Governator vs. Hunter and Aggregator: A simulation of party competition with vote-seeking and office-seeking rules

The policy positions parties choose are central to both attracting voters and forming coalition governments. How then should parties choose positions to best represent voters? Laver and Sergenti show that in an agent-based model with boundedly rational actors a decision rule (Aggregator) that takes the mean policy position of its supporters is the best rule to achieve high congruence between voter preferences and party positions. But this result only pertains to representation by the legislature, not representation by the government. To evaluate this we add a coalition formation procedure with boundedly rational parties to the Laver and Sergenti model of party competition. We also add two new decision rules that are sensitive to government formation outcomes rather than voter positions. We develop two simulations: a single-rule one in which parties with the same rule compete and an evolutionary simulation in which parties with different rules compete. In these simulations we analyze party behavior under a large number of different parameters that describe real-world variance in political parties’ motives and party system characteristics. Our most important conclusion is that Aggregators also produce the best match between government policy and voter preferences. Moreover, even though citizens often frown upon politicians’ interest in the prestige and rents that come with winning political office (office pay-offs), we find that citizens actually receive better representation by the government if politicians are motivated by these office pay-offs in contrast to politicians with ideological motivations (policy pay-offs). Finally, we show that while more parties are linked to better political representation, how parties choose policy positions affects political representation as well. Overall, we conclude that to understand variation in the quality of political representation scholars should look beyond electoral systems and take into account variation in party behavior as well.

政党所选择的政策立场，对于吸引选民与组建联合政府均至关重要。那么，政党应如何选择政策立场，以最佳方式代表选民的意志呢？拉弗（Laver）与塞尔真蒂（Sergenti）的研究表明，在包含有限理性主体的基于智能体的模型（agent-based model）中，以支持者的平均政策立场作为决策规则的"聚合者（Aggregator）"，是实现选民偏好与政党政策立场间高契合度的最优决策规则。但该结论仅适用于立法机构层面的政治代表性，而非政府层面的代表性。为评估政府层面的政治代表性，我们在拉弗与塞尔真蒂的政党竞争模型中，加入了面向有限理性政党的联合政府组建流程。同时，我们还新增了两种全新的决策规则——此类规则会对政府组建结果而非选民立场做出响应。我们开展了两类模拟仿真：其一为单规则模拟，即采用同一决策规则的政党参与竞争；其二为进化模拟，即采用不同决策规则的政党参与竞争。在上述模拟中，我们针对大量描述现实世界中政党政界动机与政党制度特征差异的参数，分析了政党的行为表现。我们的核心结论为："聚合者"规则仍能实现政府政策与选民偏好间的最优匹配。此外，尽管民众往往反感政客对竞选公职所附带的声望与职位收益（office pay-offs）的追求，但我们的研究发现，相较于以政策收益（policy pay-offs）为动机的意识形态型政客，当政客受职位收益驱动时，民众能获得更优质的政府层面政治代表性。最后，我们的研究表明，尽管政党数量越多往往越能提升政治代表性，但政党选择政策立场的方式同样会对政治代表性产生影响。综上，我们认为，若要理解政治代表性质量的差异，学者们不应仅关注选举制度，还应同时考量政党行为的差异性。