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1回予測市場勉強会資料・予測市場の概要と理論
Search
Yuya-Furusawa
June 24, 2019
Science
0
280
第1回予測市場勉強会資料・予測市場の概要と理論
第1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
June 24, 2019
Tweet
Share
More Decks by Yuya-Furusawa
See All by Yuya-Furusawa
CROP説明(仮)
yfurusawa
0
35
社内予測市場:説明会資料
yfurusawa
0
79
第4回予測市場勉強会資料・予測市場を1から学ぼう!
yfurusawa
0
250
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
560
Nefrock勉強会資料「予測市場の理論と概要」
yfurusawa
0
54
Other Decks in Science
See All in Science
トラブルがあったコンペに学ぶデータ分析
tereka114
2
1.6k
白金鉱業Meetup Vol.16_【初学者向け発表】 数理最適化のはじめの一歩 〜身近な問題で学ぶ最適化の面白さ〜
brainpadpr
11
2.2k
07_浮世満理子_アイディア高等学院学院長_一般社団法人全国心理業連合会代表理事_紹介資料.pdf
sip3ristex
0
480
Lean4による汎化誤差評価の形式化
milano0017
1
220
統計学入門講座 第2回スライド
techmathproject
0
130
機械学習 - DBSCAN
trycycle
PRO
0
890
統計的因果探索: 背景知識とデータにより因果仮説を探索する
sshimizu2006
4
910
機械学習 - 決定木からはじめる機械学習
trycycle
PRO
0
950
データベース09: 実体関連モデル上の一貫性制約
trycycle
PRO
0
680
[第62回 CV勉強会@関東] Long-CLIP: Unlocking the Long-Text Capability of CLIP / kantoCV 62th ECCV 2024
lychee1223
1
940
A Guide to Academic Writing Using Generative AI - A Workshop
ks91
PRO
0
110
Design of three-dimensional binary manipulators for pick-and-place task avoiding obstacles (IECON2024)
konakalab
0
210
Featured
See All Featured
GitHub's CSS Performance
jonrohan
1031
460k
How to train your dragon (web standard)
notwaldorf
93
6.1k
Measuring & Analyzing Core Web Vitals
bluesmoon
7
490
Improving Core Web Vitals using Speculation Rules API
sergeychernyshev
17
940
The World Runs on Bad Software
bkeepers
PRO
69
11k
Building a Scalable Design System with Sketch
lauravandoore
462
33k
CSS Pre-Processors: Stylus, Less & Sass
bermonpainter
357
30k
