We propose a design for philanthropic or publicly funded seeding to allow (near) optimal provision of a decentralized, self-organizing ecosystem of public goods. The concept extends ideas from quadratic voting to a funding mechanism for endogenous community formation. Citizens make contributions to public goods of value to them. The amount received by the public good is (proportional to) the square of the sum of the square roots of contributions received. Under the “standard model,” this mechanism yields first best public goods provision. Variations can limit the cost, help protect against collusion, and aid coordination. We discuss applications to campaign finance and highlight directions for future analysis and experimentation. This paper was accepted by Joshua Gans, business strategy.

Critics now articulate their worries about the technologies, social practices and mythologies that comprise Artificial Intelligence (AI) in many domains. In this paper, we investigate the intersection of two domains of criticism: identity and scientific knowledge. On one hand, critics of AI in public policy emphasise its potential to discriminate on the basis of identity. On the other hand, critics of AI in scientific realms worry about how it may reorient or disorient research practices and the progression of scientific inquiry. We link the two sets of concerns-around identity and around knowledge-through a series of case studies. In our case studies, about autism and homosexuality, AI figures as part of scientific attempts to find, and fix, forms of identity. Our case studies are instructive: they show that when AI is deployed in scientific research about identity and personality, it can naturalise and reinforce biases. The identity-based and epistemic concerns about AI are not distinct. When AI is seen as a source of truth and scientific knowledge, it may lend public legitimacy to harmful ideas about identity.

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This paper investigates the relationship between economic theory and theories of justice in the design of public policy. In particular, it focuses on the role of mechanism design in policy contexts beset with issues of social, racial and distributive justice. Economists’ involvement in redesigning Boston’s algorithm for allocating K-12 students to public schools serves as an instructive case study. The paper draws on the distinction between ideal theory and non-ideal theory in political philosophy and the concept of performativity in economic sociology to argue that mechanism design can enact elaborate ideal theories of justice. A normative gap thus emerges between the goals of the policymakers and the objectives of economic designs. As a result, mechanism design may obstruct stakeholders’ avenues for normative criticism of public policies, and serve as a technology of depoliticization.

We provide a novel articulation of the epistemic peril of p-hacking using three resources from philosophy: predictivism, Bayesian confirmation theory, and model selection theory. We defend a nuanced position on p-hacking: p-hacking is sometimes, but not always, epistemically pernicious. Our argument requires a novel understanding of Bayesianism, since a standard criticism of Bayesian confirmation theory is that it cannot represent the influence of biased methods. We then turn to pre-analysis plans, a methodological device used to mitigate p-hacking. Some say that pre-analysis plans are epistemically meritorious while others deny this, and in practice pre-analysis plans are often violated. We resolve this debate with a modest defence of pre-analysis plans. Further, we argue that pre-analysis plans can be epistemically relevant even if the plan is not strictly followed—and suggest that allowing for flexible pre-analysis plans may be the best available policy option.