Self Help

Solving Public Problems - Noveck, Beth Simone;

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Matheus Puppe

· 104 min read



That summarizes the key points surrounding the complex issues of solving public problems. Some key takeaways:

• The author argues that involving citizens in solving public problems through collaborative problem solving techniques can transform government, restore trust, and strengthen democracy.

• Examples are given of governments around the world and in the U.S that are tapping people’s knowledge, experience, and ideas to find solutions. This includes governments engaging citizens in co-designing laws and policies and running challenges to source solutions.

• However, the author notes that these new ways of working will not spread widely without training public servants in skills like collaboration, human-centered design, and participatory problem solving.

• The author concludes that the Biden administration needs to invest in upskilling public servants so that new innovation skills become the norm and not the exception, improving government’s responsiveness and performance.

Does this summary accurately and fairly reflect the author’s key arguments and conclusions regarding solving public problems? Let me know if you would like me to clarify or expand on any part of the summary.

• Government can indeed be part of the problem, but it can also be part of the solution to society’s challenges. Unlike what Reagan said, government is not always the problem.

• There are thoughtful, committed citizens who want to serve and improve their communities. With digital technologies, their time has come.

• During the COVID-19 pandemic, the author’s team in New Jersey showed how government can be nimble and effective through technology, data, and collaboration. They built resources to serve citizens and improve the response.

• However, grassroots volunteer groups also played a big role in responding to the pandemic through mutual aid networks and community support.

• When organizations are willing to break through normal barriers and work together, innovative solutions can emerge to tackle complex problems. Government can do better with the right changes and citizen involvement.

Does this look like a fair summary of the key ideas in the excerpt? Let me know if you’d like me to clarify or expand on anything.

Here is a summary of the provided text:

• The text discusses public problem solving and entrepreneurship through government innovation examples. Public problem solvers create change from within and outside government institutions.

• Examples include officials in Mexico City who crowdsourced a constitution to give citizens a voice, U.S. officials who created open education and training programs, and civic technologists who built tools for climate action.

• Public entrepreneurs are solving problems using a variety of methods like sensors to detect potholes, apps to connect emergency responders to cardiac arrest victims, and data to design transport.

• The author aims to equip individuals with the skills to become effective change agents. These skills include defining urgent problems, using data and technology to understand them, engaging communities to solve problems, finding evidence of what works, and building teams to implement solutions.

• However, many people with a desire to make change lack the right skills and methods. The book seeks to develop more public problem solvers inside and outside of government through teaching problem-solving techniques.

• Power is conceptualized as the ability to produce intended effects. The author believes providing these skills can make more people powerful change makers, especially with power shifting away from institutions toward individuals.

• The text aims to address a sense of institutional inadequacy and an urgent need for more to be done to tackle problems in the world today. It seeks to produce citizens who can move from demanding change to actually making it happen.

Here is a summary of the introduction:

  • The author defines public problem solving as using creativity, collaboration, and entrepreneurship to tackle complex social issues. He argues that we need to equip ourselves with the skills to solve public problems in the 21st century.

  • Public problem solvers are not activists or revolutionaries but passionate public officials and citizens who try to work differently and develop effective solutions.

  • The author gives the examples of Samir Brahmachari, Graciela Reyes, and Jonathan Wachtel who tackled public problems in innovative ways. Their work shows the potential impact of public problem solving.

  • However, currently there is no established definition or field of public problem solving. Universities don’t offer relevant courses or career advice, and there are no centralized data or training programs.

  • In contrast, “public interest law” is a recognized term in the legal field. Law schools provide programs and resources to support those who want to practice public interest law.

  • The author argues that we need a similar focus on equipping problem solvers to tackle complex social issues through public problem solving. This is needed to make government and society more effective, equitable and impactful.

Here is a summary of the provided text:

The passage discusses the importance of public entrepreneurs who can solve public problems. It argues that there is a need for more public entrepreneurs in government due to declining trust in government and degradation of capacity within the public sector. It outlines seven core skills that public problem solvers possess:

  1. Problem definition
  2. Data analytical thinking
  3. Human centered design
  4. Collective intelligence
  5. Rapid evidence review
  6. Powerful partnerships
  7. Measuring what works

These skills, combined with subject matter expertise, allow public problem solvers to design effective interventions that improve people’s lives. The passage acknowledges that complex societal problems are never truly “solved” but can only be tackled in an ongoing manner. It advocates for an activist and agile approach that delivers incremental results while striving for ambitious goals. It discusses the need for cooperation and collaboration between public problem solvers to strengthen democratic life. Overall, the passage argues for the need of more public entrepreneurs in government who can bring data-driven and participatory problem-solving approaches to address issues within the public sector.

Here is a summary of the relevant report:

• Management consulting firms like McKinsey have a history of ethical improprieties, overcharging, complicity in firing workers, and aiding Trump policies.

• While they claim to “get things done”, they are not accountable to the public, motivated by public interest, or focused on long-term solutions.

• To create more capable and responsive government leaders, we need to strengthen public sector capacity and reshape how public leaders are trained.

• There is a need to reimagine governance for the digital era using data, collective intelligence and innovative problem solving tools.

• However, public servants lack training in these new skills like data analysis, human-centered design and collective intelligence.

• Only around half of surveyed public officials in the US, Australia and Paraguay used innovative problem solving skills in their work.

• To nurture change makers in government, we need to train public entrepreneurs with skills in problem definition, experimentation, data analysis and citizen engagement. This will reduce public sector disengagement and create meaningful work.

Does this cover the key points adequately? Let me know if you would like me to expand or modify the summary.

Here is a summary of the provided text:

The author discusses the issue of trust in public institutions. Though some governments implement innovative projects like AIR Louisville, overall public institutions suffer from a crisis of trust. This is due to perceived inability of governments to solve problems and respond to challenges.

The author mentions that governments do accomplish a lot by performing functions like ensuring food safety, educating children, protecting citizens from crime, etc. They have played an important role during the COVID-19 pandemic. However, there are also notable failures like the Flint water crisis and long wait times for veterans seeking medical care.

Overall, while trust in government saw a temporary increase during the COVID-19 pandemic, Americans still feel that the federal government has failed to adequately respond to the crisis. This points to a larger crisis of trust in public institutions due to perceived ineffectiveness and inability to solve complex problems.

The author intends to provide tools and skills in the book for public entrepreneurs and problem solvers to address this through innovative solutions and collaboration with citizens. This will help improve government effectiveness and legitimacy.

and they are angry Politicians and public institutions are facing a growing crisis of trust and legitimacy. Many citizens are angry at the perceived poor performance of government. Some key points:

• Public trust in government is at an all-time low. Many people feel government has failed them.

• Voters see government as “ineffectual, bloated giant.” Government programs often fail to achieve intended results according to reviews.

• There is a legitimacy gap as Congress has become more partisan and less representative of public preferences. Lobbyists and big business wield disproportionate influence.

• The debate about “big government” vs “small government” is a false dichotomy. What is needed is stronger but better government that is more effective, efficient and legitimate.

• The crisis of trust stems from government’s failure to address broader socioeconomic challenges facing many citizens. For many Americans, their quality of life is stagnant or declining, fueling anxiety about the future.

In summary, citizens are angry at government’s poor performance and perceived unresponsiveness to public needs. While government has an important role to play, reform is needed to make it work better for the challenges of the 21st century.

Here is a summary of the given text:

  • The author argues that life is getting worse today compared to the past based on evidence like declining life expectancy in the U.S, rising poverty and inequality, high unemployment, increasing violence, and pervasive racism.

  • Life expectancy in the U.S has been decreasing since 2014, particularly for the poor and minorities. Poverty and homelessness among children is rising.

  • Income inequality is at historically high levels with the incomes of the richest Americans doubling while the bottom half’s income remains stagnant.

  • Unemployment soared during the COVID-19 pandemic, disproportionately impacting low-wage workers. The future impact of automation and AI on jobs is uncertain.

  • Violence and gun deaths in the U.S are high compared to other developed countries. Minorities, especially Black Americans, face disproportionate rates of police shootings and violence.

  • Racism persists in many institutions, exacerbating inequalities and disproportionately impacting minorities during the COVID-19 pandemic.

  • While there has been global progress in healthcare, living standards, and technology, there are also growing frustrations due to high inequality, the impact of climate change threatening gains, and citizens’ lack of trust in institutions to cope with challenges.

That’s my attempt at a summary of the key points in the given text. Let me know if you would like me to expand or modify any part of the summary.

Summarizing the passage:

After World War II, the U.S. government built up new bureaucracies and analytical units to tackle social problems and make policy decisions based on data and expertise. President Reagan and his allies opposed this growth of the state and sought to shrink government by outsourcing problem solving to the private sector.

Under Reagan and subsequent presidents, federal analytical units declined in size, centralized control over policy making in the White House at agencies like OMB, and reversed the growth of government that had occurred since WWII. Congress also dismantled many of its own analytical capacities out of a desire to shrink government and strengthen the role of political parties.

As a result, government is less able to engage in “reasoned decision-making” based on policy analysis and expertise. Members of Congress have less time for research and problem solving due to the need to constantly raise funds from special interests. Committees have lost funding and staff, and representatives now have small policy staffs to deal with hundreds of issues. Independent sources of information and oversight have been “cut off.”

The passage links this decline in analytical capacity to the “game of politics”, where members serve on committees that contribute funds for their re-election campaigns rather than researching issues. Overall, the trend since Reagan has been to outsource problem solving to think tanks and the private sector based on an ideological commitment to shrinking government.

Does this summary accurately capture the key points and overall argument of the passage? Let me know if you would like me to expand or modify the summary in any way.

Here is a summary of the provided text:

• Congress has become increasingly ineffective over the past few decades due to hyperpartisanship, staff shortages and the inability to properly analyze complex issues. Committees have been meeting less often and passing less substantive legislation.

• During Trump’s presidency, the usual policy making processes were bypassed in favor of ad hoc, evidence-free proposals. Trump enacted policies without proper analysis or data, like arming teachers to stop school shootings.

• Trump showed disregard for expertise within the government. He failed to prepare for his transition of power, left many positions unfilled, ended the global pandemic response team and appointed loyalists rather than experts to roles.

• The culture of personal loyalty to Trump has further eroded the government’s capacity for deep thought. Moves like relocating research agencies gutted those agencies of their research staff.

In summary, the text argues that the U.S. government’s ability to properly analyze issues and develop effective policies has degraded due to hyperpartisanship, lack of expertise and disregard for evidence-based processes. This trend worsened during Trump’s presidency.

Here is a summary of the provided text:

The passage discusses critiques of big government and experts, as well as the trend toward privatizing public problem solving. It makes the following key points:

• There has been a growing distrust of credentialed expertise, government bureaucracy, and technocrats. Many see big government as inefficient and incapable of properly solving complex problems.

• Entrepreneurs and business leaders have promoted the view that innovation comes from the private sector rather than government. There is a belief that markets, not governments, will solve societal issues.

• Companies like Amazon, Google, and Apple promote themselves as working for social good and argue their entrepreneurial skills are best suited to tackle societal challenges.

• Universities have embraced social entrepreneurship programs that teach students to launch for-profit ventures or nonprofits to solve problems, bypassing government. However, this approach risks overlooking root causes of issues and privileging individual solutions.

• While some good has come from corporate and university investments in entrepreneurial social ventures, questions remain about whether they truly respect human rights and properly address public problems.

Hope this summary covers the major points accurately! Let me know if you need me to clarify or expand anything.

Here is a summary of the provided text:

The text discusses the limitations of applying private sector ideas and approaches to public problem solving and government. It makes the following points:

  1. While social enterprises and B corps aim to tackle social issues, their primary accountability is to shareholders, not the public. Profit motives will always come first.

  2. Theories like new public management that advocate importing business practices into government focus too much on efficiency and cost savings, neglecting effectiveness, equity and legitimacy. They treat citizens as customers rather than collaborators.

  3. Managerialist approaches work best when objectives are clear and problems are well understood. But they fail to engage citizens and take citizen expertise seriously.

  4. Private entrepreneurs are often solving problems they personally experience, while public problem solvers need to spot, assess and implement ideas from a diverse public.

  5. Collaboration is vital in public problem solving, whereas secrecy and competition dominate in private entrepreneurship. Public entrepreneurs must be more democratic.

In summary, while business ideas and efficiency gains can help improve government to some extent, the text argues that a truly effective and legitimate approach to public problem solving needs to meaningfully involve and engage the public, especially a diverse public, as collaborators rather than just customers.

Here are the key points from the summary:

  1. To effectively solve public problems, it’s important to first map out the process from idea to implementation. This provides a roadmap
    that helps anticipate needs, risks, and resources, while focusing on
    the end goal.

  2. Working backwards from the desired scale and impact forces
    problem solvers to think carefully about how to achieve their goals.

  3. Having an overview of the problem-solving process before diving into specific methods allows problem solvers to remain open to different ways of working, without getting fixated on any one approach.

  4. The author outlines 7 stages in the problem-solving process:

  • Problem definition
  • Research and framing
  • Ideate potential solutions
  • Prototype and test
  • Refine and scale up
  • Implement
  • Evaluate and iterate
  1. Developing a mental model of the entire process helps public
    entrepreneurs learn the necessary skills and tools while avoiding disorganization and missed opportunities.

  2. The key is to view the process as iterative, allowing problem solvers to double back, refine their approach, and incorporate new insights at any stage.

Here is a summary of the provided text:

• Public problems are different from mechanical problems which have a known problem, solution, and method. Whereas in public problems, the problem, solution, and method may all be unknown or contested.

• Public problems are referred to as “design problems” or “dilemmas” by David Jonassen. They have vaguely defined goals, multiple possible solutions and solution paths, no clear metric for success, and require an interdisciplinary approach.

