2. Contents
• Introduction
• Definition
• How to make model
• Exemplary
– Dim 1 : Abstract/Concrete
– Dim 2 : Common Sense/Systemized
– Dim 3 : Similarity of methodology
– Dim 4 : How disciplinarys can be made?
• Conclusion
3. Story Line
• Introduction
• Showing actors and basis storyline
• How to make story
• Climax or Climaxes?
• Variation 1
• Variation 2
• Variation 3
• Variation 4
7. Definition
• Model
– Depict phenomenon
– Space and Relationship
• Scenario
– What can occur in “Real World”
– Realized path
8. Definition-Model
• Space
– Sets of assumption
• Actors, Outcomes, Actions, Things, etc.
– Is it measurable? (or well-defined?)
• Finite, Countable, Uncountable
– How many dimensions it have?
• Dimension is number of sets in space
– Can we make disjoint set?
9. Definition-Model
• Relationship
– Laws, Theorem, etc.
– How spaces can related with each others
• Set of n-tuples
– Is it function?
– Transitive, Symmetric, reflexive.
10. Introduction
• Why scenario matter?
– Because it shows what we can’t imagine easily,
especially making model.
– Ex: Prey-Predator
• 풀, 양, 늑대
– Ex : So many historical examples
• The Great depression, Maginot line, etc.
11. Introduction
• Why model matter?
– Though, still models are best approximation of
future.
– The diversity prediction theorem
• Collective Error = Average Individual Error-Prediction
Diversity
13. How to make model?
• There is a coin. We know P(Head) = P(Tail) = ½.
We experimented 100 times, and All the
observations are head.
• You have to put your money on this game.
Which side you want to choose?
– Head
– Tail
– Not both (Standing on)
14. How to make model
• Bayesian Approach
– X is observation. W_i = signal(Law)
– Posteriori = likelihood*Prior/evidence
15. How to make model
• Meaning of bayesian approach
– Depict humans approach of predict something
• Prior can help us to predict distribution
– We don’t even know parameters
• Parameter is critical value of relationship
• Actually, Bayesian approach assumes that parameter
follow normal distribution, which is consistent with the
law of large number
– Making model with few data
16. How to make story
• Making a novel
– It shows real worlds concern, which we cannot
imagine easily
• Making Simulation
– As we see above.
17. Exemplary
• Abstract v. Concrete
– Model always delete real world’s thing
• It want to know two thing’s relationship
– However, it makes really concrete process if we
combine models
18. Exemplary
• Common sense
– Analogy : root of Model and scenario
• Both are mixed in analogy
• Very easy to understand
• Actually, model is just another analogy to strictly logical
things.
– 개똥철학
19. Exemplary
• 개똥 철학
– 장 뤽 고다르 – 내멋대로 해라
– 장근석… ㅠ_ㅠ
• Not all people know analogy.
20. Exemplary
• Humanities
– Literature
• Novels, awesome novels.
• Laws
– 오구라 신페이(小倉進平) <향가, 이두에 관한 논문>
– 양주동 <‘청구학총’에 실린 원왕생가에 대하여> 균여전의
11수에 나온 한역을 가지고 삼대목의 실전으로 인한
삼국유사에 남은 14수를 해석
– 조윤제 <시가의 구체 결정법칙 >
» 반절성
» 전절대 후절소
21. Exemplary
• Humanities
– History
• E.H.Carr vs. John Lewis Gaddis
• History always explain particular cause and effect, but
they do not claim that it is general law of the world
• Historian always recognize that their matters are real
world’s all viewpoint things, which have infinitely many
dimension and uncountable sets.
22. Exemplary
• Philosophy
– Philosophy : deal with linguistic concept and its
property with linguistic property
• It is difficult to make disjoint concept
• Dimension of things are uncountable and infinite
dimension
• Also difficult to measure
23. Exemplary
• Similarity of Methodology
– Science : move from philosophy to mathematics
• Economics : Aristotle, Keynes and Hayek -> Arrow
– Needless to say
• Politics : Platon and Marx -> Dahl
– Incentive model of party system
• Sociology : Durkheim, Marx, Weber -> Yonghak Kim
– Network yeah!
• Psychology : We are not social science, idiot!
– Brain works!
24. Exemplary
• Similarity of mathematics in social science
– Actor’s or Societies optimization problem
• Difference of assumptions in social science
– Economics : Incentive, All model.
– Politics : Incentive, Party model.
– Sociology : (Incommensurable) Incentive, Network
model.
– Psychology : We people don’t want to bother with
your social something, jerk!
25. Exemplary
• Similarity of philosophy with Natural Science
– Superstring things in physics
– Inference how things to be done like this world
• Make physical/Biological inference from WWII.
• Absent of measurable model in Evolutionary Science
• Difference of other science club
– Almost all things can be measurable.
26. Exemplary
• Practical displinary
– Law
• Okay, you can see me philosophical things, but we are
not philosophy because we always deal with disjoint
concept which is determined by the Supreme Court!
• Also we capture every things from other disciplinary we
want to justify.
– Coase theorem in economics
– Deconstructionism in Philosophy
– Temporary Insanity from Medical school
– Etc.
