master Économie et affaires internationales paris dauphine -october 2007 dr. ramón mahía...

Master Économie et Affaires InternationalesMaster Économie et Affaires InternationalesParis Dauphine -October 2007

Dr. Ramón MahíaProfessor of Applied Economics Department

www.uam.es/ramon.mahia

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SIMULATING

http://www.uam.es/ramon.mahia

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SIMULATING STRUCTURE OF DOCUMENT AND EXPOSITION

Not a technical document

Our experience in credible simulation for

complex systems in the real world

Text focused on know-how to do useful real

simulations rather than technical skills

Simulation as a way of using econometrics

in a useful way with analytical restrictions

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SIMULATING STRUCTURE OF DOCUMENT AND EXPOSITION

I. What does Simulation mean?

Concept

Simulation Vs other topics

Some Simulation “last-names”

II. Basic elements of a Simulation Model

III. 20 Keys for an Efficient Simulation

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SIMULATING I. WHAT DOES SIMULATION MEAN?

“To represent something, feigning or

imitating what it is not”

Simplified representation of a real complex

system useful for:

Understanding the working of a real system

Experimenting with, for evaluating different

strategies to be developed on it.

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Real System:Real System:

A lot of elements A lot of elements inter-relatedinter-related

Simulated System:Simulated System:

Few elements and Few elements and selected relationsselected relations

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Arrival of powerful computers and flexible programming systems = generalized use of simulation

Modeler

Use of complex techniques

User

Use of simple interfaces

Simulation with a model is a wide spread exercise:

•Macro economic relations: trade, labor, supply – demand,•Financial markets: treasury ship, stock exchange, commodity prices…•Technology innovation processes •Business strategy games•Business organization games…

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FINANCE:FINANCE: Wall Street Raider Wall Street Raider

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MARKETING:MARKETING: MarktStrat MarktStrat

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BUSSINES ORGANISATION:BUSSINES ORGANISATION: EIS Game EIS Game

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MACRO-POLICY:MACRO-POLICY: National Budget Simulation National Budget Simulation

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Structural Analysis Forecasting

Simulation

Borders between Simulation, Forecast and

Structural Analysis are diffuse.

But the analytical approach, technical resources,

and ways of use permit us to distinguishes it.

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Simulations Vs. Optimization

Optimization systems concentrates mainly on

reaching a well predefined objective given a set of

restrictions.

Simulation is an open strategy that use the links

between inputs and outputs without setting a priori

what must be considered an optimum solution.

That’s why we usually say that simulation models are

not “resolved”, but “runned”.

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Simulations Vs. Optimization: Design car routes to

pick up employees to the factory from distant locations

OPTIMISATION SIMULATION

Objective function:

• Minimize time

Restrictions: • 3 cars• 1 hour to finish• 13 passengers

Inputs:• Cars•time to finish• # of passengers

Ouputs (results): • Route design

Results: • Route design

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Deterministic (MKT Mix effects evaluation)

Deterministic Inputs (controlled values):• Advertising effort• Price policy• Distribution policy • Sales Force

Random (Agricultural Crop Yield)

Random Inputs (not controlled values):• Climate conditions• Plagues

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Deterministic + Random (MKT Mix evaluation)

Deterministic Inputs (controlled values):• Advertising effort• Price policy• Distribution policy • Sales Force

Inputs to be randomly modeled (forecasted)• MKT Mix of existing or new competitors• Economic conditions of country• Market Demand (2nd stage input)

Reg

ress

ion

an

alys

is

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Static Vs. Dynamic: Does “passing of time” result in a key

variable (even an input) for the simulation system?

Example: Time as a basic input of a simulation system (two

real examples):

Economic impact of an immigration flow will not only depend on the

amount of immigration, but in the speed of it.

Effects on prices or trade flows of a tariff removal between two

areas, will drastically change if you consider a gradual removal or

not.

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Time as a basic input…..(example from Femise)

The inputs and outputs of the model will be

proportional distributed in a basis of a 7 years

scenarioThe inputs and outputs of the model will be

aggregated in a single year

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SIMULATING II. BASIC ELEMENTS OF A SIMULATION MODEL?

(i) System to be analyzed.

The collection of elements and its interactions which is

trying to be analysed by means of the simulation.

It is critical to identify the system (or sub-system) of

interest and concentrate the effort on a suitable

dimension, but…..

….without missing the links with the rest of the systems

or other collateral sub-systems

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SIMULATING II. BASIC ELEMENTS OF A SIMULATION MODEL

(i) System to be analyzed

TRADE BARRIES

PRODUCTION STRUCTURES

MACRO ECONOMIC ENVIROMENT

TRADE SUPPORT

TRADE FLOWS

POLITICAL ISSUES

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(ii) Analytical – Mathematical Model (an example for

international trade market equilibrium):

J

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Ad-Quantum Tariff Matrix

Ad-Valorem Tariff Matrix

Import Inverse

Function

Export Inverse

Function

Existing Quota

Regimes

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•Equilibrium reached making equal the inverse functions

of imports and exports revenues

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(iii) Inputs, Outputs, (coherent design of inputs

and outputs)

Tariffs to be removed

Span of time

Productivities

Fiscal pressure

Exchange rate

Saving rate

……

Changes in trade flows

Changes in trade prices

Changes in Va by sector

Changes in employment

Changes in fiscal revenues

……

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(v) Interface:

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SIMULATING III. 20 KEYS

Results analysis and validation of the simulation process

Try to understand the problem

Information recollection and treatment

Mathematical formulation of the model

Computer interface design

Validation of simulation model

Design of Simulation scenarios

Is there a simulation problem?