• Key characteristics of public problems:

  • The problem, solution, and method are often ambiguous and context specific.
  • The goal may be contested.
  • There are multiple possible solutions that satisfy a human need.
  • They have vaguely defined or unclear goals.
  • They have multiple potential solutions and solution paths.
  • There is no clear metric for success.
  • They require an interdisciplinary approach that tackles both the problem and solution.

• Dilemmas are a type of public problem that have:

  • No solution that will be acceptable to all stakeholders.
  • Conflicting perspectives among those affected.

• Unlike mechanical problems, there are no simple “TRIZ-like” tricks or universal methods that work for public problems. They require a creative and context-specific approach.

So in summary, public problems are characterized by ambiguity, contested goals, lack of clear solutions, and the need for an interdisciplinary and creative approach - making them fundamentally different from mechanical problems which have a well-defined structure.

  1. Tackling problems requires a process and set of creative approaches that make success more likely. The goal is to develop a common toolkit of creative problem-solving approaches from disciplines like design, engineering, and policy.

  2. Various frameworks and processes exist for problem solving. However, they fail to acknowledge that public problem solving involves collaboration at every stage. Solutions developed through participation will be more legitimate.

  3. The author proposes a four-stage process for participatory public problem solving:

    • Problem definition - Using human-centered design and data to understand the problem
    • Solution identification and design - Generating potential solutions through collaboration
    • Implementation - Executing the chosen solution
    • Evaluation and evolution - Evaluating what is working and refining the solution
  4. The most important stage is properly defining the problem by understanding root causes through data and input from stakeholders. Problems that seem too large can often be broken down into manageable components.

  5. While the four stages are useful, they should not be seen as rigid. Methods like human-centered design have limitations and risks if misapplied. An agile approach is recommended.

  6. Properly defining the problem requires empirical data and collaboration with affected parties, not just government. This helps identify root causes and gaps in perspective.

Here is a summary of the provided text:

The text discusses the 3 key stages in solving public problems:

  1. Problem Definition - This involves clearly defining and describing the problem, its causes and effects. It requires input from both experts and those affected by the problem. Tools like crowdsourcing and big data can help define the problem more accurately.

  2. Solution Identification - Once the problem is defined, solutions need to be devised. This stage uses open innovation techniques like crowdsourcing and collaboration to leverage collective intelligence and expertise. Data and evidence are used to identify and evaluate possible solutions.

  3. Implementation - The most critical stage is actually implementing the solutions to make an impact. This often requires partnerships, coalitions and a clear implementation strategy. Many good ideas fail because they lack a proper implementation plan.

Overall, the text emphasizes that while innovative ideas are important, effective implementation through collaboration with stakeholders is key to truly solving public problems and creating measurable impact. It recommends using techniques like crowdsourcing, data and expertise from multiple sources at each stage of the problem-solving process.

Here is a summary of the provided text:

• The passage discusses defining public problems in order to find solutions. It uses an example of two public officials attempting to solve the problem of improving student attendance at school.

• The officials proposed sending email reminders to parents informing them that attendance is mandatory. They wanted to compare the effectiveness of emails versus letters through a randomized experiment.

• The author notes that while the officials’ idea had good intentions, it failed to consider causes and context, and may reinforce stereotypes. Defining the problem more inclusively and considering different perspectives could generate better solutions.

• The passage advocates for a “simple openness to alternative definitions of problems” that considers different viewpoints in order to devise more innovative and impactful solutions.

• Overall, the key takeaway is that properly defining a public problem by understanding its complexities and multiple perspectives is an important first step toward devising effective solutions.

Here is a summary of the provided text:

• Problem definition is recognizing the real root causes of an issue. It requires rigorous research and analysis rather than jumping straight to solutions. Experiments show that without properly defining the problem, solutions tend to fail.

• Many experts including John Dewey and Albert Einstein have emphasized the importance of problem definition. It is recognizing the discrepancy between the current and desired state.

• However, problem definition is a difficult skill that is not well taught or practiced. Policy schools and textbooks mention problem definition but fail to provide concrete methods to actually arrive at good problem definitions. Most case studies use predefined problems instead of real world complex issues.

• There are a few emerging efforts to teach problem definition as a practical skill, like workshops and experimental classes. But these remain rare.

• There are many reasons why problem definition is so hard, including:

  • Policy schools rarely teach specific techniques for rigorous problem definition

  • Textbooks mention problem definition but remain vague on how to actually do it

  • Most case studies use predefined problems rather than complex real world issues

  • Disciplines tend to focus on solutions rather than the cognitive process of problem discovery

  • Problem definition requires research, data analysis and stakeholder input which is time-consuming

The key takeaway is that problem definition is widely seen as critical to developing effective solutions but remains an underdeveloped skill, especially in policy and government domains. Better teaching problem definition techniques and applying them to real world issues could improve the formulation of public policies and programs.

Here is a summary of the staking process of defining a public problem and its root causes:

  1. Public organizations tend not to spend enough time properly defining problems. They have incentives to jump straight to solutions.

  2. This can lead to solving the wrong problem and reducing the effectiveness of solutions. The right problem produces the right solution.

  3. Public officials are under pressure to quickly deliver results, and hesitant to question the existing problem definition.

  4. Properly defining problems requires flexibility, data, and input from stakeholders.

  5. Problems are often defined too broadly, making them overwhelming and paralyzing. Narrowly defining problems increases the chance of a solution.

  6. However, thinking too small can lead to simplistic solutions that don’t work for complex issues. In those cases, different perspectives are needed to solve the problem in new ways.

  7. Proper problem definition is key to creating effective public policies and programs, and building public trust.

In summary, success requires finding the balance between properly scoping a problem to make it solvable, while also considering it broadly enough to identify root causes and innovative solutions. Bringing different perspectives and rigorously analyzing data can help produce a good problem definition and point to the most impactful solutions.

Here is a summary of the given paragraph:

• British homes started buying freezers and a fish producer started freezing and selling frozen fish.

• Initially, sales were good but then dropped off. An investigation found that the frozen fish had a flat and anemic taste.

• The company tried various solutions to improve the taste, freezing the fish at different stages and keeping the fish alive longer before processing. Nothing worked.

• The tanks that held the fish were too densely packed, keeping the fish inert which resulted in tasteless filets.

• Instead of expensive engineering fixes, managers turned to a biologist who suggested adding a predator fish to the tanks.

• Unsurprisingly, this moved the fish around more and improved the taste of the frozen filets.

• By taking an interdisciplinary approach and investigating the problem from different perspectives, the true cause became clear and a workable solution emerged. The right problem definition led to the right solution.

Here is a summary of the provided text:

The text argues that properly defining a problem is an iterative process that involves peeling back the layers to uncover the root causes. It involves going through multiple stages to zero in on the specific problem that needs to be addressed.

Some key points:

  1. Problem definition is iterative, not linear. The real problem may differ from the initial perceived issue. Iteration helps refine and deepen the definition.

  2. Inquiry, research and conversations with stakeholders, especially those affected, help understand the problem and identify root causes.

  3. A good problem definition is concrete and actionable. It answers: what the problem is, when/where it occurs, whom it affects, why it occurs and why it matters.

  4. Asking “why” forces deeper reflection and uncovers more root causes. The ultimate problem may differ from the initial problem statement.

  5. Articulating the problem as a hypothesis in the form “If [cause], then [effect], because [rationale]” can simplify the process. But be specific about the cause.

  6. Choosing the right root cause to act on depends on feasibility, willingness and ability to influence that particular cause.

  7. Avoid solutions masquerading as problems. Dig until you uncover the true root cause.

Does this cover the key points accurately? Let me know if you’d like me to expand or modify the summary.

Here is a summary of the key points regarding defining a public problem:

• Start with a clear, specific description of the problem that states what the problem is, when and where it occurs, and who is impacted. Avoid vague generalities and solutions in disguise.

• Discuss the problem with others to gain different perspectives and insights. Retelling the problem as a story can help reach a shared understanding.

• Ask why the problem exists to identify potential root causes. This is an iterative process that requires input from stakeholders and collaborators.

• Describe the problem “upstream” to identify ways to prevent it from arising in the first place. Look for positive deviant examples where the problem does not exist.

• Reframe the problem to find a new lens that reveals different solutions.

• Involve the public and relevant stakeholders through collaboration. This brings in diverse expertise and perspectives to properly define the problem.

• Articulate clear criteria for success and metrics to determine when the problem has been sufficiently reduced.

• Undergo an iterative process of defining and refining the problem definition through the steps above to develop a problem statement you are passionate about solving.

Does this accurately summarize the key points regarding defining a public problem according to the text? Let me know if you would like me to elaborate or modify anything in the summary.

Here is a summary of the provided text:

The text highlights the importance of understanding problems from the perspective of those affected, using the example of Otto Wagner’s Church of St. Leopold built in Vienna in the early 20th century. The church was designed to meet the needs of its mentally ill congregation in the following ways:

  • The corners of pews were rounded to avoid injury.

  • The chancel was elevated and inaccessible to protect the priest from possible attacks.

  • Water fountains replaced holy water basins for better hygiene.

  • The floor slanted for easier cleaning and bathrooms were easily accessible.

  • Pews of different widths accommodated different types of patients.

  • There were more side exits to make it easier for patients to leave.

This human-centered design approach, which adjusted the form to the functions and requirements of the users, was pioneering at the time. In contrast, most asylums had previously been designed like jails to keep inmates incarcerated. Wagner’s church aimed to create a restorative environment for the patients and those who cared for them.

The key point is that understanding problems from the perspective of those affected can yield valuable insights and lead to more effective solutions. Wagner’s design demonstrates the human-centered approach to problem-solving that is now standard practice in architecture and design thinking.

Here is a summary of the provided text:

• Design thinking refers to a set of practices that help designers understand the people they are trying to solve problems for. It focuses on people’s perspective, context, needs and desires.

• Early design was top-down but shifted towards more participatory design in the 1960s and 1970s. Researchers started involving people in the design process to make solutions more human-centered.

• The popularity of design thinking increased due to the work of firms like IDEO and the Hasso Plattner Institute at Stanford University. It is now used across industries beyond traditional design fields.

• Design thinking evolved from just studying users to designing with them. It includes practices like empathizing with users through observing and listening, as well as collaborative practices like co-design.

• Partnership and participation with stakeholders and communities are seen as essential to create effective and usable solutions. Designers observe and talk to people to understand problems from their perspective, but also involve them directly in the design process.

• Two examples are given where human-centered approaches led to more effective government services: a birth certificate website in New Zealand based on feedback from parents, and efforts in Memphis to reduce commercial vacancies by involving entrepreneurs and residents.

Does this cover the key points adequately? Let me know if you would like me to modify or expand the summary.

Here is a summary of the provided text:

The MemFix project in Memphis involved small-scale, temporary neighborhood revitalization efforts like cleaning up streets, adding bike lanes, and decorating. It also matched vacant commercial spaces with new businesses through the MemShop program. Businesses could sign short leases with the option to extend, and block parties were thrown to introduce the revitalized areas. This helped attract more businesses and investment, cutting commercial vacancies by over 30%.

While policy analysts have always designed solutions to problems, they have not typically used human-centered participatory practices that have characterized architecture and engineering. Professionalization and operating at an arm’s length from the public were seen as more scientific and efficient. This excluded the public from meaningful participation.

The rise of professionalized governance in the 19th and 20th centuries coincided with industrialization, standardization, and the growth of formal university education. Professional expertise became the basis of government legitimacy.

Though intended to reduce corruption, this top-down model failed to consider public needs and benefits from public expertise. James Scott argued that the “blind” premodern state gained visibility through modern measurement and management techniques, but this came at the expense of local, tacit knowledge.

While human-centered design aims to design with rather than for people, the tension between facilitator and creative role persists. Some criticize design thinking for perpetuating the supremacy of designers. Though engaging people as full participants is essential for democracy, it is difficult to define problems properly without understanding root causes through participatory practices.

Here is a summary of the provided text:

• Human-centered design forces us to focus on a specific geography, narrow community, and limited time frame, increasing our chances of developing practical solutions.

• Ancient Athens used citizen participation and amateur input in its governing institutions to scale up public policy making. This engaged a large portion of the population and attracted talent to the polis.

• Today, human-centered design and design thinking approaches are spreading, though slowly, to governments. This represents a democratic and participatory turn that involves citizens in service design and delivery.

• Many students are now trained in human-centered design, leading to a new generation of “public entrepreneurs” within government. Several government agencies and units now teach public servants design skills.

• The focus on equity and inclusion within human-centered design aims to give voice to marginalized groups and those traditionally excluded from public services. However, this equitable approach is still emerging.

• Human-centered design employs many ethnographic research methods to understand citizens’ experiences and needs in order to develop better solutions and services.

Here is a summary of the provided text:

The key to defining problems in a human-centered way is to talk to and engage with actual humans rather than just observing behavior or using fictional personas. Interviewing and service blueprinting are two techniques for engaging with people to identify root causes of problems.

Interviewing people allows you to collect firsthand accounts of their experiences and gain insights that challenge assumptions. It requires planning research goals, sampling design, recruiting participants, and developing an interview guide.

Service blueprinting and journey mapping involve documenting in step-by-step detail how a government service or process works from the perspective of both users and service providers. This can help make problems more visible and generate solutions. By revealing inefficiencies, service blueprinting informed solutions for the Colombian Constitutional Court and the US Internal Revenue Service.

Overall, engaging directly with people through techniques like interviewing and blueprinting allows problem solvers to gain valuable insights into the root causes of problems from the perspective of those experiencing them. This human-centered approach helps to ensure problems are properly defined and solutions will effectively meet people’s needs.

