the fight against doping · by doping behavior committed in their social network. • for instance,...

The Fight against Doping An agent-based Analysis

Daniel Westmattelmann ∙ Marcel GoeldenSascha Hokamp ∙ Gerhard Schewe

© tdwsport

Can we measure the incommensurability?

1Westmattelmann/Goelden: The Fight against Doping

Situation

Different fields of our social and economic life seem to be incommensurable (extend of tax evasion; racism; organized crime; homophobia etc.)

Game theory attempt to solve this questions

Complication

Keyquenstion

Doping in elite-sport becomes increasingly important˃ Balko˃ Armstrong˃ Operation Puerto (Fuentes)˃ …

How can we provide recommendationsfor the fight against doping by using

agent-based modeling (ABM)?

What research tells us about the doping rate.

Because not all doping practices are detectable and encompassing doping controls

are not feasible, the true extent of doping can only be estimated.


Do you know the„real“ doping rate?

We found that itseem to be

between

1% - 72%...

… depends on whoyou ask!

The players be on the field „Anti-Doping“

Objects below build up the main channel of interaction within our ABM.


Athletes

Anti-doping agency Doping laboratory

• Heterogeneous population.• … compete in each period.

• Usage of doping improves chances of success in the same period.• Each athlete pursues his career for a limites periode of time.

Players

• … announces anti-doping rules

and therefore also the complexity.

• … imposes penalties.

• … issues statistics on doping.

• …executes anti-doping control.

• Doping detection depends on

test efficiency.

What types sof athletes do we have?

According to the ABM of Hokamp & Pickhardt (2010), we consider 4 agent types


Athletes

Agent types

… athletes may use doping with

respect to an expected utility

maximization approach

… athletes are strongly influenced

by doping behavior committed

in their social network.

• For instance, social network

equates to professional

cycling team.

… athletes always act compliant to the rules of the system.

… athletes want to act rule-

consistent but may commit

doping unintentional (lack of

knowledge about doping).

• Doping behavior depends on

complexity of the anti-doping rules.

Rational (A-types) Suggestible (B-types)

Moral (C-types) Erratic (D-types)

How to create/measure performance in an ABM?

The athlete‘s individual performance is the basis for the competition results.


Fitness

Fitness weighting coefficient x fitness˃ Increase wiht training and/or doping˃ Positive effect over some periods

𝑓𝑡 = 𝑓𝑡−3 1 + 𝑑𝑡−2 ∗ ψ ∗ 0,25 1 + 𝑑𝑡−1 ∗ ψ ∗ 0,5 1 + 𝑑𝑡 ∗ ψ

Consti-tution

Random factor

Constituion weighting coefficient x constitution˃ Damage of doping – adverse reaction˃ Negative effect in short and longterm

𝑐𝑡 = ct−7 1 − dt−7 ∗ ξ ∗ 0,25 1 − dt−6 ∗ ξ ∗ 0,5 1 − dt−5 ∗ ξ ∗ 0,751 − dt−4 ∗ ξ 1 − dt−3 ∗ ξ ∗ 0,75 1 − dt−2 ∗ ξ ∗ 0,5 1 − dt−1 ∗ ξ ∗ 0,25

Random weighting coefficient x random factor˃ All other factors of performance˃ Strategy, material, enviroment etc.

Performance of the

athletes

𝜶 ∗ 𝒇𝒕

𝜷 ∗ 𝒄𝒕

𝜸 ∗ 𝒓𝒕

dt doping decision

ψ doping efficiency

ξ damage from doping

Simulation process

After running initial rounds, simulation cycle will be repeated


Simulationcycle

II. Competition

III. Disposed ranking

IV.Execution

of anti-doping control

V. Renewal

of ranking

VI. Allocation of income

VII.Publication of statistics on doping

I. Ageing of athletes

I. Athletes age each period until they die and will be replaced by an agent at minimum age.

II. Doping decision is taken and competition will be conducted.

III. Ranking with clean and doped athletes.

IV. Because test efficiency and frequency is imperfect, not every doper will be caught.

V. Detected doping sinners are removed from ranking.

VI. Income will be distributed on the basis of renewal ranking.

VII. Anti-doping agency issues extent of doping and other statistics.

Can the model be extend?

Back controlling could bring the race between the hare and the tortoise to an end.


