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Agent Architecture for
Simulating Norm Dynamics. Part I


 Rosaria Conte
 rosaria.conte@istc.cnr.it



 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
Q   How do norms emerge?
Q   From which type of
    agents?                              Questions
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
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?
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




                         !"$ & '( ("
                            #% # ) *
                         + $ '-( /.
                          ( , ./,(
         /* * $ / . - . #" 2 . 4 (5''0 #(
         , , ( (/ # ( 1/0 330( " /# 7* 8
                 0         #      6
Conventions 4/5




                         !"$ & '( ("
                            #% # ) *
                         + $ '-( /.
                          ( , ./,(
         /* * $ / . - . #" 2 . 4 (5''0 #(
         , , ( (/ # ( 1/0 330( " /# 7* 8
                 0         #      6
Conventions 5/5
   In real
    scenarios,
    agents
    may not
    converge             •   Or they
    at all                   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               moral religious
        behavioural                        social
        notion                     legal
       We need an
        inlcusive notion
        of norm that
       Does justice to    What is common to them?
        its mandatory
        force
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,
                 Norms spread because
                and to the extent that the
          corresponding normative prescriptions
                      spread 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
                                         S
    EMIL project results:                o
       • To allow norm emergence         ci
       • agents need internal            et
         mechanisms and mental           y
         representations allowing
         norms to affect their
         behaviours.
                                         M
       • For a theory of immergence
         see Castelfranchi, ; Conte et   i
         al., 2007.
•   EMIL’s major outcomes
       • Conte et al. (2011) Minding
                                         n
         Norms, OUP
       • Xenatidiou and Edmonds          d
         (2011) A Dynmic View of
         Norms, CUP.
Emergence implies immergence
    EMIL project results:
                                       S
       • To allow norm emergence
       • agents need internal          o
         mechanisms and mental         ci
         representations allowing      et
         norms to affect their         y
         behaviours.
       • For a theory of immergence
         see Conte et al., 2007.
                                       M
•   EMIL’s major outcomes
       • Conte et al. (2011) Minding   i
                                       n
         Norms, OUP
       • Troitzsch and Gulyas (2011)
         EMIL-S: Smulating norm
         innovation, Wley
       • Xenatidiou and Edmonds        d
         (2011) A Dynmic View of
         Norms, CUP.
What are mental
representations?
States of the mind
   triggering and guiding        Gee, I thought    Hey, do
   behaviours                    that p’.          you know
                                 Could it be the   that p?
    Subsymbolic (eg., neural
                                 same?
      networks)
    Symbolic: representations
      of the world that can be
      compared and
      manipulated by the
      agents while
        Reasoning
        Solving problems
        Planning
        Taking decisions
Two main functions
Epistemic: agents keep their
   representations as close as            Mind
   possible to the world
    Belief, knowledge, evaluation, etc.
Pragmatic: agents try to make the         World
   world as close as possible to their
   representations
    Goal, intention, motivation, etc.
                                           Mind
    How?
    By means of planning and acting.
Lets go back to classic cybernetic        World
   circuits….
The TOTE unit (Miller et al.,
  1960)
TEST: perceived ws
       compared with
       wanted ws; If
       discrepant
OPERATE: apply action
TEST: 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.
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
                       Emotional component?




INPUT   NORM                                              CONFORMING
                          NORM                NORM
        RECOGNITION:                                      BEHAVIOR
                          ADOPTION:           DECISION:
        N-BELIEF          N-GOAL              N-
                                              INTENTION




        Epistemic
        component              Pragmatic component
Epistemic component
                                                                Vc=N-threshold
                                                                Vc=8

                                                  > vc
LTM
                                          (CandidateN-Bel “It
N-bel:It is prohibited to smoke           is prohibited to
                                          smoke”)
                                      W           < vc
          N-Board                     M




                  x         smoke   Prohibition     y
Agent x                                                           Agent y
To practice 1/2
                                                     Vc=N-threshold
At time T1                                           Vc=8



  LTM
                               (CandidateN-Bel “It
                               is prohibited to
                               smoke”) +
                           W
             N-Board       M




                  x    ?       ?         y
  Agent xi                                             Agent y
To practice 2/2
                                                     Vc=N-threshold
At time T1                                           Vc=8



  LTM
                               (CandidateN-Bel “It
                               is prohibited to
                               smoke”) -
                           W
             N-Board       M

                                   ?




                  x    ?       ?         y
  Agent xj                                             Agent y
Epistemic component
LTM
                N-board (norms arranged for salience)
  N-bel1:general                                   S
  It is prohibited to smoke in public places       m      Norm salience
                                                   o      measures how
  N-bel2:pertinence. It concerns me                ki     operative NP is
                                                   n      (perceived to be by
  N-bel3: enforcement. Violators get a fiine       g      group members).


