science

The power of prediction markets

Curated on October 20, 2016August 3, 2018 by Stefaan Verhulst

Adam Mann in Nature: “It was a great way to mix science with gambling, says Anna Dreber. The year was 2012, and an international group of psychologists had just launched the ‘Reproducibility Project’ — an effort to repeat dozens of psychology experiments to see which held up¹. “So we thought it would be fantastic to bet on the outcome,” says Dreber, who leads a team of behavioural economists at the Stockholm School of Economics.

In particular, her team wanted to see whether scientists could make good use of prediction markets: mini Wall Streets in which participants buy and sell ‘shares’ in a future event at a price that reflects their collective wisdom about the chance of the event happening. As a control, Dreber and her colleagues first asked a group of psychologists to estimate the odds of replication for each study on the project’s list. Then the researchers set up a prediction market for each study, and gave the same psychologists US$100 apiece to invest.

When the Reproducibility Project revealed last year that it had been able to replicate fewer than half of the studies examined², Dreber found that her experts hadn’t done much better than chance with their individual predictions. But working collectively through the markets, they had correctly guessed the outcome 71% of the time³.

Experiments such as this are a testament to the power of prediction markets to turn individuals’ guesses into forecasts of sometimes startling accuracy. That uncanny ability ensures that during every US presidential election, voters avidly follow the standings for their favoured candidates on exchanges such as Betfair and the Iowa Electronic Markets (IEM). But prediction markets are increasingly being used to make forecasts of all kinds, on everything from the outcomes of sporting events to the results of business decisions. Advocates maintain that they allow people to aggregate information without the biases that plague traditional forecasting methods, such as polls or expert analysis….

Prediction markets have also had some high-profile misfires, however — such as giving the odds of a Brexit ‘stay’ vote as 85% on the day of the referendum, 23 June. (UK citizens in fact narrowly voted to leave the European Union.) And prediction markets lagged well behind conventional polls in predicting that Donald Trump would become the 2016 Republican nominee for US president.

Such examples have inspired academics to probe prediction markets. Why do they work as well as they do? What are their limits, and why do their predictions sometimes fail?…(More)”

Nudging Health

Curated on October 18, 2016August 3, 2018 by Stefaan Verhulst

Book edited by I. Glenn Cohen, Holly Fernandez Lynch, and Christopher T. Robertson: “Behavioral nudges are everywhere: calorie counts on menus, automated text reminders to encourage medication adherence, a reminder bell when a driver’s seatbelt isn’t fastened. Designed to help people make better health choices, these reminders have become so commonplace that they often go unnoticed. In Nudging Health, forty-five experts in behavioral science and health policy from across academia, government, and private industry come together to explore whether and how these tools are effective in improving health outcomes.

Behavioral science has swept the fields of economics and law through the study of nudges, cognitive biases, and decisional heuristics—but it has only recently begun to impact the conversation on health care.Nudging Health wrestles with some of the thorny philosophical issues, legal limits, and conceptual questions raised by behavioral science as applied to health law and policy. The volume frames the fundamental issues surrounding health nudges by addressing ethical questions. Does cost-sharing for health expenditures cause patients to make poor decisions? Is it right to make it difficult for people to opt out of having their organs harvested for donation when they die? Are behavioral nudges paternalistic? The contributors examine specific applications of behavioral science, including efforts to address health care costs, improve vaccination rates, and encourage better decision-making by physicians. They wrestle with questions regarding the doctor-patient relationship and defaults in healthcare while engaging with larger, timely questions of healthcare reform.

Nudging Health is the first multi-voiced assessment of behavioral economics and health law to span such a wide array of issues—from the Affordable Care Act to prescription drugs….(More)”

Open Innovation: Practices to Engage Citizens and Effectively Implement Federal Initiatives

Curated on October 14, 2016August 3, 2018 by Stefaan Verhulst

United States Government Accountability Office: “Open innovation involves using various tools and approaches to harness the ideas, expertise, and resources of those outside an organization to address an issue or achieve specific goals. GAO found that federal agencies have frequently used five open innovation strategies to collaborate with citizens and external stakeholders, and encourage their participation in agency initiatives.

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GAO identified seven practices that agencies can use to effectively implement initiatives that involve the use of these strategies:

Select the strategy appropriate for the purpose of engaging the public and the agency’s capabilities.
Clearly define specific goals and performance measures for the initiative.
Identify and engage external stakeholders and potential partners.
Develop plans for implementing the initiative and recruiting participants.
Engage participants and partners while implementing the initiative.
Collect and assess relevant data and report results.
Sustain communities of interested partners and participants.

