agent architecture for simulating norm dynamics. part i rosaria conte [email protected]...
TRANSCRIPT
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Agent Architecture for
Simulating Norm Dynamics. Part I
Rosaria Conte
LABSS (Laboratory of Agent Based Social Simulation), Roma, ISTC-CNR
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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
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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?
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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
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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?
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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?
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Conventions 3/5
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Conventions 4/5
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Conventions 5/5 In real
scenarios, agents may not converge at all
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• Or they may converge on pareto-suboptimal equilibria…
• Let us simulate a congestion game
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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
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Some constraints
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General rules
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The NetLogo Model
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Findings 1/2
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Findings 2/32
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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?
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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)
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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.
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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.
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Society
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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.
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Society
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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?
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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
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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
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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.
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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.
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To practice
• Why does car driver stop in each case?
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sono necessari per visualizzare quest'immagine.
QuickTime™ e undecompressore TIFF (Non compresso)
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EMIL-A
NORMRECOGNITION:N-BELIEF
NORMADOPTION:N-GOAL
NORMDECISION:N-INTENTION
CONFORMINGBEHAVIOR
INPUT
Epistemic component Pragmatic component
Emotional component?
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(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
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(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
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(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
?
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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).
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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
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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
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Norm òatency
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The Use of Norm Recognition Module:Effects on the Environment
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Objectives
Lets compareNorm recognizersSocial conformers
in a world in which agents leave traces of their actions in the environment
Do they make a difference?
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The Agent 1/2
Each Agent is provided with:1. a Normative Board;2. a double-layer architecture;3. a vector of possible behaviors.
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N-Board:N-B1N-B2.......
level-2(D)
level-1(observedbehaviors)
The Agent 2/2
Behaviors(p1 p2 ... pn)
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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
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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)?
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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%)
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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
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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!
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Don’t smoke at work
Never smoke
Don’t smoke In public
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First conclusions
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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!
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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