innovation, and we might act vigorously to realize that innovation.  Both implicitly and explicitly we begin to push politically for specific actions.  Others do the same, only with different scenarios in mind.  As such, conflict can arise, and does arise, within and between groups of interdependent individuals.  We all know what happened to the communist society Marx envisioned―the scenario was unworkable in reality.

Indeed, the reality of our biological and cultural evolution highlights a range of potential constraints on any Utopian vision for the future.  Although we have often raged against or denied evolved constraints, we have to work within these constraints.  Steven Pinker, in his book, The Blank Slate: The Modern Denial of Human Nature describes some of the key features of human nature that influence our future scenario design―with obvious implications for any political and societal Utopia we might envision:
  • The primacy of family ties in all human societies and the consequent appeal of nepotism and inheritance.
  • The limited scope of communal sharing in human groups, the more common ethos of reciprocity, and the resulting phenomena of social loafing and the collapse of contributions to public goods when reciprocity cannot be implemented.
  • The universality of dominance and violence across human societies (including supposedly peaceable hunter-gatherers) and the existence of genetic and neurological mechanisms that underlie it.
  • The universality of ethnocentrism and other forms of group-against-group hostility across societies, and the ease with which such hostility can be aroused in people within our own society.
  • The partial heritability of intelligence, conscientiousness, and antisocial tendencies, implying some degree of inequality will arise even in perfectly fair economic systems, and that we therefore face an inherent trade-off between equality and freedom.
  • The prevalence of defense mechanisms, self-serving biases, and cognitive dissonance reduction, by which people deceive themselves about their autonomy, wisdom, and integrity.
  • The biases of the human moral sense, including a preference for kin and friends, a susceptibility to a taboo mentality, and a tendency to confuse morality with conformity, rank, cleanliness, and beauty.  (p. 294)

The more evidence we consider, the more realistic we become. Nelson Mandela’s quote may begin to resonate: I am not an optimist, but a great believer of hope. Reality invariably constrains our optimism.  Our hopes for the future become more realistic.  The future scenarios we envision become more realistic.

Scenarios are stories of a sort―they have a plot, with a set of characters that are more or less interdependent in holding the plot together.  But scenarios are stories about the world, the people in the world, and the action of people in the world―and thus realistic scenarios must draw upon real-world data.  This implies that we import knowledge from the various sciences that provide such data.  There are many relevant sciences―as many as are relevant to the scenario we envision.

Most scenarios are naturally underspecified by reference to any minimally detailed model of world dynamics.  (This is what an engineer would tell you.)  But most of the scenarios we envision are not about world dynamics―they’re about our personal, family, and community dynamics. These local circumstances and dynamics are part of the broader world dynamic, certainly, but unless our day job involves a focus on world dynamics, it’s hard to maintain a sustained focus on these things. The local context is more pertinent.  The facts of the local context, as we perceive them, are most pertinent. As the systems thinking motto goes, we may think global, but we act local.

Notwithstanding the simplicity of our scenarios, the limited import of real-world data, and our local focus, we understandably worry about our future. Uncertainty in relation to the future can itself be worrisome. But worry doesn’t always help us to develop a clear vision of reality, or our future.  When thinking about aspects of our future, it’s not easy to perceive the relevant facts clearly.  Worry, like every other emotion, is biased with respect to the facts that it has a preference for.  Much like the optimist is blinded by their rosy vision of the future, so too is the pessimist clouded by the facts they have a preference for.  The scenarios constructed from these facts result in simulations of the future that are predestined not to fit with reality.  As the mathematician might say, the model provides a poor fit the data.  And as every individual is biased one way or another, the reality of any given situation, and the simulation of increasingly realistic future scenarios, is always slow to emerge at the group level. This is why scientists check one another’s work, try to replicate one another’s research findings, and persist for many years in their efforts to collectively arrive at increasingly realistic models that explain the reality they are focused on. Science is slow. Democracy is slow. We have to accept this, even if we have a preference for speedy decision-making.

