Directions of Emergence. Reputation and Social Norms

1. Directions of Emergence.Reputation and Social Norms Rosaria Conte LABSS/ISTC-CNR AISB, Aberdeen, UK, April 1- 4, 2008

2. Emergence • An effect is said to be emergent when it is generated by micro-level entities in interaction. • Apart from debates on • Properties (such as unintentional, unpredictable, unreducible; Kim, 1995), etc. • Orders of emergence: (1st-order and 2nd-order emergence: consciousness for Dennett: is a second order emergence, in the sense that it emerges from the interactions of the parts of the mind and, through its emergence, changes how the system processes information. • Etc. it is generally perceived as an upward process • What about way back? • Existing theories of downward causation (such as Emmeche et al,, 2000) are affected by debate about reductionism and even by metaphysics (Abdoullaev, ) • A downward notion of 2nd-order emergence has been put forward (Gilbert), as implying recognition of emergent effect (see also, Goldspink and Kay, in ongoing symposium): emergent effect is represented by agents, thus contributing to its replication.

3. Need for General Theory of Downward Causation • Direct influence on behaviour (see Gilbert, 2002), newproperties at lower level (e.g., stigma, exchange power,etc.). • 2nd order emergence, as recognition of emergent effect, which contributes to replicate it (clustering in segregation model, Gilbert, 2002). Reputation is another example. • Immergence: effect cannot even emerge unless it • Immerges into the mind of generating entities’ (Castelfranchi, 1998, Andrighetto et al., 2008) • modifying representations and operating rules. Norms are one example.

4. Reputation

5. From Image to Reputation through Gossip • Reputation is the • Emrgent effects (= reported on evaluation)of a • Social process (gossip) • Starting from social evaluation ( I = image) • Twofold effect • On target: stigma • On gossipers: • social meta-belief about others’ evaluations • Through multiple loops: (meta-)belief > gossip > retroacton > meta-belief/stigma, etc.

6. Step 1: Direct material reciprocity give A B Step 2: direct informational reciprocity tell A C Step 3: Indirect material reciprocity Step 4: indirect informational reciprocity give give give tell tell tell … … A A B B n n A A Figura Interplay between informational and mateiral, direct and indirect reciprocity. Reproduced from Conte and Paolucci (2002). Why Bother? For evolutionary theorists (Dunbar, 1998; Panchanathan, 2001), reputation allowed the • evolution of indirect reciprocity and the • enlargement of hominids’ settlements

7. As a Meta-Belief… Image = social evaluation Reputation = meta-evaluation. This implies: • No personal commitment of speaker about nested beliefs’ truth value. • No responsability about their credibility (“I am told that…”) Implicit source of rumour Indefinite author of evaluation Rumours spread even when nobody believes them!

8. Simulation-based Exploration • What effect do such cognitive differences bear? • Thanks to REPAGE, a tool developed at LABSS (Sabater et al., 2005; see EU-funded eREP Project, http://erep.istc.cnr.it/ ) • Simulations on multiagent stylized scenarios

9. REP-AGE Memory includes • Predicates from • Experience (contract fulfilments) • Communication from others (I and R) • Organized in a network of dependencies, specifying which predicates contribute to the values of others: • each predicate has a set of antecedents and a set of consequents. • With new inputs, thanks to Detectors, if an antecedent is created, removed, or its value changes, predicate value is recalculated and change notified to its consequents. REPAGE runs on a JADE-X platform.

10. Simulations with REPAGE Simulations run (Paolucci et al., 2007; Quattrociocchi et al., 2008) in simplified markets, to explore trade-off of informational cooperation: • Communication is necessary to find good sellers. • But agents have an incentive to cheat. Hence: • Fixed number of sellers and buyers (respectively to 100 and 15), • Goods are represented by a 1-100 valued utility factor • Variable quality sellers with finite stocks, which, when exhausted, are replenished automatically with random quality. • Buyers • purchase, • Ask for info from one another (which is the best, which is the worst) • Answer by providing • false/truthful info (info cheating rate) • Tested (I) or untested ® information

11. Experimental Conditions • L1: market with only Image • L2: same market with Image + Reputation • Explore respective performance, considering that in L1 either • Tested image spreads, or • Retaliation (when false image is transmitted), • In L2, • less retaliation is expected and • more, although untested, information.

12. Findings. Find Out Good Sellers The two curves present a different cyclic behaviour: • in L1 (blue) agents find more good sellers than in L2 (red). • Peaks of each wave is interpreted as exhaustion of stocks: once a good seller is discovered, buyers start to buy from this one until extinction of stock. • Minimum value for each wave is interpreted as a slow process of discovery.

13. Average Quality Average productsquality in 100 turns. • L1 (blue, I only) • L2 (red, I + R). Both achieve optimal quality, with faster L2 convergence. • How is it possible? • Information spreads • more in L2: • Agents find less good sellers • do not exhaust them • try more: information circulates more (and more widely) • but what info quality?

14. Uncertainty Vs Quality: L1 Uncertainty (= “I DONT KNOW” answers) grows with quality

15. Uncertainty Vs Quality: L2 In L2 (I + R), opposite correlation:, uncertainty decreases with growing quality

16. Evolution of Uncertainty: L1 Evolution of uncertainty (I Don't Know) in 100 turns with only image circulating: values remain constantly high.

