Agent Architecture for Simulating Norm Dynamics. Part I Rosaria Conte [email protected]...

Agent Architecture for

Simulating Norm Dynamics. Part I

Rosaria Conte

[email protected]

LABSS (Laboratory of Agent Based Social Simulation), Roma, ISTC-CNR

Outline

How norms emerge? Conventions But spontaneous equilibria are not always desirable… 1st simulation model A more general notion is needed

EMIL-A: A cognitive norm-based architecture Emergence and immergence Mental representations How tell norms When is EMIL-A needed? 2nd simulation model

Why comply? Towards a theory of norms internalization 3rd simulation model

Conclusions

Questions

Q How do norms emerge?Q From which type of

agents? Q How necessary is norm

enforcement? Punishment is essential in the evolution of norms (Bowles and Gintis, 1998; 2003; Axelrod,1986 ; etc.) Norms are generally based on

enforcement Usually complied with based on

strategic reasoning Still moral education aims at

fostering compliance for the sake of norms as ends in themselves

How is this possible? Which mental processes are needed to make norms happy?

Norms in the behavioural sciences Norms are

universally present in all human societies (Roberts, 1979; Brown, 1991; Sober and Wilson, 1998);

ancient: highly elaborated in all human groups, including hunter-gatherers and groups that are culturally isolated.

ubiquitous. governing all activities, from mate choice to burial Impactful: on welfare and reproductive success.

Nonetheless (or consequently?), norms break down in too specific notions Archipelago norm includes at least

Conventions Social norms Laws QuickTime™ e un

decompressore TIFF (Non compresso)sono necessari per visualizzare quest'immagine.

Conventions 1/5 From analytical philosophy (Lewis 1969),

social sciences derived a conventionalistic view of norms as spontaneously emerging behavioral regularities based on conditioned preferences enforced by sanctions

For Lewis, conventions solve problems of coordination,

When different equivalent solutions are available, But agents must converge on one such solution Which is then arbitrary Example: telephone line falling Who is calling back?

QuickTime™ e undecompressore TIFF (Non compresso)

sono necessari per visualizzare quest'immagine.

Conventions 2/5

Why such a convention did never establish?

It seems to crash with a norm of equity…

But this does not solve problems of coordination…

Exercise: other exs?



Conventions 3/5

QuickTime e unﾪdecompressore


QuickTime™ e undecompressore


Conventions 4/5

QuickTime e unﾪdecompressore


QuickTime™ e undecompressore


Conventions 5/5 In real

scenarios, agents may not converge at all



• Or they may converge on pareto-suboptimal equilibria…

• Let us simulate a congestion game

Strategies

• Unconditioned• Aggressive: Hawks -> always

GOAHEAD, • Cooperative: Doves -> STOP if orthogonal

agents approach crossroad, else GOAHEAD

• Conditioned• Left-watchers: if orthogonal coming from left

approach crossroad STOP, else GOAHEAD

• Right-watchers: dual of LW





Some constraints

General rules

The NetLogo Model

Findings 1/2

Findings 2/32

Conclusions

How force a desirable solution? Rather than a

behavioural notion

We need an inlcusive notion of norm that

Does justice to its mandatory force

legal

moral

socialreligious

What is common to them?

A general notion

A norm “is a presribed guide for conduct which is generally complied with by the members of society” (Ullman-Margalit, 1977).

In our theory,

NormsNorms spread because spread because

and to the extent that the and to the extent that the

corresponding normative prescriptions corresponding normative prescriptions

spread as wellspread as well

(Conte et al., 2007)



What is a normative prescription?

A command that pretends to be adopted for its own sake, because it ought to be observed (Conte et al., 2009)Ideally, norms are adopted for their own sake

Sub-ideally, norms are adopted because of external enforcement

Norms’ felicity requires ideal reasons for compliance.



Emergence implies immergence

EMIL project results:• To allow norm emergence• agents need internal

mechanisms and mental representations allowing norms to affect their behaviours.

• For a theory of immergence see Castelfranchi, ; Conte et al., 2007.

• EMIL’s major outcomes• Conte et al. (2011) Minding

Norms, OUP• Xenatidiou and Edmonds

(2011) A Dynmic View of Norms, CUP.


sono necessari per visualizzare quest'immagine.Mind

Society

Emergence implies immergence

EMIL project results:• To allow norm emergence• agents need internal

mechanisms and mental representations allowing norms to affect their behaviours.

• For a theory of immergence see Conte et al., 2007.

• EMIL’s major outcomes• Conte et al. (2011) Minding

Norms, OUP• Troitzsch and Gulyas (2011)

EMIL-S: Smulating norm innovation, Wley

• Xenatidiou and Edmonds (2011) A Dynmic View of Norms, CUP.


sono necessari per visualizzare quest'immagine.Mind

Society

What are mental representations?States of the mind triggering

and guiding behaviours

Subsymbolic (eg., neural networks)

Symbolic: representations of the world that can be compared and manipulated by the agents while

Reasoning

Solving problems

Planning

Taking decisions

Gee, I thought that p’.Could it be the same?

Hey, do you know that p?

Two main functions

Epistemic: agents keep their representations as close as possible to the worldBelief, knowledge, evaluation, etc.

Pragmatic: agents try to make the world as close as possible to their representationsGoal, intention, motivation, etc.

How?

By means of planning and acting.

Lets go back to classic cybernetic circuits….

Mind

World

Mind

World

The TOTE unit (Miller et al., 1960)

TEST: perceived ws compared with wanted ws; If discrepant

OPERATE: apply actionTEST: perceived ws

compared with wanted ws; If coincident

EXIT



Norm-based mental representations

N-beliefs

N-B1, general form N-B: there is an obligation, forbearance, permission on a given set of agents to perform a given action.

