scholarly journals FaRM: Fair Reward Mechanism for Information Aggregation in Spontaneous Localized Settings

2019 ◽  
Author(s):  
Moin Hussain Moti ◽  
Dimitris Chatzopoulos ◽  
Pan Hui ◽  
Sujit Gujar
Keyword(s):  
Prior Knowledge ◽  
Incentive Mechanism ◽  
Information Aggregation ◽  
Prediction Markets ◽  
Consistency Score ◽  
Aggregate Information ◽  
Reliability Score ◽  
Random Pairing ◽  
History Of ◽  
Reward Mechanism

Although peer prediction markets are widely used in crowdsourcing to aggregate information from agents, they often fail to reward the participating agents equitably. Honest agents can be wrongly penalized if randomly paired with dishonest ones. In this work, we introduce selective and cumulative fairness. We characterize a mechanism as fair if it satisfies both notions and present FaRM, a representative mechanism we designed. FaRM is a Nash incentive mechanism that focuses on information aggregation for spontaneous local activities which are accessible to a limited number of agents without assuming any prior knowledge of the event. All the agents in the vicinity observe the same information. FaRM uses (i) a report strength score to remove the risk of random pairing with dishonest reporters, (ii) a consistency score to measure an agent's history of accurate reports and distinguish valuable reports, (iii) a reliability score to estimate the probability of an agent to collude with nearby agents and prevents agents from getting swayed, and (iv) a location robustness score to filter agents who try to participate without being present in the considered setting. Together, report strength, consistency, and reliability represent a fair reward given to agents based on their reports.

Pari-mutuel Information Aggregation Mechanisms

2012 ◽  
Vol 4 (2) ◽  
pp. 23-43
Author(s):  
Jordi McKenzie ◽  
Jared Bullen
Keyword(s):  
Information Structure ◽  
Technical Assistance ◽  
Experimental Studies ◽  
Public Information ◽  
Information Aggregation ◽  
Prediction Markets ◽  
The Public ◽  
Aggregate Information ◽  
Private And Public ◽  
Knowledge Bias

A number of experimental studies have found that pari-mutuel markets possess the ability to aggregate information privately held by individuals and therefore act as prediction markets.  However, all previous studies have assumed that information is privately and independently distributed. In real world environments the distribution of information is unlikely to take this form. This paper investigates, experimentally, an information structure in which there is both private and public information. It is found that this structure induces a ‘public knowledge bias’ which limits the market’s ability to aggregate information to the extent that the public information reduces the market’s predictive performance.The authors wish to thank Murali Agastya, Andrew Coleman, Pablo Guillen, Stefan Palan, Charles Plott, Kunal Sengupta, and Robert Slonim for useful comments and technical assistance.  We are also extremely grateful to Katarina Kálovcová and Andreas Ortmann for supplying their Ztree program for use in developing our own.

scholarly journals Knowledge and Geo-Object Based Graph Convolutional Network for Remote Sensing Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3848
Author(s):  
Wei Cui ◽  
Meng Yao ◽  
Yuanjie Hao ◽  
Ziwei Wang ◽  
Xin He ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Contextual Information ◽  
Information Aggregation ◽  
Semantic Segmentation ◽  
Spatial Correlations ◽  
Convolutional Network ◽  
Object Based ◽  
Graph Neural Networks ◽  
Salt And Pepper

Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.

scholarly journals Updating Awareness and Information Aggregation

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Spyros Galanis ◽  
Stelios Kotronis
Keyword(s):  
Mild Condition ◽  
Private Information ◽  
Dynamic Environment ◽  
Information Aggregation ◽  
Aggregate Information

AbstractThe ability of markets to aggregate information through prices is examined in a dynamic environment with unawareness. We find that if all traders are able to minimally update their awareness when they observe a price that is counterfactual to their private information, they will eventually reach an agreement, thus generalising the result of Geanakoplos and Polemarchakis (1982). Moreover, if the traded security is separable, then agreement is on the correct price and there is information aggregation, thus generalizing the result of Ostrovsky (2012) for non-strategic traders. We find that a trader increases her awareness if and only if she is able to become aware of something that other traders are already aware of and, under a mild condition, never becomes aware of anything more. In other words, agreement is more the result of understanding each other, rather than being unboundedly sophisticated.

