• J'aime
 A Unified Framework for Collective Systems
Prochain SlideShare
Chargement dans... 5
×

A Unified Framework for Collective Systems

  • 293 vues
Transféré le

Emma Hart: Edinburgh Napier University ...

Emma Hart: Edinburgh Napier University
Jeremy Pitt: Imperial College London
Ulle Endriss: University of Amsterdam

Presentation from ECAL 2013

Plus dans : Technologies , Affaires
  • Full Name Full Name Comment goes here.
    Êtes-vous sûr de vouloir
    Votre message apparaîtra ici
    Soyez le premier à commenter
    Be the first to like this
Aucun téléchargement

Vues

Total des vues
293
Sur Slideshare
0
À partir des ajouts
0
Nombre d'ajouts
5

Actions

Partages
Téléchargements
1
Commentaires
0
J'aime
0

Ajouts 0

No embeds

Signaler un contenu

Signalé comme inapproprié Signaler comme inapproprié
Signaler comme inapproprié

Indiquez la raison pour laquelle vous avez signalé cette présentation comme n'étant pas appropriée.

Annuler
    No notes for slide

Transcript

  • 1. A UNIFIED FRAMEWORK FOR COLLECTIVE SYSTEMS Emma Hart, Edinburgh Napier University Jeremy Pitt, Imperial College London Ulle Endriss, University of Amsterdam
  • 2. Grand Vi i G d Vision Applications A Software Toolkit of Design Patterns and Components p A Unified Theory of Operations for CAST Systems y
  • 3. Why do Wh d we need a new theory ? d h • Existing engineering approaches provide some theoretical basis • E.g. control theory – ensure/prove stability • But most methods don’t account for defining t f d fi i properties of CAST systems • Lead to systems that are oscillatory or at worst unfit for purpose • Existing methods often domain-driven (e g (e.g. telecoms, robotics) • Not generalisable or transferable
  • 4. CAS i M l i Di i li is Multi-Disciplinary • Many theories from individual disciplines • Hard to compare theories • Theories address different aspects of CAS • Don’t account for Don t engineering constraints
  • 5. Towards a unified theory T d ifi d h • Unifies concepts from multiple disciplines into a single framework • Qualitative theory represented in a o at c o axiomatic form • Can be formalised and analysed • Operationalised via design patterns Biological Systems Computational Social Choice Organisational Theory
  • 6. Biological Systems Bi l i l S • Immune-neuro- • Long-term stability endocrine mechanisms lead to homeostasis • Cohen’s cognitive immune system : • Adapt over multiple • Decision making via co- respondence • Swarm insects • Coordination, partial info • Symbiosis between multiple species: • C Cooperation ti timescales ti l • Coordinate multiple heterogeneous components • Deal with limited and partial information • Decision making • Conflict resolution
  • 7. Social Choice Theory S i l Ch i Th • Originates in economics and political science • Concerns design & analysis of methods for aggregating preferences of multiple agents into collective decisions • Social choice considers formal aspects of democratic decision making (e.g electoral systems) t ) • Computational Social g Choice add an algorithmic perspective • • • • • • Heterogeneous agents Multiple objectives Collective decisions Open-ness Fair division of resources Stability
  • 8. Organisational Th O i i l Theory • Elucidates principles for stable resource management • Study of engineered systems • Insights into engineering sociotechnical ‘organisations’ in a top down manner • Collective Action • Trust • Cooperation • Stable and enduring g systems
  • 9. A Unified Th U ifi d Theory of O f Operation i New CAST propertie w es… Con nflicts Engineering Requirements of CAST Systems D Diverse Objective O es Organisational Theory S Social Interaction ns Biological Systems Noisy Inf formation n Computational Social Choice Open n-ness A Unified Theory of Operations for CAST Systems
  • 10. What d Wh does synthesis give ? h i i Biological  g Systems Computational  Social Choice Biological  Biological Systems Organisational Theory Engineering Constraints Computational  p Social Choice >> Engineering Constraints • Addresses weaknesses in individual theories • Addresses conflicts • Respects engineering constraints Organisational Theory
  • 11. Individual Weaknesses I di id l W k • Biological Systems: • Tend to rely on homogeneous collectives • Global rather than individual objectives • Considerable physical differences • Computational Social Choice • Based on standard models from economics • Abstracted from human decision making (different goals but same model) • Institutional Theories • Easy to get locked into sub-optimal states due to path dependencies • Not clear how to evaluate ‘fitness’ of an institution fitness
  • 12. Conclusions C l i • Unification addresses current fragmented approach to inter-disciplinary research • Diff Different analysis t l currently hi d elucidating t l i tools tl hinder l id ti connections between fields • Many existing theories don’t account for engineering don t constraints of CAS • A unified theory will: • Enable formal comparison between concepts from different disciplines • Drive innovation in field