Designing for Performance
lara
609
69k
Typedesign – Prime Four
hannesfritz
42
2.7k
The Illustrated Children's Guide to Kubernetes
chrisshort
48
50k
Save Time (by Creating Custom Rails Generators)
garrettdimon
PRO
31
1.2k
Product Roadmaps are Hard
iamctodd
PRO
54
11k
Transcript
༧ଌࢢͷ֓ཁͱཧ ༧ଌࢢษڧձୈ̍ճ ݹᖒ ༏ 2019/06/24
Table of Contents • ࣗݾհ • ༧ଌࢢʹ͍ͭͯ • ༧ଌࢢͷཧ •
͓ΘΓʹ • Q&A
ࣗݾհ • ݹᖒ ༏ • ౦େܦࡁM2 • ઐɿήʔϜཧɺωοτϫʔΫཧ • ؔ৺ɿ༧ଌࢢɺ҉߸௨՟ɺҼՌਪ
• ༧ଌࢢαʔϏε”Eagna”ΛӡӦɾ։ൃͯ͠·͢
༧ଌࢢʹ͍ͭͯ
༧ଌͷॏཁੑ • কདྷͷ༧ଌඇৗʹॏཁ དྷ݄ͷऩೖˠࠓͷങ͍ ͷधཁˠઃඋࢿ ޙͷੈքˠݱࡏͷࡦ
None
༧ଌͱ͍͏ӦΈ • ੈͷதʹࢄΒ͍ͬͯΔใΛूͯ͠ɺকདྷ ʹؔ͢ΔใΛಋ͘ߦҝ • ͰͲͷΑ͏ʹใΛू͢Ε͍͍ͷ͔ʁ • ޮత͔ͭίετͳूํ๏ͩͱخ͍͠
༧ଌखஈ̍ɿઐՈʹฉ͘ • Pros • ৫ʹೲಘײΛੜΉ • Cons • ίετߴ͍(ۚમతɺ࣌ؒత) •
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
༧ଌࢢ Prediction Market • ܈ऺͷӥஐͱࢢϝΧχζϜΛ༻͍ͨίε τ͔ͭޮతͳใूϝΧχζϜ • ଟͷࢀՃऀ͕ࣗͷ༧ʹैͬͯɺূ݊Խ ͞Εͨ༧Λചങ͢Δ •
ຊͰ͋·ΓΒΕͯ·ͤΜͶɺɺɺ
܈ऺͷӥஐ Wisdom of Crowds • 1ਓͷ༏Εͨఱ࠽͕Լ͢அΑΓɺී௨ͷਓ ͔ΒΔूஂ͕Լ͢அͷํ͕༏Ε͍ͯΔͱ ͍͏ݱ • ྫɿΰϧτϯڭतͱ༤ڇͷମॏͯେձ
ࢢϝΧχζϜ Market Mechanism • ܦࡁతΠϯηϯςΟϒʹΑΓޮతͳΛ ୡ • ʮൃݟతखଓ͖ͱͯ͠ͷڝ૪ʯbyϋΠΤΫ • ใूϝΧχζϜͱͯ͠ͷࢢ
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 • ܦࡁతΠϯηϯςΟϒ͕༧ଌΛͨΒ͢
͍͢͝ͱ͜Ζ 1. ༧ଌ͕ਖ਼֬ “Prediction Markets”, Wolfers and Zitzewitz
“Prediction Markets”, Wolfers and Zitzewitz
͍͢͝ͱ͜Ζ 2. දݱͷଟ༷ੑ • ෳબࢶͷ༧ଌ • ͷ༧ଌ • ͖݅ͷ༧ଌ
͍͢͝ͱ͜Ζ 3. ϦΞϧλΠϜੑ • Ձ֨(ʹ༧ଌ)ͷมԽ͕Θ͔Δ • χϡʔεͳͲͰ༧ଌ͕DynamicʹมԽ • ଞͷ༧ଌखஈʹݟΒΕͳ͍ಛੑ
༧ଌࢢͷՄೳੑ • ʮްੜ࿑ಇলͷ౷ܭʹෆਖ਼͕͋Δ͔ʁʯ ɹˠ෦ͷਓͷࠂൃΛಋ͚Δ͔ʢʁʣ • ʮຊͷՁ্ঢ˓%Λ͑Δ͔ʁʯ ɹˠਓʑͷظΛԽ͠ࡦʹ͑Δ͔ʢʁʣ
μϝͳͱ͜Ζ 1. ϚʔέοτͷσβΠϯ͕͍͠ 2. ๏తͳ • ຊͩͱṌത๏ͰΞτͰ͢^^ 3. ྲྀಈੑͷ֬อɺཧऀͷଛࣦ 4.
݁Ռͷղऍ͕͍͠
༧ଌࢢઈରతʹ༏Εͨ༧ଌखஈͰͳ͍ Ή͠Ζଞͷ༧ଌखஈͱิతͳؔ
༧ଌࢢͷཧ
༧ଌࢢͷϝΧχζϜ • Ͳ͏ͬͯՁ֨ΛܾΊΕ͍͍ͷ͔ʁ • Ձ͕֨༧ଌΛදͯ͠΄͍͠ • ͦͷ༧ଌਖ਼֬ͳͷͰ͋ͬͯ΄͍͠ • ࣗͷ༧ଌ௨Γʹਖ਼ʹചങͯ͠΄͍͠
࿈ଓμϒϧΦʔΫγϣϯํࣜ Continuous Double Auction Mechanism • ূ݊Λചങ • Πϕϯτ͕ൃੜͨ͠ͱ͖ʹ$1Β͑Δূ݊ •
ചΓจͱങ͍จΛͦΕͧΕఏग़ • ͕݅Ϛον͢Εఆ • גࣜࢢɺҝସࢢͳͲͱಉ͡Γํ • ͜ͷͱ͖Ձ͕֨֬Λදͯ͘͠ΕΔʂ(Why?)
