Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-1 Model  Model Workshop - relating simulation models At EHESS,

Slides:



Advertisements
Similar presentations
Copyright 2000 Cadence Design Systems. Permission is granted to reproduce without modification. Introduction An overview of formal methods for hardware.
Advertisements

Model building. Primary purpose of modelling Quantitative and qualitative external models Model construction versus model use.
Overarching Goal: Understand that computer models require the merging of mathematics and science. 1.Understand how computational reasoning can be infused.
1 Introduction to Computability Theory Lecture12: Reductions Prof. Amos Israeli.
Analysing Systems Failures (1) Main Principles: systems thinking.
Chapter Two SCIENTIFIC METHODS IN BUSINESS
/department of mathematics and computer science Visualization of Transition Systems Hannes Pretorius Visualization Group
Distributed Cognition - outline Distributed Cognition Discussion about distributed cognition General discussion.
Computing Fundamentals 2 Overview Lecturer: Patrick Browne Room [KA] , Lab [KA] Based on Chapter 19. A Logical approach to Discrete Math.
Part III: Inference Topic 6 Sampling and Sampling Distributions
Beyond the design stance, AgentLink MSEA/ABSS SIG meeting, Barcelona 2003, slide-1 Beyond the Design Stance - losing some control.
6-1 Chapter Six DESIGN STRATEGIES. 6-2 What is Research Design? A plan for selecting the sources and types of information used to answer research questions.
Foundations This chapter lays down the fundamental ideas and choices on which our approach is based. First, it identifies the needs of architects in the.
Formal Methods 1. Software Engineering and Formal Methods  Every software engineering methodology is based on a recommended development process  proceeding.
Thinking Like a Modern Economist 6 Economics is what economists do. — Jacob Viner CHAPTER 6 Copyright © 2010 by the McGraw-Hill Companies, Inc. All rights.
RSBM Business School Research in the real world: the users dilemma Dr Gill Green.
1. An Overview of the Data Analysis and Probability Standard for School Mathematics? 2.
Checking and understanding Simulation Behaviour Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University.
Pushing the Security Boundaries of Ubiquitous Computing ACSF 2006 —————— 13 th July 2006 —————— David Llewellyn-Jones, Madjid Merabti, Qi Shi, Bob Askwith.
TEA Science Workshop #3 October 1, 2012 Kim Lott Utah State University.
When and why does haggling occur?, 1 st ESSA Conference, Groningen, September 2003, slide-1 When and why does haggling occur? -
Modeling & Simulation: An Introduction Some slides in this presentation have been copyrighted to Dr. Amr Elmougy.
Welcome to The 4 th International Workshop on MULTI AGENT BASED Melbourne, 14 th July.
1 Brief Review of Research Model / Hypothesis. 2 Research is Argument.
Formal Modelling (of social phenomena) A Specialist Method MRes, MMUBS Sepecial Methods – formal modelling, slide-1–
Validity & Practicality
The Process of Science Science is the quest to understand nature.
Big Idea 1: The Practice of Science Description A: Scientific inquiry is a multifaceted activity; the processes of science include the formulation of scientifically.
Standards for Mathematical Practice #1 Make sense of problems and persevere in solving them. I can: explain the meaning of a problem. choose the right.
1 Copyright © 2015, 2011, and 2007 Pearson Education, Inc. Start-Up Day 1.
Using the Experimental Method to Produce Reliable Self-Organised Systems, B. Edmonds, ESOA 2004, New York, July 2004, slide-1 Using.
Introduction to Research
1 Enviromatics Environmental simulation models Environmental simulation models Вонр. проф. д-р Александар Маркоски Технички факултет – Битола 2008.
Scalable Statistical Bug Isolation Authors: B. Liblit, M. Naik, A.X. Zheng, A. Aiken, M. I. Jordan Presented by S. Li.
An Examination of Science. What is Science Is a systematic approach for analyzing and organizing knowledge. Used by all scientists regardless of the field.
VIRTUAL WORLDS IN EDUCATIONAL RESEARCH © LOUIS COHEN, LAWRENCE MANION & KEITH MORRISON.
What is Modeling?. Simplifying Complex Phenomena v We live in a complex world v Most of the scientific relationships we study are very complex v Understanding.
SUPERCOMPUTING CHALLENGE KICKOFF 2015 A Model for Computational Science Investigations Oct 2015 © challenge.org Supercomputing Around.
A Model for Computational Science Investigations Supercomputing Challenge
Lesson 1 What is Science?. What do you wonder about when you see this picture? Inquiring Minds Want to Know.
M ATHEMATICAL P RACTICES For the Common Core. C ONNECTING THE S TANDARDS FOR M ATHEMATICAL P RACTICE TO THE S TANDARDS FOR M ATHEMATICAL C ONTENT The.
CMPT 880/890 The Scientific Method. MOTD The scientific method is a valuable tool The SM is not the only way of doing science The SM fits into a larger.
3.2 Semantics. 2 Semantics Attribute Grammars The Meanings of Programs: Semantics Sebesta Chapter 3.
Chapter 13 Repeated-Measures and Two-Factor Analysis of Variance
Applied Quantitative Analysis and Practices LECTURE#31 By Dr. Osman Sadiq Paracha.
Welcome to Physics--Jump in!
Bruce Edmonds, Centre for Policy Modelling 1 of 57 Supporting Social Simulation Physics vs. Biology Paradigms CPM’s approach to Social Simulation How SDML.
1 Statistics & R, TiP, 2011/12 Neural Networks  Technique for discrimination & regression problems  More mathematical theoretical foundation  Works.
Applied Quantitative Analysis and Practices LECTURE#30 By Dr. Osman Sadiq Paracha.
DEVELOPING AND USING MODELS IN SCIENCE
Yr 7.  Pupils use mathematics as an integral part of classroom activities. They represent their work with objects or pictures and discuss it. They recognise.
Scientific Method Review. Scientific Method Ask a Question: –state the purpose of what you are trying to figure out Form a Hypothesis: –a tentative explanation.
Chapter 2 The Research Process Text: Zechmeister, J. S., Zechmeister, E. B., & Shaughnessy, J. J. (2001). Essentials of research methods in Psychology.
1 Guess the Covered Word Goal 1 EOC Review 2 Scientific Method A process that guides the search for answers to a question.
Projection and the Reality of Routines – reflections of a computational modeller Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University.
Science and Engineering Practices K–2 Condensed Practices3–5 Condensed Practices6–8 Condensed Practices9–12 Condensed Practices Developing and Using Models.
Lesson 1-1 Nature of Science. QUESTIONS Communicate Observe Define scope of a Problem Form a testable Question Research the known Clarify an expected.
Stats 242.3(02) Statistical Theory and Methodology.
Programming paradigms
Common MBSE Modeling Questions and How Ontology Helps
Physical Science Chapter 1 Section 3.
Models, Scientific and Otherwise, and Theories
Web *.0 ? Combining peer production and peer-to-peer systems
Scientific Inquiry Unit 0.3.
Scientific Method Part 2.
Command Terms
Scientific Method Part 2.
Department of Computer Science Abdul Wali Khan University Mardan
Debate issues Sabine Mendes Lima Moura Issues in Research Methodology
Presentation transcript:

