Directions of Emergence. Reputation and Social Norms   Rosaria Conte LABSS/ISTC-CNR AISB, Aberdeen, UK,  April 1- 4, 2008
Emergence <ul><ul><li>An effect is said to be emergent when it is  generated  by micro-level entities in interaction. </li...
Need for General Theory of Downward Causation <ul><li>Direct influence  on behaviour  ( see Gilbert, 2002), new   properti...
Reputation
From Image to Reputation through Gossip <ul><li>Reputation is the  </li></ul><ul><ul><li>Emrgent effects (= reported on ev...
Why Bother?  <ul><li>For evolutionary theorists (Dunbar, 1998; Panchanathan, 2001), reputation allowed the  </li></ul><ul>...
As a Meta-Belief… <ul><li>No personal commitment  of speaker about  nested beliefs’ truth value. </li></ul><ul><li>No resp...
Simulation-based Exploration <ul><li>What effect do such cognitive differences bear? </li></ul><ul><li>Thanks to REPAGE, a...
REP-AGE <ul><li>Memory includes  </li></ul><ul><ul><li>Predicates from  </li></ul></ul><ul><ul><ul><li>Experience (contrac...
<ul><li>Simulations run (Paolucci et al., 2007; Quattrociocchi et al., 2008) in simplified markets, to explore  trade-off ...
Experimental Conditions <ul><li>L1: market with only Image </li></ul><ul><li>L2: same market with Image + Reputation  </li...
<ul><li>The two curves present a different cyclic behaviour:  </li></ul><ul><ul><li>in L1 (blue) agents find more good sel...
Average Quality <ul><li>Average  products   quality  in 100 turns. </li></ul><ul><li>L1 (blue, I only)  </li></ul><ul><li>...
Uncertainty Vs Quality:  L1 Uncertainty (= “I DONT KNOW” answers) grows with quality
Uncertainty Vs  Quality: L2 In L2 (I + R),  opposite correlation:, uncertainty decreases with growing quality
Evolution of uncertainty (I Don't Know) in 100 turns with only image circulating: values remain constantly high. Evolution...
Evolution of Uncertainty: L2
Preliminary Conclusions <ul><ul><li>Although both achieve good quality </li></ul></ul><ul><ul><li>But with reputation  </l...
Norms
Two Current Views <ul><li>Conventions (mainly bottom-up) </li></ul><ul><li>Legal norms (mainly top-down) </li></ul><ul><li...
2-way Dynamics of Social Norms  (EMIL project:  http://emil.istc.cnr.it/  ) <ul><li>Norm :  a behaviour that spreads thank...
<ul><li>Each input is presented as an ordered vector consisting of four elements: </li></ul><ul><ul><li>Source (x); </li><...
N-Recognition Module B  , R  , A  > v c D  , V    < v c N-bel Input  N-Board E Board of Auth. Y N
Why Bother? <ul><li>Simulations of norm-recognizers against social conformers in different populations (Campennì et al., 2...
Preliminary Findings <ul><li>Each colour represents one action </li></ul><ul><li>Social conformers: </li></ul><ul><ul><li>...
Immergence   <ul><li>At the 30th tick a normative belief starts to spread as well </li></ul><ul><li>Immergence is earlier:...
Final Remarks <ul><li>Macrosocial regularities emerge and modify the generating machines. </li></ul><ul><li>Different type...
References <ul><li>Andrighetto, G., Conte, R.,Turrini, P., Paolucci, M. (2007).  Emergence In the Loop: Simulating the two...
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  • Emergence will be shown to consist of multi-directional, in particular bottom-up and top-down, processes. The latter, also called downward causation, in turn consist of specific processes, including 2nd-order emergence (see, for example, Gilbert, 2004) and immergence (see Andrighetto et al., 2008a, and current results of the EMIL project, http://emil.istc.cnr.it/ ). Immergence is the process by means of which higher-level effects retroact on the lower-level entities that generated them, modifying their operating rules and reinforcing or reproducing the initial generative process. Two examples will be analyzed at some length, reputation and social norms. Reputation can be seen as a 2nd-order emergent effect of shared informational reciprocity (Dunbar, 1998; Conte and Paolucci, 2002; Sommerfeld et al., 2007). A computational system (REPAGE), built up in the last five years within the LABSS, will be presented, and simulation results showing how it performs in simple multi-agent systems will be discussed. Commonly, norms are either top-down (legal norms) or bottom-up (conventions) effects. In this talk, they will be defined as immergent phenomena (see Andrighetto et al., 2008b), i.e. behaviors that spread over a population because and to the extent that the corresponding normative beliefs and commands spread as well. After a short incursion on a normative agent (EMIL-A) architecture, as worked out within the EMIL project (see D.1.2 on the EMIL site), the role of normative agents will be shown by simulation findings. These illustrate the different macro-social regularities obtained within two separate populations of normative agents and social conformers.
