1. Master Économie et Affaires Internationales Paris Dauphine -October 2007 Dr. Ramón Mahía Professor of Applied Economics Department www.uam.es/ramon.mahia 20 KEYS TO BETTER SIMULATING

2. WHAT DOES SIMULATION MEAN?  WHAT DOES SIMULATION MEAN?  WHY DO WE NEED SIMULATION MODELS?  EXAMPLES OF SIMULATION TOOLS  BASIC ELEMENTS OF A SIMULATION MODEL  MORE ON SIMULATION DEFINITION  20 KEYS TO BETTER SIMULATING

3. 20 KEYS TO BETTER SIMULATING WHAT DOES SIMULATION MEAN? A simulation model is an technical tool that help us/others to take decisions in complex systems.

4. 20 KEYS TO BETTER SIMULATING WHAT DOES SIMULATION MEAN?  Using a simulation tool we can evaluate the outcome of alternative decisions (simulation) in complex interrelated system……

5. 20 KEYS TO BETTER SIMULATING WHAT DOES SIMULATION MEAN?  ….or/and we can find the best decision for achieving a particular objective (optimization)….

6. 20 KEYS TO BETTER SIMULATING WHY DO WE NEED SIMULATION MODELS?  A real system use to be complex: a lot of elements, interrelations and uncertainty must be considered in the operating of the system in a way that seems difficult or impossible to anticipate the result of a decision.  That means that we need to try out, to evaluate decisions as they goes along; to experiment on reality.

7. 20 KEYS TO BETTER SIMULATING WHY DO WE NEED SIMULATION MODELS?  Sometimes we CAN’T make real tries on the system for evaluating alternative decisions because it is impossible or it is risky  Sometimes we CAN try out but it costly (we can’t try forever)  Sometimes we have already understood how a system works but we want to settle down and formalize this operating in a technical way.

8. 20 KEYS TO BETTER SIMULATING WHY DO WE NEED SIMULATION MODELS?

9. 20 KEYS TO BETTER SIMULATING EXAMPLES OF SIMULATION TOOLS Powerful computers, flexible programming systems and new strategies to deal with uncertainty = generalized use of simulation tools, not only for simple reproduction of preexisting mathematical models but also for deductive construction of simulation tools for understanding and manage some economy dynamics that still unknown even in theory.

10. You can easily find simulation tools in: Real Life Macro economic relations: trade, labor, supply – demand, Financial markets: treasury ship, stock exchange, commodity prices… Technology innovation processes Business strategy games Business organization games… 20 KEYS TO BETTER SIMULATING EXAMPLES OF SIMULATION TOOLS

11. 20 KEYS TO BETTER SIMULATING EXAMPLES OF SIMULATION TOOLS Real Life

12. 20 KEYS TO BETTER SIMULATING EXAMPLES OF SIMULATION TOOLS FINANCE

13. 20 KEYS TO BETTER SIMULATING EXAMPLES OF SIMULATION TOOLS MARKETING

14. 20 KEYS TO BETTER SIMULATING EXAMPLES OF SIMULATION TOOLS BUSSINES ORGANISATION

15. 20 KEYS TO BETTER SIMULATING EXAMPLES OF SIMULATION TOOLS INDUSTRY

16. 20 KEYS TO BETTER SIMULATING EXAMPLES OF SIMULATION TOOLS MACRO-POLICY

17. 20 KEYS TO BETTER SIMULATING EXAMPLES OF SIMULATION TOOLS INTERNATIONAL - TRADE

18. 20 KEYS TO BETTER SIMULATING BASIC ELEMENTS OF A SIMULATION MODEL SYSTEM MODEL

19. 20 KEYS TO BETTER SIMULATING BASIC ELEMENTS OF A SIMULATION MODEL Input variablesTechnical model Output variables User Interface Technique Interface

20. 20 KEYS TO BETTER SIMULATING BASIC ELEMENTS OF A SIMULATION MODEL?  (i) System to be analyzed: The collection of elements and its interactions to be analysed by means of the simulation.  (a) It is crucial to identify the “sub-system” of interest and concentrate the effort on a suitable dimension, …… (of course without missing the links of our selected sub- system with the rest of the systems or other collateral sub-systems)

21. 20 KEYS TO BETTER SIMULATING BASIC ELEMENTS OF A SIMULATION MODEL  (i) System to be analyzed TRADE BARRIES PRODUCTION STRUCTURES MACRO ECONOMIC ENVIROMENT TRADE SUPPORT TRADE FLOWS POLITICAL ISSUES

22. 20 KEYS TO BETTER SIMULATING BASIC ELEMENTS OF A SIMULATION MODEL?  (i) System to be analyzed: The collection of elements and its interactions to be analysed by means of the simulation.  (a) It is crucial 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  (b) It is crucial to identify the right representation of the system that best fit our simulation aims.