But all these scenarios and simulations we construct are important―those of the scientists and non-scientists alike―regardless of their various limitations.  They are important because they shape our future.  They are a central part of our ongoing cultural evolution, and our current individual and group identity.  Scenarios, simulations, models, and hypotheses shape our action in the world, and these actions help us to define our individual personality and identity, and our identity and purpose as part of a group.  We simply can’t function very well without some model or simulation to guide our action. This might explain why Daoism never really caught on in the history of our cultural evolution. It’s hard to know what to do next if the only advice you’re given is to ‘go with the flow’.  People have a preference for imitating specific skills, beliefs, and behaviors that help them solve specific problem.  Human development and cultural evolution is driven by more definitive beliefs, ideas, and behavioral practices that help people adapt to specific environmental conditions.

Our cultural evolution has been ongoing for a long time.  Culture is hugely important―collectively we have access to a vast array of useful ideas, skills, and artifacts that help us to survive, adapt, and sometimes even flourish. Some scholars have argued that culture may have even shaped our recent biological evolution, at least a little. Without culture as a unique adaptation, humans would not benefit from the valuable skills and information from more knowledgeable others (Richardson & Boyd, 2005). Without a capacity for imitation, successful child and adult development would be exceedingly slow, difficult, and dangerous. Culture allows us to learn about our world rapidly and efficiently. As Lev Vygotsky describes it, culture provides the ‘scaffolding’ upon which we construct our world afresh.

Of course, cultural evolution emerged from a longer period of biological evolution.  Before we could draw, talk, write, read, compute, build, travel, explore, or coordinate our action the way we can now, our biological evolution shaped a broad range of behavioral tendencies.  I’ll talk about the interweaving of biology and culture in a future blog post.  But suffice it to say there’s great variation within us as a species―collectively, we have a very broad and varied behavioral repertoire. We can do lots of different things and we have lots of different ideas about the different things we can do.  When we bring all our variation together, in a context where we are free to express that variation, we commonly see the emergence of something new. When people come together―talking, drawing, writing, reading, computing, building, exploring―new scenarios, simulations, models, hypotheses, and artifacts often come to life and shape the future action of the group.

In this blog series, I have focused on group level dynamics and teams in particular.  I’m interested in groups because it is groups that shape the future of our cultural evolution ―there is no doubt about that.  Culture, as it is transmitted from generation to generation, from person to person, is a group-level phenomenon. And I’m interested in teams in particular, because teams are a very unique type of group. Teams have unique functional dynamics.  Well-designed teams are perhaps the most functional type of group that we have available, based on all the different group types that have been documented and studied by researchers.  Teams are unique, in part, because teams are usually designed, in some specific way, to work together. Not all groups are designed to work together.  Not all groups are teams.  Teams have a shared purpose.

But teams are groups nonetheless―and they function as a group amongst other groups. The world includes many teams, each with their own unique purpose. This reality becomes particularly obvious when we start to think about how teams might work together in the context of a broader participatory democracy – the type of democracy where a large group of people (e.g., in an organization, village, city, country) attempt to work together to co-create the policies and projects that shape their world.  When people come together in an interdependent democratic context, when they begin to deliberate about the policies and projects that shape their world, they may find it difficult to work together as a team.  Teams are not currently central to the fundamental design of politics in most organizations, villages, cities, and countries. To import the science of team dynamic into political science and public administration requires innovative design thinking, and considerable experimentation.

But we can envision a simple scenario: we can envision people deliberating and working together on specific community and country-wide projects.  We can envision a scenario where all the ideas and actions that emerge during deliberation might well be structured and coordinated in a way that benefits the group―helping them to adapt to some specific environmental challenge.  Indeed, the group may bring to life a new scenario that shapes their future action together.  Their democratic deliberations―all the facts and logic embedded in the scenarios, simulations, models, and hypotheses they consider―may come together in the form of a higher-order collective intelligence.  They may design a new collective intelligence scenario for the future.

But where do we stand in terms of our current cultural evolution?  What is needed for the design of a successful democracy into the future?  Can we use a form of collective intelligence and scenario-based design to consider what is needed?

Collective intelligence and Scenario Based Design―open data and the future design of democracy

There is reason to be hopeful about our future. There is little doubt that many aspects of cultural evolution are subject to rapid change that is somewhat predictable, at least in terms of a number of specific trajectories. Pettersson (1996) documented the following functions of culture as demonstrating accelerated change: number of different materials used by people, number of occupations involving special arts and technologies, the maximum speed of transport by mechanical means, the complexity of man-made objects and the degree of skill and knowledge required to produce them, communication speed and diversity, and data processing capabilities.  But slower to emerge in the history of our cultural evolution are methods we can use that help to synthesize and coordinate our collective intelligence in the context of the accelerating trajectories of knowledge production and occupational specialization, and the concomitant complexity in the range of perspectives we hold as to the nature of reality. Nevertheless, the push for synthesis and coordination is evident―particularly with innovations in the area of open data. Open data is data that can be freely used by anyone, and innovations have focused on publishing more and more varied data on all aspects of world dynamics, including the workings of governments. These innovations have implications for the future of democracy―forms of democracy rooted in increasingly sophisticated deliberations over open data.

