Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
第4回予測市場勉強会資料・予測市場を1から学ぼう!
Search
Yuya-Furusawa
October 15, 2019
Science
280
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
第4回予測市場勉強会資料・予測市場を1から学ぼう!
第4回予測市場勉強会で使用したスライドです。
Eagna(
https://eagna.io/
)
Yuya-Furusawa
October 15, 2019
More Decks by Yuya-Furusawa
See All by Yuya-Furusawa
CROP説明(仮)
yfurusawa
0
44
社内予測市場:説明会資料
yfurusawa
0
86
第3回予測市場勉強会資料・Googleにおける社内予測市場
yfurusawa
0
600
Nefrock勉強会資料「予測市場の理論と概要」
yfurusawa
0
63
第1回予測市場勉強会資料・予測市場の概要と理論
yfurusawa
0
310
Other Decks in Science
See All in Science
DMMにおけるABテスト検証設計の工夫
xc6da
1
1.9k
Testing the Longevity Bottleneck Hypothesis
chinson03
0
300
Physical AIを支えるWeights & Biases
olachinkei
1
350
ダメな自分の育て方―性格タイプの「劣等機能」から理解するニガテ克服術
ppillc
0
140
医療 LLM ベンチマークの現在地:多面的評価 と日本ローカライズ
analokmaus
1
480
Cross-Media Technologies, Information Science and Human-Information Interaction
signer
PRO
3
32k
CVPR2026_VGGTとその仲間たち
mickey_0226
0
710
AIPシンポジウム 2025年度 成果報告会 「因果推論チーム」
sshimizu2006
3
520
プロジェクト「Azayaka」のSARの数式とジオメトリ
syuchimu
0
320
データベース06: SQL (3/3) 副問い合わせ
trycycle
PRO
1
970
アクシズを探せ! 各勢力の位置関係についての考察
miu_crescent
PRO
1
320
Van Dare naar Durf
voginip
0
220
Featured
See All Featured
A Modern Web Designer's Workflow
chriscoyier
698
190k
Leading Effective Engineering Teams in the AI Era
addyosmani
9
2k
Ten Tips & Tricks for a 🌱 transition
stuffmc
0
130
The Limits of Empathy - UXLibs8
cassininazir
1
350
How to Ace a Technical Interview
jacobian
281
24k
