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Norms recognition

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1) The document describes a simulation study comparing norm recognizers and social conformers in different contexts. Norm recognizers form beliefs about appropriate actions based on observing others, while social conformers simply imitate the most frequent action. 2) In the simulation, social conformers show no emergent patterns over time, while norm recognizers begin to converge on common actions across contexts after 60 ticks as norms spread through their normative beliefs. 3) The introduction of a physical barrier between contexts at a certain tick allows different norms to emerge in isolated sub-populations, demonstrating norm innovation.

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N-Recognition Module Input (Candidate N-b el)‏ N-bel V c =N-threshold E N-Board LTM B  , R  , A  > v c D  , V  < v c W M
A simulation study
Norm-recognizers Vs Social conformers What are observable effects of norm recognition? Implement different populations (Andrighetto et al., 2008a, Campennì et al., 2008b): Social conformers: follow actions most frequently done in observation window (parameter)‏ Norm recognizers take input from others, form beliefs and act based on those.
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; C1 (A1, A2, A3)‏ C2 (A1, A4, A5)‏ C3 (A1, A6, A7)‏ C4 (A1, A8, A9)‏ AGENDA (C1,C2,C4,C3)‏ Time-of-Perm (nTicks/ n )‏
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. N-Board: N-B1 N-B2 ....... level-2 (D & N-V)‏ level-1 (A, R, B)‏ AGENDA (C1,C4,C2,C3)‏ Time-of-Perm (t)‏ N-Threshold (v c )‏
Social conformer Each observes other agents in same context According to conformity rate, imitates most frequent action Conformity rate = 9
Simulations' results
Preliminary Findings Social conformers (above): Each colour represents one action No difference within ticks Strong difference Among ticks (no belief)‏ Among scenarios (no memory)‏ More frequent action (dark blue) is distributed throughout the simulation: nothing emerges! Norm recognizers (below): Fuzzier Rows (autonomy)‏ Columns (beliefs)‏ After 60th tick, one action common to all scenarios: something emerges… What is it? Lets look into agents beliefs… Social conformers Norm recognizers
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
SCs Vs NRs Low Conf. Rate 3 Medium CR High CR Medium NT Lo N-Threshold Hi NT
Latency of norms Time interval between N-bels appearance and convergence on corresponding action. Actually, a complex loop from N-Bel x to N-action x from N-action x to N-bel y from N-bel y to N-action y Etc. Can help predict effect of new policies Immergence  2nd-order emergence: not a reflected upon emergent phenomenon but involved in emergence!
Follow-up questions Only common actions? What happens with physical barriers and/or (cultural) drifts ? Equally frequent norms might emerge in different sub-populations: norm innovation?
Let us simulate a barrier At a given tick, agents get stuck to current locations, they can no longer move across settings. Yes barrier No barrier
Normative Beliefs No barrier Yes barrier
But in 300 ticks…
Final remarks In a multi-scenario world, unlike social conformers, norm recognizers converge. Norms immerge in the minds before emerging in behavior. A normative belief corresponds not necessarily to the most frequent action. Barriers are sufficient (not necessary) for norm-innovation. Norms have a latency time.
For the future If agents get re-united, which norm is going to invade population? What about inertia (i.e. the time for a norm to disappear)? During inertia, norms may compete in the same population... Internalization and semi-automatic conformity Questions for future studies :-)
Why bother? Policy-making and what if analysis ICT systems: Artificial normative reasoning Electronic institutions Applications of agent systems Computational and simulation-based modelling for (social/cognitive) theory-building and testing. Thank you

More Related Content

Norms recognition

  • 1. N-Recognition Module Input (Candidate N-b el)‏ N-bel V c =N-threshold E N-Board LTM B  , R  , A  > v c D  , V  < v c W M
  • 3. Norm-recognizers Vs Social conformers What are observable effects of norm recognition? Implement different populations (Andrighetto et al., 2008a, Campennì et al., 2008b): Social conformers: follow actions most frequently done in observation window (parameter)‏ Norm recognizers take input from others, form beliefs and act based on those.
  • 4. 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; C1 (A1, A2, A3)‏ C2 (A1, A4, A5)‏ C3 (A1, A6, A7)‏ C4 (A1, A8, A9)‏ AGENDA (C1,C2,C4,C3)‏ Time-of-Perm (nTicks/ n )‏
  • 5. 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. N-Board: N-B1 N-B2 ....... level-2 (D & N-V)‏ level-1 (A, R, B)‏ AGENDA (C1,C4,C2,C3)‏ Time-of-Perm (t)‏ N-Threshold (v c )‏
  • 6. Social conformer Each observes other agents in same context According to conformity rate, imitates most frequent action Conformity rate = 9
  • 8. Preliminary Findings Social conformers (above): Each colour represents one action No difference within ticks Strong difference Among ticks (no belief)‏ Among scenarios (no memory)‏ More frequent action (dark blue) is distributed throughout the simulation: nothing emerges! Norm recognizers (below): Fuzzier Rows (autonomy)‏ Columns (beliefs)‏ After 60th tick, one action common to all scenarios: something emerges… What is it? Lets look into agents beliefs… Social conformers Norm recognizers
  • 9. 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
  • 10. SCs Vs NRs Low Conf. Rate 3 Medium CR High CR Medium NT Lo N-Threshold Hi NT
  • 11. Latency of norms Time interval between N-bels appearance and convergence on corresponding action. Actually, a complex loop from N-Bel x to N-action x from N-action x to N-bel y from N-bel y to N-action y Etc. Can help predict effect of new policies Immergence  2nd-order emergence: not a reflected upon emergent phenomenon but involved in emergence!
  • 12. Follow-up questions Only common actions? What happens with physical barriers and/or (cultural) drifts ? Equally frequent norms might emerge in different sub-populations: norm innovation?
  • 13. Let us simulate a barrier At a given tick, agents get stuck to current locations, they can no longer move across settings. Yes barrier No barrier
  • 14. Normative Beliefs No barrier Yes barrier
  • 15. But in 300 ticks…
  • 16. Final remarks In a multi-scenario world, unlike social conformers, norm recognizers converge. Norms immerge in the minds before emerging in behavior. A normative belief corresponds not necessarily to the most frequent action. Barriers are sufficient (not necessary) for norm-innovation. Norms have a latency time.
  • 17. For the future If agents get re-united, which norm is going to invade population? What about inertia (i.e. the time for a norm to disappear)? During inertia, norms may compete in the same population... Internalization and semi-automatic conformity Questions for future studies :-)
  • 18. Why bother? Policy-making and what if analysis ICT systems: Artificial normative reasoning Electronic institutions Applications of agent systems Computational and simulation-based modelling for (social/cognitive) theory-building and testing. Thank you

Editor's Notes

  1. The moduel includes a ltm and a wm. In the ltm, norms are archived once nbels got formed. The wm is a 2layers architecture, where new inputs are elaborated. So anytime a new input vector arrives, if it is a D or V, it will access the wm at the 2 layer, and a N-bel is formed. If not, but it has already been ancountered as a D or V, it will access the wm at the first florr. If not, it is matched with the NB and if…, otherwise it is discarded. This architectire allows us to treat the gradual formation of beliefs. If , within a certain time periodo, the CNB is not strengthened by new inputs (N-threshold), it will exit the wm, otherwise a fullfledged Nbel is formed and archived.