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-1 Model  Model Workshop - relating simulation models At EHESS, GREQUAM/CNRS Marseille, 2003 Welcome to the…

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-2 Organisation Juliette Rouchier durandal.cnrs-mrs.fr/GREQAM/cv/rouchier.htm David Hales Bruce Edmonds bruce.edmonds.name EHESS, GREQUAM/CNRS durandal.cnrs-mrs.fr/ehess/ehess.html Centre for Policy Modelling cfpm.org

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-3 Relating Models - an overview Bruce Edmonds (including material from David Hales and Juliette Rouchier)

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-4 Outline 1.Kinds of model, in particular comparing equation-based modelling and individual/agent-based simulation 2.Some basic ways in which models may be related or compared 3.Some uses for relating or comparing models

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-5 Part 1: Overview and Comparison of Equation-based Modelling and Agent-based Simulation

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-6 Equation-based modelling Model Target Equation-based Model Actual Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-7 Properties of (equation-based) Mathematical Models Long tradition, many techniques/results Central use of numbers and proof In simple cases can derive closed-form (i.e. general) conclusions Essentially about states –Atemporal (where time occurs it is reified) –Inference can works in may ways Assumptions necessary to represent world ‘Art’ of approximation and application

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-8 Individual-based simulation Model Target Agent-based Model Actual Outcomes Model Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes Agent-based

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-9 Properties of (agent/individual- based) Simulations Short tradition, fewer techniques/results Central use of algorithms and computation Difficult to derive general conclusions –More like an experiment than an inference Essentially about process –Temporal directionality –Process of unfolding observable More representational in practice –In time and in composition More suggestive of interpretation

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-10 Some Uses of Mathematics To predict what is currently unknown To explain what is already known To derive/infer conclusions from axioms To represent observed phenomena To explore unobserved possibilities To make an idea unambiguous To compress a representation To show one model is a special case of another To construct a formal framework/language

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-11 Some Uses of Simulation To predict what is currently unknown To explain what is already known To calculate outcomes from initial set-up To represent observed phenomena To explore unobserved possibilities To make an idea unambiguous To compress a representation (in theory) To show one model is a special case of another (in theory) To construct a formal framework/language

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-12 Part 2: Some Ways in which Models may be Compared or Related

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-13 A diagram for a simulation model Code Agents Setting Outcomes from one run

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-14 As a Summary/Abstraction of Another Model’s Results Modelling model results More abstract model can be of any kind (including simulations and equation-based models) Can be a tactic to help understand/analyse complex models

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-15 As a Generalisation/Specialisation of Another Model

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-16 As a Controlled Experiment/Comparison ?

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-17 Summary of Some Basic Kinds of Model Relation/Comparison Abstraction/approximation of another model Super/subset of another model As a Controlled Comparison The code of one model is a component of the code of another Others … ?

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-18 Part 3: Some Uses for Relating Models

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-19 To check models are equivalent Examination of the code suggests a hypothesis that the simulations are equivalent but… …this can only be disconfirmed by experiment Repeated failure to disconfirm hypothesis can lead one to rely on it Reveal hidden assumptions – understand the limits of our creations!

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-20 As a tool for staging abstraction Observations of the phenomena Descriptive simulation Equation-based models

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-21 Comparison to Inform Generalisation comparison Generalised Model

Model to Model Workshop, EHESS, Grequam/CNRS, Marseille 2003, slide-22 Summary of Some Uses of Relating Models Check if models are the same To reveal assumptions As a tool for staging abstraction To inform generalisation Communicate and compare complex phenomena between researchers And … ?