The Myth of the Modular Monolith - Day 2 Keynote - Rails World 2024
eileencodes
28
3.5k
10 Git Anti Patterns You Should be Aware of
lemiorhan
PRO
659
62k
Marketing to machines
jonoalderson
1
5.4k
Noah Learner - AI + Me: how we built a GSC Bulk Export data pipeline
techseoconnect
PRO
0
190
How Software Deployment tools have changed in the past 20 years
geshan
0
34k
Thoughts on Productivity
jonyablonski
76
5.2k
Organizational Design Perspectives: An Ontology of Organizational Design Elements
kimpetersen
PRO
1
720
Transcript
༧ଌࢢΛ͔̍Βֶ΅͏ʂ ༧ଌࢢษڧձୈ̐ճ ݹᖒ ༏ 2019/10/15
Table of Contents • ࣗݾհ • ·ͣ༧ଌʹ͍ͭͯ • ༧ଌࢢͱʁ •
༧ଌࢢͷྑ͍ͱ͜ɾѱ͍ͱ͜ • ༧ଌࢢͷՄೳੑ • ͓ΘΓʹ • Q&A
ࣗݾհ • ݹᖒ ༏ • ౦େܦࡁM2 • ઐɿήʔϜཧɺωοτϫʔΫཧ • ؔ৺ɿ༧ଌࢢɺ҉߸௨՟ɺҼՌਪ
• ༧ଌࢢαʔϏε”Eagna”ΛӡӦɾ։ൃͯ͠·͢
·ͣ༧ଌʹ͍ͭͯ
༧ଌͷॏཁੑ • কདྷͷ༧ଌඇৗʹॏཁ དྷ݄ͷऩೖˠࠓͷങ͍ ͷधཁˠઃඋࢿɺϓϩϞʔγϣϯઓུ ޙͷੈքˠͷํ
None
༧ଌͱ͍͏ӦΈ • ੈͷதʹࢄΒ͍ͬͯΔใΛूͯ͠ɺকདྷ ʹؔ͢ΔใΛಋ͘ߦҝ • ͰͲͷΑ͏ʹใΛू͢Ε͍͍ͷ͔ʁ • ޮత͔ͭ؆୯ͳूํ๏ͩͱخ͍͠
༧ଌखஈ̍ɿઐՈʹฉ͘ • Pros • ৫ʹೲಘײΛੜΉ • Cons • ͓ۚͱ͕͔͔࣌ؒΔ •
ਖ਼ʹ͑ΔΠϯηϯςΟϒʁ • ਫ਼ͦΜͳʹߴ͘ͳ͍͔͠Εͳ͍ɺɺɺ
༧ଌखஈ̎ɿଟܾʢථʣ • Pros • ؆୯ʹ࣮ߦͰ͖Δ • ࢀՃऀ͕ฏʹѻΘΕΔ • Cons •
ਖ਼ʹථ͢ΔΠϯηϯςΟϒ͕ແ͍ • ใΛ͍࣋ͬͯΔਓͱ࣋ͬͯͳ͍ਓ͕ฏʹѻΘΕ ͯ͠·͏ • ථऀͷແؾྗԽ(Voter Apathy)
༧ଌखஈ̏ɿAIͰ༧ଌ • Pros • ༧ଌਫ਼͕ඇৗʹߴ͍ • Cons • େͳσʔλ͕ඞཁ •
σʔλ͕ͳ͍͜ͱͷ༧ଌ͍͠
1. ༧ଌΛਖ਼ʹݴ͏ΠϯηϯςΟϒ͕ແ͍ 2. ࣌ؒతɾۚમతίετ͕ߴ͍ 3. େྔ͔࣭ͭͷߴ͍σʔλ͕ඞཁ
ޮతʹਫ਼ͷߴ͍༧ଌ͕͍ͨ͠ʂʂʂ
༧ଌࢢͱʁ
༧ଌࢢ Prediction Market • ܈ऺͷӥஐͱࢢϝΧχζϜΛ༻͍ͨใू ϝΧχζϜɾ༧ଌखஈ • ଟͷࢀՃऀ͕ࣗͷ༧ʹैͬͯɺূ݊Խ ͞Εͨ༧Λചങ͢Δ •
ຊͰ͋·ΓΒΕͯ·ͤΜͶɺɺɺ
܈ऺͷӥஐ Wisdom of Crowds • 1ਓͷ༏Εͨఱ࠽͕Լ͢அΑΓɺී௨ͷਓ ͔ΒΔूஂ͕Լ͢அͷํ͕༏Ε͍ͯΔͱ ͍͏ݱ • ྫɿΰϧτϯڭतͱ༤ڇͷମॏͯେձ
ࢢϝΧχζϜ Market Mechanism • ܦࡁతΠϯηϯςΟϒʹΑΓޮతͳΛ ୡ • ʮൃݟతखଓ͖ͱͯ͠ͷڝ૪ʯbyϋΠΤΫ • ใूϝΧχζϜͱͯ͠ͷࢢ
