We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are more flexible than alternative "unfolding" models previously introduced in the literature. We then use these models to estimate revealed preferences for legislators in the U.S. House of Representatives and justices on the U.S. Supreme Court. The results from these applications indicate that the new models provide superior complexity-adjusted performance to various alternatives and also that the additional flexibility leads to preferences' estimates that are closer matches to the perceived ideological positions of legislators and justices.

翻译：我们开发了一类新的针对二元偏好数据的空间投票模型，这类模型既能容纳单调响应函数，也能容纳非单调响应函数，并且比文献中先前引入的其他“展开”模型更为灵活。随后，我们利用这些模型估算了美国众议院议员和美国最高法院大法官的显性偏好。这些应用结果表明，新模型在调整复杂度后的性能优于多种替代模型，并且其额外的灵活性使得偏好估计更贴合议员和大法官公认的意识形态立场。