Silicon Valley billionaires and anti-poverty activists don't have a lot in common, but in recent years they've joined forces around a shared enthusiasm: programs that guarantee a basic income. Tech entrepreneurs like Sam Altman, chief executive of OpenAI, have promoted direct cash transfers to low-income Americans as a way to cushion them from what the entrepreneurs anticipate could be widespread job losses caused by artificial intelligence. Some local politicians and community leaders, concerned about growing wealth inequality, have also put their faith in these stipends, known as unconditional cash or, in their most ambitious form, a universal basic income. Dozens of small pilot projects testing unconditional cash transfers have popped up in communities around the country, from Alaska to Stockton, Calif. Andrew Yang, an entrepreneur, put the idea of 1,000 monthly payments for all adults at the center of his 2020 presidential campaign.

Governments and humanitarian groups can use machine learning algorithms and mobile phone data to get aid to those who need it most during a humanitarian crisis, we found in new research. The simple idea behind this approach, as we explained in the journal Nature on March 16, 2022, is that wealthy people use phones differently from poor people. Their phone calls and text messages follow different patterns, and they use different data plans, for example. Machine learning algorithms--which are fancy tools for pattern recognition--can be trained to recognize those differences and infer whether a given mobile subscriber is wealthy or poor. As the COVID-19 pandemic spread in early 2020, our research team helped Togo's Ministry of Digital Economy and GiveDirectly, a nonprofit that sends cash to people living in poverty, turn this insight into a new type of aid program. First, we collected recent, reliable and representative data.

The Research Brief is a short take about interesting academic work. Governments and humanitarian groups can use machine learning algorithms and mobile phone data to get aid to those who need it most during a humanitarian crisis, we found in newly published research. The simple idea behind this approach is that wealthy people use phones differently from poor people. Their phone calls and text messages follow different patterns, and they use different data plans, for example. Machine learning algorithms – which are fancy tools for pattern recognition – can be trained to recognize those differences and infer whether a given mobile subscriber is wealthy or poor.

We introduce a new framework to model interactions among agents which seek to trade to minimize their risk with respect to some future outcome. We quantify this risk using the concept of risk measures from finance, and introduce a class of trade dynamics which allow agents to trade contracts contingent upon the future outcome. We then show that these trade dynamics exactly correspond to a variant of randomized coordinate descent. By extending the analysis of these coordinate descent methods to account for our more organic setting, we are able to show convergence rates for very general trade dynamics, showing that the market or network converges to a unique steady state. Applying these results to prediction markets, we expand on recent results by adding convergence rates and general aggregation properties. Finally, we illustrate the generality of our framework by applying it to agent interactions on a scale-free network.