༧ଌࢢͷ࣮ྫ • Hollywood Stock Exchange • ΞϝϦΧͷฮ༧ଌࢢɺөըͷ༧ଌઐ • Augur •
ϒϩοΫνΣʔϯ্ͷ༧ଌࢢ • Google • ࣾʹ༧ଌࢢΛઃஔ
༧ଌࢢͷΈ • τϥϯϓͱώϥϦʔͷͲͪΒ͕উ͔ͭΛ༧ଌ ͢Δ༧ଌࢢΛߟ͑·͠ΐ͏ʂ
༧ଌࢢͷΈ 1. τϥϯϓτʔΫϯͱώϥϦʔτʔΫϯΛൃߦ τϥϯϓ $1 $0 τϥϯϓউར τϥϯϓഊ ώϥϦʔ $1
$0 ώϥϦʔউར ώϥϦʔഊ
༧ଌࢢͷΈ 2. τʔΫϯͷചങΛ͢Δ • উͭͱ༧͢ΔํͷτʔΫϯΛങ͏ τϥϯϓ ώϥϦʔ τϥϯϓ͕উͭ ͱࢥ͏ͳΒ… ώϥϦʔ͕উͭ
ͱࢥ͏ͳΒ…
༧ଌࢢͷΈ 2. τʔΫϯΛചങ͢Δ • ͖ͳτʔΫϯΛ͖ͳ͚ͩങ͑Δ τϥϯϓ ώϥϦʔ ×̑ ×̑ ʑ͘Β͍ͩͱ
ࢥ͏ͳΒ…
༧ଌࢢͷΈ 2. τʔΫϯΛചങ͢Δ • ༧͕มԽͨ͠ΒͦΕʹԠͯ͡ചങ τϥϯϓ ώϥϦʔ ώϥϦʔ͕উͪͦ͏ͩ ͱͳͬͨΒ…
༧ଌࢢͷΈ 3. ݁Ռ͕ܾ·ͬͨͷͪɺ͍͕͠ߦΘΕΔ τϥϯϓ ώϥϦʔ
Ձ֨ͱ༧ଌ • Ձ͕֨ߴ͍ʹΈΜͳ͕༧͍ͯ͠Δ • Ձ֨ʹࢢͷ༧ଌ • ܦࡁతΠϯηϯςΟϒ͕ਖ਼֬ͳ༧ଌΛͨΒ ͢
Ձ֨ͱࣗͷ༧ଌ • ͍ͭങ͍ͬͯͭചΔ͖͔ʁ • ྫɿτϥϯϓτʔΫϯͷՁ͕֨$0.6ͷͱ͖ τϥϯϓ͕উͭ֬ 80% ظɿ$1 × 80%
= $0.8 > $0.6 30% ظɿ$1 × 30% = $0.3 < $0.6 ങͬͨํ͕ ྑ͍ ചͬͨํ͕ ྑ͍
༧ଌࢢͷϝΧχζϜ • Ձ֨ΛͲ͏ܾͬͯΊΔ͔ʁ • Ձ͕֨༧ଌΛදͯ͠΄͍͠ • ͦͷ༧ଌਖ਼֬ͳͷͰ͋ͬͯ΄͍͠ • ࣗͷ༧ଌ௨Γʹਖ਼ʹചങͯ͠΄͍͠
࿈ଓμϒϧΦʔΫγϣϯํࣜ Continuous Double Auction Mechanism • τʔΫϯΛചങ • ചΓจͱങ͍จΛͦΕͧΕఏग़ •
͕݅Ϛον͢Εఆ • גࣜࢢɺҝସࢢͳͲͱಉ͡Γํ
࿈ଓμϒϧΦʔΫγϣϯํࣜ Continuous Double Auction Mechanism • Thin Market Problem •
ಛʹબࢶ͕ଟ͘ͳΔͱ૬ख͕ݟ͔ͭΒͳ ͍Մೳੑ • No Trade Theorem • ૬ख͕औҾ͠Α͏ͱ͢ΔͳΒʹͦΕʹԠ͡ ͳ͍ํ͕ྑ͍
ϚʔέοτϝΠΧʔํࣜ Automated Market Maker Mechanism • ࢢͷཧऀͱऔҾΛߦ͏ • ཧऀ͔ΒτʔΫϯΛߪೖ͠ɺཧऀ͕ใु Λࢧ͏
• Ձ֨ΞϧΰϦζϜʹैܾͬͯఆ͞ΕΔ →ࠓճׂѪ͠·͢ʂ
༧ଌࢢͷྑ͍ͱ͜ɾѱ͍ͱ͜
͍͢͝ͱ͜Ζ 1. ༧ଌ͕ਖ਼֬ “Prediction Markets”, Wolfers and Zitzewitz
͍͢͝ͱ͜Ζ 2. ϦΞϧλΠϜੑ • Ձ֨(ʹ༧ଌ)ͷมԽ͕Θ͔Δ • χϡʔεͳͲͰ༧ଌ͕DynamicʹมԽ • ଞͷ༧ଌखஈʹݟΒΕͳ͍ಛੑ
“Prediction Markets”, Wolfers and Zitzewitz
͍͢͝ͱ͜Ζ 3. ใͷओମతͳ֫ಘ • औҾͰಘΛ͢ΔͨΊʹใΛͨ͘͞Μू ΊΔඞཁ͕͋Δ • ྫ͑ώϥϦʔͱτϥϯϓͲ͕ͬͪউ͔ͭ ʹ͍ͭͯ͘͢͝ௐΔΑ͏ʹͳΓ·͢
μϝͳͱ͜Ζ 1. ϚʔέοτͷσβΠϯ͕͍͠ 2. ๏తͳ • ຊͩͱṌത๏ͰΞτͰ͢^^ 3. ྲྀಈੑͷ֬อɺཧऀͷଛࣦ 4.