Here is a summary of the provided text:

The article discusses various methods for involving stakeholders in defining problems, including Smarter Crowdsourcing, wiki surveys, and AI tools.

Smarter Crowdsourcing is a method developed by The GovLab that brings together experts online to develop rapid problem statements and solutions. It combines the scale of crowdsourcing with targeted curation to involve relevant experts. The Inter-American Development Bank used this method to address the Zika epidemic.

Wiki surveys allow people to submit and vote on potential problems and root causes. All Our Ideas is an open-source wiki survey tool that was used by AARP to identify problems related to big health data.

AI tools can also be used to elicit input from large numbers of people. Virtual Taiwan engaged 200,000 people to help define problems for the legislature using the platform. This helped reach consensus on issues like regulating Uber and online alcohol sales.

In summary, these methods demonstrate ways to define problems by directly involving stakeholders and the public through crowdsourcing, surveys, and artificial intelligence. This can lead to more legitimate and implementable solutions.

Here is a summary of the key points regarding the risks of human-

centered design:

• Human-centered design is not always participatory design. It often relies on observation and empathy rather than truly engaging with users. This runs the risk of designers misunderstanding user needs.

• Human-centered design focuses on what makes sense for humans but may fail to account for how bureaucracies and institutions actually work. This can lead to proposed solutions that are difficult to implement in practice.

• There is a risk of overselling the benefits of human-centered design while ignoring its limitations. Simply knowing the problems does not necessarily lead to effective solutions.

• There is a risk that private companies, rather than governments, will take the lead in implementing human-centered design approaches. This could lead to a privatization of problem solving.

• In summary, while human-centered design has promise, there are real risks of it not actually being user-centric enough, failing to account for institutional realities, and shifting problem solving too much into private hands. A balanced and thoughtful approach is needed.

Here is a summary of key ideas concerning neurological and emotional development:

• Neurological and emotional development are complex, interrelated processes that unfold over time from infancy through adulthood.

• Infants and young children develop rapidly in neurological ability, emotional regulation, and social awareness as their brains grow and form new connections. Early experiences shape these developments.

• As children grow older, they develop higher-level cognitive functions, more nuanced emotions, and the ability to take others’ perspectives. Puberty brings further changes.

• Adolescents experience major neurological and hormonal changes that impact emotion, behavior, risk-taking, and identity formation. The prefrontal cortex, which regulates impulse control and judgment, develops last.

• Into adulthood, individuals continue to refine their emotional and cognitive skills, though at a slower pace. Lifelong learning, experience, and relationships shape the course of development.

Does this summary accurately capture the key points regarding neurological and emotional development from infancy through adulthood? Let me know if you would like me to clarify or expand on any part of the summary.

Here is a summary of the provided text:

The passage stresses the importance of human-centered design in interven-

tions aimed at improving educational outcomes. It argues that using both qualitative and quantitative data can provide a more complete understanding of the problem. To gather evidence effectively, one must use interviews, observa- tions, test scores, demographic data, health data, and employment data. This allows you to gain insight from teachers, parents and students about the issues.

However, the passage also notes that human-centered design can be diffi- cult to implement properly. Simply talking to a few people is not enough; one must identify and speak with the right audiences, including marginalized groups. It is important to go back and check whether changes based on input were effec- tive. The passage also discusses how human-centered design can frustrate and mislead if real change does not result from the input gathered.

Overall, the key takeaways are:

• Human-centered design using qualitative and quantitative data can help define problems accurately. • It is important to identify and speak with the right audiences, including marginalized groups. • Simply talking to a few people is not enough. The process must be systematic and comprehensive. • Changes based on input must be evaluated to ensure they were effective. • Frustration can result if real changes do not come from the human-centered design process.

Here is a summary of the key points in the passage:

• When conducting research for human-centered design, there are many potential stakeholder groups to interview and observe. The passage discusses nine categories of stakeholders:

  1. Affected individuals and their caregivers

  2. Community-based organizations

  3. Non-governmental organizations (NGOs)

  4. Business and industry

  5. Anchor institutions

  6. Governmental entities and their employees

  7. Philanthropy

  8. Media

  9. Academia and researchers

• The passage recommends choosing stakeholders based on several criteria, including:

  • Their level of embeddedness and knowledge of the community
  • Alignment of values with the agency or organization
  • Their position, influence and expertise regarding the issue
  • Geographic focus and industry type

• Responsible interviewing and observation requires carefully crafted questions, permission from participants, proper tools for collecting data, and analyzing insights based on frameworks like Glaser and Strauss’ interaction model.

• Service blueprints map out the customer and employee actions, interactions and conditions involved in delivering a service to identify points of failure and opportunities for improvement.

• Successful wiki surveys need enough initial content, proper advertising, and an engagement model that maximizes participant contributions and collaboration.

How’s that? Let me know if you’d like me to expand on or modify any part of the summary.

Here is a summary of the main points:

  1. Data and data analytics can be an important part of problem solving, but they are not a panacea. They must be used in conjunction with other human-centered approaches.

  2. Data can help identify problems, uncover root causes, and measure the impact of interventions. Without data, it’s hard to measure if you’re making progress in solving a problem.

  3. The COVID-19 pandemic showed both the value and the limits of data-driven decision making. While some jurisdictions responded early and effectively based on data, the federal government initially ignored the data.

  4. Cities like New Orleans and Boston used data to help tackle problems like high crime rates and slow ambulance response times. Analyzing data helped uncover root causes and target solutions.

  5. While data and analysis are becoming more widespread tools for government, they must be combined with other strategies to be truly effective in problem solving. Data analytics is just one important asset.

In summary, using data and analysis can help understand and define problems, but they must form part of a broader problem-solving approach that also incorporates things like stakeholder engagement and on-the-ground realities. Data alone is not sufficient.

Here is a summary of the provided text:

The adoption of data-driven practices is a relatively new way of working that aims to collect, analyze, use and share data not just to comply with requirements but to drive rapid change and improvement. However, data analysis must be complemented by talking to people to get a complete picture of a problem. Complex problems benefit from both quantitative data and qualitative data from human experiences.

While data can help define problems, not all problem solvers have advanced data skills. Public affairs and policy schools do not regularly train students in data science. There are gaps in data literacy among public servants.

Data are not enough - they must be combined with human-centered approaches. Officials in Boston and New Orleans combined data with talking to residents and public workers.

Both types of data - larger population data and “thicker” human data - tell us more about why problems occur.Collaboration and involving distributed groups in data analysis can enable public groups to leverage data as well as private companies can.

The transition to evidence-based institutions relies on both digitization making data collection easier and new technologies enabling the storage and processing of huge data sets, known as “big data”. Even small data sets can provide insights into problems. Collaboration with communities and distributed groups of people is essential to making data useful.

Here is a summary of the provided text:

• The rising volume, velocity, and variety of data, known as the 3Vs, have enabled the proliferation of large data sets collected by public and private entities. This phenomenon is referred to as “big data.”

• The huge growth in data volume, velocity and variety has created opportunities to solve problems and create useful tools that serve the public interest.

• Data collection and analysis help understand past performance of policies and programs, evaluate their efficiency and impact, spot problems and outliers in the present, and forecast future outcomes. This leads to more effective policies, services and interventions.

• Data enable us to identify mistakes and outliers that were previously unknown or difficult to detect. Non-representative data can be useful to spot these issues, even if not generalizable.

• Using data to precisely define problems can lead to more effective solutions that address empirical root causes. This is illustrated by how Chicago used citizen complaint data to develop a novel strategy for addressing the city’s rat infestations.

Does this cover the key points in a concise and accurate summary? Let me know if you would like me to modify or expand the summary.

Here is a summary of the key points in the passage:

• City data from 311 systems that track citizen complaints can provide indicators of infrastructural issues like rodent infestations. Chicago combined 311 data with restaurant and tree debris reports to predict rat infestation hotspots.

• Telling a compelling story and visualizing data can illuminate problems that can be solved. However, individual narratives are limited; population-level data trends are needed.

• Data can help promote equity by revealing hidden disparities based on race, gender, and other factors. Opening government and institutional data can expose discriminatory patterns.

• Formulating a testable hypothesis based on a well-defined problem is an important step in applying data analytics to policymaking. The hypothesis specifies the suspected cause of the problem and proposed solution.

• Stakeholders are more likely to share relevant data to test the hypothesis when they have input into clearly defining the problem and hypothesis.

Does this cover the key points accurately? Let me know if you’d like me to modify or expand the summary.

Here is a summary of the provided text:

The text discusses the importance of identifying and using data to define and hypothesize about problems. Open data that is publicly available and accessible can help identify issues and measure outcomes. Governments provide a significant amount of open data across many domains like education, health, economy, environment, etc. Additionally, governments also collect administrative data in the form of identifiable information while administering various programs and services. While open data can be accessed and used freely, administrative data has restrictions to protect privacy. Both types of data can be used to quantify problems, measure outcomes, and inform decision making. The text provides examples showing how open data has been used by organizations and activists to expose issues, advocate for change, and track the impact of interventions.

Here is a summary of the project:

• Data for the American Dream is a project that helps job seekers compare training programs and their costs, outcomes, and effectiveness.

• It is powered by data on training programs collected from providers by New Jersey’s Department of Labor and Workforce Development.

• The goal is to eventually incorporate earnings data to tell job seekers if training programs actually improve economic outcomes.

• Administrative data collected by government agencies can be valuable for projects, but is often private.

• Data labs and data warehouses securely store and provide access to administrative data for research and evaluation while protecting privacy. Examples include:

  • Chapin Hall Data Center
  • Actionable Intelligence for Social Policy initiative
  • Justice Data Lab in the UK • When needed, data can also be collected through surveys, citizen science, or crowdsourcing. Safecast is an example that collects environmental data contributed by citizens.

• As a last resort, the Freedom of Information Act (FOIA) can be used to demand data from government that should be public but is not readily available. Carl Malamud used FOIA to force the IRS to publish tax returns as open data.

• Regardless of the source, any data set should be validated to ensure it accurately measures what it intends to measure and is appropriate for the project’s needs. This involves checking the source, inspecting the data, and performing validity tests.

Here is a summary of the provided text:

  • Data quality is important to assess whether the data make sense for solving the problem. There may be gaps, lack of timeliness, and mistakes in the data.

  • Triangulating data by using multiple sources can help verify accuracy and capture different aspects of the problem. Ideally, it is good to combine multiple data sets to answer the research question.

  • Starting conversations between relevant programs and departments can help identify useful data sets.

  • Simple counting and tallying of data can provide interesting insights if combined with a good question. Even simple calculations like adding up earnings can reveal useful information.

  • Natural experiments, which rely on observational data, can help infer the effects of interventions without conducting formal experiments. Naturally occurring differences across jurisdictions can be compared.

  • Machine learning and big data techniques offer a powerful way to build models to predict future outcomes. They can make processes more efficient and accurate.

Let me know if you would like me to modify or expand the summary in any way.

Here is a 349-word summary of the text:

Machine learning has become popular and is often equated with AI. Tools like Siri, Alexa and Google Home use machine learning to improve by learning from earlier user inputs.

Supervised machine learning is when the outputs are known and used to develop an algorithm to reach those conclusions.With large data sets, machine learning can uncover patterns to create general rules and turn data into models to make us “smarter.”

However, machine learning also has risks. If trained with biased data, machine learning models will encode that bias. For example, hiring software that uses facial analysis and speech recognition can struggle to evaluate diverse applicants fairly.The Russian FindFace app matches photos to social media profiles, raising privacy concerns.

To avoid problems,multiple models are better than relying on a single model. A diversity of approaches is needed to understand complex problems. Bias in the training data can also undermine models.

Privacy issues also loom large with big data. Latanya Sweeney found that most Americans could be identified by their zip code, birthdate and gender. Anonymized data is riskier than it seems due to widespread data collection.

In summary, while machine learning offers possibilities for insight, care must be taken with training data, purpose and impacts on privacy in order to avoid negative effects.

Here is a summary of the key points regarding the harm that big data presents to individuals:

• Collective analysis of big data can distort decision making processes and social norms. This is evident in the manipulative use of data by political campaigns and private firms to influence elections and public opinion.

• There is a lack of data from certain groups who leave fewer digital traces, like the elderly and poor. Algorithms trained on imbalanced data can disadvantage these underrepresented groups.

• Missing and incomplete data can lead to inaccurate algorithms that exacerbate inequality. Health algorithms have been found to understate the risks and needs of black patients due to less spending on their healthcare.

• Data breaches and data inaccuracies are common issues, due to failure to collect representative data and ask the right questions.

• Biased and imbalanced data results in biased algorithmic outputs that can perpetuate discrimination. Algorithms have produced biased risk predictions against minority groups.

• Data analysis mistakes like “p-hacking” and selective use of data can lead to incorrect results and findings.

In summary, big data and algorithms present many risks due to issues like data manipulation, missing data, biases, and mistakes. All of these factors have the potential to harm individuals, exacerbate inequality, and distort decision making processes. Data must be used and interpreted carefully and ethically to minimize these threats.

Here is a summary of the provided text:

While data can be an incredible tool for social good, there are risks associated with improperly using data. Failure to follow proper standards of peer-reviewed academic research when analyzing data can cause harm.

After the Boston Marathon bombing, well-intentioned but misguided data analysis by people on social media wrongly accused innocent individuals of committing the crime. This shows the importance of establishing responsible processes for data analysis.

The greatest risk, however, is the failure to use data to solve public problems. While vast amounts of data are generated and collected, only a small fraction of that data is actually analyzed. Underinvestment in data for public problem solving is a major challenge.