23. 20 KEYS TO BETTER SIMULATING BASIC ELEMENTS OF A SIMULATION MODEL

24. 20 KEYS TO BETTER SIMULATING BASIC ELEMENTS OF A SIMULATION MODEL  (ii) Technical Model:  There exists different technical solutions for different objectives (forecasting, evaluating, optimizing, …….)  …. and restrictions given (uncertainty, data, time, skills, theoretical requirements)…  So choosing the technique wont be easy....  If different alternatives can be technically chosen, let simplicity lead your decision (simplicity of construction, of updating, of use…)

25. 20 KEYS TO BETTER SIMULATING BASIC ELEMENTS OF A SIMULATION MODEL  (ii) Technical Model (an example for international trade market equilibrium): Ad-Quantum Tariff Matrix Ad-Valorem Tariff Matrix Import Inverse Function Export Inverse Function Existing Quota Regimes Equilibrium reached making equal the inverse functions of imports and exports revenues

26. 20 KEYS TO BETTER SIMULATING BASIC ELEMENTS OF A SIMULATION MODEL  (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  ……

27. 20 KEYS TO BETTER SIMULATING BASIC ELEMENTS OF A SIMULATION MODEL  (v) Interface:

28. 20 KEYS TO BETTER SIMULATING MORE ON SIMULATION DEFINITION  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 “runned” and optimization models are “solved”.

29. 20 KEYS TO BETTER SIMULATING MORE ON SIMULATION DEFINITION  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

30. 20 KEYS TO BETTER SIMULATING MORE ON SIMULATION DEFINITION  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

31. 20 KEYS TO BETTER SIMULATING MORE ON SIMULATION DEFINITION  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) Regression analysis

32. 20 KEYS TO BETTER SIMULATING MORE ON SIMULATION DEFINITION  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.

33. 20 KEYS TO BETTER SIMULATING MORE ON SIMULATION DEFINITION  Time as a basic input….. (example from Femise)

34. 20 KEYS TO BETTER SIMULATING

35. 1.- Be sure that you a have a simulation 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

36. 20 KEYS TO BETTER SIMULATING 2.- Offer feasibility of prospects and involve the end users 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

37. 20 KEYS TO BETTER SIMULATING Program enough time to study deeply the system to be analysed 3.- Program enough time to study deeply the system to be analysed  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.

38. 20 KEYS TO BETTER SIMULATING Integrate in your team theoretical experts been familiar with the system 4.- Integrate in your team theoretical experts been familiar with 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

39. 20 KEYS TO BETTER SIMULATING 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.

40. 20 KEYS TO BETTER SIMULATING 6.- Study “in depth” the work carried out by others  Originality must never be an aim in itself  Explore previous main sources of data - limitations of the exercise - different techniques available

41. 20 KEYS TO BETTER SIMULATING 7.- Program enough time for exploring deeply the 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.

42. 20 KEYS TO BETTER SIMULATING 8.- Explore the analytical - mathematical techniques that best 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

43. 20 KEYS TO BETTER SIMULATING 9.- Try to adapt the analytical technique to the problem and not 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

44. 20 KEYS TO BETTER SIMULATING 10.- Do not complicate the technical models if it does not lead to clear benefits  "If your intention is to discover the truth, do it with simplicity and leave the elegance for the tailors."

45. 20 KEYS TO BETTER SIMULATING 11.- Take care about the forecast power 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.

46. 20 KEYS TO BETTER SIMULATING 12.- If a prediction exercise is necessary try to involve the user criteria in the interface  "If you have to forecast, forecast often."

47. 20 KEYS TO BETTER SIMULATING 13.- If inferential statistics are used, check the sensitivity of the system to changes in the estimations  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

48. 20 KEYS TO BETTER SIMULATING 14.- Do not underestimate the political or qualitative aspects of 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.

49. 20 KEYS TO BETTER SIMULATING 15.- Let simplicity guide the design of the interface in all of its score  "The majority of the fundamental ideas in science are essentially simple and, as a general rule, they can be expressed in a way that everyone can understand them."

50. 20 KEYS TO BETTER SIMULATING 16.- 16.- Call for software professionals into the design of the interface

51. 20 KEYS TO BETTER SIMULATING 17.- Set different levels of interface users  Directors, politicians, media technicians, technical experts, etc.  “There is no inept user, only badly designed systems”.

52. 20 KEYS TO BETTER SIMULATING 18.- Instruct the users on the correct use of the system

53. 20 KEYS TO BETTER SIMULATING 19.- Limit the use of the model to real 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.

54. 20 KEYS TO BETTER SIMULATING 20.- Ensure the perfect display of the 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.