The Perils of Experimentation

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Paper by Michael A. Livermore: “More than eighty years after Justice Brandeis coined the phrase “laboratories of democracy,” the concept of policy experimentation retains its currency as a leading justification for decentralized governance. This Article examines the downsides of experimentation, and in particular the potential for decentralization to lead to the production of information that exacerbates public choice failures. Standard accounts of experimentation and policy learning focus on information concerning the social welfare effects of alternative policies. But learning can also occur along a political dimension as information about ideological preferences, campaign techniques, and electoral incentives is revealed. Both types of information can be put to use in the policy arena by a host of individual and institutional actors that have a wide range of motives, from public-spirited concern for the general welfare to a desire to maximize personal financial returns. In this complex environment, there is no guarantee that the information that is generated by experimentation will lead to social benefits. This Article applies this insight to prior models of federalism developed in the legal and political science literature to show that decentralization can lead to the over-production of socially harmful information. As a consequence, policy makers undertaking a decentralization calculation should seek a level of decentralization that best balances the costs and benefits of information production. To illustrate the legal and policy implications of the arguments developed here, this Article examines two contemporary environmental rulemakings of substantial political, legal, and economic significance: a rule to define the jurisdictional reach of the Clean Water Act; and a rule to limit greenhouse gas emissions from the electricity generating sector….(More)”.

 

The Values of Public Library in Promoting an Open Government Environment

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Djoko Sigit Sayogo et al in the Proceedings of the 17th International Digital Government Research Conference on Digital Government Research: “Public participation has been less than ideal in many government-implemented ICT initiatives. Extant studies highlight the importance of public libraries as an intermediary between citizens and government. This study evaluates the role of public libraries as mediating the relationship between citizens and government in support of an open government environment. Using data from a national survey of “Library and Technology Use” conducted by PEW Internet in 2015, we test whether a citizen’s perception of public values provided by public libraries influence the likelihood of the citizen’s engagement within open-government environment contexts. The results signify a significant relationship between certain public values provided by public libraries with the propensity of citizens engaging government in an online environment. Our findings further indicate that varying public values generate different results in regard to the way citizens are stimulated to use public libraries to engage with government online. These findings imply that programs designed and developed to take into account a variety of values are more likely to effectively induce citizen engagement in an open government environment through the mediation of public libraries….(More)”

Private Data and the Public Good

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Gideon Mann‘s remarks on the occasion of the Robert Khan distinguished lecture at The City College of New York on 5/22/16: and opportunities about a specific aspect of this relationship, the broader need for computer science to engage with the real world. Right now, a key aspect of this relationship is being built around the risks and opportunities of the emerging role of data.

Ultimately, I believe that these relationships, between computer science andthe real world, between data science and real problems, hold the promise tovastly increase our public welfare. And today, we, the people in this room,have a unique opportunity to debate and define a more moral dataeconomy….

The hybrid research model proposes something different. The hybrid research model, embeds, as it were, researchers as practitioners.The thought was always that you would be going about your regular run of business,would face a need to innovate to solve a crucial problem, and would do something novel. At that point, you might choose to work some extra time and publish a paper explaining your innovation. In practice, this model rarely works as expected. Tight deadlines mean the innovation that people do in their normal progress of business is incremental..

This model separated research from scientific publication, and shortens thetime-window of research, to what can be realized in a few year time zone.For me, this always felt like a tremendous loss, with respect to the older so-called “ivory tower” research model. It didn’t seem at all clear how this kindof model would produce the sea change of thought engendered byShannon’s work, nor did it seem that Claude Shannon would ever want towork there. This kind of environment would never support the freestanding wonder, like the robot mouse that Shannon worked on. Moreover, I always believed that crucial to research is publication and participation in the scientific community. Without this engagement, it feels like something different — innovation perhaps.

It is clear that the monopolistic environment that enabled AT&T to support this ivory tower research doesn’t exist anymore. .

Now, the hybrid research model was one model of research at Google, butthere is another model as well, the moonshot model as exemplified byGoogle X. Google X brought together focused research teams to driveresearch and development around a particular project — Google Glass and the Self-driving car being two notable examples. Here the focus isn’t research, but building a new product, with research as potentially a crucial blocking issue. Since the goal of Google X is directly to develop a new product, by definition they don’t publish papers along the way, but they’re not as tied to short-term deliverables as the rest of Google is. However, they are again decidedly un-Bell-Labs like — a secretive, tightly focused, non-publishing group. DeepMind is a similarly constituted initiative — working, for example, on a best-in-the-world Go playing algorithm, with publications happening sparingly.