Aspects of these practices are illustrated by the 15 open innovation initiatives GAO reviewed at six selected agencies: the Departments of Energy, Health and Human Services, Housing and Urban Development, and Transportation (DOT); the Environmental Protection Agency; and the National Aeronautics and Space Administration (NASA).

For example:

• With the Asteroid Data Hunter challenge, NASA used a challenge and citizen science effort, beginning in 2014, to improve the accuracy of its asteroid detection program and develop an application for citizen scientists.

• Since 2009, DOT’s Federal Highway Administration has used an ideation initiative called Every Day Counts to identify innovations to improve highway project delivery. Teams of federal, state, local, and industry experts then implement the ideas chosen through this process….(More)”

Remote Data Collection: Three Ways to Rethink How You Collect Data in the Field

Curated on October 10, 2016October 24, 2018 by Stefaan Verhulst

Magpi : “As mobile devices have gotten less and less expensive – and as millions worldwide have climbed out of poverty – it’s become quite common to see a mobile phone in every person’s hand, or at least in every family, and this means that we can utilize an additional approach to data collection that were simply not possible before….

In our Remote Data Collection Guide, we discuss these new technologies and the:

Key benefits of remote data collection in each of three different situations.
The direct impact of remote data collection on reducing the cost of your efforts.
How to start the process of choosing the right option for your needs….(More)”

USGS expands sensor network to track monster hurricane

Curated on October 10, 2016August 3, 2018 by Stefaan Verhulst

Mark Rockwell at FCW: “The internet of things is tracking Hurricane Matthew. As the monster storm draws a bead on the south Atlantic coast after wreaking havoc in the Caribbean, its impact will be measured by a sensor network deployed by the U.S. Geological Survey.

USGS hurricane response crews are busy installing two kinds of sensors in areas across four states where the agency expects the storm to hit hardest. The information the sensors collect will help with disaster recovery efforts and critical weather forecasts for the National Weather Service and the Federal Emergency Management Agency.

As is the case with most things these days, the storm will be tracked online.

The information collected will be distributed live on the USGS Flood Viewer to help federal and state officials gauge the extent and the storm’s damage as it passes through each area.

FEMA, which tasked USGS with the sensor distribution, is also talking with other federal and state officials further up the Atlantic coastline about whether the equipment is needed there. Recent forecasts call for Matthew to take a sharp easterly turn and head out to sea as it reaches the North Carolina coast.

USGS crews are in installing storm-surge sensors at key sites along the coasts of North Carolina, South Carolina, Georgia, and Florida in anticipation of the storm, said Brian McCallum, associate director for data at the USGS South Atlantic Water Science Center.

In all, USGS is deploying more than 300 additional weather and condition sensors, he told FCW in an interview on Oct. 5.

The devices come in two varieties. The first are 280 storm surge sensors, set out in protective steel tubes lashed to piers, bridges and other solid structures in the storm’s projected path. The low-cost devices will provide the highest density of storm data, such as depth and duration of the storm surge, McCallum said. The devices won’t communicate their information in real time, however; McCallum said USGS crews will come in behind the storm to upload the sensor data to the Internet.

The second set of sensors, however, could be thought of as the storm’s “live tweets.” USGS is installing 25 rapid-deployment gauges to augment its existing collection of sensors and fill in gaps along the coast….(More)”

Data Ethics: Investing Wisely in Data at Scale

Curated on October 6, 2016August 3, 2018 by Stefaan Verhulst

Report by David Robinson & Miranda Bogen prepared for the MacArthur and Ford Foundations: ““Data at scale” — digital information collected, stored and used in ways that are newly feasible — opens new avenues for philanthropic investment. At the same time, projects that leverage data at scale create new risks that are not addressed by existing regulatory, legal and best practice frameworks. Data-oriented projects funded by major foundations are a natural proving ground for the ethical principles and controls that should guide the ethical treatment of data in the social sector and beyond.

This project is an initial effort to map the ways that data at scale may pose risks to philanthropic priorities and beneficiaries, for grantmakers at major foundations, and draws from desk research and unstructured interviews with key individuals involved in the grantmaking enterprise at major U.S. foundations. The resulting report was prepared at the joint request of the MacArthur and Ford Foundations.

Grantmakers are exploring data at scale, but currently have poor visibility into its benefits and risks. Rapid technological change, the scarcity of data science expertise, limited training and resources, and a lack of clear guideposts around emergent risks all contribute to this problem.