Notably, developments in political philosophy, science, technology, and open data information systems have prompted a range of innovations in the domain of governance and public administration.  The availability of open data relevant to community and national projects can help citizens and public administrators work together in new ways. Broadly speaking, citizens can engage democratically in a range of different ways – they can monitor government policy and the consequences of policy; they can deliberate and discuss policies and shape the policy decision making process; or they can participate directly in policy development and both local and national projects.  Open data can inform their activity in all of these situations. The use of open data has the potential to enhance transparency and trust in government, as data provides a window into the real-world dynamics of both local and national projects and the functioning of government. There are well over 8,000 datasets available on the European Union Open Data Portal (Ojo et al., 2016), with hundreds of open data portals provided at different levels of government to enhance transparency and spur data-driven innovation.

However, acts of co-creation and innovation amongst large groups of people can be challenging.  Collaboration over open data presents many unique design challenges―what data should we collect and report, how should we organize the data, how do we support people to understand and use the data, what kinds of issues provide a good focus for collaboration, what tools will help people to deliberate and innovate, and so on. The ideal of transparent democratic governance involving the use of open data implies many social and technical design challenges, and these design challenges also vary depending on the political and social scenario where open data is being used.  Envisioning specific future scenarios where open data is used by citizens and public administrators to achieve specific goals can help shape our design thinking.  The scenarios can help us to design new open data platforms that allow us to experiment with new forms of democratic activity.  Specific scenarios help us to think about how we put open data to use.

Below, I will describe our approach to the design of an open data collaboration platform in the context of an EU open data transparency project.  Briefly, we used a collective intelligence scenario-based design process to identify system requirements that have shaped the design of our open data platform.  We have also used versions of these same scenarios to attract users to the system and to experiment with the system, such that we can examine how collaborative groups put open data to use. In essence, scenarios have shaped both the design and application of our open data portal.

Conceptualising transparency and approaching transparency design

In a democracy, one of the key things that open data can help to do is to enhance transparency and trust in government.  Trust in government has been consistently low over the past decade, and citizens are looking for ways to gain greater control in the direction and control of their government.  Meijer (2015) points to two eras of transparency―transparency in an era of representative democracy and transparency in an era of participatory democracy. Representative democracy is founded on the principle of elected officials representing a group of people. In a representative democracy, the people, or citizens, can monitor and discuss policies and policy outcomes, but they have no direct influence over policy.  Any influence they have is indirect―and thus representative democracy is sometimes called indirect democracy.  Participatory democracy is different―it’s more direct: it emphasizes the collaboration of citizens and public administrators in the operation of political systems and the co-creation of public value.  Participatory democracy represents an ideal that has yet to be fully realized―it will require considerable effort in design and experimentation.  However, the era of participatory democracy is upon us, and is associated with widespread availability of government documents and data on websites and open data portals (Meijer, 2015). But there is a major problem―the social and collaborative features of these platforms are currently very limited.  In other words, open data platforms do not facilitate quality collaboration, and thus they limit our capacity for participatory democracy.  Thus, quality collaboration over open data is not yet possible, but if we can design a usable socio-technical infrastructure, openly available data can be drawn upon by groups who seek to collaborate in the formulation of policy and the co-creation of public value.

Indeed, some of the political and socio-technical design challenges that need to be addressed in the future design of democracy are rooted in the challenge of conceptualizing transparency. Conceptualizations of transparency are often grounded in deeper world views (Pepper, 1942), which can lead to the development of different frameworks shaping research and innovation (Hayes et al., 1988).  Consistent with John Warfield’s approach to collective intelligence design (Warfield, 2006), we have adopted a contextualist approach to research and innovation.  This implies a focus on a specific scenario or situation where open data is being used to support democratic processes, the specific purpose or goal(s) of actors in the scenario, with success determined by the extent to which their purpose or goal(s) are achieved.  We draw upon the collective intelligence scenario-based design thinking of stakeholders (i.e., prospective users of the open data portal) to identify barriers to accessing, understanding, and using open data and options to overcome these barriers. Stakeholders also identified specific information, decision-making, and social collaborative needs of the actors in our scenarios―what do the actors in these scenarios (i.e., citizens and public administrators) need to ensure a successful democratic process and outcome for their group?