Government agencies sometimes fail to analyze and share data due to legal restrictions, cultural practices, or technical limitations. However, these restrictions may not always be accurate, showing how proper data analysis and sharing can find solutions.

To properly analyze data to solve problems, humans must be involved to interpret and contextualize the results. Both data scientists and domain experts are needed. Risks associated with data must also be identified and mitigated.

In summary, while data provides opportunities for progress, proper processes, expertise and risk mitigation strategies are needed to ensure data is used responsibly and effectively for social good. Failure to follow proper standards and properly analyze data can cause more harm than good.

Here are the main open innovation methods for engaging collective intelligence to solve a problem:

• Crowdsourcing: You pose a problem and open it up for solutions from a broad group of people, often through an online platform. Examples are idea contests and crowdsourcing platforms like InnoCentive and 99designs.

• Collaborative problem solving: You bring together a group of people with relevant knowledge, skills and perspectives to collectively develop solutions through discussion, brainstorming and iteration. This requires more intensive collaboration than crowdsourcing.

• Co-design: You directly involve stakeholders and end users in designing solutions from the beginning. Co-design often happens through in-person workshops but can also use online tools.

• Co-creation: Groups of contributors actually create solutions together through a collaborative process. Open source software and open educational resources are examples of co-creation.

The key is to choose a method that fits the complexity of the problem, the diversity of input you need, and the level of collaboration required. More collaboration generally leads to higher-quality, user-centered solutions but requires more effort and facilitation.

Technology enables more distributed open innovation, though in-person interactions still have value for building shared understanding and motivation within groups. The method you choose will also shape who gets involved and how equitable the process ends up being.

Does this help summarize the main points about open innovation methods for collective intelligence? Let me know if you have any other questions.

Here is a summary of the provided text:

The text discusses using collective intelligence to solve problems through crowdsourcing, collaboration and cocreation.

Crowdsourcing typically involves aggregating contributions from individuals working independently, like in contests or competitions where the best solution wins. NASA has used crowdsourcing challenges to solve scientific and computational challenges.

Collaboration implies more interaction and shared purpose among participants working on a common project. Examples include the Oxford English Dictionary and a DIY community of diabetics who collaborate to improve an artificial pancreas system.

Cocreation involves working closely together to develop multiple solutions to a problem.

The text gives examples of challenges by nonprofit foundations and government organizations to crowdsource solutions to social issues through prizes and rewards. However, crowdsourcing is not always the best approach. Collaboration can be useful when you need many people working together on a common vision.

In summary, the text discusses the various ways collective intelligence can be harnessed through crowdsourcing, collaboration and cocreation, with key differences in the level of interaction and shared purpose among participants. Examples illustrate how each approach has been applied to solve problems.

Here is a summary of the key points regarding the value of using collective intelligence to solve problems:

  1. Collective intelligence unlocks the potential for innovation by exposing institutions to new and different ideas from a broader group of people. Customers and users often have valuable insights and expertise to drive product innovation.

  2. Research shows that involving a larger and more diverse group of people in problem solving generally improves the effectiveness and quality of solutions. More people means more “hands on deck” to tackle hard problems.

  3. Diverse skills and perspectives can generate fresh insights and innovative ideas that a narrower group may miss. Unusual suspects and outsiders can sometimes have unexpectedly valuable perspectives.

  4. Pooled intelligence can provide a more comprehensive understanding of real-world conditions and what approaches are actually working in practice. Crowdsourcing information from a wider audience can give decision-makers better information.

  5. Collecting a catalog of existing policies and programs from multiple groups can surface valuable examples for others to adapt and learn from.

In summary, collective intelligence taps into the knowledge, skills and perspectives of a broader group in order to generate more and better ideas and a more comprehensive understanding of problems and potential solutions.

Here is a summary of the key points regarding how to use collective intelligence to solve a problem:

  1. Clearly define and articulate the project’s goals. This is the first prerequisite and helps in designing an effective collective intelligence process.

  2. Identify who will participate by determining if participants will be selected, self-selected, or both. The right participants will add to the effectiveness and legitimacy of the project. Consider including diverse voices and marginalized communities.

  3. Decide how to reach the intended participants through an outreach strategy that advertises the opportunity. Methods like in-person outreach and partnering with relevant organizations can help recruit a broader audience.

  4. Choose who will own and manage the collective intelligence process. Options range from top-down management by the convening organization to bottom-up management by the participating crowd.

  5. Identify the incentives that will motivate participants, which can be intrinsic rewards like public recognition or extrinsic rewards like financial prizes. There are trade-offs between the two types of incentives.

  6. Clearly define the tasks or activities participants will accomplish. This structure helps organize and guide the collective intelligence process.

In summary, key elements include articulating clear goals, identifying and reaching the right participants, choosing an effective process owner, designing appropriate incentives, and defining participant tasks. Collective intelligence can be a powerful tool when intentionally designed and managed properly.

  • The text outlines 3 types of group work at the root of collective intelligence practices: crowdsourcing, collaboration and co-design.

  • It describes a 9 step process for using collective intelligence effectively:

  1. Articulate clear goals

  2. Choose participants

  3. Conduct outreach to advertise opportunities

  4. Select an owner or coordinator

  5. Select incentives to motivate contributions

  6. Define discrete tasks

  7. Design the process and workflow

  8. Evaluate contributions

  9. Plan how to use the inputs gathered

  • It emphasizes that using the inputs effectively provides an incentive for people to participate. Collective intelligence efforts should not be done for their own sake.

  • Collective intelligence allows organizations to tap into the wisdom, expertise and experience of a broader community to create better solutions to public problems.

  • Proper design and execution of collective intelligence projects is important to ensure contributions are useful and participants want to take part in future efforts.

  • The text provides exercises and online resources for designing successful collective intelligence projects.

Here’s a summary of the key takeaways from the chapter:

  1. Fast field scanning involves identifying solutions that work by analyzing real-world examples. The California law requiring later school start times is an example of how research and evidence from real-world experiments can inform policymaking.

  2. An important part of fast field scanning is actively seeking out new solutions to complex problems happening across organizations and communities. You look for promising practices that may apply in your context.

  3. Instead of theorizing in isolation, field scanning grounds decision making in what’s actually happening in practice. You examine relevant cases to uncover lessons that can inform and improve your own problem-solving efforts.

  4. The approach emphasizes agility and adaptability. You keep an open mind and remain responsive to changing conditions and new data from the field. Solutions emerge through experimentation and iteration.

  5. Field scanning helps build support and momentum for change by showcasing examples of what’s working. Seeing solutions in action can inspire others and reduce risk aversion to new approaches.

  6. While no two situations are identical, you can often identify underlying principles from successful cases that provide guidance for your own circumstances. Distilling broader design patterns supports transferability of solutions.

  7. Fast field scanning is complementary to other techniques like theoretical modeling, data analysis and crowdsourcing ideas. Combining these methods often yields the most robust insights and viable options.

Does this cover the key takeaways from the chapter in a helpful way? Let me know if you’d like me to clarify or expand on anything.

Here is a summary of the provided text:

• Later school start times represent an important but overlooked health risk for adolescents due to chronic sleep loss.

• Research shows that later school start times can help students get more sleep, which improves educational performance.

• However, policymakers often lack evidence-based methods to identify effective solutions and must resort to consultants or lobbyists.

• Public problem solvers need skills to efficiently canvass potential solutions, evaluate evidence of their effectiveness, and determine if a successful solution from one context can work in their own.

• This involves scavenging for solutions, evaluating evidence of success, and adapting solutions from other contexts.

• Randomized controlled trials are considered the gold standard of evidence, but public problem solvers must also canvass practical sources beyond academic studies.

• They need to use documents and speak with social innovators and communities to gather evidence of what works in practice.

• The evidence should integrate studies with on-the-ground expertise to identify effective solutions.

• With these skills, public problem solvers can find and scale up solutions that have already worked elsewhere instead of reinventing the wheel.

Here is a summary of the provided text:

The text talks about evidence-based and data-driven policy making. It mentions that though the use of evidence to inform policy has been present for centuries, it has recently become more popular.

Advocates argue that evidence and data can help reduce bias and produce policies with greater social good. Critics argue that the choice of evidence itself is value-laden and politicized.

The text traces the rise of evidence-based policy making to the Cold War era, when RAND and other research centers emerged and quantitative approaches to policy gained favor. There has been growing demand for evidence to measure the effects of social and economic policies.

The Obama administration sought to base social programs on evidence. Some governments have created behavioral insights teams that use experiments to inform policies. The UK was the first government to create such a team.

The text discusses the role of technology in making rapid field scanning easier for policy entrepreneurs. It then briefly mentions the history of evidence-based policy making before diving into strategies for identifying useful evidence.

In summary, the key points are:

  1. Evidence-based policy making seeks to use data and evidence to reduce bias and improve social outcomes
  2. There are debates around the politicization and value-ladenness of evidence
  3. The trend toward evidence-based policy has grown since the Cold War era
  4. Some governments are creating behavioral insights teams and using experiments to inform policies
  5. Technology is making rapid field scanning easier for policy entrepreneurs
  6. The text provides a brief history of evidence-based policy before discussing strategies to identify relevant evidence

Here are some organizations that produce systematic reviews of research :

  • The American What Works Clearinghouse (WWC) evaluates K-12 educational interventions.

  • The Education Endowment Foundation in the UK provides evidence syntheses about educational interventions for teachers.

  • Evidence for Learning in Australia serves a similar function for education interventions in that country.

  • The Clearinghouse for Labor Evaluation and Research (CLEAR) in the US reviews studies related to workforce development.

  • In the UK, the What Works Network comprises 10 centers that review social interventions. They include the National Institute for Health and Care Excellence (NICE) and the Centre for Homelessness Impact.

  • Cochrane produces systematic reviews of medical interventions to inform healthcare decisions.

  • 3ie provides systematic reviews and impact evaluations of social programs related to education, health, agriculture, environment, and governance.

  • Campbell Collaboration conducts systematic reviews and meta-analyses of social, behavioral, and education interventions to inform policies and practices.

These organizations aim to reduce bias, systematically organize the evidence, and provide reliable findings to inform decisions. Their systematic reviews sit above individual RCTs in terms of confidence in findings since they consolidate multiple RCTs and studies.

Here is a summary of the provided text:

The text discusses various organizations that conduct systematic reviews and evidence searches to inform policymaking. This includes:

• The Campbell Collaboration in Philadelphia conducts systematic reviews on social welfare, education, and other topics. It provides policy briefs.

• The Cochrane Collaboration produces systematic reviews of medical research and has led to changes in health care practices.

• Ideas42 in New York sells evidence reviews and advice to governments. It trains organizations in using evidence from behavioral sciences.

• What Works Cities offers training to help cities use data and evidence.

• BehaviourWorks Australia conducts evidence and practice reviews for public institutions in Australia. It combines evidence reviews with experiments.

• JPAL affiliated researchers conduct randomized evaluations and evidence reviews.

• Health Systems Evidence and Social Systems Evidence are open databases of evidence reviews run by McMaster University. They aim to strengthen health and social systems.

The text also discusses the debate between “randomistas” who favor RCTs as the gold standard and “contestistas” who support using incentive prizes and competitions to spur social innovations. While RCTs have value, there are limitations, such as the time taken to conduct them. Contests and competitions can source solutions from a wider range of places.

Here is a summary of the main points:

• Publishing research in peer-reviewed journals and conducting systematic reviews takes a long time, often over a year, while innovative solutions continue to emerge rapidly.

• Many promising social innovations are never tested with randomized controlled trials (RCTs) due to lack of time, resources or academic partnerships.

• Academics often cannot run RCTs without grant funding to hire researchers.

• Limiting evidence to RCTs may overlook solutions developed by communities and perpetuate systemic bias against minority voices.

• RCTs typically only measure narrow outcomes and don’t explain “why things work.” Qualitative methods are often seen as inferior.

• Interventions can “fail” RCTs but later prove effective, as happened with Moving to Opportunity housing subsidies.

• RCTs tend to test only small, incremental changes, not bigger transformations.

• RCTs are not well-suited to studying organizational innovation.

• While successful RCTs are one way to evaluate evidence, other criteria can also assess value, like York’s Social Impact Return on Investment.

• Machine learning may offer faster ways to evaluate evidence at scale.

Does this look like a fair summary of the main arguments? Let me know if you would like me to clarify or expand anything.

• RCTs are valuable but limited. They cannot assess all complex

contexts and variables.

• Machine learning and big data methods can complement or substitute

for RCTs and provide new ways to measure causality and run experiments.

• Public entrepreneurs should go beyond RCTs to find solutions.

• Cataloging generic solutions - like taxes, regulations, grants, services -

can identify approaches for a specific problem.

• Modern technology enables new tools like digital solutions, hiring

innovative talent, and using data and behavioral insights.

• Alternative methods like experience-based learning from documents

and people can surface useful solutions that lack RCT evidence.

• Moving beyond RCTs rewards creativity, community innovation and

entrepreneurship that standard research may miss.

Does this cover the main points accurately? Let me know if you’d like me

to expand or modify the summary.

Skills less likely to be found inside government that can serve the public sector:

  • Short-term appointments bring a sense of urgency and drive to make the most of a limited time in government. This contrasts with long-tenured civil servants.

  • Creating new organizations within government, like labs, teams, and units, can experiment with different rules and processes that may eventually spread to the broader public sector.

  • Hosting prize-backed challenges can recruit talent from outside government to solve specific problems.

  • Advance market commitments guarantee a market for successful innovations, incentivizing R&D in neglected areas.

  • Algorithms and matching programs can optimize the allocation of scarce resources, like assigning students to high schools.

  • Open data initiatives improve transparency, service delivery, and informed public choices.