17. Evolution of Uncertainty: L2

18. Preliminary Conclusions • Although both achieve good quality • But with reputation • Uncertainty decreases: information does not get lost • No exhaustion of resources • What is the use of reduced uncertainty, if quality is the same? • Results indicate three directions for further exploration • Reputation might favour and be compatible with larger networks (effect to be checked with open networks) • Information can be transmitted to future generations (effect to be checked with evolution of the market, spin-off, etc,). • What about not only scarce but also finite resources? One might think that reputation is more robust than image with non-self replenishing resources.

19. Norms

20. Two Current Views • Conventions (mainly bottom-up) • Legal norms (mainly top-down) • Open questions • As to conventions: • What about social norms? • Why are they enforced? • What about mandatory social norms? • As to legal norms • How do they evolve? • How do agents find them out? • As to both • What about a unifying view?

21. 2-way Dynamics of Social Norms (EMIL project: http://emil.istc.cnr.it/ ) • Norm: a behaviour that spreads thanks to the spreading of normative beliefs and commands • Normative belief: a belief that a given action , in a given context, for a given set of agents, is forbidden, obligatory, permitted, etc. • Normative command:a command based upon a normative belief (more precisely,a command that wants to be adopted via the formation of a normative belief).

22. The Input Each input is presented as an ordered vector consisting of four elements: • Source (x); • Modal (M) through which the message is presented: assertions (A), behaviours (B), requests (R), deontics (D), evaluations (V), sanctions (S); • Observer (y); • Action transmitted (α).

23. N-Recognition Module Board of Auth. Y N-bel N N-Board > vc D, V < vc E B, R, A Input

24. Why Bother? • Simulations of norm-recognizers against social conformers in different populations (Campennì et al., 2008a, papers submitted to WCSS; 2008b, submitted to NORMAS) • The model: • Multi scenario world • Four different multi-action scenarios (social settings) • With one common + two scenario-specific actions (total nine actions). • Agents • move from one scenario to the next • are endowed with • Personal agendas • Individual fixed time of permanence in each scenario • Two populations • Social conformers: follow actions most frequently done in observation window (parameter). • Norm recognizers take input from others, form beliefs and act based on those.

25. Preliminary Findings • Each colour represents one action • Social conformers: • 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: • Fuzzier • Rows (autonomy) • Columns (beliefs) • After 60th ticks, one action common to all scenarios: something emerges… • What is it? Lets look into agents beliefs…

26. Immergence • At the 30th tick a normative belief starts to spread as well • Immergence is earlier: it takes time for effect to emerge (loops). • What has happened in the meantime? • Other normative beliefs were formed, although earlier is more frequent • If same-norm agents get separated (genetic or cultural drift): norm innovation! (equally frequent norms might emerge in different subpopulations). • If they then get re-united, which norm is going to invade population? • Question for future studies :-)

27. Final Remarks • Macrosocial regularities emerge and modify the generating machines. • Different types and degrees of top-down influence: • agents recognize emerged effects • sometimes effects don’t emerge unless they immerge. • Hence, we need to understand this process to • Understand agents • Understand different patterns of macrosocial regularities: • With reputation, observable marosocial effects of reduced uncertainty might include larger networks, higher stability, more robustness. • With social norms, observable macrosocial effects of normative beliefs • actually include effective convergence across scenarios, • Potentially, norm-innovation

28. References • Andrighetto, G., Conte, R.,Turrini, P., Paolucci, M. (2007). Emergence In the Loop: Simulating the two way dynamics of norm innovation. In Proceedings of the Dagstuhl Seminar on Normative Multi-agent Systems, 18-23 March 2007, Dagstuhl, Germany. • Andrighetto, G., Campennì, M, Conte, R., Paolucci, M.(2007). On the Immergence of Norms: a Normative Agent Architecture. In Proceedings of AAAI Symposium, Social and Organizational Aspects of Intelligence, Washington DC. • Conte, R., Andrighetto, G., Campennì, M, Paolucci, M.(2007). Emergent and Immergent Effects in Complex Social Systems. In Proceedings of AAAI Symposium, Social and Organizational Aspects of Intelligence, Washington DC. • Andrighetto, G., Campennì, M, Conte, R., Cecconi, F. (2008). Conformity in Multiple Contexts: Imitation Vs Norm Recognition, The second World Congress on Social Simulation (WCSS-08), George Mason University, Fairfax - July 14-17, 2008. Submitted. • Andrighetto, G., Campennì, M, Conte, R., Cecconi, F. (2008). How Agents Find out Norms: A Simulation Based Model of Norm Innovation, 3rd International Workshop 
on Normative Multiagent Systems 
(NorMAS 2008),Luxembourg, 15-18 July, 2008. Submitted. • Andrighetto, G.; Campennì, M.; Conte, R. (2007). EMIL-M: MODELS OF NORMS EMERGENCE, NORMS IMMERGENCE AND THE 2-WAY DYNAMIC, Technical Report, 00507, LABSS-ISTC/CNR.