Unfortunately, both of these approaches, the hybrid research model and the moonshot model stack the deck towards a particular kind of research — research that leads to relatively short term products that generate corporate revenue. While this kind of research is good for society, it isn’t the only kind of research that we need. We urgently need research that is longterm, and that is undergone even without a clear financial local impact. Insome sense this is a “tragedy of the commons”, where a shared public good (the commons) is not supported because everyone can benefit from itwithout giving back. Academic research is thus a non-rival, non-excludible good, and thus reasonably will be underfunded. In certain cases, this takes on an ethical dimension — particularly in health care, where the choice ofwhat diseases to study and address has a tremendous potential to affect human life. Should we research heart disease or malaria? This decisionmakes a huge impact on global human health, but is vastly informed by the potential profit from each of these various medicines….

Private Data means research is out of reach

The larger point that I want to make, is that in the absence of places where long-term research can be done in industry, academia has a tremendous potential opportunity. Unfortunately, it is actually quite difficult to do the work that needs to be done in academia, since many of the resources needed to push the state of the art are only found in industry: in particular data.

Of course, academia also lacks machine resources, but this is a simpler problem to fix — it’s a matter of money, resources form the government could go to enabling research groups building their own data centers or acquiring the computational resources from the market, e.g. Amazon. This is aided by the compute philanthropy that Google and Microsoft practice that grant compute cycles to academic organizations.

But the data problem is much harder to address. The data being collected and generated at private companies could enable amazing discoveries and research, but is impossible for academics to access. The lack of access to private data from companies actually is much more significant effects than inhibiting research. In particular, the consumer level data, collected by social networks and internet companies could do much more than ad targeting.

Just for public health — suicide prevention, addiction counseling, mental health monitoring — there is enormous potential in the use of our online behavior to aid the most needy, and academia and non-profits are set-up to enable this work, while companies are not.

To give a one examples, anorexia and eating disorders are vicious killers. 20 million women and 10 million men suffer from a clinically significant eating disorder at some time in their life, and sufferers of eating disorders have the highest mortality rate of any other mental health disorder — with a jaw-dropping estimated mortality rate of 10%, both directly from injuries sustained by the disorder and by suicide resulting from the disorder.

Eating disorders are particular in that sufferers often seek out confirmatory information, blogs, images and pictures that glorify and validate what sufferers see as “lifestyle” choices. Browsing behavior that seeks out images and guidance on how to starve yourself is a key indicator that someone is suffering. Tumblr, pinterest, instagram are places that people host and seek out this information. Tumblr has tried to help address this severe mental health issue by banning blogs that advocate for self-harm and by adding PSA announcements to query term searches for queries for or related to anorexia. But clearly — this is not the be all and end all of work that could be done to detect and assist people at risk of dying from eating disorders. Moreover, this data could also help understand the nature of those disorders themselves…..

There is probably a role for a data ombudsman within private organizations — someone to protect the interests of the public’s data inside of an organization. Like a ‘public editor’ in a newspaper according to how you’ve set it up. There to protect and articulate the interests of the public, which means probably both sides — making sure a company’s data is used for public good where appropriate, and making sure the ‘right’ to privacy of the public is appropriately safeguarded (and probably making sure the public is informed when their data is compromised).

Next, we need a platform to make collaboration around social good between companies and between companies and academics. This platform would enable trusted users to have access to a wide variety of data, and speed process of research.