  • Aberdeen2 1

    1. 1. Directions of Emergence. Reputation and Social Norms Rosaria Conte LABSS/ISTC-CNR AISB, Aberdeen, UK, April 1- 4, 2008
    2. 2. Emergence <ul><ul><li>An effect is said to be emergent when it is generated by micro-level entities in interaction. </li></ul></ul><ul><ul><li>Apart from debates on </li></ul></ul><ul><ul><ul><li>Properties (such as unintentional , unpredictable, unreducible; Kim, 1995), etc . </li></ul></ul></ul><ul><ul><ul><li>Orders of emergence: (1st-order and 2nd-order emergence: c onsciousness 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. </li></ul></ul></ul><ul><ul><ul><li>Etc. </li></ul></ul></ul><ul><ul><li>it is generally perceived as an upward process </li></ul></ul><ul><ul><li>What about way back? </li></ul></ul><ul><ul><ul><li>Existing theories of downward causation (such as Emmeche et al,, 2000) are affected by debate about reductionism and even by metaphysics ( Abdoullaev, ) </li></ul></ul></ul><ul><ul><ul><li>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. </li></ul></ul></ul>
    3. 3. Need for General Theory of Downward Causation <ul><li>Direct influence on behaviour ( see Gilbert, 2002), new properties at lower level (e.g., stigma, exchange power,etc.). </li></ul><ul><li>2nd order emergence , as recognition of emergent effect, which contributes to replicate it (clustering in segregation model, Gilbert, 2002). Reputation is another example. </li></ul><ul><li>Immergence : effect cannot even emerge unless it </li></ul><ul><ul><li>Immerges into the mind of generating entities’ (Castelfranchi, 1998, Andrighetto et al., 2008) </li></ul></ul><ul><ul><li>modifying representations and operating rules. </li></ul></ul><ul><ul><li>Norms are one example. </li></ul></ul>
    4. 4. Reputation
    5. 5. From Image to Reputation through Gossip <ul><li>Reputation is the </li></ul><ul><ul><li>Emrgent effects (= reported on evaluation) of a </li></ul></ul><ul><ul><li>Social process (gossip) </li></ul></ul><ul><ul><li>Starting from social evaluation ( I = image) </li></ul></ul><ul><li>Twofold effect </li></ul><ul><ul><li>On target: stigma </li></ul></ul><ul><ul><li>On gossipers: </li></ul></ul><ul><ul><ul><li>social meta-belief about others’ evaluations </li></ul></ul></ul><ul><ul><ul><li>Through multiple loops: (meta-)belief > gossip > retroacton > meta-belief/stigma, etc. </li></ul></ul></ul>
    6. 6. Why Bother? <ul><li>For evolutionary theorists (Dunbar, 1998; Panchanathan, 2001), reputation allowed the </li></ul><ul><ul><li>evolution of indirect reciprocity and the </li></ul></ul><ul><ul><li>enlargement of hominids’ settlements </li></ul></ul>Figura Interplay between informational and mateiral, direct and indirect reciprocity. Reproduced from Conte and Paolucci (2002). Step 1: Direct material reciprocity A B give Step 2: direct informational reciprocity A C tell … A n B A tell tell tell Step 4: indirect informational reciprocity give give … A n B A give Step 3: Indirect material reciprocity
    7. 7. As a Meta-Belief… <ul><li>No personal commitment of speaker about nested beliefs’ truth value. </li></ul><ul><li>No responsability about their credibility (“I am told that…”) </li></ul>Implicit source of rumour Indefinite author of evaluation Image = social evaluation Reputation = meta-evaluation. This implies: Rumours spread even when nobody believes them!