2004 2008 2012

• Novel performance enhancing drugs are used before corresponding analytical detection methods exist.

• WADA is empowered to keep doping control samples safe for up to eight years.

Gap between use of new doping practices and its detectability will be closed.

2000

?

2016

?

2013

Conclusion and outlook

Basic framework is build, and within the next time it needs to be extended by

adapting various features.


>Backcontrolling>Prevention>Increasing testing frequency>Diagnostics

Model extensions andbudget allocation Assistance to WADA, NADOs, etc.

>Stress Tests for every situation>Generating new ideas>Discussion of concepts before

launching

>True detection probability is unknown>Ex-post validation>Differents sports can be handle

different

>Detailled distinction between different prohibited substances and methods.>Intelligent testing

Conclusion and outlook

Model validation Support of other research fields

The Fight against Doping An agent-based Analysis

Daniel Westmattelmann ∙ Marcel GoeldenSascha Hokamp ∙ Gerhard Schewe

© tdwsport

Back-up – epidemiology – literature overview


Category Author Year Title NExtend of

doping

Direct analytical

evidence

WADA 2012 Laboratory Testing Figures 243.193 ~2%

NADA Germany 2012 NADA Jahresbericht 5.087 ~1%

Forensic

approach (ABP) Sottas et al. 2011

Prevalence of Blood Doping in Samples Collected

from Elite Track and Field Athletes7.289 14-22%

Self reports

Plessner/ Musch 2002 Wie verbreitet ist Doping im Leistungssport? 467 >34%

Pitsch et al. 2007Doping in elite sports in Germany: results of a www

survey448 7%

Striegel et al. 2010Randomized response estimates for doping and

illicit drug use in elite athletes480 25-48%

Breuer/ Hallmann 2013 Dysfunktionen des Spitzensports 1.154 6%

Projections

Anshell et al. 1991A survey of elite athletes on the perceived causes

of using banned drugs in sport126 72%

Waddington et al. 2005 Drug use in English professional football 706 6%

Petróczi et al. 2008Comfort in big numbers: Does over-estimation of

doping prevalence in others indicate self-

involvement?

124 35%

Uvacsek et al. 2011Self-admitted behavior and perceived use of

performance-enhancing vs psychoactive drugs

among competitive athletes

82 36%

James et al. 2013 A potential inflating effect in estimation models 513 58%

ABM – performance function

The athlete‘s individual performance is the basis for the competition results.


𝑷𝒕 = 𝜶 ∗ 𝒇𝒕 + 𝜷 ∗ 𝒄𝒕 + 𝜸 ∗ 𝒓𝒕

𝑓𝑡 = 𝑓𝑡−3 1 + 𝑑𝑡−2 ∗ ψ ∗ 0,25 1 + 𝑑𝑡−1 ∗ ψ ∗ 0,5 1 + 𝑑𝑡 ∗ ψ

𝑐𝑡 = 𝑐𝑡−7 1 − 𝑑𝑡−7 ∗ ξ ∗ 0,25 1 − 𝑑𝑡−6 ∗ ξ ∗ 0,5 1 − 𝑑𝑡−5 ∗ ξ ∗ 0,75 1 − 𝑑𝑡−4 ∗ ξ1 − 𝑑𝑡−3 ∗ ξ ∗ 0,75 1 − 𝑑𝑡−2 ∗ ξ ∗ 0,5 1 − 𝑑𝑡−1 ∗ ξ ∗ 0,25

Label Variable Value Label Variable Value

Pt Performance [0; 100] ft fitness [0; 100]

dt doping decision [0,1] α fitness weighting coefficient 0.5

ψ doping efficiency [0; 1] ct constitution [0; 100]

ξ damage from doping [0; 1] β constitution weighting coefficient 0.4

rt random factor [0; 100]

γ random weighting coefficient 0.1

How can we provide recommendations for the fight

against doping by using agent-based modeling?


Situation

Doping in elite-sport becomes increasingly important˃ Balko˃ Armstrong˃ Operation Puerto (Fuentes)˃ …

Complication

Keyquenstion

“Statistically the numbers of people being caught is between one to two per cent, that’s the numbers of positives against the number of tests. But the number of people doping are in the double digits.”

David Howman

How can we provide recommendationsfor the fight against doping by using

agent-based modeling?

the fight against doping · by doping behavior committed in their social network. • for instance,...

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