Funders have important opportunities to invest in, learn from, and innovate around data-intensive projects, in concert with their grantees. Grantmakers should not treat the new ethical risks of data at scale as a barrier to investment, but these risks also must not become a blind spot that threatens the success and effectiveness of philanthropic projects. Those working with data at scale in the philanthropic context have much to learn: throughout our conversations with stakeholders, we heard consistently that grantmakers and grantees lack baseline knowledge on using data at scale, and many said that they are unsure how to make better informed decisions, both about data’s benefits and about its risks. Existing frameworks address many risks introduced by data-intensive grantmaking, but leave some major gaps. In particular, we found that:

Some new data-intensive research projects involve meaningful risk to vulnerable populations, but are not covered by existing human subjects regimes, and lack a structured way to consider these risks. In the philanthropic and public sector, human subject review is not always required and program officers, researchers, and implementers do not yet have a shared standard by which to evaluate ethical implications of using public or existing data, which is often exempt from human subjects review.
Social sector projects often depend on data that reflects patterns of bias or discrimination against vulnerable groups, and face a challenge of how to avoid reinforcing existing disparities. Automated decisions can absorb and sanitize bias from input data, and responsibly funding or evaluating statistical models in data-intensive projects increasingly demands advanced mathematical literacy which foundations lack.
Both data and the capacity to analyze it are being concentrated in the private sector, which could marginalize academic and civil society actors.Some individuals and organizations have begun to call attention to these issues and create their own trainings, guidelines, and policies — but ad hoc solutions can only accomplish so much.

To address these and other challenges, we’ve identified eight key questions that program staff and grantees need to consider in data-intensive work:

For a given project, what data should be collected, and who should have access to it?
How can projects decide when more data will help — and when it won’t?
How can grantmakers best manage the reputational risk of data-oriented projects that may be at a frontier of social acceptability?
When concerns are recognized with respect to a data-intensive grant, how will those concerns get aired and addressed?
How can funders and grantees gain the insight they need in order to critique other institutions’ use of data at scale?
How can the social sector respond to the unique leverage and power that large technology companies are developing through their accumulation of data and data-related expertise?
How should foundations and nonprofits handle their own data?
How can foundations begin to make the needed long term investments in training and capacity?

Newly emergent ethical issues inherent in using data at scale point to the need for both a broader understanding of the possibilities and challenges of using data in the philanthropic context as well as conscientious treatment of data ethics issues. Major foundations can play a meaningful role in building a broader understanding of these possibilities and challenges, and they can set a positive example in creating space for open and candid reflection on these issues. To those ends, we recommend that funders:…(More)”

Playful Cities: Crowdsourcing Urban Happiness with Web Games

Curated on October 4, 2016August 3, 2018 by Stefaan Verhulst

Daniele Quercia in Built Environment: “It is well known that the layout and configuration of urban space plugs directly into our sense of community wellbeing. The twentieth-century city planner Kevin Lynch showed that a city’s dwellers create their own personal ‘mental maps’ of the city based on features such as the routes they use and the areas they visit. Maps that are easy to remember and navigate bring comfort and ultimately contribute to people’s wellbeing. Unfortunately, traditional social science experiments (including those used to capture mental maps) take time, are costly, and cannot be conducted at city scale. This paper describes how, starting in mid-2012, a team of researchers from a variety of disciplines set about tackling these issues. They were able to translate a few traditional experiments into 1-minute ‘web games with a purpose’. This article describes those games, the main insights they offer, their theoretical implications for urban planning, and their practical implications for improvements in navigation technologies….(More)”

Europe Should Promote Data for Social Good

Curated on October 3, 2016August 3, 2018 by Stefaan Verhulst

Daniel Castro at Center for Data Innovation: “Changing demographics in Europe are creating enormous challenges for the European Union (EU) and its member states. The population is getting older, putting strain on the healthcare and welfare systems. Many young people are struggling to find work as economies recover from the 2008 financial crisis. Europe is facing a swell in immigration, increasingly from war-torn Syria, and governments are finding it difficult to integrate refugees and other migrants into society.These pressures have already propelled permanent changes to the EU. This summer, a slim majority of British voters chose to leave the Union, and many of those in favor of Brexit cited immigration as a motive for their vote.

Europe needs to find solutions to these challenges. Fortunately, advances in data-driven innovation that have helped businesses boost performance can also create significant social benefits. They can support EU policy priorities for social protection and inclusion by better informing policy and program design, improving service delivery, and spurring social innovations. While some governments, nonprofit organizations, universities, and companies are using data-driven insights and technologies to support disadvantaged populations, including unemployed workers, young people, older adults, and migrants, progress has been uneven across the EU due to resource constraints, digital inequality, and restrictive data regulations. renewed European commitment to using data for social good is needed to address these challenges.

