Transforming legal rules into virtual world rules: a case study in the VirtualLife platform
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Transforming legal rules into virtual world rules: a case study in the VirtualLife platform

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Vytautas Čyras, Kevin Glass, Francesco Zuliani. ...

Vytautas Čyras, Kevin Glass, Francesco Zuliani.
In: Schweighofer, E., Geist, A., Staufer, I. (eds.) Globale Sicherheit und proaktiver Staat – Die Rolle der Rechtsinformatik. Tagungsband des 13. Internationalen Rechtsinformatik Symposions IRIS 2010, 25.-27. Februar 2010, Universität Salzburg, pp. 579-586. Österreichische Computer Gesellschaft, Wien. ISBN 978-3-85403-259-5, http://d-nb.info/1000977358. Also in e-journal Jusletter IT 1 September 2010, Editions Weblaw, Bern, ISSN 1664-848X, http://jusletter-eu.weblaw.ch/magnoliaPublic/issues/2010/102.html.
Also in e-journal Jusletter IT, 1 September 2010, Editions Weblaw, Bern, ISSN 1664-848X, http://jusletter-eu.weblaw.ch/magnoliaPublic/issues/2010/102.html.
ABSTRACT:
The paper addresses operationalisation of legal rules in online 3D virtual world software. The development is performed in the frame of a virtual world platform within the FP7 VirtualLife project, which pursues the goal to create a secure and legally ruled collaboration environment. The novelty of the platform is an in-world legal framework, which is real world compliant. Legally ruled behavior of avatars is addressed. We call this kind of ruling virtual law. A sample (toy) rule is "Keep off the grass". We follow the legal approach "From rules in law to rules in artefact". It accords with the thesis "Computer code is law" of Lawrence Lessig. We also approach the concept of a code of avatars that is concerned by Raph Koster. VirtualLife implies the transformation of legal rules (which are formulated in a human language) into machine-readable format. Such a translation requires natural intelligence.

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Transforming legal rules into virtual world rules: a case study in the VirtualLife platform Presentation Transcript

