towards hybrid and diversity‐aware collective adaptive systems

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Towards Hybrid and DiversityAware Collective Adaptive Systems Collective Adaptive Systems Fausto Giunchiglia Vincenzo Maltese d l d 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 UHopper & CREATENET, Trento, Italy 26/09/2013 www.smartsocietyproject.eu

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

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Page 1: Towards Hybrid and Diversity‐Aware Collective Adaptive Systems

Towards Hybrid and Diversity‐Aware Collective Adaptive SystemsCollective Adaptive Systems

Fausto Giunchiglia Vincenzo Maltese d l dStuart Anderson Daniele Miorandi

DISI, University of Trento, Trento, Italy

School of Informatics University of Edinburgh Edinburgh UKSchool of Informatics, University of Edinburgh, Edinburgh, UK

U‐Hopper & CREATE‐NET, Trento, Italy

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Page 2: Towards Hybrid and Diversity‐Aware Collective Adaptive Systems

Smart Society

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d h lRide sharing application

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h ll d ?How is this Collective Adaptive? Reputation (Joe should have one too) Reputation (Joe should have one too) Reputation is a collective asset.R t ti d i l ti Reputation drives selection process

Reputation aggregates behaviour

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/ fAggregation/Stratification Aggregation builds collective assets Aggregation builds collective assets. Goes together with stratificationSt tifi ti d t i th “ l t” l ti Stratification determines the “relevant” population

Stratification is driven by particular observations on the l tipopulation.

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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dLayered Systems Layer 2: Incentivising the creation of self organising Layer 2: Incentivising the creation of self organisingtransport 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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lSocial Computation 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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lCompositionality Slogan – “the meaning of the whole is a function of the Slogan – the meaning of the whole is a function of the 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.M i i t t d d t “ d h” ti Meaning is context dependent, “good enough” semantics could deploy humans to resolve context/calculate semantics could be relativised to the contextsemantics, 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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lConclusions 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 Computationtractability of Social Computation.

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