    8. 8. Simulation-based Exploration <ul><li>What effect do such cognitive differences bear? </li></ul><ul><li>Thanks to REPAGE, a tool developed at LABSS (Sabater et al., 2005; see EU-funded eREP Project, http://erep.istc.cnr.it/ ) </li></ul><ul><li>Simulations on multiagent stylized scenarios </li></ul>
    9. 9. REP-AGE <ul><li>Memory includes </li></ul><ul><ul><li>Predicates from </li></ul></ul><ul><ul><ul><li>Experience (contract fulfilments) </li></ul></ul></ul><ul><ul><ul><li>Communication from others (I and R) </li></ul></ul></ul><ul><ul><li>Organized in a network of dependencies, specifying which predicates contribute to the values of others: </li></ul></ul><ul><ul><ul><li>each predicate has a set of antecedents and a set of consequents. </li></ul></ul></ul><ul><ul><ul><li>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. </li></ul></ul></ul><ul><li>REPAGE runs on a JADE-X platform. </li></ul>
    10. 10. <ul><li>Simulations run (Paolucci et al., 2007; Quattrociocchi et al., 2008) in simplified markets, to explore trade-off of informational cooperation: </li></ul><ul><ul><li>Communication is necessary to find good sellers. </li></ul></ul><ul><ul><li>But agents have an incentive to cheat. </li></ul></ul><ul><li>Hence: </li></ul><ul><ul><li>Fixed number of sellers and buyers (respectively to 100 and 15), </li></ul></ul><ul><ul><li>Goods are represented by a 1-100 valued utility factor </li></ul></ul><ul><ul><li>Variable quality sellers with finite stocks, which, when exhausted, are replenished automatically with random quality. </li></ul></ul><ul><ul><li>Buyers </li></ul></ul><ul><ul><ul><li>purchase, </li></ul></ul></ul><ul><ul><ul><li>Ask for info from one another (which is the best, which is the worst) </li></ul></ul></ul><ul><ul><ul><li>Answer by providing </li></ul></ul></ul><ul><ul><ul><ul><li>false/truthful info (info cheating rate) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Tested (I) or untested ® information </li></ul></ul></ul></ul>Simulations with REPAGE
    11. 11. Experimental Conditions <ul><li>L1: market with only Image </li></ul><ul><li>L2: same market with Image + Reputation </li></ul><ul><li>Explore respective performance, considering that in L1 either </li></ul><ul><ul><li>Tested image spreads, or </li></ul></ul><ul><ul><li>Retaliation (when false image is transmitted), </li></ul></ul><ul><li>In L2, </li></ul><ul><ul><li>less retaliation is expected and </li></ul></ul><ul><ul><li>more, although untested, information. </li></ul></ul>
    12. 12. <ul><li>The two curves present a different cyclic behaviour: </li></ul><ul><ul><li>in L1 (blue) agents find more good sellers than in L2 (red). </li></ul></ul><ul><ul><li>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. </li></ul></ul><ul><ul><li>Minimum value for each wave is interpreted as a slow process of discovery. </li></ul></ul>Findings. Find Out Good Sellers
    13. 13. Average Quality <ul><li>Average products quality in 100 turns. </li></ul><ul><li>L1 (blue, I only) </li></ul><ul><li>L2 (red, I + R). </li></ul><ul><li>Both achieve optimal quality, with faster L2 convergence . </li></ul><ul><li>How is it possible? </li></ul><ul><li>Information spreads </li></ul><ul><li>more in L2: </li></ul><ul><ul><li>Agents find less good sellers </li></ul></ul><ul><ul><li>do not exhaust them </li></ul></ul><ul><ul><li>try more: information circulates more (and more widely) </li></ul></ul><ul><li>but what info quality ? </li></ul>
    14. 14. Uncertainty Vs Quality: L1 Uncertainty (= “I DONT KNOW” answers) grows with quality
    15. 15. Uncertainty Vs Quality: L2 In L2 (I + R), opposite correlation:, uncertainty decreases with growing quality
    16. 16. Evolution of uncertainty (I Don't Know) in 100 turns with only image circulating: values remain constantly high. Evolution of Uncertainty: L1
    17. 17. Evolution of Uncertainty: L2
    18. 18. Preliminary Conclusions <ul><ul><li>Although both achieve good quality </li></ul></ul><ul><ul><li>But with reputation </li></ul></ul><ul><ul><ul><li>Uncertainty decreases: information does not get lost </li></ul></ul></ul><ul><ul><ul><li>No exhaustion of resources </li></ul></ul></ul><ul><ul><li>What is the use of reduced uncertainty, if quality is the same? </li></ul></ul><ul><ul><li>Results indicate three directions for further exploration </li></ul></ul><ul><ul><ul><ul><li>Reputation might favour and be compatible with larger networks (effect to be checked with open networks) </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Information can be transmitted to future generations (effect to be checked with evolution of the market, spin-off, etc,). </li></ul></ul></ul></ul><ul><ul><ul><ul><li>What about not only scarce but also finite resources? One might think that reputation is more robust than image with non-self replenishing resources. </li></ul></ul></ul></ul>
    19. 19. Norms