݁Ռͷղऍ͕͍͠
AIʹΑΔ༧ଌͱͷҧ͍ • ҧ͍ɿσʔλɾϞσϧΛඞཁͱ͠ͳ͍ • σʔλ͕ແ͍ɺऔΕͳ͍ྖҬ • Ϟσϧ͕ෳࡶͳྖҬ • ٯʹϦονͳσʔλɾཱ֬͞ΕͨϞσϧ͕͋ ΕAIͷํ͕༏Ε͍ͯΔ
༧ଌࢢઈରతʹ༏Εͨ༧ଌखஈͰͳ͍ Ή͠Ζଞͷ༧ଌखஈͱิతͳؔ
༧ଌࢢͷՄೳੑ
ࣾձɾ࣏ʹؔ͢Δ༧ଌ • ྫɿʮؖटձஊ͕Ҏʹ࣮ݱ͢Δ ͔ʁʯ • σʔλͰ༧ଌࠔ • Ϗδωεతʹඇৗʹॏཁͳ༧ଌ
͖݅ͷ༧ଌ • ྫɿʮফඅ੫Λ૿੫ͨ͠Βɺ̑ޙɺຊͷ ܦࡁ˓%Λ͑Δ͔ʁʯ • ࡦͷஅʹ͏͜ͱ͕Մೳ • ੈௐࠪͷΘΓ • ϙδγϣϯτʔΫͳ͘ͳΔʢʁʣ
ਅ࣮Λ͘(ʁ)༧ଌ • ྫɿʮްੜ࿑ಇলͷ౷ܭʹෆਖ਼͕͋Δ͔ʁʯ • ෦ͷਓͨͪෆਖ਼͕͋Δͱ༧ଌ͢ΔΠϯη ϯςΟϒ͕͋Δ • Ձ͕֨ߴ͔ͬͨΒʮͳΜ͔ո͍͠ɺɺɺʯͬ ͯͳΔ
͓ΘΓʹ
ͬͱΓ͍ͨਓ • ʮී௨ͷਓͨͪΛ༬ݴऀʹม͑Δʰ༧ଌࢢʱͱ͍ ͏৽ઓུʯɺυφϧυɾτϯϓιϯ • ʮʰΈΜͳͷҙݟʱҊ֎ਖ਼͍͠ʯɺδΣʔϜζɾ εϩΟοΩʔ • “Prediction Market
: Theory and Application”, Leighton Vaughan Williams
Eagna • ”Eagna”ͱ͍͏αʔϏεΛӡӦɾ։ൃͯ͠·͢ • PCɺεϚϗͷϒϥβ্Ͱ༧ଌࢢΛແྉͰ ମݧͰ͖·͢ʢsign upඞཁɺεϚϗਪʣ • Ϛʔέοτ͝ͱʹίΠϯΛ͢ΔͷͰɺͦ ΕΛͨ͘͞Μ૿͍ͯͩ͘͠͞ʂ
Eagna • ใु͋Γ·͢ʂ • ֫ಘͨ͠ίΠϯʹൺྫͯ֬͠తʹελόͱ͔ͷ Ϊϑτ݊Λͬͯ·͢ • eagna.ioͰݕࡧʂ • ϑΟʔυόοΫେେେܴͰ͢ʂ
None
༧ଌࢢษڧձ • ຊͷ༧ଌࢢίϛϡχςΟͱͯ͠ຖ݄ߦͬ ͍ͯ͘༧ఆͰ͢ • ࣌ɺձɺςʔϚconnpassͰʂ • ੋඇ࣍ճ͝ࢀՃԼ͍͞ʂ
Q&A
͋Γ͕ͱ͏͍͟͝·ͨ͠ʂ