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Bill Fulkerson

Why Positive Thinking Won't Get You Out of Poverty | naked capitalism - 0 views

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    "n a recent article in the New York Times, the development economist Seema Jayachandran discusses three studies that used Randomised Controlled Trials (or RCTs) to understand the benefits of enhancing the self-worth of poor people. Despite wide differences in context, all the cases explore the viability of 'modest interventions' to 'instill hope' in marginalised communities, concluding that 'remarkable improvements' in the quest for poverty reduction are possible."
Bill Fulkerson

Behind The Magical Thinking | Center for a New American Security - 0 views

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    "Drones' greatest attraction for the national security world is that they create options where there were none - or none at a cost policymakers feel comfortable with. With a public tired of large scale military interventions, drones and other approaches that gave the U.S. options to study or intervene against security challenges in a low profile, low risk way fit perfectly into the Obama administration's comfort zone. These platforms came to symbolize and enable much of the Obama national security team's approach. But the enthusiastic embrace of drone technology, particularly in counterterrorism, left some former Obama officials questioning whether they'd been clutching a Pandora's box they should have opened more deliberately. Overall, such "light footprint" strategies generate enduring disagreements about their efficacy, risk, and oversight. Unlike any other recent military platform, drones in particular engender strong emotion - hope, revulsion, overconfidence, demonization - and magical thinking, even among those who know them best. And the attributes that make them so compelling - that they are precise, remote, sensing, and unmanned - may sometimes be too reassuring."
Bill Fulkerson

New AI system will help us discover the most effective behaviour change strategies - 0 views

  • hanging people’s behaviour is key to tackling the world’s health, social and environmental problems, such as obesity, sustainable living and cybersecurity. To change behaviour, though, we need to know what works, for whom, where and how. But research is generated far faster than humans can access and use it. A search on Google Scholar for “behaviour change” produced over 60,000 hits for the first six months of 2018. Evidence on behaviour change interventions is also messy. Different terms are often used to describe similar things – “physical activity” and “exercise”, for example. This means different people can often mean different things when discussing or researching behaviour change. This lack of shared vocabulary limits our progress in discovering what interventions work best.
Bill Fulkerson

Gambling research: The 'fun' can stop with unemployment, ill-health and even death - 0 views

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    High levels of gambling are associated with a 37% increase in mortality, according to a new study, which reveals that the top 1% of gamblers surveyed spent 58% of their income and one in ten are spending 8% on the habit. Published today [4 Feb] in Nature Human Behavior, the study led by Dr. Naomi Muggleton, of Oxford's Department of Social Policy and Intervention, highlights the financial damage, negative lifestyles and health of gamblers, who can move from 'social' to high-level gambling in months.
Bill Fulkerson

Systems | Free Full-Text | Developing a Preliminary Causal Loop Diagram for Understandi... - 0 views

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    COVID-19 is a wicked problem for policy makers internationally as the complexity of the pandemic transcends health, environment, social and economic boundaries. Many countries are focusing on two key responses, namely virus containment and financial measures, but fail to recognise other aspects. The systems approach, however, enables policy makers to design the most effective strategies and reduce the unintended consequences. To achieve fundamental change, it is imperative to firstly identify the "right" interventions (leverage points) and implement additional measures to reduce negative consequences. To do so, a preliminary causal loop diagram of the COVID-19 pandemic was designed to explore its influence on socio-economic systems. In order to transcend the "wait and see" approach, and create an adaptive and resilient system, governments need to consider "deep" leverage points that can be realistically maintained over the long-term and cause a fundamental change, rather than focusing on "shallow" leverage points that are relatively easy to implement but do not result in significant systemic change
Steve Bosserman