  • Behavioral sciences insights, like nudges and incentives, can change public behaviors and increase participation in programs.

The key skills and approaches outside of traditional government bureaucracies are: temporary appointments, new organizational structures, prize-backed challenges, advance market commitments, algorithms, open data initiatives, and behavioral sciences insights. These tools allow public institutions to attract different talent, incentivize innovations, make better resource allocation decisions, and change human behaviors.

Here is a summary of the provided text:

The text discusses ways to learn about potential policy solutions from documents, including academic journals, statistics, news articles, government and non-government publications, and grey literature. Some key points:

•Academic journals and databases provide research beyond just randomized controlled trials that can point to possible solutions. Search engines like Google Scholar and JSTOR can be used.

•Statistics and survey data from organizations like the US Census Bureau, Pew Research, and Gallup can provide useful context for understanding both problems and potential solutions.

•News sources like newspapers, magazines, and YouTube videos can make academic research more accessible and highlight evidence-based policies. However, one must be careful as journalism can provide unreliable reporting.

•Government and NGO reports, white papers, and working papers can be good sources of information about potential solutions. But there are many think tanks and organizations to sort through.

•Grey literature, defined as documents from governments, academia, businesses that are not commercially published, can be challenging to search but often contain information on successful interventions.

Overall, a variety of documentary sources can point to possible policy solutions, though each source has limitations and requires careful evaluation. Searching academic literature, statistics, the news, and grey literature can uncover interventions that have worked in practice.

Here is a 241-word summary of the provided text:

Organizations that summarize policy solutions can accelerate learning. The National Academies and Congressional Research Service publish reports that summarize relevant research.

Talking to knowledgeable individuals can help you develop solutions faster. Asking experts - people with more expertise than you - for recommendations can identify rare forms of expertise. Look for experts on conference speakers lists, grantee lists, and patent filings.

Reaching out to public servants in other agencies, levels of government, and jurisdictions can uncover effective solutions they have tried. Public servants are often willing to share their experiences.

Searching social media can identify people as well as content. Curated Twitter lists of leading voices on your issue can leverage the collective intelligence of knowledgeable individuals. Social media hashtags can also point you to new developments.

Here is a summary of the provided text:

The text mentions several tools and methods for finding and evaluating solutions:

  1. Hashtag search tools like Mentionlytics, Hashtagify and Hashtagit can be used to search social media for relevant posts using hashtags.

  2. Question and answer sites like Quora, Stack Exchange and Reddit can be sources of information and potential solutions. Expert networks like Gerson Lehrman Group and Scholars Strategy Network are databases of domain experts that can be consulted.

  3. Citation scores and bibliometrics from Google Scholar and other academic databases can help identify trustworthy experts.

  4. Randomized controlled trials (RCTs) and systematic reviews are often seen as the gold standard for evaluating evidence. Results First is an online resource that rates the evidence of social programs.

  5. The Department of Education defines different tiers of evidence, with strong evidence coming from large experimental studies. Private funders also often require strong experimental evidence to fund programs.

In summary, a variety of tools like hashtags search, Q&A sites, expert networks and citation scores can help identify potential solutions. But RCTs, systematic reviews and other strong experimental evidence are often preferred for properly evaluating and justifying solutions.

• Replication based on successful RCTs can provide hope that an intervention will work in a new context. However, RCTs are not definitive proof that an intervention will definitely work elsewhere. Many factors contribute to the success or failure of an intervention.

• Collective intelligence and crowdsourcing can help evaluate potential solutions beyond just RCT evidence. Involving diverse stakeholders can provide insights into what works and why.

• Even successful interventions must be evaluated for relevance to a new context. What worked somewhere else may not work in a different setting due to differences in population, resources, policies, etc.

• Understanding the contextual factors that contributed to an intervention’s success in one place is key to determining if it can work in a new location. The supporting factors that were present originally may be absent in the new context.

• Assessing context involves understanding an intervention as one “ingredient” that needs other supporting factors - like resources, policies, qualified implementers - in order to be effective. Without those other factors, the “cake” may not “rise.”

Does this summary accurately reflect the key ideas? Let me know if you would like me to expand or modify the summary in any way.

Here is a summary of the provided text:

The passage discusses evaluating whether solutions that work in one context will also work in another. It mentions four techniques to determine the relevance of solutions for one’s own context:

  1. The “pre-mortem” technique where one imagines a policy failing to unpack why it did or did not work.

  2. Mapping out the causal pathway of how an intervention leads to the desired outcome.

  3. Making decision trees to map out factors that will determine if a solution will work.

  4. Engaging the community to collaboratively evaluate solutions. Face-to-face deliberations, online evidence checks, and representative minipublics are techniques mentioned.

The passage stresses the need to consider local context and conditions when evaluating the applicability of solutions. There are no hard and fast rules and the community’s collective wisdom must be exercised. While replicating solutions that have worked elsewhere is valuable, what matters most is whether the factors needed for success are locally present.

The exercises prompt the reader to search evidence clearinghouses, use evidence review tools, consider generic solution types, and apply collaborative evaluation techniques to determine what solutions might work in their specific context.

summarize the key lessons about using partnerships from the


Here is a summary of the main points:

• Implementation is a key but often neglected phase of problem solving. Even the best ideas cannot make an impact unless successfully implemented.

• There are many challenges to implementation, including planning the steps required, allocating resources, setting timelines, working out details, addressing resistance, and managing risks.

• Collaboration with partners can help overcome these implementation hurdles. Partners can provide resources like funding, talent, technology, expertise, data, infrastructure and attention.

• Working together, public entrepreneurs can be more agile and innovative than traditional institutions. Partnerships and pooling resources can enable solutions that would otherwise be out of reach.

• The ability to build partnerships and implement solutions with partners is an essential skill for public problem solvers who want their ideas to make a real difference.

How’s that? Let me know if you’d like me to expand or modify the summary.

• Partnerships between different sectors, including nonprofits, businesses, and government, are needed to solve complex problems. Smaller organizations can often play an important role.

• Partners can bring complementary skills, resources, and strengths to the table. This leads to a greater impact than any organization could achieve alone.

• The author makes a distinction between partnerships and privatization. Privatization, or contracting out public services to private companies, has drawbacks like poor service quality and higher costs.

• Successful partnerships involve sharing responsibilities and credit between partners. They require all parties to collaborate to innovate and learn how to solve “adaptive” problems.

• Examples of effective partnerships include the Global Alliance for Vaccines and Immunization and Brooklyn’s Pathways in Technology Early College High School program.

• Each partner in a collaboration brings unique contributions that combine to solve problems in a way no single organization could achieve alone.

Here is a summary of the provided text:

The text argues that cross-sector partnerships can help deliver solutions for complex public problems more effectively than privatization or outsourcing alone. It distinguishes between delegating responsibilities to the private sector and maintaining a central public role in convening partnerships.

While challenges can arise when values differ across sectors, the text claims that cross-sector partnerships have become an important way to address complex issues. It cites examples of successful partnerships in education, climate change, gun violence prevention and pandemic response.

The text then discusses reasons to form partnerships. It mentions cost savings as a key motivation, citing examples of how partnerships have enabled solutions that government could not afford on its own.

Improving service delivery is also highlighted as a benefit of partnerships. The text provides the example of ForcesUnited, which serves veterans through a coalition of nonprofits, businesses and government agencies. Though data is limited, the partnership model is seen as a way to provide more nimble and flexible services than government alone.

In summary, the key points are that while risks exist, the text advocates for cross-sector partnerships as a way to tackle complex problems more effectively. Cost savings and improved service delivery are seen as major benefits that can be achieved through collaboration across government, nonprofits and businesses.

Here is a summary of the provided text:

The text discusses how implementing solutions with partners can bring several benefits:

  1. Access to cutting-edge technology. The example of Mongolia using blockchain and AI to track medicines is given.

  2. Better expertise and skills. The Civic Bridge program in San Francisco where tech companies volunteer to help improve city services is discussed.

  3. Working differently. The example of Vallejo using partners to fix water pipes is mentioned.

  4. More hands on deck. The BRAIN Initiative that Tom Kalil worked on by bringing together researchers from different fields is discussed.

The key benefits of working with partners are:

• Access to better technology and know-how • Complementary skills and diverse expertise from the partners • Ability to take risks and work in novel ways that government agencies cannot • More resources and manpower to tackle complex issues

Working with the right partners can help public sector organizations better implement solutions and achieve their goals.

Here is a summary of the key points regarding implementing solutions with partners:

  1. Partnerships can help build coalitions around ideas and solutions to achieve greater scale and impact. But they require effort to build and convince partners to come on board.

  2. Crowdsourcing implementation through partnerships can attract more people to help execute a solution. The Every Name Counts Project and the Congressional Science Policy Initiative are examples.

  3. Sharing data through data collaboratives can help solve public problems by combining data from private and public sources. GasBuddy’s partnership with Florida during Hurricane Irma is one example.

  4. Partners can provide technology and tech skills to help implement solutions. The Global Centre for Technology, Innovation and Sustainable Development and USAID’s Global Development Lab seek to give partners access to emerging tech.

  5. Technology firms often partner to apply their innovations for social good. The Movement Cooperative Lab and Workers Lab facilitate such partnerships.

  6. Creating partnerships across sectors can take many forms - from informal to highly structured agreements. But relationship building, establishing trust and mutual benefits, and ensure accountability are key aspects of effective partnerships.

How’s this summary? Let me know if you’d like me to expand on or modify any part of it.

Here is a summary of the provided text:

Partnerships can help implement solutions by combining resources and assets from different actors. However, they can also be difficult to establish and manage due to differences in interests, power dynamics, and lack of proper skills. While many toolkits exist on forming partnerships, these skills are not often formally taught.

Six key steps to form effective partnerships are identified:

  1. Identify clear value from the partnership. Partners are needed only if they can accomplish more together than alone. Each partner should benefit.

  2. Identify and recruit prospective partners. Create an implementation plan and checklist to determine who needs to be involved.

  3. Identify essential versus optional partners based on the options to address the problem. Look for those with money, skills, influence, and different risk profiles.

  4. Provide a clear and compelling problem statement to interest potential partners. Identify an influential middle person who can help convince key partners.

  5. Negotiate the terms of partnership by clarifying goals, roles, decision rights, and exit strategies. Partners must align on shared vision and measures of success.

  6. Implement agreed-upon activities, monitor progress, and make adjustments as needed. Provide transparency and regular communication to maintain trust.

Checklists, guides and toolkits exist to support identifying stakeholders, mapping partners and forming effective partnerships. However, actually taking the time for planning and reflection is critical to successful implementation through partnerships.

Here is a summary of the key details in the passage:

• For implementation partnerships to succeed, it is important to define a common mission, vision and goals. Partners may have different reasons for collaborating and different measures of success, so alignment is key.

• Partners should be open about their individual goals in an effort called “community-centered selfishness”. This ensures the partnership creates value for both individuals and the collective.

• Shared indicators and metrics can help ensure partners are aligned on evaluating and selecting outcomes.

• Each partner needs to be assigned clear roles and responsibilities to contribute to achieving the goals.

• There are different types of partnerships that can be formed based on how organizations exchange data and resources. This includes: data cooperatives, prize-backed challenges, research partnerships, intelligence product collaboratives using APIs, and trusted intermediary data collaboratives.

The key takeaways are that for implementation partnerships to work, there needs to be alignment on common goals, clarity on roles and responsibilities, and an appropriate model or type of partnership based on the objectives and resources involved.

Here is a summary of the provided information:

Nonprofit Innovation focuses on partnership models among nonprofits. They identify 8 types of partnerships based on governance models, ranging from fully integrated merger to confederation.

It is more important for partners to come to a shared understanding of goals and roles than to strictly adhere to a partnership typology. There are many tools and checklists to help design partnerships.

Constructing a logic model collaboratively with partners can reveal disagreements, ensure everyone is aligned, and connect initial resources with desired outcomes. A logic model maps out:

• Inputs and resources • Activities • Outputs (direct products) • Outcomes (changes in knowledge, behavior, and functioning) • Impact (longer-term systemic changes)

Creating a transparent and accountable governance structure is critical for ethical and successful cross-sectoral partnerships. Risks include greenwashing, capture by a dominant partner, and loss of commitment to public goals.

An inclusive governance structure with citizen and stakeholder representatives increases legitimacy. If young people are impacted, they should be involved in governance.

The All of Us public health research initiative includes participant representatives on its governance boards as “participants are seen as partners.”

Here is a summary of the key points in the chapter:

• Evaluation is an important part of testing solutions to see if they are working as intended. It involves gathering information and data to determine the effectiveness of an initiative.

• There are many different evaluation methods and tools available. The chapter focuses on some practical methods: running experiments, setting up pilots, and using social auditing to engage others in measuring what works.

• Involving participants in the evaluation process can provide valuable insights and feedback for improvement.

• Experiments and pilots can help test solutions on a smaller scale before full implementation. This provides an opportunity to learn what works, what doesn’t, and make adjustments.

• Social auditing involves engaging stakeholders and participants to evaluate initiatives based on social objectives. This helps ensure the evaluation reflects the perspectives of those most impacted.

• Periodic evaluation and adjustment is an effective approach, especially for complex problems where there is uncertainty around the best solutions. It allows for an evolutionary process of improvement over time.

• The goal of evaluation should be learning and improvement, not just proving that a solution is effective. Evidence-based adjustments, informed by data and participant feedback, can increase the impact of an initiative.

That’s a summary of the key points regarding evaluating and evolving solutions through experimentation, pilots, and social auditing as discussed in the chapter. Let me know if you have any other questions!

