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Policymaking is hard. Policymakers typically have imperfect information about which policies produce which outcomes, and they are left with little choice but to fumble their way through the policy space via a trial-and-error process. This raises a question at the heart of democracy: Do democratic systems identify good policies? To answer this question I introduce a novel model of policymaking in complex environments. I show that good policies are often but not always found and I identify the possibility of policymaking getting stuck at outcomes that are arbitrarily bad. Notably, policy stickiness occurs in the model even in the absence of institutional constraints. This raises the question of how institutions and the political environment impact experimentation and learning. I show how a simple political friction—uncertainty over voter preferences—interacts with political competition and policy uncertainty in a subtle way that, surprisingly, improves the quality of policymaking over time.

I study a dynamic model of trial-and-error search in which agents do not have complete knowledge of how choices are mapped into outcomes. Agents learn about the mapping by observing the choices of earlier agents and the outcomes that are realized. The key novelty is that the mapping is represented as the realized path of a Brownian motion. I characterize for this environment the optimal behavior each period as well as the trajectory of experimentation and learning through time. Applied to new product development, the model shares features of the data with the well-known Product Life Cycle. (JEL D81, D83, D92, L26)

The role of expertise in policy making has been a focus of political science research in recent decades. Underlying formal models in this area is a conception of expertise that is very simple: expertise is a single piece of information. Combined with a condition on the set of possible processes, this simplicity implies that expertise is invertible. Thus, a single recommendation by an expert can render a layperson an expert. In this paper, I offer a broader representation of expertise and policy making that relaxes these features. To demonstrate that this generality matters to political behavior, I develop a simple model of delegation and show that imperfect invertibility of expertise provides a resolution of the commitment problem of legislative-bureaucratic policy making. The theory predicts that only issues of sufficient complexity can be delegated, consistent with anecdotal evidence. Adapted from the source document.

I analyze a model of electoral competition with entry under the run-off rule. I consider both two- and multiple-party systems. The principal result is that two-party systems may prove stable under the run-off rule: I show that a continuum of equilibria exists in which only two parties enter and subsequent entry is deterred. This finding conflicts with the accepted wisdom on the run-off rule encapsulated by Duverger's Hypothesis. The results of the model are then reconciled with Duverger's Hypothesis and a more precise formulation is proposed.

This paper considers a model of elections in which parties compete simultaneously for multiple districts. I show that if districts are heterogeneous, then a unique two-party equilibrium exists under plurality rule in which further entry is deterred. The equilibrium requires that parties choose noncentrist policy platforms & not converge to the ideal policy of the median voter. These characteristics are consistent with empirical observation, in contrast to those of single-district models. Necessary & sufficient conditions for the existence of the equilibrium are then characterized & related to Duverger's law. The existence of multiple-party equilibria in this environment is also considered. Tables, Figures, References. Adapted from the source document.

AbstractModels of agenda setting are central to the analysis of political institutions. Elaborations of the classical agenda‐setting model of Romer–Rosenthal have long been used to make predictions about policy outcomes and the distribution of influence among political actors. Although the canonical model is based on complete and perfect information about preferences and policy outcomes, some extensions relax these assumptions to include uncertainty about preferences and reversion points. We consider a different type of uncertainty: incomplete knowledge of the mapping between policies and outcomes. In characterizing the optimal agenda setting under this form of uncertainty, we show that it amends substantively the implications of the Romer–Rosenthal model. We then extend the model dynamically and show that rich dynamics emerge under policy uncertainty. Over a longer horizon, we find that agenda control suppresses the incentive of legislators to experiment with policy, leading to less policy learning and worse outcomes than are socially efficient.

Fixed statutes and regulations often have variable consequences over time. If left unattended, such drift can severely erode the performance of government as an institution of representation. To better understand the mechanics of policy‐making in a changing world, we develop a positive theory that captures political drift in a dynamic separation‐of‐powers system. We show analytically that a distinctive combination of legislative supermajoritarianism and agency discretion—institutional features that, in isolation, elicit widespread criticism—can effectively ameliorate policies' susceptibility to the vicissitudes of exogenous change. The critical mechanism for governmental accommodation of drift is delegation, which increases all decision makers' well‐being by reducing fluctuations in outcomes. Although the complete smoothing of outcomes is attainable in a separation‐of‐ powers system, we show that this is typically not achieved in equilibrium. The presence of drift provides an opportunity for self‐interested legislators to extract a distributional benefit from their fellow legislators at the expense of overall policymaking efficiency.