Transparency Design and the Route-to-PA project

Our project—the Route-to-PA project—is an innovation project focused on the design of an open data collaboration platform that can be flexibly used by citizens and public administrators across a wide variety of scenarios. The goal is to create a new technology for public administrations and citizens across a range of EU countries categorized by the Open Data Barometer (2015) as high capacity (UK, France, and the Netherlands) and emerging and advancing (Italy and Ireland).  Thus, it was important in the first instance to understand the varied political and social contexts, both at the national and local levels, where our design and innovation is to be realized. This involved an analysis of the open data readiness of each country, and analysis of the local open data context for specific usage scenarios that reflect key priorities of citizens and public administrations in each of our pilot sites.  What types of democratic scenarios are citizens and public administrations focused on, and is there open data available that is relevant to these scenarios?

To help our technical design team think carefully about the platform design, we needed to ask key stakeholders and users in each pilot site about the most important barriers to accessing and using open data, options to overcome these barriers, and the key needs and requirements of users in specific scenarios.  It was essential to focus not only on the information (or data) needs of users, but also their social-collaborative and decision-making needs.  In order words, the system design needed to allow for collaboration, shared learning, and decision making in the context of accessible, usable, understandable open data.  This is where the collective intelligence scenario-based design approach became particularly useful.  Specifically, we combined collective intelligence (Warfield, 2006) with scenario-based design (Carroll, 2000) and agile user story (Cohn, 2004) methods. The collective intelligence methods help to structure ideas and ensure input from a diverse range of representative stakeholders in the design process; the use of scenario-based design methods ensures that identified needs and requirements of users are grounded in an understanding of specific political and social scenarios; and finally, the use of agile user stories allows for the specification of user needs, and reasons for those needs, at a level of detail that allows for agile software development of specific functionalities. Working across four EU countries and five pilot sites, we used these methods in a series of carefully designed workshops, one in each pilot site, for the purpose of developing a comprehensive set of user needs, as proposed by key stakeholders.

Our workshops brought together academics, industry specialists, open data practitioners, representatives of governments, open data researchers, and potential users (including citizens, representatives of citizens and social service institutes, and journalists).  The scenarios that shaped their thinking in relation to user needs included multiple actors. The scenarios were specific to each workshop site, and the political priorities identified in each site. For example, in Dublin, stakeholders focused on community networking and opportunity creation; stakeholders in Groningen focused on the challenge of population decline; Den Haag focused on employment and opportunity creation; Prato focused on local policy and budget issues; and Issy-les-Moulineaux focused on the facilitation of start-up companies and the digital economy. The research team conducted a meta-analysis of barriers, options, and needs across all sites and used this analysis to inform the design of the Route-to-PA platform.  The full report describing the method and results can be found here. Below is a summary of some key findings.

Barriers, options, and needs

The design of an open data collaboration platform is a collective activity that takes place within a larger societal and technical problem field.  Designers need to understand this problem field as completely as possible before they begin thinking about specific needs of users and specific system requirements and design features.  It is often the case that technical design teams jump to design solutions without thinking either about the larger problem field or any specific scenario of usage for their technology. This is one reason why so many technologies fail.  When we ran a meta-analysis across all our pilot sites, drawing upon the collective intelligence across all our workshops, we identified 12 categories of barriers to accessing, understanding, and using open data. These are important to consider.

One major cluster of categories focused on government and organizational barriers, including resistance to open data initiatives and fear of losing control of data. Notably, transparency is not valued by everyone in government; there is often refusal by politicians to transfer knowledge or power, fear of loss of data ownership once data is released in an open format, and fear that the government will lose its reputation if it pursues the path of transparency.  We asked our stakeholders to consider options in response to these barriers.  Options proposed highlighted, for example, the need to design enjoyable and intuitive interfaces for local government staff to publish data as open data; celebrating open data innovation leaders in organizations to highlight the importance and value of their work; and providing information, training and education for all government agencies on the benefits of an open data portal. Naturally, these barriers and options have implications for the way in which any new technology is introduced to a community of users.

Other government and organizational barriers focused on privacy and security and conflict and cooperation.  For example, personal information accessed by the public can lead to data protection infringement; some data are commercially sensitive; and privacy and security may be compromised by conflicting roles and interests between politicians, management, and the public. These are complex issues, and decisions have to be made on a case-by-case basis, and on a planned basis, as to what kinds of data are acceptable to collect and publish online.  Options for overcoming barriers in this category included efforts to organize multi-level training on how to use data safely; initiatives showcasing good practice; and research examining how potentially sensitive data is used in an open environment in other countries.

The conflict and cooperation category of barriers highlighted related issues, including the inevitable conflict and lack of progress in the development of open data initiatives due to contrary interests; and lack of cooperation between government and citizens. Stakeholders suggested a range of options in response to this category of barriers, including efforts to introduce procedures to standardize/simplify data release; establish the practice of asking and having to justify “why not” around data release; along with an option to establish a data review board to help public administrators with data release decisions.

Frequently noted by stakeholders were barriers linked to technical, data, and resource issues. Key barriers to accessing, understanding, and using open data included: lack of engaging activities and information for users; lack of standard approaches in data organization and storage; data displayed in a technical way or use unfamiliar technical language; data that is accessible by a limited number of systems; and cost and resource issues associated with maintaining open data platforms. Options in response to these barriers included efforts to understand who exactly would like to use the different types of data; implement a set of policies and standards for publishing user friendly open data; develop engagement strategies that allow for step by step discovery of how to access and use open data; create normalized formats that can be embedded in most data analysis tools; and set up a fund to commercialize open data projects.

The third major set of barrier categories related to training and citizen engagement issues. Key barriers included lack of awareness of the existence of open data; lack of training to go about finding data that is relevant for the purpose required; and available open datasets are not “relevant” or “speaking to” peoples interests. Stakeholders proposed a range of options to overcome these barriers including, for example, promotion programs aimed at the public to create not just awareness of data availability but knowledge about the uses and benefits of open data; support mechanisms for open data entrepreneurs; and efforts to identify and publish data that is relevant and engaging.

To the extent that we will ground our democratic decisions in collaboration over open data, overcoming barriers to accessing, understanding, and using open data is critical for the future success of democracy. Deliberative and participatory democracy is not some vague ideal—it implies group decision-making and action in relation to specific issues. Specific issues, and specific scenarios where open data platforms are being used, imply specific user needs. The unique scenarios used by stakeholders in our workshops prompted thinking in relation to the unique needs of platform users.  This was clearly reflected, in the first instance, in the variety of open data information or data needs across sites.  For example, while the scenario in Den Haag focused on employment and opportunity creation, resulting in a high proportion of jobseekers information needs, the Dublin scenario, which focused on community engagement and planning, generated information needs across a much wider range, including community, planning, services, amenities, business, and education information.  Unsurprisingly, what we have since found is that the data and information needs across sites, continues to develop further as each pilot site works to realize their scenarios and promote effective collaboration between citizens and public administrators. More generally, essential for the future success of open data portals is that more varied high-quality open data is made available to stakeholders in an increasingly accessible, understandable and usable manner. Ongoing work by the Route-to-PA team has involved profiling the extent to which open data is available, matched to, and useful for, the scenarios of interest to stakeholders in each pilot site. This profiling of data is being used to feedback to public administrators and key data providers to highlight some of the key gaps in the data and specific data needs moving forward, that is, in efforts to realize the scenarios in each pilot site.

The Route-to-PA team has also designed a range of affordances matched to the keysocial-collaborative needs identified by stakeholders across pilot sites. Notably, stakeholders highlighted the need for different forms of interaction over open data, including dialogue and discussion spaces; feedback, moderation and maintenance of these spaces; varied forms of interaction over the data; sharing and requesting data; and also coaching and support in the use of social-collaborative and data analysis affordances.  Currently, the Route-to-PA platform includes a number of key social-collaborative affordances, including a dialogue and collaboration spaces that allows for sharing and discussion of data visualizations, knowledge of network connections and engagement and exchange dynamics between users, and the capacity to work in private rooms, co-creation rooms, or public spaces on the platform.

