1 / 33

NORMAL = NORMATIVE? THE ROLE OF INTELLIGENT AGENTS IN NORM EMERGENCE Giulia Andrighetto

NORMAL = NORMATIVE? THE ROLE OF INTELLIGENT AGENTS IN NORM EMERGENCE Giulia Andrighetto Marco Campennì, Federico Cecconi, Rosaria Conte Laboratory of Agent Based Social Simulation, Institute of Cognitive Science and Technology, CNR, Rome. Italy. Normative Multi-Agent Systems Dagstuhl

kalea
Télécharger la présentation

NORMAL = NORMATIVE? THE ROLE OF INTELLIGENT AGENTS IN NORM EMERGENCE Giulia Andrighetto

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. NORMAL = NORMATIVE? THE ROLE OF INTELLIGENT AGENTS IN NORM EMERGENCE Giulia Andrighetto Marco Campennì, Federico Cecconi, Rosaria Conte Laboratory of Agent Based Social Simulation, Institute of Cognitive Science and Technology, CNR, Rome. Italy Normative Multi-Agent Systems Dagstuhl 15-20. 03.09

  2. OUTLINE • A social cognitive view of norms • A Normative MAgent architecture: • EMIL-A • Simulation model and results • Conclusions and future work. NORMAS09 15-20/3/2009

  3. A SOCIAL COGNITIVE DEFINITION OF NORMS • Norms = behaviors spreading in population (Pi) as long as • Corresponding prescriptions and mental constructs (Conte and Castelfranchi, 1995; 2006) spread over Pi • N-beliefs: beliefs that for given sets of agents, in given set of contexts, given actions are obliged/forbidden/permitted • N-goals: goals to (not) achieve/accomplish obligatory/forbidden/permitted actions. NORMAS09 15-20/3/2009

  4. PROPERTIES OF SOCIAL NORMS • Multi agent • Involving more than one agent • More than one social role: Observer, Legislator/Source, Addressee/Recipient, Defender • Hybrid • behaviour • mental construct • dynamic: undergoing two processes • emergence: process by means of which a norm not deliberately issued spreads through a society • immergence: process by means of which a normative belief is formed into the agents’ minds (Castelfranchi, 1998; Conte et al., 2007) NORMAS09 15-20/3/2009

  5. EMERGENCE AND IMMERGENCE Emergencethe process by means of which effects are generated by (inter)acting micro(-social) entities, and implemented upon into their rules. Immergence, the process by means of which the emergent effect modifies the way of functioning of the generating system, affecting its generating rules or mechanisms in such a way that it is likelier to be reproduced. NORMAS09 15-20/3/2009

  6. COMPLEX LOOP OF NORM EMERGENCE Emergence of social norms is due to the agents’ behaviors, but the agents’ behaviors are due the mental mechanisms and representations controlling and (re)producing them (immergence). NORMAS09 15-20/3/2009

  7. Game-theoretical approach (Ullman-Margalit, E. 1977, Axelrod, R. 1986, Bicchieri, C. 1990, Young, P.H. 1996) Bottom-up approach Norms as conventions Norms emerge from interaction among agents, driven by non normative internal mechanisms: Moral dispositions Strategical reasoning Social learning (imitation) Investigation of the emergence of social regularities MAS approach (Shoham, Y. and Tennenholtz, M. 1992; Conte, R., and Castelfranchi, C. 1995; Jones, A. and Sergot, M. 1996; Dignum, F. 1999; Van der Torre. L. and Tan, Y. 1999) Prescriptive approach, where an institutional mechanism specifies how agents should behave Norms as prescriptions Cognitively rich agentsreason and decide upon norms Investigation of the effects of norms, i.e. a functionl analysis. NORMAS09 15-20/3/2009

  8. Objective bringing together the strength of both traditions • high resolution of both aspects of norms: inter and intra agent processes • and the recursive impact of inter and intra agent processes on each other emergent features. • Modelling normative agents paying attention to norm recognition will help to show how norm emergence is affected by cognition • A simulation of the two way dynamics of intra- and inter agent processes will advance the understanding of how actors produce and are in the same time a product of human society NORMAS09 15-20/3/2009

  9. IMPLEMENTING NORMS ON AGENTS: EMIL-A How do agents find out norms? • tick red arrows represent the standard information flow • dotted black arrows represent alternative directions of the information flow. NORMAS09 15-20/3/2009

  10. NORM RECOGNITION NORMAS09 15-20/3/2009

  11. X a T Y INPUT Each input is presented as an ordered vector • Source (x); • Action transmitted (a) (potential norm) • Type of input: • Behaviors • Messages: assertions (A), requests (R), deontics (D), normative evaluations (V) • Observer (y); • (sanctions (S)); NORMAS09 15-20/3/2009

  12. N-Board LTM E W M x smoke Prohibition y Agent x Agent y N-RECOGNITION MODULE Vc=N-threshold Vc=8 > vc (CandidateN-Bel “It is prohibited to smoke”) N-bel:It is prohibited to smoke < vc NORMAS09 15-20/3/2009

