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THis paper studies the relationship between voters' preferences and teh composition of party platforms in teh two-party democractice elections with adaptive parties. In the model, preferences determine an electoral landscape on which parties locally adapt platforms. Varying the distribution of voter's preferences alters the landscape's ruggedness and may effect parties' responsiveness. We find that in atwo-party democratic elections, adaptive parties genearlly locate in regions of high social utiliyt but cannot always find winning platforms. We also show that parties' ability to locate winning plaforms as well as teh rate of convergence of party platforms depends upon the

This paper introduces specialized elections. A specialized election randomly assigns each voter to one election, freeing her of voting responsibilities in other elections. By reducing voters' responsibilities, specialized elections encourage more information acquisition. Specialized elections also make campaigning less costly. A shortcoming of specialized elections is the increase in outcome variance resulting from the sampling effect. Whether or not specialized elections improve democratic outcomes hinges upon the tradeoff between more informed voters and greater outcome variance. Sufficient conditions are derived for the increase in information to generate an outcome nearer to that which would be chosen by a fully informed electorate.

We develop a model of spatial elections that departs from the standard model in three important respects. Our parties' information of voters' preferences is limited to polls; our parties can be either office-seeking of ideological; and our parties are not perfect optimizers, i.e. they are modeled as boundedly rational actors. Since our imperfect parties do not necessarily find optimal positions, rather than concern ourselves with existence and location of equilibria, we trace the trajectory of winning party positions. The outcomes are subsequently evaluated with respect to a measure of social welfare, centrality. Our results suggest that in fair voting systems, two party elections lead to normatively appealing outcomes. We are seeking to introduce the role of computers genearlly and adaptive artificial agents (AAA) specifically, to the study of parties, voters, and elections in spatial models. We argue for using adaptive parties to add behavioral complexity to stand formal models of politics without sacrificing a logical foundation. Doing so may not only revise our judgments about the relevance of spatial voting models to real elections, but it may lead us to important insights about what occurs in those elections.

We provide commentaries on the papers included in the Dynamics of Political Polarization Special Feature. Baldassarri reads the contribution of the papers in light of the theoretical distinction between ideological partisanship, which is generally rooted in sociodemographic and political cleavages, and affective partisanship, which is, instead, mostly fueled by emotional attachment and repulsion, rather than ideology and material interests. The latter, she argues, is likely to lead to a runaway process and threaten the pluralistic bases of contemporary democracy. Page sees the contribution of the many distinct models in the ensemble as potentially contributing more than the parts. Individual papers identify distinct causes of polarization as well as potential solutions. Viewed collectively, the papers suggest that the multiple causes of polarization may self-reinforce, which suggests that successful interventions would require a variety of efforts. Understanding how to construct such interventions may require larger models with greater realism.

Evidence suggests that the cultural context influences the performance of laws, policies, and political institutions. Descriptive accounts reveal that outcomes and behaviors often depend on the array of historical institutions. This article presents a multi-institutional framework that can account for those findings through path-dependent behavioral spillovers. Individuals learn equilibrium behaviors when interacting in a new institutional setting. Initially, some individuals choose behaviors that align with their behaviors in similar extant institutions, creating a cultural context that can lead to inefficient outcomes. The article shows how avoiding path dependence requires sequencing (or designing) institutions to maintain behavioral diversity. Optimal sequencing thus requires positioning institutions with clear incentives early in the sequence as well as avoiding strong punishments that can stifle attempts to break established behavioral patterns.

Consensus plays an ambiguous role in deliberative democracy. While it formed the horizon of early deliberative theories, many now denounce it as an empirically unachievable outcome, a logically impossible stopping rule, and a normatively undesirable ideal. Deliberative disagreement, by contrast, is celebrated not just as an empirically unavoidable outcome but also as a democratically sound and normatively desirable goal of deliberation. Majority rule has generally displaced unanimity as the ideal way of bringing deliberation to a close. This article offers an epistemic perspective on this question of consensus versus disagreement. For ensuring the production of better decisions, we argue, the normative appeal of consensus varies depending on the deliberative task – whether it entails problem solving or prediction. We argue that in pure problem-solving contexts, consensus retains a strong normative appeal and forms the ideal deliberative outcome of deliberation. In contrast, on predictive tasks, consensus should generally not be used as a stopping rule noris it likely to be epistemically desirable as an outcome. Instead deliberators may be better served by ending the deliberation with a form of deliberative disagreement we call 'positive dissensus', which paves the way for more accurate aggregate predictions.

The Colonel Blotto game captures strategic situations in which players attempt to mismatch an opponent's action. We extend Colonel Blotto to a class of General Blotto games that allow for more general payoffs and externalities between fronts. These extensions make Blotto applicable to a variety of real-world problems. We find that like Colonel Blotto, most General Blotto games do not have pure strategy equilibria. Using a replicator dynamics learning model, we show that General Blotto may have more predictable dynamics than the original Blotto game. Thus, adding realistic structure to Colonel Blotto may, paradoxically, make it less complex. Adapted from the source document.