Introduction to Ubiquitous Computing

2011 ◽  
pp. 1-20
Author(s):  
Max Mühlhäuser ◽  
Iryna Gurevych
Keyword(s):  
Ubiquitous Computing ◽  
Prior Knowledge ◽  
Computer Networks ◽  
Related Literature ◽  
History Of

The present chapter is intended as a lightweight introduction to ubiquitous computing as a whole, in preparation for the more specific book parts and chapters that cover selected aspects. This chapter thus assumes the preface of this book to be prior knowledge. In the following, a brief history of ubiquitous computing (UC) is given first, concentrating on selected facts considered as necessary background for understanding the rest of the book. Some terms and a few important standards are subsequently mentioned that are considered necessary for understanding related literature. For traditional standards like those widespread in the computer networks world, at least superficial knowledge must be assumed since their coverage is impractical for a field with such diverse roots as UC. In the last part of this chapter, we will discuss two kinds of reference architectures, explain why they are important for the furthering of Ubiquitous Computing and for the reader’s understanding, and briefly sketch a few of these architectures by way of example.

scholarly journals Response to Lynn Cavanagh

2013 ◽  
Vol 22 (2) ◽  
pp. 27-29
Author(s):  
Harald Krebs
Keyword(s):  
Prior Knowledge ◽  
Music Theory ◽  
History Of Ideas ◽  
The Past ◽  
Active Reading ◽  
The Future ◽  
History Of ◽  
Metrical Dissonance

Krebs's unusual monograph demonstrates that the writing of pure music theory remains an essential enterprise—particularly in the relatively neglected rhythmic domain—but only if the reader undertakes active reading and has prior knowledge of how the music goes. Within the monograph, perpetuation of the image of the misunderstood and suffering artist as a clue to the extramusical meaning Schumann associated with metrical dissonance leads to biased results. This essay's supplementary analysis of the first movement of Schumann's Symphony No. 3, informed by the history of ideas, proposes that in this movement metrical dissonance is used to reinterpret an emblem of the past so as to convey a vision of the future.

scholarly journals A Game Model for Incentive Mechanism of Distributed Nodes in Supply Chains

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jingjing Jiang ◽  
Aobo Lyu
Keyword(s):  
Supply Chain ◽  
Incentive Mechanism ◽  
Operation Mode ◽  
Game Model ◽  
The Public ◽  
Digital Currency ◽  
Distributed Ledger ◽  
Distributed Ledger Technology ◽  
The Incentive Mechanism ◽  
Reward Mechanism

This study aims to solve the credit problems in the supply chain commodity and currency circulation links from the perspective of the ledger, while the game model method has been adopted. The research firstly reviews the relationship between distributed ledger technology and the essential functions of currency. Then, by constructing two-agent single-period and multi-period game models in the entire supply chain, the researchers analysed the incentive mechanism and equilibrium solution of distributed nodes of Central Bank Digital Currency (CBDC). The results of this study include the incentive mechanism and optimization of distributed nodes based on licensed distributed ledger technology, which is an important issue that CBDC faces when performing currency functions. The implications of this study mainly cover the limitations of the underlying technology of the public chain and its reward mechanism in the supply chain management and provide support for the rationality of the CBDC issuance mechanism based on state-owned commercial banks, which provides a reference for the CBDC practice. The main value of the research not only serves the decision-making department of the CBDC issuance but also provides ideas on the operation mode of digital currency for the field of digital currency research.