Try another approach

NO

YES

Does it work?

YES

NO

Prepare an offer to the client

If you have convinced…

Calibrate

Results analysis and validation of the simulation process

Try to understand the problem

Information recollection and treatment

Mathematical formulation of the model

Computer interface design

Validation of simulation model

Design of Simulation scenarios

Is there a simulation problem?

Try another approach

NO

YES

Does it work?

YES

NO

Prepare an offer to the client

If you have convinced…

Calibrate

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1.- 1.- Be sure that you a have a Be sure that you a have a simulation problemsimulation problem

Have to deal with a multivariate problem Can clearly identify inputs and outputs Input variables can vary in a wide range of values Output variables clearly respond to changes in inputs There is not a single scenario to be established

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2.- 2.- Offer feasibility of its Offer feasibility of its prospects and involve the end prospects and involve the end

users in the whole proposalusers in the whole proposal

Don’t make the mistake of offer maximum proposals and bear in mind its realism.

The final user usually prefers work proposals in which they feel an active part from the beginning

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3.- Program enough time to study Program enough time to study deeply the system to be deeply the system to be

analysedanalysed

The largest part of the technical decisions regarding the estimation, calibrating, scenario and interface design are conditioned by the comprehension of the elements and interrelations of the system to be analysed.

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4.- Integrate in your team Integrate in your team theoretical experts been familiar theoretical experts been familiar

with the systemwith the system “Research needs more heads than hands”. Save time, which could be used to improve the

technical issues for the simulation model Help even for taking technical decisions Establish a theoretical rigour to the whole of the

analysis Give reality to the simulation mechanism

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5.- Prioritise the wishes of users in all the stages of the

construction of the simulation model and take their advices

Nobody is interested in a technically refined tool that does not serve their interests

The users only want the model to be adjusted to their demands, nor the other way round.

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6.- 6.- Study “in deep” the work Study “in deep” the work carried out by otherscarried out by others

Originality must never be an aim in itself Explore previous main sources of data - limitations of

the exercise - different techniques available

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7.- 7.- Dedicate time to analyze all of Dedicate time to analyze all of the available datathe available data

"Measure twice, and cut once". Use homogeneous data Choose carefully the samples Assess the data provided by the end user Be extremely scrupulous in the handling of data Agree with the user that data to be used responds

faithfully to the reality perceived by him.

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8.- 8.- Explore the analytical - Explore the analytical - mathematical techniques that mathematical techniques that

best adapt to the systembest adapt to the system Its adaptation towards capturing the specific

phenomena observed in the specific system its feasibility in calculating its flexibility the quantity of theoretical hypothesis required its robustness towards eventual changes its simplicity the available resources

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9.- 9.- Try to adapt the analytical Try to adapt the analytical technique to the problem and technique to the problem and

not the other way roundnot the other way round

Guide the technical procedures by the suitability of the real characteristics observed in the system.

The technique used is only valid if it works, independently of the “objective” scientific considerations

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10.- 10.- Do not complicate the Do not complicate the technical models if it does not technical models if it does not

lead to clear benefitslead to clear benefits

"If your intention is to discover the truth, do it with simplicity and leave the elegance for the tailors."

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11.- 11.- Take care about the forecast Take care about the forecast power of the modelpower of the model

Try to anticipate the needs with regard to the prediction at the time of choosing the variables

Evaluate with the focus on “cross validation” its vulnerability to eventual prediction errors in the key variables

Avoid using single results as regards to the prediction; use always intervals of variation or alternative values with probabilities of occurrence.

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12.- I12.- If a prediction exercise is f a prediction exercise is necessary try to involve the necessary try to involve the user criteria in the interfaceuser criteria in the interface

"If you have to forecast, forecast often."

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13.- 13.- If inferential statistics are If inferential statistics are used, check the sensitivity of used, check the sensitivity of the system to changes in the the system to changes in the

estimationsestimations When the analytical procedure means the use of

statistical inference, the system can depend more or less critically on these estimations

Check the sensitivity of the results of the system against variations in the estimated coeficients

Check the robustness of the estimations with question such as changes in the data sample

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14.- 14.- Do not underestimate the Do not underestimate the political or qualitative aspects political or qualitative aspects of which experts advise youof which experts advise you

The systems are not capable of being modelled using purely quantitative structures.

It is not a question of choosing between a quantitative or qualitative approximation, rather than knowing how to combine both.

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15.- 15.- Let simplicity guide the Let simplicity guide the design of the interface in all of design of the interface in all of

its scoreits score

"The majority of the fundamental ideas in science are essentially simple and, as a general rule, they can be expressed in language understandable to everyone."

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16.- 16.- Call for software professionals into the design of

the interface

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17.- 17.- Establish different levels of Establish different levels of user for the handling of the user for the handling of the

interfaceinterface

Directors, politicians, media technicians, technical experts, etc.

“There is no inept user, only badly designed systems”.

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18.- 18.- Instruct the users on the Instruct the users on the correct use of the systemcorrect use of the system

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19.- 19.- Limit the use of the model to Limit the use of the model to real scenariosreal scenarios

The greatest part of the simulation systems can be labelled as “rubbish in / rubbish out”.

Design an interface which stops, or at least warns the user, of possible errors in the design of scenarios.

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20.- 20.- Ensure the perfect display of Ensure the perfect display of the resultsthe results

Pretest: Before giving the final ok to designs in this area, carry out several tests among your work colleagues to make sure that the results are understandable and do not oppose the proposals suggested to improve it.

master Économie et affaires internationales paris dauphine -october 2007 dr. ramón mahía...

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