    20. 20. Two Current Views <ul><li>Conventions (mainly bottom-up) </li></ul><ul><li>Legal norms (mainly top-down) </li></ul><ul><li>Open questions </li></ul><ul><ul><li>As to conventions: </li></ul></ul><ul><ul><ul><li>What about social norms ? </li></ul></ul></ul><ul><ul><ul><li>Why are they enforced ? </li></ul></ul></ul><ul><ul><ul><li>What about mandatory social norms? </li></ul></ul></ul><ul><ul><li>As to legal norms </li></ul></ul><ul><ul><ul><li>How do they evolve? </li></ul></ul></ul><ul><ul><ul><li>How do agents find them out? </li></ul></ul></ul><ul><ul><li>As to both </li></ul></ul><ul><ul><ul><li>What about a unifying view? </li></ul></ul></ul>
    21. 21. 2-way Dynamics of Social Norms (EMIL project: http://emil.istc.cnr.it/ ) <ul><li>Norm : a behaviour that spreads thanks to the spreading of normative beliefs and commands </li></ul><ul><li>Normative belief : a belief that a given action  , in a given context, for a given set of agents, is forbidden, obligatory, permitted, etc. </li></ul><ul><li>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). </li></ul>
    22. 22. <ul><li>Each input is presented as an ordered vector consisting of four elements: </li></ul><ul><ul><li>Source (x); </li></ul></ul><ul><ul><li>Modal (M) through which the message is presented: assertions (A), behaviours (B), requests (R), deontics (D), evaluations (V), sanctions (S); </li></ul></ul><ul><ul><li>Observer (y); </li></ul></ul><ul><ul><li>Action transmitted ( α ). </li></ul></ul>The Input
    23. 23. N-Recognition Module B  , R  , A  > v c D  , V  < v c N-bel Input N-Board E Board of Auth. Y N
    24. 24. Why Bother? <ul><li>Simulations of norm-recognizers against social conformers in different populations (Campennì et al., 2008a, papers submitted to WCSS; 2008b, submitted to NORMAS) </li></ul><ul><li>The model: </li></ul><ul><ul><li>Multi scenario world </li></ul></ul><ul><ul><ul><li>Four different multi-action scenarios (social settings) </li></ul></ul></ul><ul><ul><ul><li>With one common + two scenario-specific actions (total nine actions). </li></ul></ul></ul><ul><ul><li>Agents </li></ul></ul><ul><ul><ul><li>move from one scenario to the next </li></ul></ul></ul><ul><ul><ul><li>are endowed with </li></ul></ul></ul><ul><ul><ul><ul><li>Personal agendas </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Individual fixed time of permanence in each scenario </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Two populations </li></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Social conformers: follow actions most frequently done in observation window (parameter). </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>Norm recognizers take input from others, form beliefs and act based on those. </li></ul></ul></ul></ul></ul>
    25. 25. Preliminary Findings <ul><li>Each colour represents one action </li></ul><ul><li>Social conformers: </li></ul><ul><ul><li>No difference within ticks </li></ul></ul><ul><ul><li>Strong difference </li></ul></ul><ul><ul><ul><li>Among ticks (no belief) </li></ul></ul></ul><ul><ul><ul><li>Among scenarios (no memory) </li></ul></ul></ul><ul><ul><ul><li>More frequent action (dark blue) is distributed throughout the simulation: nothing emerges! </li></ul></ul></ul><ul><li>Norm recognizers: </li></ul><ul><ul><li>Fuzzier </li></ul></ul><ul><ul><ul><li>Rows (autonomy) </li></ul></ul></ul><ul><ul><ul><li>Columns (beliefs) </li></ul></ul></ul><ul><ul><li>After 60th ticks, one action common to all scenarios: something emerges… </li></ul></ul><ul><ul><li>What is it? Lets look into agents beliefs… </li></ul></ul>
    26. 26. Immergence <ul><li>At the 30th tick a normative belief starts to spread as well </li></ul><ul><li>Immergence is earlier: it takes time for effect to emerge (loops). </li></ul><ul><li>What has happened in the meantime? </li></ul><ul><li>Other normative beliefs were formed, although earlier is more frequent </li></ul><ul><li>If same-norm agents get separated ( genetic or cultural drift): norm innovation! (equally frequent norms might emerge in different subpopulations). </li></ul><ul><li>If they then get re-united, which norm is going to invade population? </li></ul><ul><li>Question for future studies :-) </li></ul>
    27. 27. Final Remarks <ul><li>Macrosocial regularities emerge and modify the generating machines. </li></ul><ul><li>Different types and degrees of top-down influence: </li></ul><ul><ul><li>agents recognize emerged effects </li></ul></ul><ul><ul><li>sometimes effects don’t emerge unless they immerge. </li></ul></ul><ul><li>Hence, we need to understand this process to </li></ul><ul><ul><li>Understand agents </li></ul></ul><ul><ul><li>Understand different patterns of macrosocial regularities: </li></ul></ul><ul><ul><ul><li>With reputation, observable marosocial effects of reduced uncertainty might include larger networks, higher stability, more robustness. </li></ul></ul></ul><ul><ul><ul><li>With social norms, observable macrosocial effects of normative beliefs </li></ul></ul></ul><ul><ul><ul><ul><li>actually include effective convergence across scenarios, </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Potentially, norm-innovation </li></ul></ul></ul></ul>
    28. 28. References <ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>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 -1 8 July, 2008. Submitted. </li></ul><ul><li>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. </li></ul>
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