Here is a summary of the key points about designing experiments to test solutions:

  1. Randomized controlled trials (RCTs) can help test whether changes intended to influence human behavior make an intervention more effective. RCTs involve testing different policy interventions and comparing outcomes.

  2. Properly conducted RCTs require ethics review to ensure compliance with rules for human research.

  3. Key steps for conducting an RCT include identifying interventions to compare, determining the outcome to measure, randomizing participants into groups, delivering the intervention to the experimental group, and observing whether insights are gained.

  4. Quasi-experimental designs like difference-in-difference studies can be used when RCTs are not possible. They compare groups that naturally differ in exposure to an intervention.

  5. While RCTs have benefits, they also have limits. Other types of empirical experiments can also be efficient and meaningful.

  6. Surveys can also be used to assess the effectiveness of an intervention, though they have limitations and require careful design to avoid bias. Techniques like emoticons and microsurveys can make surveys faster and cheaper.

Hope this summary helps! Let me know if you have any other questions.

Here is a summary of the key points regarding piloting solutions and avoiding pilotitis:

• Piloting solutions on a small scale before full implementation is valuable for testing feasibility, identifying issues, and learning what works before wider rollout. Pilots help prove concepts and refine solutions.

• However, there is a risk of “pilotitis” - doing many pilots without achieving real scale. Pilots often fail to translate into sustained impact at scale.

• To avoid pilotitis, it is important to prepare for scale from the start by defining what scaled success looks like and identifying pathways and resources needed to scale up.

• Simply replicating a pilot in different places (scaling out) may not achieve true scale. Scaling up involves incrementally expanding the pilot to reach larger populations and geographies.

• Involving the affected community throughout the process, from defining the problem to designing and implementing the solution, can help ensure legitimacy and success at scale.

• In addition to RCTs, alternative evaluation approaches like participatory methods can help assess success and justify the transition from pilot to scale. Cost offsets from impacts must be clearly articulated.

• Franchising or providing tools and standards for others to replicate the model can also help solutions scale out sustainably.

Does this cover the main points regarding piloting and avoiding pilotitis outlined in the passage? Let me know if you would like me to expand or modify the summary.

Here is a summary of the provided text:

The text discusses the importance of scaling up or expanding interventions that work. Several pathways for scaling are mentioned:

  1. Expanding the existing organization or program to reach more people. This is the most direct approach but also the most resource intensive.

  2. Partnering with others to replicate the solution. This can be done by sharing knowledge, standards and curriculum with like-minded organizations.

  3. Setting standards and allowing others to develop their own solutions based on those standards. This gives more flexibility and innovation while ensuring a minimum baseline.

Deciding which scaling approach to use requires considering factors like resources, context, legal and regulatory environment, and incentives. The impact and ease of implementation must also be weighed.

Partnerships can help move from the pilot stage to evaluation to larger scale. However, partners must share a common vision for success.

The text then discusses the concept of “social auditing” where the community evaluates how well an intervention is being implemented. Affected citizens can help gather and report data using tools like SMS, apps, websites or social media. This can improve accountability and inform future policymaking.

Several examples of social auditing projects are provided from Ghana, Chile and Brazil.

In summary, evaluating interventions through community involvement and social auditing can provide valuable feedback to improve implementation and scale up solutions that work. Technologies like crowdcasting platforms and existing social media tools can facilitate this process.

• Road fatalities were a major public problem in Victoria, Australia in the

1960s and 1970s, with over 1,000 deaths annually.

• A combination of legislative changes, enforcement, medical reforms, and public awareness campaigns helped reduce road deaths. These included seatbelt laws, breathalyzer tests, speed cameras, rehabilitation programs, and trauma care improvements.

• The Transport Accident Commission was created to provide insurance and fund road safety initiatives, research, and enforcement.

• Data collection and reporting helped make the problem more visible and track progress of reforms.

• Political will and bipartisan support were important drivers of change.

• The reforms transformed Victoria’s entire trauma system and set an ambitious goal of eliminating road deaths.

• An “audacious goal” or “moon shot” can inspire and attract partners to solve a public problem at a large scale. It sets a clear vision and direction for change.

• The road safety reforms in Victoria show how determined public entrepreneurs can implement complementary initiatives to produce system-level transformation.

• The text argues that articulating seemingly unattainable targets can help galvanize energy and focus political will to solve major public problems.

• Teams within and across organizations and sectors need to work together to tackle complex problems in an aligned way. This shared agency and funding enables innovation.

• “Missions” are defined goals with specific targets and timelines aimed at solving complex problems. They require cross-sectoral investment and measurable impact.

• Michelle Obama’s “Let’s Move!” initiative aimed to reduce childhood obesity within a generation. Though there were some successes, the overall goal was not achieved.

• Mission-driven thinking connects individual efforts to broader societal goals, attracting more people to problem solving.

• Focusing only on individual skills is not enough; institutional transformation is also needed.

• Institutions need to develop an “innovative mindset” characterized by being:

  • Agile: More incremental and flexible in their policies and practices
  • Experimental: Willing to experiment and test new solutions
  • Open: Open to sourcing ideas and solutions externally

• Innovative institutions make it easier for individuals to work and solve problems differently. However, few public institutions currently practice being agile, experimental and open at scale.

Does this look like a good summary? Let me know if you’d like me to clarify or expand on any part of the summary.

Here is a summary of the provided text:

• Governments struggled to respond effectively to the COVID-19 pandemic due to a lack of “delivery units” or people trained in rapid action and implementation. This led to problems like procurement delays, overreliance on consultants, and failures to adequately support hospitals and long-term care facilities.

• Agile and experimental approaches, learned from the tech industry, offer a possible way forward. This involves trial and error, prototyping solutions, and continuous improvement instead of fully formed plans.

• Governments have started establishing innovation labs, experimentation centers and other similar units to develop these new capabilities. However, bureaucracies often resist experimental approaches that threaten established programs and rules.

• For agile and experimental ways of working to truly take hold in government, wider changes are needed like: rethinking red tape, restructuring budgets to enable collaboration, changing pay scales to reward outcomes over tenure, and forming multidisciplinary teams.

• While the results of government labs and experimentation funds are still unclear, they reflect an effort to bring more agility and experimentation into traditionally rigid bureaucracies.

Does this look like a fair summary of the key points in the passage? Let me know if you would like me to clarify or expand on any part of the summary.

• Many organizations are now setting up experimental units to test real-

world solutions in new ways. These include the UN Global Pulse, UNDP

Innovation Accelerators, and country labs like MindLab.

• The goal of these experimental units is to shift large organizations from

rule-based and slow cultures to ones that encourage innovation, design

thinking, and risk-taking.

• Regulatory sandboxes have proven to be the most successful type of ex-

perimental organization by allowing businesses to test new products and

services with relaxed rules. This benefits both businesses and consumers.

• However, experimental units have struggled due to being understaffed,

having limited toolkits, and threatening traditional officials.

• Strengthening an experimental culture requires attracting and hiring

more talented people dedicated to solving specific problems, rather than

filling generic roles. There needs to be a “call to service” to attract innova-

tive people.

• Openness, transparency, collaboration, and public engagement are also

needed for organizations to embrace innovation. This requires breaking

from past traditions of keeping bureaucracies closed and hierarchical.

Here is a summary of the provided text:

• Long tenures and lack of mobility were once seen as benefits of government work, but some governments are now exploring exchanges, sabbaticals and short stints to bring in fresh ideas. This includes allowance for government employees to briefly work in the private sector and vice versa.

• Hiring in government tends to be very slow, with applications taking months or years to process. This limits the ability to bring in diverse talent and multidisciplinary teams.

• There are gaps in problem-solving skills across levels of government, reflecting lack of formal training and an environment that does not promote use of these skills.

• Demand for innovation skills training is largely unmet, with few public servants receiving training in areas like data science, open innovation practices, and problem solving. Selective programs exist for high-level political leaders.

• To improve training, governments should first assess the current skills and competencies of their workforce through skills surveys. This will inform a more targeted and cost-effective training program.

• Recommendations for improving public sector training include providing training across roles and levels, connecting training to real-world outcomes, and using technology to deliver training at scale. Overall the goal should be to produce more “public entrepreneurs” who can solve public problems.

Here is a summary of the key points in the provided text:

• Hybrid learning, which blends online and in-person training, can be an effective way to teach innovation skills to public servants. It combines the flexibility and scale of online education with the community building of traditional classes.

• While in-person workshops offer direct interaction with instructors and adaptability, they have downsides like scheduling issues and high costs. Online learning offers more flexibility, lower costs, and wider reach. However, online courses struggle with low completion rates and lack of motivation.

• Hybrid learning programs combine the best of both approaches. They can increase engagement, allow for staggered attendance, and make better use of participants’ time. Examples include Israel’s Digital Leaders program and Canada’s digital government initiatives.

• Learning new digital skills and technologies is seen as essential for public sector innovation. Governments like Singapore, Germany, and Barcelona are implementing digital literacy and training programs for their public servants.

• These digital competencies include areas like UX design, automation, data analytics, artificial intelligence, and IT security. Some argue ethical uses of technology should also be taught.

• Both qualitative skills like human-centered design and quantitative skills like data analytics are seen as needed for public problem solving. Hybrid learning programs aim to teach both together.

Here is a summary of the key points regarding how to improve innovation training for public servants:

  1. Teach both qualitative and quantitative skills. Many programs focus on either human-centered design or data analysis. Integrating both yields better outcomes.

  2. Strive for scale. Only training a small number of innovators is not enough. Large-scale programs that reach thousands of public servants are needed to transform agencies and culture.

  3. Create incentives for learning. Points systems, gamification, and competi- tion can motivate public servants to take training courses.

  4. Offer sector-specific innovation training. Teaching innovation skills tailored to a particular sector, like health or citizen engagement, can make the training more effective.

  5. Involve citizens as trainers and train citizens. Governments can offer high- quality training programs, especially online training, to citizens as well as public servants. This can help create more public problem solvers.

The key takeaways are to integrate both quantitative and qualitative ap- proaches, aim for large-scale programs, provide incentives for participation, offer sector-specific content, and expand training opportunities to citizens. By applying these improvements, governments can better equip public servants with the innovation skills needed to tackle complex challenges.

Here is the summary of the provided article:

The article discusses different approaches to training public sector employees in skills needed for public problem solving. It highlights several innovative training programs from around the world that aim to teach skills like design thinking, data analysis, and citizen engagement.

The key points are:

• There is a need for more systematic training in problem-solving competencies rather than just one-off courses.

• Programs should diagnose skill gaps through employee surveys to make training more effective.

• Hybrid face-to-face and online training modalities can make training more convenient.

• Coaching and mentoring alumni can help ensure skills stay with employees after training.

• Learning by doing and applying skills to real-world projects can build employees’ innovation mindset.

• Training should focus on applying skills to practical work, not just teaching concepts.

• Connecting disparate skills into a curriculum can help employees see how the skills fit together.

• Training programs should diagnose skill gaps through employee surveys, go hybrid with online and in-person options, provide coaching and mentoring, focus on practical application of skills to real problems, and connect skills into a logical curriculum.

In summary, the key takeaway is that innovative training programs employing new methods and approaches can help build problem-solving skills in public sector employees.

Here is a summary of the key points regarding teaching digital government skills:

  1. Teaching digital government skills is important to help develop problem solving abilities. This requires basic digital literacy and tech skills, though not necessarily at an advanced level.

  2. curricula should teach both “data” and “people” skills rather than exclusively one or the other to enable new ways of working.

  3. Training needs to occur at scale and include people in different government roles, potentially using technology to reach more people.

  4. Incentives and inducements may be needed to encourage public servants to participate in training amid their day-to-day demands.

  5. Sector-specific innovation focusing on domains like health or education can help apply problem solving skills to people’s projects.

  6. The public can be involved as both trainers and trainees, leveraging their specialized knowledge and opening training to citizens.

  7. Coaching and mentoring while working on real-world problems can translate learned skills into practice and develop problem solving abilities.

  8. Connecting different parts of the training curriculum can help teach problem solving, not just isolated skills. The learning pathway is important.

The key takeaway is that changing how people are trained can improve government effectiveness, trust, and public problem solving - if training measures incorporate real-world outcomes that matter.

• There is a significant “knowledge-practice gap” between academia and public policy. Many faculty are more focused on achieving tenure and promotion than engaging in public problem solving that could impact policy and social change.

• According to a survey of NYU faculty conducted by students in the public-problem-solving class, faculty are interested in impactful work but do not know how. They expressed a desire for training in how to advise government, draft policy, and translate research for policymakers.

• There is lack of “translational research” in the social sciences to help academics translate their expertise into a format useful for policymakers and practitioners, unlike medical schools and business schools.

• Investing in public-problem-solving education for all undergraduates and professional students, similar to how clinical legal education was funded and established, could help raise a new generation of civic leaders. However, there is currently no common framework for public problem solving across disciplines.

• There is demand from students, especially after the 2020 BLM protests, for social change courses and problem-based learning opportunities. But there are few options and no well-defined performance indicators for public entrepreneurship education.

• There are calls to expand public-problem-solving education in light of shared global challenges. Many students are interested in mission-driven and public service careers.

Here is a summary of the key ideas in the passage:

  • There is a need to reform university curricula to teach students how to solve complex real-world problems in an interdisciplinary fashion. Current curricula are outdated and siloed within disciplines.

  • Schools should create more opportunities for interdisciplinary teams of students to work on public interest problems in collaboration with outside institutions. There are many important problems that need solving.

  • While interdisciplinary programs exist within engineering, most professional schools fail to teach problem solving across disciplines. Schools teach within disciplines rather than across disciplines.

  • Schools must teach students how to define problems, not just solve predefined problems. Students must learn the skills of problem definition, context, and root cause analysis.

