The document discusses the development of hybrid and diversity-aware collective adaptive systems, with a focus on reputation as a collective asset that drives selection processes. It emphasizes the importance of data aggregation and stratification in creating reliable and self-organizing transport groups, as well as policy experimentation. Finally, it highlights social computation and compositionality as essential elements for managing hybridity and diversity in these systems.

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.