Finally, I wonder if there is a way that government could support research sabbaticals inside of companies. Clearly, the opportunities for this research far outstrip what is currently being done…(more)”

Do Open Comment Processes Increase Regulatory Compliance? Evidence from a Public Goods Experiment

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Stephen N. Morgan, Nicole M. Mason and Robert S. Shupp at EconPapers: “Agri-environmental programs often incorporate stakeholder participation elements in an effort to increase community ownership of policies designed to protect environmental resources (Hajer 1995; Fischer 2000). Participation – acting through increased levels of ownership – is then expected to increase individual rates of compliance with regulatory policies. Utilizing a novel lab experiment, this research leverages a public goods contribution game to test the effects of a specific type of stakeholder participation scheme on individual compliance outcomes. We find significant evidence that the implemented type of non-voting participation mechanism reduces the probability that an individual will engage in noncompliant behavior and reduces the level of noncompliance. At the same time, exposure to the open comment treatment also increases individual contributions to a public good. Additionally, we find evidence that exposure to participation schemes results in a faster decay in individual compliance over time suggesting that the impacts of this type of participation mechanism may be transitory….(More)”

How innovation agencies work

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Kirsten Bound and Alex Glennie at NESTA: “This report considers how governments can get better at designing and running innovation agencies, drawing on examples from around the world.

Key findings

  • There is no single model for a ‘successful’ innovation agency.  Although there is much to learn from other countries about best practice in institution and programme design, attempts to directly replicate organisational models that operate in very different contexts are likely to fail.
  • There are a variety of roles that innovation agencies can play. From our case studies, we have identified a number of different approaches that an innovation agency might take, depending on the specific nature of a country’s innovation system, the priorities of policymakers, and available resources.
  • Innovation agencies need a clear mission, but an ability to adapt and experiment. Working towards many different objectives at once or constantly changing strategic direction can make it difficult for an innovation agency to deliver impactful innovation support for businesses. However, a long-term vision of what success looks like should not prevent innovation agencies from experimenting with new approaches, and responding to new needs and opportunities.
  • Innovation agencies should be assessed both quantitatively and qualitatively. Evaluations tend to focus on the financial return they generate, but our research suggests that more effort needs to be put into assessing some of the more qualitative aspects of their role, including the quality of their management, their ability to take (and learn from) strategic risks, and the skill with which they design and implement their programmes.
  • Governments should be both ambitious and realistic about what they expect an innovation agency to achieve. An innovation agency’s role will inevitably be affected by shifts in government priorities. Understanding how innovation agencies shape (and are shaped by) the broader political environment around innovation is a necessary part of ensuring that they are able to deliver on their potential.

Governments around the world are looking for ways to nurture innovative businesses, as a way of solving some of their most urgent economic and societal challenges. Many seek to do this by setting up national innovation agencies: institutions that provide financial and other support to catalyse or drive private sector innovation. Yet we still know relatively little about the range of approaches that these agencies take, what programmes and instruments are likely to work best in a given context, and how to assess their long-term impact.

We have been investigating these questions by studying a diverse group selection of innovation agencies in ten different countries. Our aim has been to improve understanding of the range of existing institutional models and to learn more about their design, evolution and effectiveness. In doing so, we have developed a broad framework to help policymakers think about the set of choices and options they face in the design and management of an innovation agency….(More)”

Open data + increased disclosure = better public-private partnerships

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David Bloomgarden and Georg Neumann at Fomin Blog: “The benefits of open and participatory public procurement are increasingly being recognized by international bodies such as the Group of 20 major economies, the Organisation for Economic Co-operation and Development, and multilateral development banks. Value for money, more competition, and better goods and services for citizens all result from increased disclosure of contract data. Greater openness is also an effective tool to fight fraud and corruption.

However, because public-private partnerships (PPPs) are planned during a long timeframe and involve a large number of groups, therefore, implementing greater levels of openness in disclosure is complicated. This complexity can be a challenge to good design. Finding a structured and transparent approach to managing PPP contract data is fundamental for a project to be accepted and used by its local community….

In open contracting, all data is disclosed during the public procurement process—from the planning stage, to the bidding and awarding of the contract, to the monitoring of the implementation. A global open source data standard is used to publish that data, which is already being implemented in countries as diverse as Canada, Paraguay, and the Ukraine. Using open data throughout the contracting process provides opportunities to innovate in managing bids, fixing problems, and integrating feedback as needed. Open contracting contributes to the overall social and environmental sustainability of infrastructure investments.

In the case of Mexico’s airport, the project publishes details of awarded contracts, including visualizing the flow of funds and detailing the full amounts of awarded contracts and renewable agreements. Standardized, timely, and open data that follow global standards such as the Open Contracting Data Standard will make this information useful for analysis of value for money, cost-benefit, sustainability, and monitoring performance. Crucially, open contracting will shift the focus from the inputs into a PPP, to the outputs: the goods and services being delivered.