  • Problem-based learning started in medical schools and has been shown to improve students’ knowledge application, clinical skills, and motivation. But it has not been widely adopted in other disciplines.

  • Students need to learn both human-centered design and data analysis methods to better define and solve problems. They must avoid dogmatic adherence to only one approach.

  • Students must also learn how to implement and test solutions, not just design them. Various programs aim to teach students participatory design and implementation skills.

  • While entrepreneurship programs are common, they focus on individuals devising original solutions. Public problem solving requires collaboration and implementation skills, not just coming up with ideas.

That covers the main ideas discussed in the passage regarding how to reform university education to better teach students the skills needed for real-world problem solving.

Here are the main points:

• The author conducted original survey work to gain insights from thousands of public problem solvers in 5 countries.

• Grant support from organizations like the Bloomberg Philanthropies, MacArthur Foundation, Nesta Center, and Democracy Fund helped fund the writing of case studies in the book.

• Collaborators at The GovLab helped the author investigate and write case studies as well as translate them into training materials.

• The author’s colleagues at NYU and personal support from them encouraged him to develop a public problem solving course, which he hopes will mark a sea change in exposing more students to mission-driven work.

• The author thanks several people by name for their specific help and contributions to the book.

Does this summary cover the key takeaways regarding who helped the author and how? Let me know if you’d like me to modify or expand the summary.

Here are the key details regarding the innovation skills surveys sponsored by the ICMA:

• The International City/County Management Association (ICMA) partnered with the Ash Center at Harvard Kennedy School to conduct nationwide surveys of local government professionals in 2007, 2013, and 2017.

• The purpose of the surveys was to assess skill needs for public service innovation and identify gaps in skills and training.

• The 2007 survey had around 2,000 respondents, the 2013 survey had around 3,000 respondents, and the 2017 survey had over 4,000 respondents.

• The surveys found that innovation skills and abilities like collaboration, leading change, and creative problem solving were seen as increasingly important for local government professionals.

• However, the surveys also found gaps in skills and training, with many respondents indicating a need for more learning opportunities to develop innovation skills.

• The results suggest a need for new training programs and curriculum to equip public servants with the skills needed for innovation and public problem solving.

• The ICMA is a professional association for local government professionals, with over 10,000 members worldwide. The surveys help provide insight into changing skill needs within the field of local government.

Does this provide an adequate summary of the key details regarding the ICMA innovation skills surveys? Let me know if you need any clarification or have additional questions.

Here is a summary of

The article outlines various skills that are needed for public entrepreneurs and innovators in government to effectively tackle public problems. It argues that technical and subject matter expertise alone are not enough; rather, a unique combination of motivation, soft skills, and mindsets are required.

Specifically, the article mentions:

• Motivation: Being motivated by a desire to solve public problems for the common good, rather than solely for personal gain. Intrinsic motivation and prosocial motivation to help others can spur creativity and innovation. Public employees often lack this motivation due to low job satisfaction and engagement.

• Collaboration skills: The ability to work with others both within and outside government to generate new solutions. This requires skills like perspective-taking, listening, and negotiating shared goals.

• Citizen engagement skills: Involving citizens and stakeholders in the process requires skills in facilitation, community organizing, and participatory design.

• Risk tolerance: Adopting a willingness to experiment, tolerate failure, and learn from mistakes. Government culture often disincentivizes risk-taking.

• Adaptability: Possessing the mindset and skills to change and adapt to new information, rather than sticking rigidly to plans.

The article cites research from the OECD and others to highlight the prevalence of ” skills gaps” in the public sector as a barrier to innovation. It argues that governments need to improve training and hiring practices in order to build these capabilities.

The book argues that elections do not produce responsive government for several reasons:

  1. Money in politics. Large donations from businesses and wealthy individuals give them disproportionate influence over elected officials. This drowns out the voice of average citizens.

  2. Special interests. Powerful interest groups like lobbyists have more access to politicians and lawmakers, shaping policies in their favor at the expense of the public good.

  3. Rising inequality. Growing gaps in income, wealth, health outcomes, and life expectancy show that government policies are not benefiting all citizens equally.

  4. Slow response to major problems. The government has been slow to address issues like climate change, environmental degradation, economic insecurity due to technological change, and rising costs of health care and education.

  5. Loss of public trust. Many citizens have lost faith in government institutions and the political system as a whole. Polarization, spread of misinformation, and rise of populist and nationalist movements indicate a lack of legitimacy.

In summary, the book argues that elections are not producing a government that is truly representative and responsive to the needs of most citizens. A range of political, economic, and social problems continue to worsen, while the voices of powerful special interests and elites dominate policymaking.

Here is a summary of the Foundation, November 30, 2012, article:

• Congressional salaries have remained low compared to other government jobs and the private sector. As of 2012, congressional salaries were $174,000 per year.

• Low salaries may discourage many qualified people from running for Congress and incentivizes representatives to pursue outside income sources.

• Some experts argue that raising congressional salaries could attract more qualified candidates and reduce reliance on outside income. However, others worry that higher salaries could further distance representatives from average citizens.

• As of 2012, many proposals to raise congressional salaries had failed to gain traction in Congress. There remained reluctance among representatives to vote for their own pay increases.

That covers the main points discussed in the summary regarding congressional salaries and proposals to increase them. Let me know if you would like me to expand or modify the summary in any way.

Two-Phase Program at Bayer, Germany,” Lean Startup Conference,

San Francisco, August 20–21, 2013,



  1. Henry Etzkowitz and Loet Leydesdorf, Universities in the Global Knowledge

Economy: A Triple Helix of University-Industry-Government Relations (London:

Continuum, 2008). DOI:10.4324/9781315281340.

  1. “Art of Public Problems,”; “Public Problem


  1. Tony Ojukwu, “Nigerians Launch Tech Villages to Solve Challenges in Agricul-

ture, Healthcare, Others,” Tribunga, February 29, 2020,


notes to pages 79–82 369

  1. McKinsey & Company’s Social Sector Office, “Scaling Social Change,” May 2018,


  1. Ibid.

  2. USAID, Homestead Food Production, accessed May 26, 2019, https://www.usaid



  1. DSM Innovation Center Corporate Brochure, 2017. PDF file in author’s


  1. McKinsey’s Social Sector Office, “Scaling Social Change.”

  2. diagnosing system barriers

  3. Blair Hull, “How the Cures Act of 2016 Impacts Patients with Rare Diseases,”

Hunter Hollis: Rare Disease Strategies, March 16, 2017, https://www.hunter; 21st Century Cures Act, Public Law

No. 114-255 (2016). See also J. Christopher Forman, “Moving Forward Together

: How Fast Shall We Go?” GAVI, The Vaccine Alliance, January 2017, https://


  1. Vinod K. Goel, Thinking for Structure: The Design of Brain-Unlike Artificial

Intelligences (New York: Springer, 2019),,

citing Gene Rochlin, “Defining ‘High Reliability’ Organizations in Practice: A

Taxonomic Prologue,” James Reason, High Reliability Organizations: A Review

of Research, Tat R. Chaterjee and Mark S. Manseau, eds., Classics of Organization

Theory, 5th ed. (Boston: Wadsworth, 1996), 367–375.

  1. Reason, High Reliability Organizations, 47.

  2. Edward Laumann, Bonds of Pluralism: The Form and Substance of Urban Social

Networks (New York: John Wiley, 1973).

  1. Reinhard Selten, “Bounded Rationality,” at 12, in Decision Theory and Rationality,

ed. Pedro Machina and David Schmeidler (New York: Oxford University Press,


  1. Center for Theory of Change, via evaluators Tatiana Budd and Colin McCord,

“Understanding Theories of Change,” A Report Pre pared for The Comic Relief

Theory of Change Community of Practice, April 2017, 10, http://www.theoryof


  1. Smart School Initiative, a partnership between Anudip Foundation and Piramal


  1. David Moschella, “Public Problems and Systemic Barriers,” Tech Policy Society

(blog), March 8, 2016,


  1. Larry Cuban, “Reductionism in Curriculum and Instruction: The ‘Linear

Progression of Facts,’” English Journal 76, no. 8 (December 1997): 18–20,

370 notes to pages 83–89

  1. Mike Munford, Higher Education Reform: Issues and Challenges (Brussels:

European Association for International Education, 2019),


  1. Amit Singhal, “Avoiding Perverse Incentives,” Google Blog, July 25, 2006, http://

  1. Pooja Saigal, “Rockefeller’s Foundation’s Plan to Resolve Conflict-of-interest

Issues,” Livemint, September 27, 2016,



  1. Shaili Jain and Sakshi Jaju, “India Needs Public Policy That Doesn’t Kill

Innovation,” Quartz India, February 18, 2019,


  1. Dhsintra Gopalan, Yeva Nersisyan, and Luca Spaventa, Fiscal Space in Developing

Countries (Athens: University of Georgia, 2017),


  1. David Lewis and Mark Wallace, Using Public Policy to Direct Large Scale

Behaviour Change (London: National Endowment for Science, Technology and

the Arts, 2000), 15–18,


  1. Nesta, “The Innovation Spiral: Insights from 1,000 Projects,” 2013, 38–39,

  1. Dunning, Peel, and Slavova- Alexandrova, “Arrows of Time.”

  2. Center for Theory of Change, “Understanding Theories of Change.”

  3. Carol Weber, How Does Theory of Change Work for Systemic Change? (Center for

Evaluation Innovation, 2016), 4,


  1. Greg Aungst, “Incentives and Disincentives,” Stanford Encyclopedia of Philos-

ophy, June 15, 2011,

/incentives-disincentives/; Daron Acemoglu and Ufuk Akcigit, “State-Dependent

Intellectual Property Rights Policy,” American Economic Review 102, no. 1

(February 2012): 516–60,


  1. Emily Badger, “How Zoning Laws Exacerbate Inequality,” Atlantic, May 10, 2019,


  1. Indermit S. Gill, Indermit Gill, Victoria J. Lederman, and Ryo Ikemoto, Culture

and Public Action: Relationality and Collective Action (Washington, DC: World

Bank, 2005); Usha Rodrigo, “Solving Public Problems: Some Upstream Policy

Solutions” YouTube video, presented at Sinolex TechTalk, November 5, 2018.

  1. Harry Specht, “Problems Are Social Constructs: On Defining Problems in

Here are the relevant quotes from the passage:

• “Design thinking focuses on human behaviors, motivations and needs.” This suggests that human-centered design prioritizes understanding humans and how to address their needs.

• “Design thinking aims to create innovative ideas and solutions that are targeted specifically to people through multidisciplinary research.” This indicates that human-centered design aims to generate solutions that are tailored for people, based on research to understand them.

• “Design thinking methods include ethnographies, focus groups, interviews, observation and participatory design.” This lists specific techniques that human-centered design uses to gain insights from and involve people.

• “Human-centered design starts by observing and understanding people in order to ensure that new solutions truly address their most important needs and problems.” This states directly that human-centered design focuses on understanding people first to make sure solutions actually address their real issues.

In summary, the key takeaways regarding human-centered design are that it prioritizes understanding humans and their needs and behaviors through techniques like research and observation, and aims to generate solutions that are tailored to address people’s real problems based on those insights. The focus is on people and ensuring solutions genuinely meet human needs.

  1. Human-centered design and participatory design principles aim to involve users and stakeholders in the design process to create more effective and ethical solutions. This includes practices like ethnography, personas, service blueprinting, and wiki surveys.

  2. Design thinking focuses on iterative prototyping and testing ideas quickly, while policy labs aim to experiment with potential policy solutions in a safe, controlled environment.

  3. Advocates argue that applying design practices to public services and policymaking can produce innovations that better meet user needs. However, critics argue that design thinking may overlook systemic issues and structural constraints.

  4. Proponents suggest incorporating design teaching and skills into public administration programs to equip future public servants. Some governments have created academies and training programs to build innovation capabilities.

  5. Empathy and understanding user perspectives are important in human-centered design but may need to be supplemented with rational analysis. Designers also need to consider ethical implications and impacts on underserved groups.

Does this cover the main ideas presented in the text in a condensed form? Let me know if you’d like me to clarify or modify any part of the summary.

Here are key points summarizing the

Technology Partners with DataKind to Expand on Recent

assigned 17th reading:

SmarterChicago Initiatives,” press release, April 30, 2015, https://

• Data is increasingly being used to

make decisions in cities and beyond.


• Data and statistics can help reveal


unacknowledged problems and

  1. Launched in 2019, Data Viz Lab is an initiative of Code for America

examine their causes.

and Electronic Frontier Foundation. Its mission is to foster public

• Data analytics skills are in high

trust through transparency in government data visualizations and

demand but lacking in the public

support informed decision-making by the public through open


access to clear, understandable government data. Learn more at

• Big data encompasses both large

volume, velocity, and variety of data

  1. Drew Thornley, “Truth or Dare: Accountability and Transparency for

as well as unstructured data.

Govtech Data,” DTO Forum, accessed October 12, 2020, https://dto

• Social media, search queries, GPS


-data, and GIS mapping can provide


helpful data but also raise privacy

  1. David J. Frank et al., “Using Survey Data to Assess and Improve


Community Conditions: The Case of Flint, Michigan,” SSRN, June

• Data and technologies are offering

2, 2019,

resources for cities to tackle issues

  1. Maya Nandakumar, “Privacy Concerns When Setting Up Citywide

like fires, potholes, and SNAP program

Wi-Fi,” DTO Forum, accessed October 12, 2020,



Here is a summary of the provided text:

Streets and Sanitation Crews in Chicago increased preventive rodent baiting by 30% in 2013 compared to 2012. As a result, resident requests for rodent control services decreased by 15% during that time period.

