Abstract

Voting on legislative bills to form new laws serves as a key function of most of the legislatures. Predicting the votes of such deliberative bodies leads better understanding of government policies and generate actionable strategies for social good. However, it is very difficult to predict legislative votes due to the myriad factors that affect the political decision-making process. In this paper, we present a novel prediction model that maximizes the usage of publicly accessible heterogeneous data, i.e., bill text and lawmakers' profile data, to carry out effective legislative prediction. In particular, we propose to design a probabilistic prediction model which archives high consistency with past vote recorders while ensuring the minimum uncertainty of the vote prediction reflecting the firm legal ground often hold by the lawmakers. In addition, the proposed legislative prediction model enjoys the following properties: inductive and analytical solution, abilities to deal with the prediction on new bills and new legislators, and the robustness to missing vote issue. We conduct extensive empirical study using the real legislative data from the joint sessions of the United States Congress and compare with other representative methods in both quantitative political science and data mining communities. The experimental results clearly corroborate that the proposed method provides superior prediction accuracy with visible performance gain.

Original languageEnglish (US)
Pages (from-to)107-120
Number of pages14
JournalStatistical Analysis and Data Mining
Volume10
Issue number2
DOIs
StatePublished - Apr 1 2017

Fingerprint

Vote
Uncertainty
Prediction
Prediction Model
Voting
Probabilistic Model
Empirical Study
Data Mining
Analytical Solution
Decision Making
Maximise
Robustness
Predict
Data mining
Decision making
Experimental Results

Keywords

  • dual-view learning
  • heterogeneous information
  • legislative prediction

ASJC Scopus subject areas

  • Analysis
  • Information Systems
  • Computer Science Applications

Cite this

Legislative prediction with dual uncertainty minimization from heterogeneous information. / Cheng, Yu; Agrawal, Ankit; Liu, Huan; Choudhary, Alok.

In: Statistical Analysis and Data Mining, Vol. 10, No. 2, 01.04.2017, p. 107-120.

Research output: Contribution to journalArticle

Cheng, Yu ; Agrawal, Ankit ; Liu, Huan ; Choudhary, Alok. / Legislative prediction with dual uncertainty minimization from heterogeneous information. In: Statistical Analysis and Data Mining. 2017 ; Vol. 10, No. 2. pp. 107-120.
@article{d42bf7dccbcf4b2d817918886351342f,
title = "Legislative prediction with dual uncertainty minimization from heterogeneous information",
abstract = "Voting on legislative bills to form new laws serves as a key function of most of the legislatures. Predicting the votes of such deliberative bodies leads better understanding of government policies and generate actionable strategies for social good. However, it is very difficult to predict legislative votes due to the myriad factors that affect the political decision-making process. In this paper, we present a novel prediction model that maximizes the usage of publicly accessible heterogeneous data, i.e., bill text and lawmakers' profile data, to carry out effective legislative prediction. In particular, we propose to design a probabilistic prediction model which archives high consistency with past vote recorders while ensuring the minimum uncertainty of the vote prediction reflecting the firm legal ground often hold by the lawmakers. In addition, the proposed legislative prediction model enjoys the following properties: inductive and analytical solution, abilities to deal with the prediction on new bills and new legislators, and the robustness to missing vote issue. We conduct extensive empirical study using the real legislative data from the joint sessions of the United States Congress and compare with other representative methods in both quantitative political science and data mining communities. The experimental results clearly corroborate that the proposed method provides superior prediction accuracy with visible performance gain.",
keywords = "dual-view learning, heterogeneous information, legislative prediction",
author = "Yu Cheng and Ankit Agrawal and Huan Liu and Alok Choudhary",
year = "2017",
month = "4",
day = "1",
doi = "10.1002/sam.11309",
language = "English (US)",
volume = "10",
pages = "107--120",
journal = "Statistical Analysis and Data Mining",
issn = "1932-1864",
publisher = "John Wiley and Sons Inc.",
number = "2",

}

TY - JOUR

T1 - Legislative prediction with dual uncertainty minimization from heterogeneous information

AU - Cheng, Yu

AU - Agrawal, Ankit

AU - Liu, Huan

AU - Choudhary, Alok

PY - 2017/4/1

Y1 - 2017/4/1

N2 - Voting on legislative bills to form new laws serves as a key function of most of the legislatures. Predicting the votes of such deliberative bodies leads better understanding of government policies and generate actionable strategies for social good. However, it is very difficult to predict legislative votes due to the myriad factors that affect the political decision-making process. In this paper, we present a novel prediction model that maximizes the usage of publicly accessible heterogeneous data, i.e., bill text and lawmakers' profile data, to carry out effective legislative prediction. In particular, we propose to design a probabilistic prediction model which archives high consistency with past vote recorders while ensuring the minimum uncertainty of the vote prediction reflecting the firm legal ground often hold by the lawmakers. In addition, the proposed legislative prediction model enjoys the following properties: inductive and analytical solution, abilities to deal with the prediction on new bills and new legislators, and the robustness to missing vote issue. We conduct extensive empirical study using the real legislative data from the joint sessions of the United States Congress and compare with other representative methods in both quantitative political science and data mining communities. The experimental results clearly corroborate that the proposed method provides superior prediction accuracy with visible performance gain.

AB - Voting on legislative bills to form new laws serves as a key function of most of the legislatures. Predicting the votes of such deliberative bodies leads better understanding of government policies and generate actionable strategies for social good. However, it is very difficult to predict legislative votes due to the myriad factors that affect the political decision-making process. In this paper, we present a novel prediction model that maximizes the usage of publicly accessible heterogeneous data, i.e., bill text and lawmakers' profile data, to carry out effective legislative prediction. In particular, we propose to design a probabilistic prediction model which archives high consistency with past vote recorders while ensuring the minimum uncertainty of the vote prediction reflecting the firm legal ground often hold by the lawmakers. In addition, the proposed legislative prediction model enjoys the following properties: inductive and analytical solution, abilities to deal with the prediction on new bills and new legislators, and the robustness to missing vote issue. We conduct extensive empirical study using the real legislative data from the joint sessions of the United States Congress and compare with other representative methods in both quantitative political science and data mining communities. The experimental results clearly corroborate that the proposed method provides superior prediction accuracy with visible performance gain.

KW - dual-view learning

KW - heterogeneous information

KW - legislative prediction

UR - http://www.scopus.com/inward/record.url?scp=85017013651&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85017013651&partnerID=8YFLogxK

U2 - 10.1002/sam.11309

DO - 10.1002/sam.11309

M3 - Article

AN - SCOPUS:85017013651

VL - 10

SP - 107

EP - 120

JO - Statistical Analysis and Data Mining

JF - Statistical Analysis and Data Mining

SN - 1932-1864

IS - 2

ER -