                                                        Signaling
                                                        (visibility)
           Source                                       Transgression
           (Cred.              Norm salience            rate
           & legitimacy                                 Sanctions (pr. &
                                                        severity
                                                        Norm invocation
                                                        Norm's effect
activate             pursue


generate         interact
                              Pragmatic component
  Norm recognition            Norm adoption   Norm decision-making
                                       NG1
    N-bel1:general
                                                   Active goals

    N-bel2:pertinence
                                      Gn
    N-bel3: enforcement




                                                       Output
                                                (compliance/violation
Emergence of norms in artificial
                                              populations
                                     (www.emil.istc.cnr.it )
Artificial wikipedia (Emde and Troitzsch,
   2008)
Traffic scenario (Lotzmann et al., 2008)
Microcredit (Lucas et al., 2009)
Multicontext world (Campennì et al, 2010)
models                    available         at
   http://mass.aitia.ai/applications/emil




                                                  Norm òatency




                                                                 33
The Use of Norm Recognition
Module:
Effects on the Environment
Objectives
 Lets compare
   Norm recognizers
   Social 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.
The Agent 2/2


                   level-2
      N-Board:       (D)
        N-B1                    Behaviors
        N-B2        level-1    (p1 p2 ... pn)
        .......   (observed
                  behaviors)
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, whether
   they prefer gray and follow it
   they modify their preference according to others’
It is the normative belief that generates compliance
First conclusions
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:                 Never smoke
   Norms compete in the mind before
   competing in society.                 Don’t smoke at work

Norm latency: it takes time before            Don’t smoke
   norms surface. Candidate norms             In public
   may never surface!
First conclusions
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              Don’t smoke
   capacity to represent and adopt                In public

   prescriptions.
Immergence precedes emergence:                 Never smoke
   Norms compete in the mind before      Don’t smoke at work
   competing in society.
Norm latency: it takes time before            Don’t smoke
                                              In public
   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

