evolution of discrimination (experimental ethics) chung tue “duck” nguyen bse (hon.) project...

Evolution of Discrimination(Experimental Ethics)

Chung Tue “DuCK” NguyenBSE (Hon.) ProjectSupervisors: Ann Nicholson, Kevin Korb

25/10/2006 Evolution of Discrimination - Chung Tue "DuCK" Nguyen

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Outline Introduction & Motivation & Main goal Related concepts Experimental Design

The world The agents

Observations & Results Conclusion References


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Introduction & Motivation Discrimination:

The ability to recognise differences. The tendency to offer different

treatement on the basis of those differences.

Positive: telling useful from harmful. Negative: unfair treatement: racism,

sexism, etc. Does discrimination evolve?


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Main goal

The goal of this study is:

To observe the evolution of discriminatory tendencies in intelligent species through social interactions.


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Related concepts Evolutionary Theory

Natural selection – survival of the fittest Evolutionary Psychology

Evolution of behaviours/mental traits Baldwin Effect

Roles of learning in evolution Ethocentrism

Ingroup hyper-evaluation Outgroup negative streotype

Iterated Prisoner’s Dilemma (IPD) Alife Simulation


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Experimental Design

Using simulations to study discrimination

Standard Alife setup: The world The agents


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The world

A wrap-around board of n-by-m locations

Each location can contain an infinite number of agents


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The agent

Chromosome: mutable genotype

Behaviour: flexible phenotype

Discrimination is modelled using chromosome and behaviour


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Chromosome affects behaviour at birth.

Behaviour affects the tendency of possible actions.

Chromosome is modified through mutation.

Behaviour is modified through learning. Social learning (mimic) Individual learning (learning through

pain/pleasure)

Chromosome vs Behaviour


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Chromosome, Behaviour and the Environment


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Modelling discrimination Chromosome:

“discrimination gene” Behaviour:

“discriminatory behaviour”

Discrimination <-> Preference Preferential genotype Preferential behaviour Measurable


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Preferences How positive/negative an agent “feels” about agents in

his own group/the other group

Represenation: two numbers, each between 0 and 1

Two types of preferences: Ingroup preference: towards the ingroup Outgroup preference: towards the outgroup E.g.: Ingroup preference = 0.7, outgroup preference = 0.4

Preferential behaviours <-> discriminatory actions


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Preference test Test for preference (ingroup/outgroup):

Choose randomly: x in [0,1] Compare x with p x <= p : like x > p : dislike

x =Test(preference)

Like Dislike

successfailure

p0 1


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Main genotypes Based on the values of preferences

(chromosome), there are 5 main genotypes, each may evolve differently.

Out-group-pref.

>0.5 <0.5 =0.5

In-group-pref.

>0.5 Do-gooder Supremacist Supremacist

<0.5 Betrayer Hater Betrayer

=0.5 Do-gooder Hater Moderate


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The agent (cont.)

Other traits: Race (can also be: religion, ideology, etc.) Health Age Location Fitness = #children + 2 x #grandchildren


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A typical cycle During 1 cycle of the simulation,

every agent can perform all actions, in the following order: Move Learn socially (mimic the best) Mate + reproduce Interact + learn through pain and

pleasure Note: Agents don’t eat, health is affected

directly through interaction


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Preferences in action - Moving Agents can move in four directions

Agents choose their location based on how much they like the majority of agents in a particular location.

Agents considers their current location as well as four cells around them.

Considerations are done in random order


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Moving example

In_pref = 0.8Out_pref = 0.3Majority = RedTest(Out_pref) = 0.4Result: 0.4 > 0.3 => Failed

In_pref = 0.8Out_pref = 0.3Majority = BlueTest(In_pref) = 0.6Result: 0.6 > 0.8 => Success

Note: much more likely to move in with its own kind


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Preference in action – Social Learning (mimicking)

Agents learn from other agents in the same location

Agents learn from the race they like first Agents learn from the agents the see as

most successful (the idol) Success = fitness x (maxAge – age) Mimicking:

Depends on social learning success rate Change preferences towards the idol’s preferences