࿈ଓμϒϧΦʔΫγϣϯํࣜ Continuous Double Auction Mechanism • Thin Market Problem •
ಛʹબࢶ͕ଟ͘ͳΔͱ૬ख͕ݟ͔ͭΒͳ ͍Մೳੑ • No Trade Theorem • ૬ख͕औҾ͠Α͏ͱ͢ΔͳΒʹͦΕʹԠ͡ ͳ͍ํ͕ྑ͍
ϚʔέοτϝΠΧʔํࣜ Automated Market Maker Mechanism • ࢢͷཧऀͱऔҾΛߦ͏ • ཧऀ͔ΒτʔΫϯΛߪೖ͠ɺཧऀ͕ใु Λࢧ͏
• Ձ֨ΛͲ͏ܾΊΕྑ͍͔ʁ →ϞσϧԽ͠·͠ΐ͏ʂ
είΞϦϯάϧʔϧ Scoring Rule • ֬Λਃࠂ͢Δɿ • είΞϦϯάϧʔϧ • ਃࠂ͞Εͨ֬ʹର͢ΔใुͷׂΓͯϧʔϧ S
= {s1 (r), ⋯, sn (r)} r = {r1 , ⋯, rn } si (r) r i Λਃࠂ͠Πϕϯτ ͕ൃੜͨ͠߹ʹΒ͑Δใु 120%, 225%, …
ϓϩύʔείΞϦϯάϧʔϧ Proper Scoring Rule • ࣗͷຊͷ༧ɿ • ϓϩύʔείΞϦϯάϧʔϧ • ਖ਼ʹਃࠂ͢Δ͜ͱͰظใु͕࠷େԽ͞
ΕΔΑ͏ͳείΞϦϯάϧʔϧ ̂ r = { ̂ r1 , ⋯, ̂ rn } ̂ r ∈ arg max r n ∑ i=1 ̂ ri Si (r)
ϓϩύʔείΞϦϯάϧʔϧͷྫ • Logarithmic Scoring Rule • Quadratic Scoring Rule si
(r) = ai + b log(ri ) si (r) = ai + 2bri − b n ∑ j=2 r2 j
ϚʔέοτείΞϦϯάϧʔϧ Market Scoring Rule • Scoring Rule͚ͩͩͱ̍ճਃࠂͯ͠ऴΘΓɺෳ ਓͷਃࠂΛѻ͑ͳ͍ • ஞ࣍తʹείΞϦϯάϧʔϧΛద༻
• ਃࠂΛɹɹɹɹɹɹɹͱ͍͏Α͏ʹࢀՃऀશ ମͰมԽ͍ͤͯ͘͞ r0 → r1 → ⋯ → r
ϚʔέοτείΞϦϯάϧʔϧ Market Scoring Rule • ਃࠂΛม͑ͨ࣌ͷใु • ࢀՃऀ࠷ऴతʹɹɹɹɹɹ͚ͩΒ͏ • ཧऀଛΛ͢ΔՄೳੑ
si (r) − si (r0) rold rnew ਃࠂΛ ͔Β ʹมߋͨ͠߹ɺ i Πϕϯτ ͕ൃੜͨ࣌͠ʹ si (rnew) − si (rold)Λࢧ͏ Proper Scoring Rule
LMSR • είΞϦϯάϧʔϧʹLogarithmic Scoring Rule Λ༻͍Δ߹ɺ Logarithmic Market Scoring Rule