N-B2, pertincence N-B: I am a member of the set of agents interested by the norm.

N-B3, enforcement N-B concerning positive or negative sanctions consequent to compliance or violation.

N-goals: a goal relativised to at least N-B1.

N-G1 N-adoption: want to act as prescribed, as long as and because this is prescribed

N-G2 N-invocation: want others to form NBs

N-G3 N-defence: want others to comply with N

N-G4 Sanction: want violators be punished.

N-intentions: NGs chosen for execution.

To practice

• Why does car driver stop in each case?











EMIL-A

NORMRECOGNITION:N-BELIEF

NORMADOPTION:N-GOAL

NORMDECISION:N-INTENTION

CONFORMINGBEHAVIOR

INPUT

Epistemic component Pragmatic component

Emotional component?

(CandidateN-Bel “It is prohibited to smoke”)

N-Board

LTM

WM

Vc=N-thresholdVc=8

x smoke Prohibition yAgent x Agent y

> vc

Epistemic component

N-bel:It is prohibited to smoke

< vc

(CandidateN-Bel “It is prohibited to smoke”) +

N-Board

LTM

WM

Vc=N-thresholdVc=8

x ? ? yAgent xi Agent y

To practice 1/2

At time T1

(CandidateN-Bel “It is prohibited to smoke”) -

N-Board

LTM

WM

Vc=N-thresholdVc=8

x ? ? yAgent xj Agent y

To practice 2/2

At time T1

?

LTM

Epistemic component

N-bel1:generalIt is prohibited to smoke in public places

N-bel2:pertinence. It concerns me

N-bel3: enforcement. Violators get a fiine

Smoking

N-board (norms arranged for salience)

Signaling (visibility)Transgression rateSanctions (pr. & severityNorm invocationNorm's effect

Norm salienceSource (Cred. & legitimacy

Norm salience measures how operative NP is (perceived to be by group members).

Pragmatic component

N-bel1:general

N-bel2:pertinence

N-bel3: enforcementGn

NG1

Norm adoption Norm decision-making

Active goals

Output(compliance/violation

activate

generate

pursue

interact

Norm recognition

Emergence of norms in artificial populations

(www.emil.istc.cnr.it )Artificial wikipedia (Emde and Troitzsch, 2008)Artificial wikipedia (Emde and Troitzsch, 2008)

Traffic scenario (Lotzmann et al., 2008)Traffic scenario (Lotzmann et al., 2008)

Microcredit (Lucas et al., 2009) Microcredit (Lucas et al., 2009)

Multicontext world (Campennì et al, 2010)Multicontext world (Campennì et al, 2010)

models available at models available at http://mass.http://mass.aitiaaitia..ai/applications/emilai/applications/emil

3333



Norm òatency

The Use of Norm Recognition Module:Effects on the Environment

Objectives

Lets compareNorm recognizersSocial conformers

in a world in which agents leave traces of their actions in the environment

Do they make a difference?

The Agent 1/2

Each Agent is provided with:1. a Normative Board;2. a double-layer architecture;3. a vector of possible behaviors.

N-Board:N-B1N-B2.......

level-2(D)

level-1(observedbehaviors)

The Agent 2/2

Behaviors(p1 p2 ... pn)

The Model 1/2

Agents try to be compliant with surrounding environment; follow preferred color (if switched on);

Social Conformers tend to assimilate others’ preferences (to a certain speed)

Norm Recognizers form normative beliefs and goals

All randomly move in the world (if they do not follow preferred colors) color the patches with one of three possible colors:

Red Black Gray

The Model 2/2

Gray is more environmentally suitable than black and red: if agents, in a portion of the world with lots of black and red patches, color patches gray, they perturb the environment less than would be the case otherwise (red if most patches are black and vice-versa)

What is the relationship between environmental responsiveness (color of patches) and norm compliance (follow the salience of normative beliefs to choose the action to be performed)?

Concluding Remarks

Social Conformers: Rarely converge on one color

Sometimes GRAY with Uphill switched on

Norm Recognizers: No case where the result is different from GRAY (they converge

very clearly on gray)

Mixed Populations: More the population is composed by norm recognizers, more the

result tends to GRAY (small markers indicate mixed populations – 50%)

Why?As soon as the norm immerges, NR bring it around:

They compare it with current state of the envirnment

If conflict (2 cases out of 3), they act GRAY (to perturb environment as little as possible)

Instead, SC act GRAY 1 out of 3, whetherthey prefer gray and follow it

they modify their preference according to others’

It is the normative belief that generates compliance

First conclusionsWhile regularities can emerge in

populations of simple agents

“Prescribed guides of conduct” emerge while immerging in the mind of rich cognitive agents endowed with the capacity to represent and adopt prescriptions.

Immergence precedes emergence: Norms compete in the mind before competing in society.

Norm latency: it takes time before norms surface. Candidate norms may never surface!



Don’t smoke at work

Never smoke

Don’t smoke In public

First conclusions



Don’t smoke at work

Never smoke

Don’t smoke In public

Don’t smokeIn public

While regularities can emerge in populations of simple agents

“Prescribed guides of conduct” emerge while immerging in the mind of rich cognitive agents endowed with the capacity to represent and adopt prescriptions.

Immergence precedes emergence: Norms compete in the mind before competing in society.

Norm latency: it takes time before norms surface. Candidate norms may never surface!

For discussion• When are simple architectures (say SC) fit?• Which real-world setting does 2nd simulation

model refer to?– Which actions– Which norms– Which domain?

• How about – Evolutionary scenario– Envirnmental policy

Agent Architecture for Simulating Norm Dynamics. Part I Rosaria Conte [email protected]...

Documents

Transcript of Agent Architecture for Simulating Norm Dynamics. Part I Rosaria Conte [email protected]...