  13. NORM RECOGNIZER AT WORK 1/6 NORMAS09 15-20/3/2009

  14. NORM RECOGNIZER AT WORK 2/6 NORMAS09 15-20/3/2009

  15. NORM RECOGNIZER AT WORK3/6 NORMAS09 15-20/3/2009

  16. NORM RECOGNIZER AT WORK 4/6 NORMAS09 15-20/3/2009

  17. NORM RECOGNIZER AT WORK 5/6 NORMAS09 15-20/3/2009

  18. NORM RECOGNIZER AT WORK 6/6 NORMAS09 15-20/3/2009

  19. A SIMULATION STUDY Do agents behave differently depending on whether they focus of what people do or on what society prescibes? NORMAS09 15-20/3/2009

  20. NORM-RECOGNIZERS VS SOCIAL CONFORMERS • What are observable effects of norm recognition? • Implement different populations (Andrighetto et al., 2008, Campennì et al., 2008): • Social conformers: follow actions most frequently done in observation window (parameter) • Norm recognizers take input from others, form beliefs and act based on those. NORMAS09 15-20/3/2009

  21. AGENDA (C1,C2,C4,C3)‏ Time-of-Perm (nTicks/n)‏ AGENT AND WORLD 4 contexts: • following its agenda and time of permanence, each agent moves among contexts; • in each context, agents can produce 1 out of 3 actions; • 1 action is the same for all of the contexts. C2 (A1, A4, A5) C1 (A1, A2, A3) C4 (A1, A8, A9) C3 (A1, A6, A7) NORMAS09 15-20/3/2009

  22. level-2 (D & N-V)‏ level-1 (A, R, B)‏ NORM RECOGNIZER Each is provided with: • Normative Board; • Double-layer architecture; • Agenda: individual time of permanence (in contexts); • New normative beliefs contribute to choose action; • If normative board is empty, action is randomly chosen. AGENDA (C1,C4,C2,C3)‏ N-Board: N-B1 N-B2 ....... Time-of-Perm (t)‏ N-Threshold (vc) NORMAS09 15-20/3/2009

  23. Each observes other agents in same context According to conformity rate, imitates most frequent action SOCIAL CONFORMER Conformity rate = 9 NORMAS09 15-20/3/2009

  24. SIMULATIONS' RESULTS NORMAS09 15-20/3/2009

  25. PRELIMINARY FINDINGS Social conformers • Social conformers (above): • Each colour represents one action • No difference within ticks • Strong difference • Among ticks (no normative beliefs) • Among scenarios (no normative beliefs) • Most frequent action (dark blue) is distributed throughout the simulation: nothing emerges! • Norm recognizers (below): • Fuzzier • Rows • Columns • After 60th tick, the action common to all scenarios spreads: something emerges… • What is it? Lets look into agents minds… Norm recognizers NORMAS09 15-20/3/2009

  26. IMMERGENCE • At the 30th tick a normative belief starts to spread • What has happened in the in the interval? • Other normative beliefs got formed, although earlier is more frequent • Immergence is earlier: it takes time for effect to emerge NORMAS09 15-20/3/2009

  27. LATENCY OF NORMS • Time interval between N-bels appearance and convergence on corresponding action. • Actually, a complex loop • from N-Belx to N-actionx • from N-actionx to N-bely • from N-bely to N-actiony • Etc. NORMAS09 15-20/3/2009

  28. TO SUM UP Social Conformers Norm Recognizers • Social conformers do not converge on one action • Normative agents converge on the common action. NORMAS09 15-20/3/2009

  29. FINAL REMARKS • In a multi-scenario world, unlike social conformers, norm recognizers converge. • Norms immerge in the minds before emerging in behavior. • Norms have a latency time • Norms are prescribed behaviours undergoing internal and external dynamics. NORMAS09 15-20/3/2009

  30. SOCIAL CONFORMERS VS NORM RECOGNIZERS: NEXT STEPS • Add more heterogeneity: Agents endowed with different individual abilities to recognize norms and to comply with them • Agents with a different degree of credibility • Capability to violate a norm, detect violation and sanction it • Add more complexity: designing more realistic and dynamic scenarios • What about inertia (i.e. the time for a norm to disappear)? NORMAS09 15-20/3/2009

  31. SALIENCE 1/2 • Now the salience of a specific norm • can only increase, depending on how many instances of the same norm are stored in the LTM during the simulation; • is dependent only on the inputs received by other agents. Negative effect: some norms have an exponential growth, with the consequence of a eccessive interference with the decisional processes of the agent. NORMAS09 15-20/3/2009

  32. SALIENCE 2/2 Future work: the salience of a specific norm • can decrease; • is dependent also on the the goals of the agents and on the environment. Organize norms in the LTM as network structure: a node is a norm linked to other norms, and the links correspond to associations between related norms. (Whenever a specific norm is made salient, the agent has access to the representation of that norm and of other norms closely related). NORMAS09 15-20/3/2009

  33. THANK YOU FOR YOUR ATTENTION References and online simulations can be found onhttp://labss.istc.cnr.it/ NORMAS09 15-20/3/2009

More Related