ESSA10 - Summer School

955 views
905 views

Published on

Published in: Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
955
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
0
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

ESSA10 - Summer School

  1. 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. 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. 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. 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. 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. 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. 7. Conventions 3/5 !"$ & '( (" #% # ) * + $ '-( /. ( , ./,( /* * $ / . - . #" 2 . 4 (5''0 #( , , ( (/ # ( 1/0 330( " /# 7* 8 0 # 6
  8. 8. Conventions 4/5 !"$ & '( (" #% # ) * + $ '-( /. ( , ./,( /* * $ / . - . #" 2 . 4 (5''0 #( , , ( (/ # ( 1/0 330( " /# 7* 8 0 # 6
  9. 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. 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
  11. 11. Some constraints
  12. 12. General rules
  13. 13. The NetLogo Model
  14. 14. Findings 1/2
  15. 15. Findings 2/32
  16. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 26. To practice • Why does car driver stop in each case?
  27. 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. 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. 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. 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. 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. 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. 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. 34. The Use of Norm Recognition Module: Effects on the Environment
  35. 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. 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. 37. The Agent 2/2 level-2 N-Board: (D) N-B1 Behaviors N-B2 level-1 (p1 p2 ... pn) ....... (observed behaviors)
  38. 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. 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. 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. 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. 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. 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. 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

×