The Next System Project, Reconsidered | Grassroots Economic Organizing - 0 views

  • That is, our situation is much like that of colonized peoples: we can vote for our rulers, but cannot control them; our voices, at times, can be expressed, but can almost always be dismissed or over-ruled; our tax revenues mainly fund military interventions and corporate interests, leaving us to battle with each other over tiny trickle-downs. We are, in effect, walking in the dreams and demands of our captors, doomed to sit like docile passengers, who can only watch as the USA train goes wherever the MIC takes it. Despite the incessant rhetoric of “freedom”, we as a people are unwilling and indeed, unknowing, captives.
Steve Bosserman

The Conversation About Basic Income is a Mess. Here's How to Make Sense of It. | naked ... - 0 views

  • UBI is in fact not a single proposal. It’s a field of proposals that’s perhaps better thought of as a philosophical intervention, a new conception of macro-economic and political structure. It’s unusual to argue wholeheartedly against representative government, taxation or universal suffrage, while it is common to disagree on which party should govern, whether taxes should be raised or cut, and particular elements of voting procedure. In the same way, we shouldn’t argue all-out for or against UBI but instead inspect the make-up of each approach to it – that’s where we can find not only meaningful debate, but also possibilities for working out what we might actually want.
  • The most important distinguishing feature between the different iterations of UBI is where the funding comes from. Wrapped up in this are ancillary questions: what would a UBI replace, compensate for, or complement in the rest of the economy? What would the knock-on effects be for social welfare and the government’s responsibility to its citizens? Who gets the money is another question worth looking at (just how ‘universal’ is the income?), as is its amount and regularity.
Steve Bosserman

How We Made AI As Racist and Sexist As Humans - 0 views

  • Artificial intelligence may have cracked the code on certain tasks that typically require human smarts, but in order to learn, these algorithms need vast quantities of data that humans have produced. They hoover up that information, rummage around in search of commonalities and correlations, and then offer a classification or prediction (whether that lesion is cancerous, whether you’ll default on your loan) based on the patterns they detect. Yet they’re only as clever as the data they’re trained on, which means that our limitations—our biases, our blind spots, our inattention—become theirs as well.
  • The majority of AI systems used in commercial applications—the ones that mediate our access to services like jobs, credit, and loans— are proprietary, their algorithms and training data kept hidden from public view. That makes it exceptionally difficult for an individual to interrogate the decisions of a machine or to know when an algorithm, trained on historical examples checkered by human bias, is stacked against them. And forget about trying to prove that AI systems may be violating human rights legislation.
  • Data is essential to the operation of an AI system. And the more complicated the system—the more layers in the neural nets, to translate speech or identify faces or calculate the likelihood someone defaults on a loan—the more data must be collected.
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  • But not everyone will be equally represented in that data.
  • And sometimes, even when ample data exists, those who build the training sets don’t take deliberate measures to ensure its diversity
  • The power of the system is its “ability to recognize that correlations occur between gender and professions,” says Kathryn Hume. “The downside is that there’s no intentionality behind the system—it’s just math picking up on correlations. It doesn’t know this is a sensitive issue.” There’s a tension between the futuristic and the archaic at play in this technology. AI is evolving much more rapidly than the data it has to work with, so it’s destined not just to reflect and replicate biases but also to prolong and reinforce them.
  • Accordingly, groups that have been the target of systemic discrimination by institutions that include police forces and courts don’t fare any better when judgment is handed over to a machine.
  • A growing field of research, in fact, now looks to apply algorithmic solutions to the problems of algorithmic bias.
  • Still, algorithmic interventions only do so much; addressing bias also demands diversity in the programmers who are training machines in the first place.
  • A growing awareness of algorithmic bias isn’t only a chance to intervene in our approaches to building AI systems. It’s an opportunity to interrogate why the data we’ve created looks like this and what prejudices continue to shape a society that allows these patterns in the data to emerge.
  • Of course, there’s another solution, elegant in its simplicity and fundamentally fair: get better data.
Steve Bosserman

Spend a Dollar on Drug Treatment, and Save More on Crime Reduction - The New York Times - 0 views

  • For a dollar spent on treatment, up to three are saved in crime reduction. An earlier study found that interventions to address substance use disorders save more in reduced crime than they save in reduced health care spending.
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