Benefits of open data for PPPs

We think that better and open data will lead to better PPPs. Here’s how:

1. Using user feedback to fix problems

The Brazilian state of Minas Gerais has been a leader in transparent PPP contracts with full proactive disclosure of the contract terms, as well as of other relevant project information—a practice that puts a government under more scrutiny but makes for better projects in the long run.

According to Marcos Siqueira, former head of the PPP Unit in Minas Gerais, “An adequate transparency policy can provide enough information to users so they can become contract watchdogs themselves.”

For example, a public-private contract was signed in 2014 to build a $300 million waste treatment plant for 2.5 million people in the metropolitan area of Belo Horizonte, the capital of Minas Gerais. As the team members conducted appraisals, they disclosed them on the Internet. In addition, the team held around 20 public meetings and identified all the stakeholders in the project. One notable result of the sharing and discussion of this information was the relocation of the facility to a less-populated area. When the project went to the bidding phase, it was much closer to the expectations of its various stakeholders.

2. Making better decisions on contracts and performance

Chile has been a leader in developing PPPs (which it refers to as concessions) for several decades, in a range of sectors: urban and inter-urban roads, seaports, airports, hospitals, and prisons. The country tops the list for the best enabling environment for PPPs in Latin America and the Caribbean, as measured by Infrascope, an index produced by the Economist Intelligence Unit and the Multilateral Investment Fund of the IDB Group.

Chile’s distinction is that it discloses information on performance of PPPs that are underway. The government’s Concessions Unit regularly publishes summaries of the projects during their different phases, including construction and operation. The reports are non-technical, yet include all the necessary information to understand the scope of the project…(More)”

Smart crowds in smart cities: real life, city scale deployments of a smartphone based participatory crowd management platform

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Tobias Franke, Paul Lukowicz and Ulf Blanke at the Journal of Internet Services and Applications: “Pedestrian crowds are an integral part of cities. Planning for crowds, monitoring crowds and managing crowds, are fundamental tasks in city management. As a consequence, crowd management is a sprawling R&D area (see related work) that includes theoretical models, simulation tools, as well as various support systems. There has also been significant interest in using computer vision techniques to monitor crowds. However, overall, the topic of crowd management has been given only little attention within the smart city domain. In this paper we report on a platform for smart, city-wide crowd management based on a participatory mobile phone sensing platform. Originally, the apps based on this platform have been conceived as a technology validation tool for crowd based sensing within a basic research project. However, the initial deployments at the Notte Bianca Festival1 in Malta and at the Lord Mayor’s Show in London2 generated so much interest within the civil protection community that it has gradually evolved into a full-blown participatory crowd management system and is now in the process of being commercialized through a startup company. Until today it has been deployed at 14 events in three European countries (UK, Netherlands, Switzerland) and used by well over 100,000 people….

Obtaining knowledge about the current size and density of a crowd is one of the central aspects of crowd monitoring . For the last decades, automatic crowd monitoring in urban areas has mainly been performed by means of image processing . One use case for such video-based applications can be found in, where a CCTV camera-based system is presented that automatically alerts the staff of subway stations when the waiting platform is congested. However, one of the downsides of video-based crowd monitoring is the fact that video cameras tend to be considered as privacy invading. Therefore,  presents a privacy preserving approach to video-based crowd monitoring where crowd sizes are estimated without people models or object tracking.

With respect to the mitigation of catastrophes induced by panicking crowds (e.g. during an evacuation), city planners and architects increasingly rely on tools simulating crowd behaviors in order to optimize infrastructures. Murakami et al. presents an agent based simulation for evacuation scenarios. Shendarkar et al. presents a work that is also based on BSI (believe, desire, intent) agents – those agents however are trained in a virtual reality environment thereby giving greater flexibility to the modeling. Kluepfel et al. on the other hand uses a cellular automaton model for the simulation of crowd movement and egress behavior.

With smartphones becoming everyday items, the concept of crowd sourcing information from users of mobile application has significantly gained traction. Roitman et al. presents a smart city system where the crowd can send eye witness reports thereby creating deeper insights for city officials. Szabo et al. takes this approach one step further and employs the sensors built into smartphones for gathering data for city services such as live transit information. Ghose et al. utilizes the same principle for gathering information on road conditions. Pan et al. uses a combination of crowd sourcing and social media analysis for identifying traffic anomalies….(More)”.