This report examines how the EU, member-states, and the private sector are using data to support social inclusion and protection. Examples include programs for employment and labor-market inclusion, youth employment and education, care for older adults, and social services for migrants and refugees. It also identifies the barriers that prevent European countries from fully capitalizing on opportunities to use data for social good. Finally, it proposes a number of actions policymakers in the EU should take to enable the public and private sectors to more effectively tackle the social challenges of a changing Europe through data-driven innovation. Policymakers should:

Support the collection and use of relevant, timely data on the populations they seek to better serve;
Participate in and fund cross-sector collaboration with data experts to make better use of data collected by governments and non-profit organizations working on social issues;
Focus government research funding on data analysis of social inequalities and require grant applicants to submit plans for data use and sharing;
Establish appropriate consent and sharing exemptions in data protection regulations for social science research; and
Revise EU regulations to accommodate social-service organizations and their institutional partners in exploring innovative uses of data….(More)”

Data governance: a Royal Society and British Academy project

Curated on September 30, 2016August 3, 2018 by Stefaan Verhulst

Call for Evidence from The British Academy and the Royal Society: “…The project seeks to make recommendations for cross-sectoral governance arrangements that can ensure the UK remains a world leader in this area. The project will draw on scholars and scientists from across disciplines and will look at current and historical case studies of data governance, and of broader technology governance, from a range of countries and sectors. It will seek to enable connected debate by creating common frameworks to move debates on data governance forward.

Background

It is essential to get the best possible environment for the safe and rapid use of data in order to enhance UK’s wellbeing, security and economic growth. The UK has world class academic expertise in data science, in ethics and aspects other of governance; and it has a rapidly growing tech sector and there is a real opportunity for the UK to lead internationally in creating insights and mechanisms for enabling the new data sciences to benefit society.

While there are substantial arrangements in place for the safe use of data in the UK, these inevitably were designed early in the days of information technology and tend to rest on outdated notions of privacy and consent. In addition, newer considerations such as statistical stereotyping and bias in datasets, and implications for the freedom of choice, autonomy and equality of opportunity of individuals, come to the fore in this new technological context, as do transparency, accountability and openness of decision making.

Terms of Reference

The project seeks to:

Identify the communities with interests in the governance of data and its uses, but which may be considering these issues in different contexts and with varied aims and assumptions, in order to facilitate dialogue between these communities. These include academia, industry and the public sector.
Clarify where there are connections between different debates, identifying shared issues and common questions, and help to develop a common framework and shared language for debate.
Identify which social, ethical and governance challenges arise in the context of developments in data use.
Set out the public interests at stake in governance of data and its uses, and the relationships between them, and how the principles of responsible research and innovation (RRI) apply in the context of data use.
Make proposals for the UK to establish a sustained and flexible platform for debating issues of data governance, developing consensus about future legal and technical frameworks, and ensuring that learning and good practice spreads as fast as possible….(More)”

Measuring Scientific Impact Beyond Citation Counts

Curated on September 28, 2016August 3, 2018 by Stefaan Verhulst

Robert M. Patton, Christopher G. Stahl and Jack C. Wells at DLib Magazine: “Measuring scientific progress remains elusive. There is an intuitive understanding that, in general, science is progressing forward. New ideas and theories are formed, older ideas and theories are confirmed, rejected, or modified. Progress is made. But, questions such as how is it made, by whom, how broadly, or how quickly present significant challenges. Historically, scientific publications reference other publications if the former publication in some way shaped the work that was performed. In other words, one publication “impacted” a latter one. The implication of this impact revolves around the intellectual content of the idea, theory, or conclusion that was formed. Several metrics such as h-index or journal impact factor (JIF) are often used as a means to assess whether an author, article, or journal creates an “impact” on science. The implied statement behind high values for such metrics is that the work must somehow be valuable to the community, which in turn implies that the author, article, or journal somehow has influenced the direction, development, or progress of what others in that field do. Unfortunately, the drive for increased publication revenue, research funding, or global recognition has lead to a variety of external factors completely unrelated to the quality of the work that can be used to manipulate key metric values. In addition, advancements in computing and data sciences field have further altered the meaning of impact on science.

The remainder of this paper will highlight recent advancements in both cultural and technological factors that now influence scientific impact as well as suggest new factors to be leveraged through full content analysis of publications….(More)”