Can an algorithm help solve political paralysis?

After meeting in person and virtually over six weekends between January and May, the participants recently published their final report of policy recommendations, which range from levying a tax on frequent air travelers to investing in low-carbon public transportation.

With a mathematician’s precision, Hennig explains how an algorithm he created generated the 110-person “mini public” from the U.K.’s population of 67 million. The process began by sending invitations to 30,000 households from the nation’s postal database. Hennig says completely random selection would have skewed the responses toward people with higher incomes (who are more likely to have the time and money to participate). So 20 percent of the sampled individuals were randomly invited from the “most deprived areas,” and 80 percent were chosen at random from every region. To further reduce the effects of income-related selection bias, participants were promised a small stipend and travel reimbursements.

Out of the 30,000 people invited, nearly 2,000 accepted and completed an online survey indicating seven characteristics: their gender identity, age, ethnicity, educational attainment, location, description of their residence as urban or rural, and level of concern about climate change. Hennig applied his algorithm to those 2,000 respondents to select 110 participants who would proportionally represent the U.K. with respect to those seven categories.