Agent Architecture for Simulating Norm Dynamics. Part I

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. Questions How do norms emerge? From which type of agents? 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. 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 2/5

7. Conventions 3/5

8. Conventions 4/5

9. 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

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. Some constraints

12. General rules

13. The NetLogo Model

14. Findings 1/2

15. Findings 2/32

16. 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 moral religious social legal What is common to them?

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, • Normsspread 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 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. Society Mind

20. 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. Society Mind

21. 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?

22. Two main functions • Epistemic: agents keep their representations as close as possible to the world • Belief, knowledge, evaluation, etc. • Pragmatic: agents try to make the world as close as possible to their representations • Goal, intention, motivation, etc. • How? • By means of planning and acting. • Lets go back to classic cybernetic circuits…. Mind World Mind World

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. CONFORMING BEHAVIOR INPUT EMIL-A Emotional component? NORM RECOGNITION: N-BELIEF NORM ADOPTION: N-GOAL NORM DECISION: N-INTENTION Epistemic component Pragmatic component

28. N-Board LTM W M x smoke Prohibition y Agent x Agent y Epistemic component Vc=N-threshold Vc=8 > vc (CandidateN-Bel “It is prohibited to smoke”) N-bel:It is prohibited to smoke < vc

29. N-Board LTM W M x ? ? y Agent xi Agent y To practice 1/2 Vc=N-threshold Vc=8 At time T1 (CandidateN-Bel “It is prohibited to smoke”) +

30. N-Board LTM W M x ? ? y Agent xj Agent y To practice 2/2 Vc=N-threshold Vc=8 At time T1 (CandidateN-Bel “It is prohibited to smoke”) - ?

31. LTM Epistemic component N-board (norms arranged for salience) Smoking N-bel1:general It is prohibited to smoke in public places • Norm salience measures how operative NP is (perceived to be by group members). N-bel2:pertinence. It concerns me N-bel3: enforcement. Violators get a fiine • Signaling (visibility) • Transgression rate • Sanctions (pr. & severity • Norm invocation • Norm's effect • Source • (Cred. • & legitimacy Norm salience

32. pursue activate Pragmatic component interact generate Norm recognition Norm adoption Norm decision-making Active goals NG1 N-bel1:general 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. Each Agent is provided with: a Normative Board; a double-layer architecture; a vector of possible behaviors. The Agent 1/2

37. The Agent 2/2 level-2 (D) N-Board: N-B1 N-B2 ....... Behaviors (p1 p2 ... pn) level-1 (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: Norms compete in the mind before competing in society. • Norm latency: it takes time before norms surface. Candidate norms may never surface! Never smoke Don’t smoke at work Don’t smoke In public

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 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 In public Never smoke Don’t smoke at work Don’t smoke In public

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