Information Aggregation System Research Articles

Erratum to Examining the limits of the Condorcet Jury Theorem: Tradeoffs in hierarchical information aggregation systems

Erratum to Examining the limits of the Condorcet Jury Theorem: Tradeoffs in hierarchical information aggregation systems

Examining the limits of the Condorcet Jury Theorem: Tradeoffs in hierarchical information aggregation systems

Condorcet’s Jury Theorem states that the correct outcome is reached in direct majority voting systems with sufficiently large electorates as long as each voter’s independent probability of voting for that outcome is greater than 1/2. Previous research has found that switching to a hierarchical system always leads to an inferior result. Yet, in many situations direct voting is infeasible (e.g., due to high implementation or infrastructure costs), and hierarchical voting may provide a reasonable alternative. This paper examines differences in accuracy rates of hierarchical and direct voting systems for varying group sizes, abstention rates, and voter competences. We derive three main results. First, we prove that indirect two-tier systems differ most from their direct counterparts when group size and number are equal (i.e., when each equals [Formula: see text], where Nd is the total number of voters in the direct system). In multitier systems, we prove that this difference is maximized when group size equals [Formula: see text], where n is the number of hierarchical levels. Second, we show that while direct majority rule always outperforms indirect voting for homogeneous electorates, hierarchical voting gains in accuracy when either the number of groups or the number of individuals within each group increases. Third, we prove that when voter abstention and competency are correlated within groups, hierarchical systems can outperform direct voting. The results have implications beyond voting, including information processing in the brain, collective cognition in animal groups, and information aggregation in machine learning.

Lie on the Fly: Strategic Voting in an Iterative Preference Elicitation Process

A voting center is in charge of collecting and aggregating voter preferences. In an iterative process, the center sends comparison queries to voters, requesting them to submit their preference between two items. Voters might discuss the candidates among themselves, figuring out during the elicitation process which candidates stand a chance of winning and which do not. Consequently, strategic voters might attempt to manipulate by deviating from their true preferences and instead submit a different response in order to attempt to maximize their profit. We provide a practical algorithm for strategic voters which computes the best manipulative vote and maximizes the voter's selfish outcome when such a vote exists. We also provide a careful voting center which is aware of the possible manipulations and avoids manipulative queries when possible. In an empirical study on four real-world domains, we show that in practice manipulation occurs in a low percentage of settings and has a low impact on the final outcome. The careful voting center reduces manipulation even further, thus allowing for a non-distorted group decision process to take place. We thus provide a core technology study of a voting process that can be adopted in opinion or information aggregation systems and in crowdsourcing applications, e.g., peer grading in Massive Open Online Courses (MOOCs).

Wiki surveys: open and quantifiable social data collection.

In the social sciences, there is a longstanding tension between data collection methods that facilitate quantification and those that are open to unanticipated information. Advances in technology now enable new, hybrid methods that combine some of the benefits of both approaches. Drawing inspiration from online information aggregation systems like Wikipedia and from traditional survey research, we propose a new class of research instruments called wiki surveys. Just as Wikipedia evolves over time based on contributions from participants, we envision an evolving survey driven by contributions from respondents. We develop three general principles that underlie wiki surveys: they should be greedy, collaborative, and adaptive. Building on these principles, we develop methods for data collection and data analysis for one type of wiki survey, a pairwise wiki survey. Using two proof-of-concept case studies involving our free and open-source website www.allourideas.org, we show that pairwise wiki surveys can yield insights that would be difficult to obtain with other methods.

Development of Immediate Earthquake Intensity Information Aggregation System Using PC Built-In Sensors and IP Multicast

本論では，サーバを持たずに複数端末間で同時且つ即時に地震動情報の共有を可能にする方法としてマルチキャスト方式の通信方法に着目し，高密度の地震動把握及び個別建物の建物応答，室内被害の把握を目的として，ノートパソコン内蔵加速度センサーとマルチキャストによる即時震度情報収集システムを開発した．本システムでは，マルチキャストの特性を生かして新たに誤検知情報の処理方法及び地震データの正規化機能，端末間の存在通知機能を開発した．そして，加速度センサーの性能実験及び振動時のノートPCの挙動確認試験，マルチキャストの配信実験から本システムの有用性を示した．

Capability-Aware Information Aggregation in Peer-to-Peer Grids

Information aggregation is the process of summarizing information across the nodes of a distributed system. We present a hierarchical information aggregation system tailored for Peer-to-Peer Grids which typically exhibit a high degree of volatility and heterogeneity of resources. Aggregation is performed in a scalable yet efficient way by merging data along the edges of a logical self-healing tree with each inner node providing a summary view of the information delivered by the nodes of the corresponding subtree. We describe different tree management methods suitable for high-efficiency and high-scalability scenarios that take host capability and stability diversity into account to attenuate the impact of slow and/or unstable hosts. We propose an architecture covering all three phases of the aggregation process: Data gathering through a highly extensible sensing framework, data aggregation using reusable, fully isolated reduction networks, and application-sensitive data delivery using a broad range of propagation strategies. Our solution combines the advantages of approaches based on Distributed Hash Tables (DHTs) (i.e., load balancing and self-maintenance) and hierarchical approaches (i.e., respecting administrative boundaries and resource limitations). Our approach is integrated into our Peer-to-Peer Grid platform Cohesion. We substantiate its effectiveness through performance measurements and demonstrate its applicability through a graphical monitoring solution leveraging our aggregation system.