Twelve principles for open innovation 2.0

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Martin Curley in Nature: “A new mode of innovation is emerging that blurs the lines between universities, industry, governments and communities. It exploits disruptive technologies — such as cloud computing, the Internet of Things and big data — to solve societal challenges sustainably and profitably, and more quickly and ably than before. It is called open innovation 2.0 (ref. 1).

 The promise is sustainable, intelligent living: innovations drive economic growth and improve quality of life while reducing environmental impact and resource use. For example, a dynamic congestion-charging system can adjust traffic flow and offer incentives to use park-and-ride schemes, guided by real-time traffic levels and air quality. Car-to-car communication could manage traffic to minimize transit times and emissions and eliminate road deaths from collisions. Smart electricity grids lower costs, integrate renewable energies and balance loads. Health-care monitoring enables early interventions, improving life quality and reducing care costs.

Such innovations are being tested in ‘living labs’ in hundreds of cities. In Dublin, for example, the city council has partnered with my company, the technology firm Intel (of which I am a vice-president), to install a pilot network of sensors to improve flood management by measuring local rain fall and river levels, and detecting blocked drains. Eindhoven in the Netherlands is working with electronics firm Philips and others to develop intelligent street lighting. Communications-technology firm Ericsson, the KTH Royal Institute of Technology, IBM and others are collaborating to test self-driving buses in Kista, Sweden.

Yet many institutions and companies remain unaware of this radical shift. They often confuse invention and innovation. Invention is the creation of a technology or method. Innovation concerns the use of that technology or method to create value. The agile approaches needed for open innovation 2.0 conflict with the ‘command and control’ organizations of the industrial age (see ‘How innovation modes have evolved’). Institutional or societal cultures can inhibit user and citizen involvement. Intellectual-property (IP) models may inhibit collaboration. Government funders can stifle the emergence of ideas by requiring that detailed descriptions of proposed work are specified before research can begin. Measures of success, such as citations, discount innovation and impact. Policymaking lags behind the market place….

Keys to collaborative innovation

  1. Purpose. Efforts and intellects aligned through commitment rather than compliance deliver an impact greater than the sum of their parts. A great example is former US President John F. Kennedy’s vision of putting a man on the Moon. Articulating a shared value that can be created is important. A win–win scenario is more sustainable than a win–lose outcome.
  2. Partner. The ‘quadruple helix’ of government, industry, academia and citizens joining forces aligns goals, amplifies resources, attenuates risk and accelerates progress. A collaboration between Intel, University College London, Imperial College London and Innovate UK’s Future Cities Catapult is working in the Intel Collaborative Research Institute to improve people’s well-being in cities, for example to enable reduction of air pollution.
  3. Platform. An environment for collaboration is a basic requirement. Platforms should be integrated and modular, allowing a plug-and-play approach. They must be open to ensure low barriers to use, catalysing the evolution of a community. Challenges in security, standards, trust and privacy need to be addressed. For example, the Open Connectivity Foundation is securing interoperability for the Internet of Things.
  4. Possibilities. Returns may not come from a product but from the business model that enabled it, a better process or a new user experience. Strategic tools are available, such as industrial designer Larry Keeley’s breakdown of innovations into ten types in four categories: finance, process, offerings and delivery.
  5. Plan. Adoption and scale should be the focus of innovation efforts, not product creation. Around 20% of value is created when an innovation is established; more than 80% comes when it is widely adopted7. Focus on the ‘four Us’: utility (value to the user); usability; user experience; and ubiquity (designing in network effects).
  6. Pyramid. Enable users to drive innovation. They inspired two-thirds of innovations in semiconductors and printed circuit boards, for example. Lego Ideas encourages children and others to submit product proposals — submitters must get 10,000 supporters for their idea to be reviewed. Successful inventors get 1% of royalties.
  7. Problem. Most innovations come from a stated need. Ethnographic research with users, customers or the environment can identify problems and support brainstorming of solutions. Create a road map to ensure the shortest path to a solution.
  8. Prototype. Solutions need to be tested and improved through rapid experimentation with users and citizens. Prototyping shows how applicable a solution is, reduces the risks of failures and can reveal pain points. ‘Hackathons’, where developers come together to rapidly try things, are increasingly common.
  9. Pilot. Projects need to be implemented in the real world on small scales first. The Intel Collaborative Research Institute runs research projects in London’s parks, neighbourhoods and schools. Barcelona’s Laboratori — which involves the quadruple helix — is pioneering open ‘living lab’ methods in the city to boost culture, knowledge, creativity and innovation.
  10. Product. Prototypes need to be converted into viable commercial products or services through scaling up and new infrastructure globally. Cloud computing allows even small start-ups to scale with volume, velocity and resilience.
  11. Product service systems. Organizations need to move from just delivering products to also delivering related services that improve sustainability as well as profitability. Rolls-Royce sells ‘power by the hour’ — hours of flight time rather than jet engines — enabled by advanced telemetry. The ultimate goal of open innovation 2.0 is a circular or performance economy, focused on services and reuse rather than consumption and waste.
  12. Process. Innovation is a team sport. Organizations, ecosystems and communities should measure, manage and improve their innovation processes to deliver results that are predictable, probable and profitable. Agile methods supported by automation shorten the time from idea to implementation….(More)”