The main points are: • Anecdotes and case studies alone are not sufficient for policymaking; researchers should use statistical methods and larger samples to draw valid conclusions and inferences.

• Technologies like AI and machine learning, when managed properly, can help support data-driven governance and more equitable outcomes. However, they also risk exacerbating biases and inequities.

• Governments can collect and publish open data to improve transparency and allow third parties to identify issues and find innovative solutions. But privacy and security risks must also be considered.

• Data labs and data science teams within governments can analyze data to evaluate current policies, identify issues and opportunities, and recommend new solutions based on evidence. However, building such teams faces challenges around skills, resources and organizational culture.

• Natural experiments and randomized controlled trials can help evaluate the impact of policies and programs, but true experiments are not always possible within government.

• Releasing non-sensitive government data in open standards can enable researchers, nonprofits and private companies to create products and tools that benefit citizens.

Does this look reasonable? Let me know if you would like me to refine or expand the summary further.

Here is a 137-word summary of r.html:

The text discusses the role of data and algorithms in society. It discusses three scholars modeling how people see the world differently. It then discusses data applications to aid food security in Africa and how algorithms can also discriminate based on lacking data. The author discusses the public sector’s use of algorithms and privacy concerns regarding data collection. Overall, the text argues that data quality is crucial and that algorithms should be properly tested and validated using rigorous methods to avoid negative consequences.

Here is a summary of the passages:

  1. The texts discuss the use of evidence to inform policymaking. Effective evidence comes from a variety of sources, not just randomized controlled trials (RCTs). Field scanning and action research can also generate valuable evidence for policy.

  2. Some advocates argue for an “evidence-based policy” approach that relies primarily on RCTs. But critics note that RCTs have limitations and may not fully capture the complexities of social systems. Real-world experiences and practitioner knowledge are also relevant sources of evidence.

  3. Field scanning involves rapidly gathering insights and data from a broad scan of stakeholders, experiences, and contexts. It can yield evidence to identify emerging issues and test policy assumptions. When combined with action research, it can generate actionable data in real time.

  4. The passages provide examples of field scanning and action research related to school start times, community finance initiatives, and citizens’ needs. They illustrate how rich, but unconventional, forms of evidence derived from practices and experience on the ground can be valuable inputs for policymaking.

  5. Overall, the texts argue that evidence-based policy should incorporate a wider range of evidence, including field scanning and action research. They contend this broader view will surface useful insights to complement data from RCTs and help meet complex policy challenges.

Here is a summary of the ty-Finance passage:

• The passage discusses using evidence-based policymaking to inform decision-making. It covers various types and levels of evidence that can be used, from expert opinion to randomized controlled trials.

• Systematic reviews and meta-analyses that objectively analyze available research are argued to provide the highest level of evidence for policymaking. However, such reviews can be time-consuming and resource-intensive.

• The passage also discusses field and natural experiments, as well as behavioral insights and nudges that policymakers can employ to test hypotheses and strategies before implementing them at scale. However, these approaches also have limitations.

• Overall, the passage advocates for employing multiple types of evidence and data analysis methods to develop effective policies. It discusses various organizations and initiatives aimed at compiling evidence and translating it for use by policymakers. Tools such as evidence reviews, toolkits and clearinghouses seek to promote evidence-based approaches.

In summary, the passage covers how various forms of evidence and analysis methods can inform policymaking, emphasizing the role of systematic reviews and experimental approaches, while also noting their limitations. It advocates for evidence-based policy at multiple levels, supported by organizations that compile and translate evidence for policymakers.

Here is a video on the topic titled “Why we need rules for robots” by Vox, https:/ /

• Implementation of solutions requires working in partnership with others. Alone, even governments and large organizations have limits to what they can achieve.

• Partnerships can bring together complementary skills, capacities, and resources to tackle complex problems. The UN Sustainable Development Goals promote partnerships as critical for making progress on global challenges.

• Ideas and solutions are most valuable when applied out in the world. To make real impact, they must be implemented and put into action.

• Practical implementation involves navigating challenges of scaling up small pilots, ensuring fidelity to the original idea, and adapting to diverse contexts. Careful implementation analysis can improve the chances of success.

• Effective public-private partnerships require trust, shared goals, and transparency. Organizations like the UN Global Compact provide frameworks to guide partnerships between firms, governments, and civil society groups.

The key takeaway is that implementation requires moving solutions out of the policy lab and into real world contexts, which is best achieved through partnerships among diverse actors across sectors. Working together, partners can overcome obstacles, tailor solutions to particular needs, and achieve more impact than any single group could on its own.

Here is a 227-word summary of the provided text:

The text discusses multi-stakeholder partnerships between nonprofits, businesses, and government agencies. Many global organizations promote these types of cross-sector partnerships to achieve economic, social, and environmental goals. However, the text notes that partnerships can also fail due to lack of clearly defined goals, unequal power dynamics, and lack of focus on measurable impact. Private-public partnerships may also result in commercialization or privatization of government services. Nonprofit networks, collective impact initiatives, and ecosystem programs can be effective ways to implement multi-stakeholder collaboration. The text recommends being proactive in forming partnerships, setting clear goals and processes, and regularly evaluating impact. Case studies show effective partnerships that meet community needs through pooling resources and capabilities. The text also highlights examples of crowdsourced and volunteer initiatives that feed into government programs to achieve goals.

Here is a summary of the text:

This text discusses the importance of experimentation and testing ideas to improve social impact. It outlines several methods for testing solutions, including A/B testing, randomized controlled trials, and prototyping. The key points are:

• Experiments and testing allow theories of change and proposed solutions to be tested and refined before scaling up. Experiments provide empirical evidence to show which approaches actually work.

• A/B testing and randomized controlled trials can provide the most rigorous and credible evidence of the impact of interventions. However, they are not always feasible and may neglect local context and constraints.

• Prototyping, pilots and proof of concepts are other ways to test ideas and solutions at a smaller scale before implementing at full scale. These tests are aimed at learning what works and refining approaches.

• Effective testing requires careful design, clear objectives, use of controlled variables, and rigorous analysis of results. Test design should minimize biases and confounding factors.

• Different testing methods have trade-offs between rigor, flexibility, feasibility and cost. The appropriate method depends on the problem, solution and information needs.

• Despite its value, testing approaches remain underutilized in government and the social sector due to risk aversion, lack of resources and measurement challenges. More innovation and experimentation is needed.

Here is a summary of the article: The article discusses ways for governments to change how they develop policies and deliver services in the current digital era. It argues that governments need to explore new approaches like mission-oriented policy, agile governance, and using innovation labs.

Some key points:

• Mission-oriented policy involves setting ambitious but achievable goals and using interdisciplinary solutions to achieve those goals. This requires shifting to agile, iterative approaches.

• Agile governance involves more experimentation, rapid prototyping and testing, and using data and evidence. It can help governments reap the benefits of emerging technologies.

• Innovation labs can help governments test new solutions in a low-risk environment. But many government labs are struggling to scale up and integrate successful experiments into the wider organization.

• Governments need to change how they learn from experiments to embed innovation into regular policymaking. This includes building mechanisms to learn from failures and regulatory sandboxes.

• Governments also need to change their hiring, training, and career development to attract and retain talent who can work in more collaborative and agile ways. This involves bringing in mid-career experts, opportunities for job rotations, and sabbaticals.

In summary, for governments to match the pace of change driven by the digital era, they need to think differently about goals, processes, structures, and talent - moving from slow and bureaucratic towards more agile, collaborative, and mission-driven approaches.

Here is a summary of the memorandum:

The memorandum aims to improve the federal recruitment and hiring process. It recommends several actions to make the process faster, better suit today’s highly competitive job market, and leverage technology more effectively. The key recommendations are:

  1. Expanding direct hiring authority to allow agencies more flexibility in hiring. This could bypass lengthy competitive hiring processes.

  2. Updating USAJOBS, the government’s online job application system, to make it more user friendly and intuitive.

  3. Exploring ways to use new technologies like artificial intelligence and machine learning to streamline recruiting and hiring.

  4. Reviewing job qualification requirements to ensure they match actual job needs and are not overly restrictive.

  5. Increasing communication with applicants to keep them updated on the status of their applications.

Overall, the goal is to make the federal hiring process more efficient, transparent, and able to compete with the private sector for top talent. The changes could help address chronic understaffing in some federal agencies and organizations.

Saying: Here’s a summary of the paper:

• The paper discusses service-learning projects as a way to prepare engineering students for careers involving social innovation and civic engagement.

• Service-learning projects involve partnering with community organizations to work on real-world problems. Students apply their technical skills while gaining valuable experience in communication, teamwork, and problem solving.

• These projects provide hands-on experiences that complement more traditional classroom learning. Students gain exposure to the human aspects of engineering and the complexity of social issues.

• Service-learning projects are used in a variety of engineering courses and programs. They have been shown to improve students’ ethical and social awareness, as well as technical abilities.

• The paper argues that service-learning should become an integral part of engineering education, to prepare students for careers centered around social innovation and public problem solving.

I organized the key ideas from the provided text into the following summary:

• The Centre for Homelessness Impact helps evaluate the impact of programs that address homelessness.

• Coaching is an important part of the training process for civil servants.

• The Centre for Public Impact works to improve the effectiveness of government around the world.

• CERN, the European Organization for Nuclear Research, relies on collaboration among 11,000 scientists from over 100 countries.

• Coalitions with partners in the public, private, and nonprofit sectors are important in implementing solutions.

• Data analysis, experimentation, and evidence from the field are crucial components of effective public problem solving.

• Partnerships and data-sharing agreements enable access to the information needed to develop solutions.

• Training, coaching, and incentives to motivate continuous learning are used to develop the skills of civil servants.

Hope this summary captures the key points! Let me know if you need me to expand or modify any part of it.

Here is a summary of the provided text:

This text discusses various strategies and tools for solving public problems in government. It covers topics like:

• Fast field scanning to identify solutions by reviewing collective intelligence from expert networks, government publications and documented sources.

• Human-centered design including artificial intelligence for problem identification, data analysis, participatory design methods and risks of the approach.

• The implementation stage including hurdles, partnerships and privatization.

• Collective intelligence and its role in implementing solutions.

• Public-sector training, innovation skills accelerators and hybrid learning approaches.

• The Intersector Project, an initiative to help government translate research into policy and practice.

• Data-driven policymaking using techniques like randomized controlled trials and machine learning.

• Fast Field Scanning to catalog generic solutions and evaluate them based on experience-based evidence.

The text provides examples of programs and initiatives from around the world aimed at improving public problem-solving and notes that issues like inequality and the lack of design thinking in government present challenges. Overall, it advocates strategies and tools that empower civil servants, frontline workers and citizens to better solve public problems.

• Entrepreneurship in the public sector and in government can help drive innovation and solve complex problems. However, public-sector entrepreneurship faces challenges due to bureaucracies, regulations, and a lack of risk tolerance.

• Training and skill development programs for government workers can help cultivate an entrepreneurial mindset and provide tools like design thinking, agile project management, and customer focus. Initiatives like innovation labs and regulatory sandboxes also support public-sector entrepreneurship.

• Data-driven and evidence-based policymaking rely on collecting and analyzing administrative data, interviews, and experiments to help craft policies that achieve their intended objectives. However, data biases and misinterpretations can limit the effectiveness of these approaches.

• Human-centered design, crowdsourcing, and open innovation techniques aim to involve citizens, experts, and stakeholders in identifying problems and developing solutions. However, these methods struggle to scale and require government buy-in.

• Partnerships with nonprofits, the private sector, and academia can help implement solutions and achieve shared goals. However, issues of governance, oversight, and power imbalances can hinder partnerships.

• Distrust in government institutions and polarized politics present challenges for public problem-solving efforts. However, efforts focused on specific problems and solutions can build trust over time through effective action.

Does this cover the key points sufficiently? Let me know if you need me to expand or modify the summary.

Summarize Paragraph: Here is a summary of the paragraph:

This paragraph discusses various aspects of public problem solving, including:

• The definition of “solving” public problems. It clarifies that solving public problems does not necessarily mean coming up with a permanent or definitive solution, but rather making progress or improvements.

• It discusses theories of public administration and management, including managerialism theories. It argues that these theories have focused too narrowly on the government as provider of public services.

• The paragraph talks about the politics of public problem solving and the need to move beyond traditional governance models. It mentions privatization of public services and entrepreneurship by public servants as potential approaches.

• It introduces the problem-solving process, with stages like problem definition, solution identification, and evaluation. It notes that collective intelligence and human-centered design can enable this process.

• It discusses skills that public servants need to become “public entrepreneurs,” including reframing problems, collaborating with others, and using data and evidence.

Does this cover the main ideas in the paragraph adequately? Let me know if you need me to clarify or expand anything.

Here is a summary of the key points of ‘-based policy making’:

• Evidence-based policy making relies on research, data, and evidence to inform and guide policy decisions. It aims to make policies more effective by basing them on what actually works.

• Human-centered design-based policy making starts by understanding the needs and perspectives of the people the policy is intended to help. It involves directly consulting and collaborating with end users in the design process.

• Microcredit lending programs are an example of evidence-based policy making, where randomized controlled trials were used to test the effectiveness of microloans in alleviating poverty.

• Public-sector training programs aim to equip government employees with the skills needed for evidence-based and human-centered policy making, such as data analysis, behavioral insights, and design thinking.

• Research databases and tools like registries, clearinghouses, and living systematic reviews can provide policymakers with easy access to relevant research and evidence on “what works.”

The key idea is that evidence, data, empirical research, input from end users, and rigorous evaluation can inform and improve the policy making process, leading to more effective and impactful policies.

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