“We May be Through with the Past …”

2016 ◽  
Vol 20 (4) ◽  
pp. 459-490
Author(s):  
Jason M. Silverman
Keyword(s):  
Prior Knowledge ◽  
Hebrew Bible ◽  
Cultural Context ◽  
Test Case ◽  
The Past ◽  
History Of

Recurring themes and traditions within the biblical corpus have attracted much scholarship. This article uses P. T. Anderson’s Magnolia (1999) as a test case for the ways traditions may or may not interact, and what that means for ‘tradition history.’ While the film has many features which strike many viewers as biblical, the author-director denies prior knowledge of these connections. The film is analyzed in terms of structure, Anderson’s claimed sources, the American cultural context of the 1990s, and its biblical resonances. After assessing the import of these observations, the tradition history of Magnolia is compared to the tradition history of the exodus in the Hebrew Bible. Within the framework of attention to media contexts, the article concludes by noting the importance of authority, canon, and multiple lines of transmission. In so doing, our understanding of the transmission of traditions is problematized, and a broader, less text-centric paradigm is called for.

Exploring the Use of Prediction Markets as Digital Games to Support Learning in a Project Management Context

2019 ◽  
pp. 239-260
Author(s):  
Herbert Remidez ◽  
Michael Stodnick ◽  
Sri Beldona
Keyword(s):  
Project Management ◽  
Digital Games ◽  
Prediction Markets ◽  
Game Based Learning ◽  
Aggregate Information ◽  
Complex Projects ◽  
Management Context ◽  
Future Events ◽  
Potential Risks ◽  
Learning Principles

A growing area of study is the management of complex projects involving stakeholders dispersed across organizations. Key to the success of complex projects is encouraging stakeholders to learn and communicate useful information about work progress and potential risks. Increasingly, companies are using a gaming approach to encourage workers to learn and communication useful information. This chapter looks at one such gaming vehicle, namely prediction markets. Prediction markets are games in the form of marketplaces that adapt many of the same structures found in stock markets to aggregate information about the probability of future events. This chapter traces the developmental history and application of prediction markets, discusses issues in marketplace design, and explores how game-based learning principles can support the use of prediction markets in this context. The concluding section discusses the application of a prediction market to support the management of an IT project.

REAL-MONEY VS. PLAY-MONEY FORECASTING ACCURACY IN ONLINE PREDICTION MARKETS – EMPIRICAL INSIGHTS FROM IPREDICT

2012 ◽  
Vol 4 (3) ◽  
pp. 21-58 ◽  
Author(s):  
Sebastian Deimer ◽  
Joaquin Poblete
Keyword(s):  
Stock Exchange ◽  
Forecast Accuracy ◽  
Information Aggregation ◽  
Prediction Markets ◽  
Trading Activity ◽  
Market Prices ◽  
Forecasting Accuracy ◽  
Real Money ◽  
Incentive Schemes ◽  
Online Trading

Prediction markets are online trading platforms where contracts on future events are traded with payoffs being exclusively linked to event occurrence. Scientific research has shown that market prices of such contracts imply high forecasting accuracy through effective information aggregation of dispersed knowledge. This phenomenon is related to incentives for truthful aggregation in the form of real-money or play-money rewards. The question whether real- or play-money incentives enhance higher relative forecast accuracy has been addressed by previous works with diverse findings. The current state of empirical research in his field is subject to two inherent deficiencies. First, inter-market studies suffer from market disparities and differences in the definition of underlying events. Comparisons between two different platforms (one for play-money contracts, one for real-money contracts) are potentially biased by different trading behaviour. Second, the majority of studies are based upon identical datasets of market platforms (IOWA stock exchange, Tradesports/Intrade, NewsFutures).This paper contributes new insights by analysing 44,169 trading observations on ipredict, where real-money and play-money contracts are traded on a variety of events. Forecasting accuracy is analysed on overall trading activity as well as comparison of equal contracts under different monetary incentive schemes. Statistical models are built to analyse the influence of order volumes and days to expiry under both incentive schemes. Ignoring different events in underlying trading activity, play-money contracts imply statistically insignificant excess accuracy. In direct comparison of equal events, real-money contracts, however, real-money contracts predict at significantly higher accuracy. This paper finds a relationship between order volumes and forecasting accuracy whereas the influence of days to expiry and aggregated volumes showed lower R² than was expected by formed hypotheses.

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