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.

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Need for General Theory of Downward Causation

• Direct influence on behaviour (see Gilbert,
  2002), new properties 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.

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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 gt gossip gt
  retroacton gt meta-belief/stigma, etc.

6
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

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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.

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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

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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.

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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.

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Average Quality

• Average products quality 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?

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Uncertainty Vs Quality L1
Uncertainty ( I DONT KNOW answers) grows with
quality
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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.
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Evolution of Uncertainty L2
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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.

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Norms
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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?

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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).

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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 (a).

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N-Recognition Module
Board of Auth.
Y
N-bel
N
N-Board
gt vc D?, V? lt vc
E
B?, R?, A?
Input
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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.

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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

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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 -)

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Final Remarks

• Macrosocial regularities emerge and modify the
  generating machines.
• Different types and degrees of top-down
  influence
• agents recognize emerged effects
• sometimes effects dont 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

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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.