000 03499nam a22005295i 4500
001 978-3-031-01577-9
003 DE-He213
005 20240730164843.0
007 cr nn 008mamaa
008 220601s2017 sz | s |||| 0|eng d
020 _a9783031015779
_9978-3-031-01577-9
024 7 _a10.1007/978-3-031-01577-9
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aFaltings, Boi.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986378
245 1 0 _aGame Theory for Data Science
_h[electronic resource] :
_bEliciting Truthful Information /
_cby Boi Faltings, Goran Radanovic.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXV, 135 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aPreface -- Acknowledgments -- Introduction -- Mechanisms for Verifiable Information -- Parametric Mechanisms for Unverifiable Information -- Nonparametric Mechanisms: Multiple Reports -- Nonparametric Mechanisms: Multiple Tasks -- Prediction Markets: Combining Elicitation and Aggregation -- Agents Motivated by Influence -- Decentralized Machine Learning -- Conclusions -- Bibliography -- Authors' Biographies .
520 _aIntelligent systems often depend on data provided by information agents, for example, sensor data or crowdsourced human computation. Providing accurate and relevant data requires costly effort that agents may not always be willing to provide. Thus, it becomes important not only to verify the correctness of data, but also to provide incentives so that agents that provide high-quality data are rewarded while those that do not are discouraged by low rewards. We cover different settings and the assumptions they admit, including sensing, human computation, peer grading, reviews, and predictions. We survey different incentive mechanisms, including proper scoring rules, prediction markets and peer prediction, Bayesian Truth Serum, Peer Truth Serum, Correlated Agreement, and the settings where each of them would be suitable. As an alternative, we also consider reputation mechanisms. We complement the game-theoretic analysis with practical examples of applications in prediction platforms, community sensing, and peer grading.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_986380
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aRadanovic, Goran.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_986383
710 2 _aSpringerLink (Online service)
_986386
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031004490
776 0 8 _iPrinted edition:
_z9783031027055
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_986387
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01577-9
912 _aZDB-2-SXSC
942 _cEBK
999 _c85945
_d85945