Fausto Giunchiglia & Vincenzo Maltese: University of Trento Stuart Anderson: Edinburgh University Daniele Miorandi: U‐Hopper Presentation from ECAL 2013

1. Towards Hybrid and Diversity‐Aware
Collective Adaptive Systems
Collective Adaptive Systems
Fausto Giunchiglia Vincenzo Maltese
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Stuart Anderson Daniele Miorandi
DISI, University of Trento, Trento, Italy
School of Informatics, University of Edinburgh, Edinburgh, UK
School of Informatics University of Edinburgh Edinburgh UK
U‐Hopper & CREATE‐NET, Trento, Italy
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2. Smart Society
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3. Ride sharing application
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4. 26/09/2013
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5. How is this Collective Adaptive?
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 Reputation (Joe should have one too)
Reputation (Joe should have one too)
 Reputation is a collective asset.
 R
Reputation drives selection process
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 Reputation aggregates behaviour
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6. Aggregation/Stratification
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 Aggregation builds collective assets
Aggregation builds collective assets.
 Goes together with stratification
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Stratification determines the “relevant” population
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 Stratification is driven by particular observations on the
population.
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 Aggregation/Stratification builds layered systems
 Aggregation/Stratification support collectives as actors
 Empirically there are ethical concerns
 Aggregation used to justify lack of transparency
 Stratification can identify
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7. Layered Systems
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 Layer 2: Incentivising the creation of self organising
Layer 2: Incentivising the creation of self organising
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transport groups – analyse data, bring people together,
improved reliability, stable cost.
improved reliability, stable cost.
Aggregation of Trip data is essential to achieve this and
stratification drives specificity.
stratification drives specificity.
The extra layer changes evidence from the first layer.
Layer 3: Incentivise the creation of policy experimentation
Layer 3: Incentivise the creation of policy experimentation
based on evidence from layer 1 and 2.
Requires aggregation of modes of organisation/provision…
Requires aggregation of modes of organisation/provision
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8. Social Computation
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 Programming model that taking humans and programs
Programming model that taking humans and programs
working in close cooperation.
 Human computation depends on resources (e g
Human computation depends on resources (e.g.
communication) and incentives
 There are many emerging models:
There are many emerging models:
 Mechanical turk
 Games with a purpose
Games with a purpose
 Crowdsourcing
…
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9. Compositionality
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 Slogan – “the meaning of the whole is a function of the
Slogan – the meaning of the whole is a function of the
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meaning of the parts”.
Key property if systems are to be intellectually tractable.
Key property if systems are to be intellectually tractable
Many components don’t compose nicely.
Meaning is context dependent, “good enough” semantics
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could deploy humans to resolve context/calculate
semantics, could be relativised to the context.
semantics could be relativised to the context
Compositionality potentially generalises ideas about
aggregation suggests architectures of social machines
aggregation suggests architectures of “social machines”
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10. Conclusions
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 Social computation is the key to support Hybridity
Social computation is the key to support Hybridity.
 Compositionality is the key tool to support Diversity and
tractability of Social Computation.
tractability of Social Computation
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