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ESSA10 - Summer School

  • 1. Agent Architecture for Simulating Norm Dynamics. Part I Rosaria Conte rosaria.conte@istc.cnr.it LABSS (Laboratory of Agent Based Social Simulation), Roma, ISTC-CNR
  • 2. 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
  • 3. Q How do norms emerge? Q From which type of agents? Questions 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?
  • 4. 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
  • 5. 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?
  • 6. 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?
  • 7. Conventions 3/5 !"$ & '( (" #% # ) * + $ '-( /. ( , ./,( /* * $ / . - . #" 2 . 4 (5''0 #( , , ( (/ # ( 1/0 330( " /# 7* 8 0 # 6
  • 8. Conventions 4/5 !"$ & '( (" #% # ) * + $ '-( /. ( , ./,( /* * $ / . - . #" 2 . 4 (5''0 #( , , ( (/ # ( 1/0 330( " /# 7* 8 0 # 6
  • 9. Conventions 5/5  In real scenarios, agents may not converge • Or they at all may converge on pareto- suboptimal equilibria… • Let us simulate a congestion game
  • 10. 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
  • 16. Conclusions  How force a desirable solution?  Rather than a moral religious behavioural social notion legal  We need an inlcusive notion of norm that  Does justice to What is common to them? its mandatory force
  • 17. 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, Norms spread because and to the extent that the corresponding normative prescriptions spread as well (Conte et al., 2007)
  • 18. 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.
  • 19. Emergence implies immergence S EMIL project results: o • To allow norm emergence ci • agents need internal et mechanisms and mental y representations allowing norms to affect their behaviours. M • For a theory of immergence see Castelfranchi, ; Conte et i al., 2007. • EMIL’s major outcomes • Conte et al. (2011) Minding n Norms, OUP • Xenatidiou and Edmonds d (2011) A Dynmic View of Norms, CUP.
  • 20. Emergence implies immergence EMIL project results: S • To allow norm emergence • agents need internal o mechanisms and mental ci representations allowing et norms to affect their y behaviours. • For a theory of immergence see Conte et al., 2007. M • EMIL’s major outcomes • Conte et al. (2011) Minding i n Norms, OUP • Troitzsch and Gulyas (2011) EMIL-S: Smulating norm innovation, Wley • Xenatidiou and Edmonds d (2011) A Dynmic View of Norms, CUP.
  • 21. What are mental representations? States of the mind triggering and guiding Gee, I thought Hey, do behaviours that p’. you know Could it be the that p? Subsymbolic (eg., neural same? networks) Symbolic: representations of the world that can be compared and manipulated by the agents while Reasoning Solving problems Planning Taking decisions
  • 22. Two main functions Epistemic: agents keep their representations as close as Mind possible to the world Belief, knowledge, evaluation, etc. Pragmatic: agents try to make the World world as close as possible to their representations Goal, intention, motivation, etc. Mind How? By means of planning and acting. Lets go back to classic cybernetic World circuits….
  • 23. The TOTE unit (Miller et al., 1960) TEST: perceived ws compared with wanted ws; If discrepant OPERATE: apply action TEST: perceived ws compared with wanted ws; If coincident EXIT
  • 24. 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.
  • 25. 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.
  • 26. To practice • Why does car driver stop in each case?
  • 27. EMIL-A Emotional component? INPUT NORM CONFORMING NORM NORM RECOGNITION: BEHAVIOR ADOPTION: DECISION: N-BELIEF N-GOAL N- INTENTION Epistemic component Pragmatic component
  • 28. Epistemic component Vc=N-threshold Vc=8 > vc LTM (CandidateN-Bel “It N-bel:It is prohibited to smoke is prohibited to smoke”) W < vc N-Board M x smoke Prohibition y Agent x Agent y
  • 29. To practice 1/2 Vc=N-threshold At time T1 Vc=8 LTM (CandidateN-Bel “It is prohibited to smoke”) + W N-Board M x ? ? y Agent xi Agent y
  • 30. To practice 2/2 Vc=N-threshold At time T1 Vc=8 LTM (CandidateN-Bel “It is prohibited to smoke”) - W N-Board M ? x ? ? y Agent xj Agent y
  • 31. Epistemic component LTM N-board (norms arranged for salience) N-bel1:general S It is prohibited to smoke in public places m Norm salience o measures how N-bel2:pertinence. It concerns me ki operative NP is n (perceived to be by N-bel3: enforcement. Violators get a fiine g group members). Signaling (visibility) Source Transgression (Cred. Norm salience rate & legitimacy Sanctions (pr. & severity Norm invocation Norm's effect
  • 32. activate pursue generate interact Pragmatic component Norm recognition Norm adoption Norm decision-making NG1 N-bel1:general Active goals N-bel2:pertinence Gn N-bel3: enforcement Output (compliance/violation
  • 33. Emergence of norms in artificial populations (www.emil.istc.cnr.it ) Artificial wikipedia (Emde and Troitzsch, 2008) Traffic scenario (Lotzmann et al., 2008) Microcredit (Lucas et al., 2009) Multicontext world (Campennì et al, 2010) models available at http://mass.aitia.ai/applications/emil Norm òatency 33
  • 34. The Use of Norm Recognition Module: Effects on the Environment
  • 35. Objectives Lets compare Norm recognizers Social conformers in a world in which agents leave traces of their actions in the environment Do they make a difference?
  • 36. The Agent 1/2 Each Agent is provided with: 1. a Normative Board; 2. a double-layer architecture; 3. a vector of possible behaviors.
  • 37. The Agent 2/2 level-2 N-Board: (D) N-B1 Behaviors N-B2 level-1 (p1 p2 ... pn) ....... (observed behaviors)
  • 38. 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
  • 39. 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)?
  • 40. 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%)
  • 41. 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, whether they prefer gray and follow it they modify their preference according to others’ It is the normative belief that generates compliance
  • 42. First conclusions 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: Never smoke Norms compete in the mind before competing in society. Don’t smoke at work Norm latency: it takes time before Don’t smoke norms surface. Candidate norms In public may never surface!
  • 43. First conclusions 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 Don’t smoke capacity to represent and adopt In public prescriptions. Immergence precedes emergence: Never smoke Norms compete in the mind before Don’t smoke at work competing in society. Norm latency: it takes time before Don’t smoke In public norms surface. Candidate norms may never surface!
  • 44. 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