Model and Scenario

Byeongsu Yu

Yonsei, Univ.

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

Story Line

• Introduction

• Showing actors and basis storyline

• How to make story

• Climax or Climaxes?

• Variation 1

• Variation 2

• Variation 3

• Variation 4

Introduction

• Fired Theater model

Introduction

• Fired Theater model

Introduction

• How about this?

– 기둥을 중간에 설치하면 어떻게 될까?

– 사람들이 가는 속도가 오히려 더 빨라진다!

Definition

• Model

– Depict phenomenon

– Space and Relationship

• Scenario

– What can occur in “Real World”

– Realized path

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?

Definition-Model

• Relationship

– Laws, Theorem, etc.

– How spaces can related with each others

• Set of n-tuples

– Is it function?

– Transitive, Symmetric, reflexive.

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.

Introduction

• Why model matter?

– Though, still models are best approximation of future.

– The diversity prediction theorem

• Collective Error = Average Individual Error-Prediction Diversity

Introduction

• Why model matter?

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)

How to make model

• Bayesian Approach

– X is observation. W_i = signal(Law)

– Posteriori = likelihood*Prior/evidence

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

How to make story

• Making a novel

– It shows real worlds concern, which we cannot imagine easily

• Making Simulation

– As we see above.

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

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

Exemplary

• 개똥 철학

– 장 뤽 고다르 – 내멋대로 해라

– 장근석… ㅠ_ㅠ

• Not all people know analogy.

Exemplary

• Humanities

– Literature

• Novels, awesome novels.

• Laws

– 오구라신페이(小倉進平) <향가, 이두에관한논문>

– 양주동 <‘청구학총’에실린원왕생가에대하여> 균여전의11수에나온한역을가지고삼대목의실전으로인한삼국유사에남은 14수를해석

– 조윤제 <시가의구체결정법칙 >

» 반절성

» 전절대후절소

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.

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

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!

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!

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.

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.

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!

Exemplary

• Et cetera

– Culinary

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.

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)

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

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.

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.

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.

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

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;

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?

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.

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.

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.

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.

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.

The End and QnA