(LMSR) ͱݺΕΔ • Ұ൪Α͘ΘΕΔϧʔϧ
ίετؔͱϚʔέοτϝΠΧʔ Cost-function-based Market Maker • ΑΓʮࢢΒ͘͠ʯ͍ͨ͠ʂ ূ݊ͷചങͱ͍͏Θ͔Γ͍͢ܗʹ • ূ݊ Πϕϯτɹ͕ൃੜͨ࣌͠ʹˈ̍ͦΕҎ֎ˈ̌
• ֤ূ݊ͷ૯ൃߦྔϕΫτϧ i i q = {q1 , ⋯, qn }
MSRͷ࠶ղऍ • ɹɹʮɹΛ༧ͨ࣌͠ʹ֤τʔΫϯ͕͍ͭ͘ ͑Δ͔ʯʹରԠ͍ͯ͠Δ • ͭ·ΓɹɹɹʹରԠ͢Δ • औҾʹΑͬͯɹɹɹɹɹɹɹɹͱมԽ͍ͯ͘͠ • ɹͷมԽɹͷมԽΛͨΒ͢
• ɹɹɹɹͰมԽ͍ͯ͘͠ q0 → q1 → ⋯ → q q s(r) r s(r) q r r s−1(q)
MSRͷ࠶ղऍ • Ձ֨ɹɹͱҰக͢ΔΑ͏ʹऔҾ͞ΕΔ • ͭ·ΓՁ͕֨ͪΌΜͱ༧ଌΛදͯ͘͠ΕΔʂ • Ձ֨ɹɹɹɹɹͰܾఆ͞ΕΔ • औҾͷࡍͷࢧֹ͍Ձ֨ؔͷੵ •
ͬ͘͟Γͱɹɹɹɹɹɹɹͭ·Γ • ίετؔɹɿՁ֨ؔͷݪ࢝ؔ r p = s−1(q) C C(qnew) − C(qold) p ∫ qnew qold p(q)dq
ίετؔ with LMSR • LMSRͷ߹ɺ ίετؔ Ձ֨ C(q) = b
log n ∑ j=1 exp ( qj − aj b ) pi = exp ( qi − ai b ) ∑n j=1 exp ( qj − aj b )
͓ΘΓʹ
ͬͱΓ͍ͨਓ • ʮී௨ͷਓͨͪΛ༬ݴऀʹม͑Δʰ༧ଌࢢʱͱ͍ ͏৽ઓུʯɺυφϧυɾτϯϓιϯ • ʮʰΈΜͳͷҙݟʱҊ֎ਖ਼͍͠ʯɺδΣʔϜζɾ εϩΟοΩʔ • “Prediction Market
: Theory and Application”, Leighton Vaughan Williams
Eagna • ”Eagna”ͱ͍͏αʔϏεΛӡӦɾ։ൃͯ͠·͢ • PCɺεϚϗͷϒϥβ্Ͱ༧ଌࢢΛແྉͰ ମݧͰ͖·͢ʢsign upඞཁʣ • Ϛʔέοτ͝ͱʹίΠϯΛ͢ΔͷͰɺͦ ΕΛͨ͘͞Μ૿͍ͯͩ͘͠͞ʂ
Eagna • ใु͋Γ·͢ʂ • ֫ಘͨ͠ίΠϯʹൺྫͯ֬͠తʹίʔώʔͷΪ ϑτ݊ΛΓ·͢ • eagna.ioͰݕࡧʂ • ϑΟʔυόοΫେܴͰ͢ʂ
None
༧ଌࢢษڧձ • ຊͷ༧ଌࢢίϛϡχςΟͱͯ͠ຖ݄ߦͬ ͍ͯ͘༧ఆͰ͢ • ݄݄̓͘Β͍ʹߦ͍·͢ • ࣌ɺձɺςʔϚconnpassͰʂ • ੋඇ࣍ճ͝ࢀՃԼ͍͞ʂ
Q&A
͋Γ͕ͱ͏͍͟͝·ͨ͠ʂ