27. Exemplary
• Practical Law
– Business
• Actually, we are not practical law. We are pure law,
because many pure scientists come to me to do
something. Hahaha, money talks!
– Information Retrieval and Pattern recognition in Accounting
– Actuarial Science
– Finance : Please call me Stochastic Calculus, not business.
– Strategy : Porter –> Christenson
– Marketing : Call me Quant marketing.
– Mgmt Science : We even are not business, idiot!
29. Exemplary
• Summary
– All models have two parts;
• Measurable part and not or difficult to measurable part
– Method is similar between sciences
– So what really matter is find the line between
measurable set and unmeasurable set
• Something we dared not to measure can be measured
by many tools.
– CERN, Microscope, Flow, etc.
30. Making Disciplinary
• How we can make these method?
– Model of making model
• There are three scenario for model of making
model
– Falsifiability (Popper)
– Revolution! (Kuhn)
– Proof and Refutation(Lakatos)
31. Making Disciplinary
• Falsifiability
– First of all, all theories have to be falisifiable.
Namely, It can be false.
– Secondly, for getting approach to truth, we have
to alter prior theories with posterior theory which
have more explanatory power.
– Actually, Popper suppose Falisfiability to solving
problem of demarcation
32. Making Disciplinary
• Falsifiability
– In my point, falsifiability means we have to deal
with models on only measurable set.
– Also, what we have to do is comparing between
models in point of sets and relation sets.
33. Making Disciplinary
• Problem of Falsifiability
– Without loss of generality, our first model is
randomly chosen and adopting new model is
continuously differ.
– Then, we can find a relation between model and
its explanatory power contiuously on infinite set.
– If this relation is not linear, our maximum quantity
of truth is lower than absolute maximum quantity.
34. Making Disciplinary
• No free lunch Theorem
– From viewpoint of local maximization, finding
model is finding optimal solution for truth.
– Wolpert and Macready proved that “any two
optimization algorithms are equivalent when their
performance is averaged across all possible
problem.”
– It says we have to adopt one model with
continuously mapping, its explanatory power
must be reduced on some problems.
35. Making Disciplinary
• Scientific Revolution
– Paradigm : universally recognized scientific
achievements that, for a time, provide model
problems and solutions for a community
researchers
• What is to be observed and scrutinized
• Questions that are supposed to be asked
• How these questions are to be structured
• How interpreted, experimented
36. Making Disciplinary
• Scientific Revolution
– Normal Science
– Scientific Revolution
– (Altered) Normal Science
• Changing Model is not gradual process; It is
radically changed.
• Also, two models are incommensurable;
37. Making Disciplinary
• Problem of Kuhn
– Relativism; Then how we can choose one model
with another model? Just do what others do?
– Are these models really incommensurable?
38. Making Disciplinary
• Proof and Refutation
– First suppose some conjecture exists
– Three types of refutation
• Global refutation without local refutation
• Local refutation without Global refutation
– Making Monsters!
– Excluding Monsters.
• Global and also local refutation
– Change conjecture with more general version.
39. Making Disciplinary
• Sketch Book for v-e+f =2
– Imagine God as a math teacher and Geniuses
(Euler, Gauss, Cauch, etc.) as his/her students on
math class.
40. Conclusion
• Don’t exclude persons who have different
model. But exclude persons without agreeing
that comparing models with you on decision
making.
• Do simulation or imagine a lot of scenario.
Even if we have models, we actually don’t
know how the model works. Even if these
things are messy, please read novels.
41. Conclusion
• Independent n model’s is better than one
fancy model. There is no free lunch on model.
– So modulation really matter on your organization
or your personal decision.
– Make your own team for getting better decision
• Learning is different with knowing. Please
have a proficiency on your selected model
– Proficiency really matter on blink.
42. Conclusion
• Model and scenario are equally important.
Model says on structures we cannot see with
limited information, but scenario says on
plausible things with full information but
without structure.
– Always we have to concern both.
6.6.2 이때 오구라 신페이가 이걸보고 ‘으음 대략 조선에도 학문하는 애가 있구나.’ 하고 인정하게 됨. 6.7 이때 오구라 신페이는 동경제대 교수로 옮겨갔는데, 여기서 일본 교수와 조선 향가의 완성이 8구체냐 10구체냐를 가지고 논쟁을 벌이게 됨. 오구라 신페이는 조선 향가의 완성이 4구체 -> 8구체로 보고, 일본 교수중 한명은 4->8->10구체로 봤음.6.7.1 이때 조현재가 소설사를 때려치고(...) 시가연구로 들어가게 됨.6.8 조윤제가 연구해서 법칙화한 시가의 구체 결정 법칙이 나옴.6.8.1 (A) 반절성 : 4-> 8 반으로 잘라서 볼 수 있음. 또한 10구체 역시 앞 8구의 전절과 뒤 2구의 후절로, 반으로 나눌 수 있음. 이는 8구 끝의 감탄사와 9구 초의 감탄사로 알 수 있음.6.8.2 (B) 전절대 후절소 법칙 : 8구까지가 전절인데, 이 전절은 길고, 9-10구의 후절은 짧음. 절의 구분은 감탄사로 됨.6.8.3 시조는 매우 잘 적용이 되었음. 그러나 가사는 잘 안됨.