Society’s biggest problems need more than a nudge

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 at the Conversation: “So-called “nudge units” are popping up in governments all around the world.

The best-known examples include the U.K.’s Behavioural Insights Team, created in 2010, and the White House-based Social and Behavioral Sciences Team, introduced by the Obama administration in 2014. Their mission is to leverage findings from behavioral science so that people’s decisions can be nudged in the direction of their best intentions without curtailing their ability to make choices that don’t align with their priorities.

Overall, these – and other – governments have made important strides when it comes to using behavioral science to nudge their constituents into better choices.

Yet, the same governments have done little to improve their own decision-making processes. Consider big missteps like the Flint water crisis. How could officials in Michigan decide to place an essential service – safe water – and almost 100,000 people at risk in order to save US$100 per day for three months? No defensible decision-making process should have allowed this call to be made.

When it comes to many of the big decisions faced by governments – and the private sector – behavioral science has more to offer than simple nudges.

Behavioral scientists who study decision-making processes could also help policy-makers understand why things went wrong in Flint, and how to get their arms around a wide array of society’s biggest problems – from energy transitions to how to best approach the refugee crisis in Syria.

When nudges are enough

The idea of nudging people in the direction of decisions that are in their own best interest has been around for a while. But it was popularized in 2008 with the publication of the bestseller “Nudge“ by Richard Thaler of the University of Chicago and Cass Sunstein of Harvard.

A common nudge goes something like this: if we want to eat better but are having a hard time doing it, choice architects can reengineer the environment in which we make our food choices so that healthier options are intuitively easier to select, without making it unrealistically difficult to eat junk food if that’s what we’d rather do. So, for example, we can shelve healthy foods at eye level in supermarkets, with less-healthy options relegated to the shelves nearer to the floor….

Sometimes a nudge isn’t enough

Nudges work for a wide array of choices, from ones we face every day to those that we face infrequently. Likewise, nudges are particularly well-suited to decisions that are complex with lots of different alternatives to choose from. And, they are advocated in situations where the outcomes of our decisions are delayed far enough into the future that they feel uncertain or abstract. This describes many of the big decisions policy-makers face, so it makes sense to think the solution must be more nudge units.

But herein lies the rub. For every context where a nudge seems like a realistic option, there’s at least another context where the application of passive decision support would be either be impossible – or, worse, a mistake.

Take, for example, the question of energy transitions. These transitions are often characterized by the move from infrastructure based on fossil fuels to renewables to address all manner of risks, including those from climate change. These are decisions that society makes infrequently. They are complex. And, the outcomes – which are based on our ability to meet conflicting economic, social and environmental objectives – will be delayed.

But, absent regulation that would place severe restrictions on the kinds of options we could choose from – and which, incidentally, would violate the freedom-of-choice tenet of choice architecture – there’s no way to put renewable infrastructure options at proverbial eye level for state or federal decision-makers, or their stakeholders.

Simply put, a nudge for a decision like this would be impossible. In these cases, decisions have to be made the old-fashioned way: with a heavy lift instead of a nudge.

