Explaining doping A macro-micro-macro model

1. Explaining dopingA macro-micro-macro model Werner PitschEike EmrichMarkus Klein Saarland University Institute for Sport Sciences Frank Schröder, Jens Flatau, Monika Frenger, Peter Maats, Benedikt Dessauer

2. Explaining doping Outline • Theories, applicable to the phenomenon „Doping“ • Development of a multi-level-explanation: • Methodological challenges and solutions from computer science • A macro-model of the doping market • A micro-model of individual agents • Linking the models together • Planned research and already started projects

3. Explaining doping Theories for Explaining doping • Systems theory: Doping as an immanent phenomenon in modern elite sports, that compels or at least pushes athletes to dope via „doping traps“ emerging from non-intended consequences of unrelated individual behaviour. • Economics: game theoretical models (mostly based on the prisoners’ dilemma scheme; p.i. Haugen 2004) which explain why a Nash equilibrium in the field of doping implies D-D-strategies.

4. Explaining doping Theories for Explaining doping • Problem 1: Both theories can explain, why athletes engage in doping, but none of them gives a positive answer to the question, why athletes do not dope! • Problem 2: None of the explanations is suitable to empirical testing if it is appropriate.

5. Explaining doping The “bathtub”-methodology collective phenomenon (t2) macro-level individual behaviour individual situation micro-level • The problem that is to be explained is a phenomenon at the collective level. • The phenomenon emerges from independent individual decisions. • Individual agents interact and produce the phenomenon at the collective level. social situation (t1)

6. Explaining doping The “bathtub”-methodology social situation (t1) collective phenomenon (t2) macro-level individual situation individual behaviour micro-level • Collective opportunities and restrictions to individual behaviour. a c b • The logic of selecting the appropriate behaviour depending on individual needs and collective preconditions for action. • The logic of aggregating individual decisions to a collective phenomenon.

7. Explaining doping Sketching out a macro-micro-macro-theory • Model the consequences of (anti-)doping behaviour at a macro-level in the tradition of the economic analysis of law (p.i. Becker 1974; Posner 1986) and/or the economics of drug use (p.i. Koboldt 1995). • Model the individual decision processes in the course of individual careers determined by changing restrictions over time and differing restrictions by different sports.

8. Explaining doping Sketching out a macro-micro-macro-theory Difficulties in finding an analytic solution: Success in sport is a result of a complex function, taking account of • genetic, environmental, biographical and – in the doping-case – pharmaceutical preconditions of performance • the performance density • the impact of random processes in competition • collective preconditions for individual success emerging from overlapping generations of athletes

9. Explaining doping Sketching out a macro-micro-macro-theory Limited use of an analytic solution: Analytic solutions argue basing on an idealized average athlete. Sport organizations are not only interested in the overall effect of shifts in Anti-Doping strategies but also in specific effects for subgroups among the collective of athletes. Solution: Implementing a multi agent based simulation model

10. Explaining doping Social scientific multi-agent-based modelling   Model System

11. Explaining doping Step 1: modelling a doping-market basic model equation (cf. Berentsen, 2002): with:

12. Explaining doping Step 1: modelling a doping-market Expanding this basic model to a model of a ‘doping market’ shows: • The higher the sanctions and the higher the probability of sanctions are the more ressources are used for masking doping. With restrictive anti-doping-rules, threatening also the doping racketeers: • The market will diminish to fewer suppliers of doping substances/methods, complemented by masking techniques and … • … to fewer athletes with high incomes who can afford doping.

13. Explaining doping Step 2: „Doping“ as a result of a rational choice costs uses Doping Supply costs Moral costs Health costs Sanctions Prestige loss Prize money Promotion contracts Non-monetary uses Prestige gains Psychic use Non-Doping Prize money Promotion contracts Non-monetary uses Prestige gains, Psychic use • Basic assumption: people (also athletes) try to maximize their use. • Decisions for doping are met basing on an analysis of costs and uses.

14. Step 2.1: modelling the use of sport The use of sport in the non-doping-case: Explaining doping

15. Explaining doping Step 2.2: modelling the use of doping The doping-non-sanction-case:

16. Explaining doping Step 2.2: modelling the use of doping The doping-sanction-case:

17. Explaining doping „Doping“ as a result of a rational choice Erfolgs-wahrschein-lichkeit probability of success probability of ascription costs uses Doping Supply costs Moral costs Health costs Sanctions Prestige costs Prize money Promotion contracts Non-monetary uses Prestige gains Psychic use Non-Doping Prize money Promotion contracts Non-monetary uses Prestige gains, Psychic use individual determinants for performance performance density • Costs as well as uses arise only with certain (sport-, level- and ranking-specific) probabilities which have to be calculated for secure widely unknown risk of being sanctioned

18. Explaining doping Step 2.3: modelling doping decisions The use of competing:

19. Explaining doping Step 3: linking the models together Model 1: high-level abstract derivations of collective effects in a doping market identification of desiderata for research empirical testing parameters:prices, suppliers, prob. of sanction parameters: average seU‘s,number of ‘dopers’ empirical description Model 2: Individual doping decisions

20. Explaining doping Step 4: calibrating the model „Calibration“ means (in the terms of classical test theory) to construct links to the empirical relative. Need for calibration:

21. Explaining doping Step 4: calibrating the model Projects started so far: • Description of the monetary and non-monetary use of elite sport (Umon, Unmon) by document analysis of prize money payments and regular non-monetary rewards from elite sport supporting systems. • Measuring the perceived shift in ranking probability by a web-based questionnaire

22. Explaining doping Step 4: calibrating the model Projects just in the beginning: • Description of the public prestige use from elite sport (Upre) by analyzing the media coverage of competitions. • Estimating the Tradeoff-Factors for prestige use of sport basing on in-depth-interviews with elite athletes. • Estimating the psychic use of doping and of non-doping, basing on in-depth-interviews with elite athletes.

23. Explaining doping Step 4: calibrating the model Planned projects: • Estimating the proportion of deviant/non-deviant subcultures in elite sport (Cooperation with Prof. Møller, Aarhus University)‏

24. Explaining doping Thank you very much for your attention

25. Explaining doping References Becker, G. S. (1974). Crime and Punishment: An Economic Approach. In G. S. Becker & W. M. Landes (Hrsg.), Essays in the economics of crime and punishment (S. 1-54). New York: Columbia University Press. Berentsen, A. (2002). The economics of doping. European Journal of Political Ecomomy, 18, 109-127. Haugen, K. K. (2004). The Performance Enhancing Drug Game Journal of Sport Economics, 5, 67-86. Koboldt, C. (1995). Ökonomik der Versuchung. EIne Erweiterung der ökonomischen Sozialvertragstheorie und ihre Anwendung auf das Drogenverbot. Tübingen: Mohr. Posner, R. A. (1986). Economic analysis of law (3. Aufl.). Boston: Little, Brown and Co.