Stakeholders also identified a range of understandability, usability, and decision-making needs, including decision-making support tools, data visualization tools, data personalization features, and data analysis and reporting tools. The ability to search, filter, aggregate, visualize, modify, customize, and analyze data were central needs identified across all pilot sites.  More advanced data analysis and reporting tools were also seen as central for decision-making, including data mining tools, modeling tools, metadata tools, data merging tools, data wrangling and labelling tools, among others. A key challenge in this context is the design of tools that people can readily learn to use without advanced training in data analysis.  What level of data and tool competency is needed to match the complexity of the societal issues collaborative groups are working on?  One potential solution to this challenge is to design tools that have a range of affordances, and design collaborative groups that include stakeholders with a range of skills, including a sub-group who specialize in more advanced data analysis and visualisation work that supports the deliberation and decision-making of the larger team.

Going forward

As noted above, participatory democracy is an ideal that has yet to be realized.  It implies a form of networked governance, where increasingly large groups of people work together.  The efficacy of governance networks is contingent on the inclusion of citizens in the networks (Ojo and Mellouli, 2016), and thus mobile social-media platforms could constitute a key infrastructure for enabling citizen participation in this regard. However, Ojo and Mellouli (2016) note that new and emerging governance networks are still largely steered by government.  Thus, governments must initiate and demonstrate deep commitments to partnerships with citizens for collaborative governance networks to be effective.  Government is ultimately responsible for building trust with partners and are accountable for the overall outcome of the networked governance arrangement. This implies ongoing investment and iterative design, innovation and experimentation with tools and methods that may support networked governance, including open data platforms that support collaboration. Considering the specificity of the user needs identified by the stakeholders in our collective intelligence workshops, it is clear that governments and citizens need to work with social scientists and technology experts to design open data platforms that include a range of data analysis and decision-making affordances that support collaborative societal problem solving and policy development. This needs to be coupled with appropriate training in the use of these affordances.  Ojo and Mellouli (2016) highlight, based on extensive case study analyses, the need to motivate citizen participation in governance networks and align divergent views of different actors collaborating in the network.

From a contextualist perspective, the collective intelligence scenario-based design thinking of stakeholders in our workshops suggest that motivating citizens may be contingent on meeting their needs.  Designers need to understand these needs, and the specific scenarios of usage, before they design their solutions.  We need to work together to design socio-technical infrastructure that support our social-collaborative and decision-making needs, as this will be critical to sustain motivation in the use of our innovations.

Democracy may be slow to emerge and develop historically, and it may be slow as a method of resolving societal problems, but my hope is that it will evolve as a method we can use to good effect to support our collective intelligence and collective action.  Indeed, our current trajectory of cultural evolution suggests that democracy will evolve. We just need to push cultural evolution on a bit—and push it in the right direction.

© Michael Hogan

References

Caroll, J. (2000). Five reasons for scenario-based design. Interacting with Computers, 13, 43-60.

Cohn, M. (2004) User Stories Applied for Agile Software Development, Boston, MA: Addison-Wesley.

De Duve, C. (2002). Life evolving: molecules, mind, and meaning. Oxford: Oxford University Press.

Gebser, J. (1985). The ever-present origin. Athens, Ohio: Ohio University Press.

Hayes, S. C., Hayes, L. J., & Reese, H. W. (1988). Finding the Philosophical Core: A Review of Stephen C. Pepper’s World Hypotheses: A Study in Evidence. Journal of the Experimental Analysis of Behaviour, 1(1), 97–111.

Meijer, A. (2015). Government Transparency in Historical Perspective: From the Ancient Regime to Open Data in The Netherlands. International Journal of Public Administration, 38(3), 189–199. doi:10.1080/01900692.2014.934837

Ojo, A., Mellouli, S. (2016). Deploying Governance Networks for Societal Challenges, Government Information Quarterly, doi:10.1016/j.giq.2016.04.001

Open Data Barometer (January 2015) – http://barometer.opendataresearch.org/

Pepper, S.C. (1942). World hypotheses: A study in evidence. University of California Press.

Pettersson, M. (1996). Complexity and evolution. New York: Cambridge University Press.

Pinker, S. (2002).  The Blank Slate. Penguin Books.

Richerson, P. J., & Boyd, R. (2005). Not by genes alone: how culture transformed human evolution. Chicago: University of Chicago Press.

Warfield, J. N. (2006). An introduction to systems science. Singapore: World Scientific.

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