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Preference in action – Mating

There is no gender Inter-racial mating Mating is consensual

Consent is given on the basis of racial preference

I.e. both partners must pass the preference test

Mating hapens locally


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Reproduction When mating is successful, a new agent is

created in the same location as its parents New agent has a 50-50 chance of getting:

Either parent’s race Either parent’s ingroup preference

(chromosome), or mutate Either parent’s outgroup preference

(chromosome), or mutate A small chance of mutation:

Change the value to any x in [0,1]


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Reproduction example

Chromosome: 0.3/0.5Behaviour:0.6/0.9




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Preference in action – Interaction & Indiv. Learning

Every agent interacts with another agent in the same location, randomly chosen.

Interaction is essentially an IPD* game: Each agent does a preference test on the other If success: the agent cooperates If failure: the agent defects Agent’s health is updated accordingly


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Interaction trade-offs

Cooperate Defect

Cooperate 3 | Pleasure -3 | Pain

Defect 4 | Pleasure -2 | Pain

A typical IPD is characterised with the following order of trade-offs:1. Defect – Cooperate2. Cooperate – Cooperate3. Defect – Defect4. Cooperate – Defect


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Learning through pleasure and pain

Agents change their preference according to the result of their interaction with another agent.

If Pleasure: increase preference If Pain: decrease preference Preference test also applies:

Only increase preference if success Only decrease preference if failure


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Experiments

Difference between genotypes Relationship between fitness and

preference Relationship between ingroup and

outgroup preference Population development and

geographical distribution of the races


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Experimental modes

Separated communities: Two races start in isolation

Mixed communities: Two race mixed together from the start


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Running the simulation

Run 30 simulations in each experimental mode

Each simulation is 1000-cycle long Starting population is 7000 World size: 50x50 All agents start off as “Moderate”:

In_pref = Out_pref = 0.5


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Results - Overview The genotypes did evolve Order of fitness (most -> least):

Supremacist Do-gooder Hater Betrayer

Ethnocentric behaviours Most affected by the Supremacist and

Do-gooder


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Results - Overview Separated communities different from

mixed communities: Separated:

Less variations in genotype More variations in races Complete domination + extinction

Mixed: More variations in genotype Less variations in races Domination changes hands


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Results - Experiment 1

Difference between genotypesMixed communitiesSeparated communities


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Results - Experiment 1Mixed communitiesSeparated communities


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Results – Experiment 2 Relationship between fitness and preference

Correlations:Inpref-Fitness = 0.887, p = 0.01Outpref-Fitness = -0.519, p = 0.01

Correlations:Inpref-Fitness = 0.920, p = 0.01Outpref-Fitness = -0.660, p = 0.01

Mixed communitiesSeparated communities


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Results – Experiment 3 Relationship between ingroup and outgroup

preference

Correlations:Inpref-Outpref = -0.750, p = 0.01




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Results – Experiment 3

Correlation:Inpref-Outpref = 0.736, p = 0.01


Separated communities Mixed communities


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Results – Experiment 4 Population development and geographical

distribution of the races



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Clear domination: 70%Extinction: 13.3%

Clear domination: 46.6%Extinction: 0%


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Separated communities

Mixed communities


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Separated communities

Mixed communities


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More geographical distribution if time permits…


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Conclusion Discrimination can be demonstrated to be

evolutionary A successful discriminatory strategy includes a

reasonable level of cooperation and defection Ingroup cooperation has more impact on fitness than

outgroup defection: don’t have to love thine neighbours but don’t mess up with thine family

Ethnocentric behaviours may have been evolutionary Mixed communities allow more variation and balanced

competition. Think: multiculturalism, immigration…


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Futher work More + longer simulation

Race domination will stabilised or lead to extinction eventually?

Increase separation space/time to observe development before and after contact (in separated communities mode)

More efficient simulation Find a better way to observe geographical

distribution of genotypes in 3D. Thesis


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References

Refer to References section in the Thesis


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Questions?

Thank you.

evolution of discrimination (experimental ethics) chung tue “duck” nguyen bse (hon.) project...

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