Dynamic public good provision under time preference heterogeneity

Philip Trammell (Global Priorities Institute and Department of Economics, University of Oxford)

GPI Working Paper No. 9-2021

I explore the implications of time preference heterogeneity for the private funding of public goods. The assumption that players use a common discount rate is knife-edge: relaxing it yields substantially different equilibria, for two reasons. First, time preference heterogeneity motivates intertemporal polarization, analogous to the polarization seen in a static public good game. In the simplest settings, more patient players spend nothing early in time and less patient players spending nothing later. Second, and consequently, time preference heterogeneity gives less patient players a “first-mover advantage”. Departures from the common-discounting assumption are economically significant: a patient player’s payoff in equilibrium, relative to that obtained when he is constrained to act according to a higher discount rate, typically grows unboundedly as his share of the initial budget falls to zero. Finally I discuss applications of these results to the debate over legal disbursement minima.

Other working papers

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We set out longtermist political philosophy as a research field. First, we argue that the standard case for longtermism is more robust when applied to institutions than to individual action. This motivates “institutional longtermism”: when building or shaping institutions, positively affecting the value of the long-term future is a key moral priority. Second, we briefly distinguish approaches to pursuing longtermist institutional reform along two dimensions: such approaches may be more targeted or more broad, and more urgent or more patient.

AI alignment vs AI ethical treatment: Ten challenges – Adam Bradley (Lingnan University) and Bradford Saad (Global Priorities Institute, University of Oxford)

A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching…