by Fausto Giunchiglia, Vincenzo Maltese, Stuart Anderson & Daniele Miorandi. Presentation given by Stuart Anderson at the Focas workshop @ ECAL 2013.

1. Fausto Giunchiglia Vincenzo Maltese
Stuart Anderson Daniele Miorandi
DISI, University of Trento, Trento, Italy
School of Informatics, University of Edinburgh, Edinburgh, UK
U-Hopper & CREATE-NET, Trento, Italy
Towards Hybrid and Diversity-Aware
Collective Adaptive Systems
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2. Smart Society
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3. Ride sharing application
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5. How is this Collective Adaptive?
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 Reputation (Joe should have one too)
 Reputation is a collective asset.
 Reputation drives selection process
 Reputation aggregates behaviour

6. Aggregation/Stratification
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 Aggregation builds collective assets.
 Goes together with stratification
 Stratification determines the “relevant” population
 Stratification is driven by particular observations on the
population.
 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

7. Layered Systems
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 Layer 2: Incentivising the creation of self organising
transport groups – analyse data, bring people together,
improved reliability, stable cost.
 Aggregation of Trip data is essential to achieve this and
stratification drives specificity.
 The extra layer changes evidence from the first layer.
 Layer 3: Incentivise the creation of policy experimentation
based on evidence from layer 1 and 2.
 Requires aggregation of modes of organisation/provision…

8. Social Computation
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 Programming model that taking humans and programs
working in close cooperation.
 Human computation depends on resources (e.g.
communication) and incentives
 There are many emerging models:
 Mechanical turk
 Games with a purpose
 Crowdsourcing
 …

9. Compositionality
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 Slogan – “the meaning of the whole is a function of the
meaning of the parts”.
 Key property if systems are to be intellectually tractable.
 Many components don’t compose nicely.
 Meaning is context dependent, “good enough” semantics
could deploy humans to resolve context/calculate
semantics, could be relativised to the context.
 Compositionality potentially generalises ideas about
aggregation suggests architectures of “social machines”

10. Conclusions
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 Social computation is the key to support Hybridity.
 Compositionality is the key tool to support Diversity and
tractability of Social Computation.