Complex policy decisions like this require what we call active decision support….(More)”

Insights On Collective Problem-Solving: Complexity, Categorization And Lessons From Academia

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Part 3 of an interview series by Henry Farrell for the MacArthur Research Network on Opening Governance: “…Complexity theorists have devoted enormous energy and attention to thinking about how complex problems, in which different factors interact in ways that are hard to predict, can best be solved. One key challenge is categorizing problems, so as to understand which approaches are best suited to addressing them.

Scott Page is the Leonid Hurwicz Collegiate Professor of Complex Systems at the University of Michigan, Ann Arbor, and one of the world’s foremost experts on diversity and problem-solving. I asked him a series of questions about how we might use insights from academic research to think better about how problem solving works.

Henry: One of the key issues of collective problem-solving is what you call the ‘problem of problems’ – the question of identifying which problems we need to solve. This is often politically controversial – e.g., it may be hard to get agreement that global warming, or inequality, or long prison sentences are a problem. How do we best go about identifying problems, given that people may disagree?

Scott: In a recent big think paper on the potential of diversity for collective problem solving in Scientific American, Katherine Phillips writes that group members must feel validated, that they must share a commitment to the group, and they must have a common goal if they are going to contribute. This implies that you won’t succeed in getting people to collaborate by setting an agenda from on high and then seeking to attract diverse people to further that agenda.

One way of starting to tackle the problem of problems is to steal a rule of thumb from Getting to Yes, by getting to think people about their broad interests rather than the position that they’re starting from. People often agree on their fundamental desires but disagree on how they can be achieved. For example, nearly everyone wants less crime, but they may disagree over whether they think the solution to crime involves tackling poverty or imposing longer prison sentences. If you can get them to focus on their common interest in solving crime rather than their disagreements, you’re more likely to get them to collaborate usefully.

Segregation amplifies the problem of problems. We live in towns and neighborhoods segregated by race, income, ideology, and human capital. Democrats live near Democrats and Republicans near Republicans. Consensus requires integration. We must work across ideologies. Relatedly, opportunity requires more than access. Many people grow up not knowing any engineers, dentists, doctors, lawyers, and statisticians. This isolation narrows the set of careers they consider and it reduces the diversity of many professions. We cannot imagine lives we do not know.

Henry: Once you get past the problem of problems, you still need to identify which kind of problem you are dealing with. You identify three standard types of problems: solution problems, selection problems and optimization problems. What – very briefly – are the key differences between these kinds of problems?

Scott: I’m constantly pondering the potential set of categories in which collective intelligence can emerge. I’m teaching a course on collective intelligence this semester and the undergraduates and I developed an acronym SCARCE PIGS to describe the different types of domains. Here’s the brief summary:

  • Predict: when individuals combine information, models, or measurements to estimate a future event, guess an answer, or classify an event. Examples might involve betting markets, or combined efforts to guess a quantity, such as Francis Galton’s example of people at a fair trying to guess the weight of a steer.
  • Identify: when individuals have local, partial, or possibly erroneous knowledge and collectively can find an object. Here, an example is DARPA’s Red Balloon project.
  • Solve: when individuals apply and possibly combine higher order cognitive processes and analytic tools for the purpose of finding or improving a solution to a task. Innocentive and similar organizations provide examples of this.
  • Generate: when individuals apply diverse representations, heuristics, and knowledge to produce something new. An everyday example is creating a new building.
  • Coordinate: when individuals adopt similar actions, behaviors, beliefs, or mental frameworks by learning through local interactions. Ordinary social conventions such as people greeting each other are good examples.
  • Cooperate: when individuals take actions, not necessarily in their self interest, that collectively produce a desirable outcome. Here, think of managing common pool resources (e.g. fishing boats not overfishing an area that they collectively control).
  • Arrange: when individuals manipulate items in a physical or virtual environment for their own purposes resulting in an organization of that environment. As an example, imagine a student co-op which keeps twenty types of hot sauce in its pantry. If each student puts whichever hot sauce she uses in the front of the pantry, then on average, the hot sauces will be arranged according to popularity, with the most favored hot sauces in the front and the least favored lost in the back.
  • Respond: when individuals react to external or internal stimuli creating collective responses that maintains system level functioning. For example, when yellow jackets attack a predator to maintain the colony, they are displaying this kind of problem solving.
  • Emerge: when individual parts create a whole that has categorically distinct and new functionalities. The most obvious example of this is the human brain….(More)”