  • 1. Transforming Legal Rules into Virtual  World Rules: A Case Study in the  VirtualLife Platform Vytautas ČYRAS Vilnius University, Lithuania Vytautas.Cyras@mif.vu.lt 1 Kevin Glass Tavae S.a.r.l., Aix‐en‐Provence, France Francesco Zuliani Nergal S.r.l., Rome, Italy
  • 2. Virtual Worlds • Serious, e.g. Second Life,  Active Worlds Educational  Universe • Leisure purposed – a game – e.g. World of Warcraft • I am neither proponent nor  opponent of them 2 – Consider negative factors such as addiction. Thus “pro” and “contra” arguments • Research & software development project – FP7 ICT VirtualLife project, 3 years 2008‐2010 – Title “Secure, Trusted and Legally Ruled Collaboration Environment in Virtual Life” – Goal: software platform – peer‐to‐peer  architecture – Learning support as a use scenario, e.g. “University Virtual Campus”
  • 3. About FP7 VirtualLife project • Objective  – safe, legally ruled collaboration • Novelties – issues of security and trust – in‐world legal framework. Implemented as shrink‐wrap agreements  1. a “Supreme Constitution” 2. a “Virtual Nation Constitution” 3. a set of contracts – peer‐to‐peer network communication architecture 3
  • 4. Legal framework of VirtualLife Three tiers: 1. A “Supreme Constitution” – Code of Conduct • values that the user has to respect, e.g. avatars integrity, sanctity of  property, reputation, etc. – A part of EULA (End User License Agreement) 2. A “Virtual Nation Constitution” – authentication procedure to become a member of Nation – copyright law of a Nation, e.g. “CopyLeft” or “CopyRight” 3. A set of different sample contracts – sales contract – teacher employment contract – student contract 4
  • 5. Sample scenarios Web 2.0 • information as a content Virtual world • interaction as a content 5
  • 6. From legal rules – to virtual world rules – to rules in software 6 6 This translation complies with: – Lawrence Lessig’s conception “Code is law” – Raph Koster’s “Declaration of the Rights of Avatars” ‘Keep off the grass’ ‘The subject – avatar – is forbidden the  action – walking on the grass’ A software program, i.e. a script.  Implemented by triggers which control the avatar Natural intelligence – a team of  • legal expert • virtual world developer Natural intelligence • a programmer Translation Translation
  • 7. Examples of rules 1. An avatar is forbidden to touch objects not owned by  him or a certain group. 2. An avatar not belonging to a given group is forbidden  to a given area of the zone. 3. An avatar is forbidden to create more than a given  number of objects during a given time interval. 4. An avatar is forbidden to use a given dictionary of  words (slang) while chatting with other avatars. 5. An avatar of age is forbidden to chat with avatars  under age. 7
  • 8. The editor of rules • A law is composed of Norms, see (Vázquez‐Salceda et al. 2008). • Norm is composed by: (1) NORM_CONDITION, (2) VIOLATION_CONDITION, (3) DETECTION_MECHANISM, (4) SANCTION (5) REPAIR. • NORM_CONDITION is expressed by: – TYPE {Obliged, Permitted, Forbidden} – SUBJECT {Avatar, Zone, Nation} – ACTION {ENTER, LEAVE, CREATE, MODIFY, MOVE, CREATE, TRADE, SELL,  BUY, CHAT, etc.} – COMPLEMENT {AREA, AVATAR, OBJECT, etc.} – IF {logical_expresssion_using_subjects_properties} 8
  • 9. Norm example (1) Norm condition: FORBIDDEN Student_Avatar ENTER Library IF Student_Avatar.age < 18 (2) Violation condition: NOT over_age(Student_Avatar) AND admit(Student_Avatar, Library) (3) Detection mechanism: call    over_age(Student_Avatar) when Student_Avatar enters Library (4) Sanction: decrease_reputation(Student_Avatar); notify avatar (5) Repair: log and roll back if applicable 9
  • 10. Facing the problems of translation • Abstractness of norms. Legal rules are formulated abstractly. • Open texture. Hart’s example of “Vehicles are forbidden in  the park”. • Legal interpretation methods. The meaning of a legal text  cannot be extracted from the sole text. – grammatical interpretation – teleological interpretation • Legal teleology. The purpose of a legal rule usually can be  achieved by a variety of actions. • Heuristics. The ability to translate abstract high level concepts  and invent low level ones. • Consciousness of the society. Law enforcement is a complex  social phenomenon. 10
  • 11. Spatialization – a virtual world as a whole Virtual space.    Frame: constitutive.  ~ Theatre Stage ActionsAvatar Avatar Avatar
  • 12. F. Lachmayer’s spatialization Stage Actions Virtual space.    Frame: constitutive.  ~ Theatre Rules 1.  Technical Factual limitations,  e.g. to fence the  grass.  Regimes, paradigms, ethics, professional morality Avatar Avatar Avatar
  • 13. F. Lachmayer’s spatialization Stage Actions Virtual space.    Frame: constitutive.  ~ Theatre Rules 1.  Technical Factual limitations,  e.g. to fence the  grass.  Rules 2. Legal obligations, permissions,  prohibitions.  Regimes, paradigms, ethics, professional morality Authorities: virtual procedures,  e.g. online dispute resolution Avatar Avatar Avatar
  • 14. F. Lachmayer’s spatialization Stage Actions Virtual space.    Frame: constitutive.  ~ Theatre Rules 1.  Technical Factual limitations,  e.g. to fence the  grass.  Rules 2. Legal obligations, permissions,  prohibitions .  Rules 3.  Reputation economic, social, civic. Rules n. Energy… Regimes, paradigms, ethics, professional morality Authorities: virtual procedures,  e.g. online dispute resolution Avatar Avatar Avatar
  • 15. An example of reputation rules Reputation: • economic, • social, • civic. 15
  • 16. Principles of construction Rules 1.  Technical Rules 2. Legal Rules 3.  Reputation Rules n. Energy … Core ontology Special  ontology 1 Special ontology 2 Special ontology 3 Special ontology  n… Stage ActionsAvatar Avatar Avatar
  • 17. Principles of construction … … Stage Rules 1.  Technical Rules 2. Legal Rules 3.  Reputation Rules n. Energy … Core ontology Special  ontology 1 Special ontology 2 Special ontology 3 Special ontology  n Different modes of effect (Wirkung) or relevance Barrier. Strict Occasional. Probability p% Step‐by‐step. “Entering without stop is refused” “Policeman fines you  for stepping the grass”. But this happens with  p% probability – if you  do not succeed. “Reputation/energy is  decreased by 10 points”
  • 18. Example of a technical rule • E‐law project, Austria if document.XML_format = OK then put_on_legislative_workflow ( document ) 18 Legislative workflow in Austria “Running sushi” transport belt
  • 19. 19 RoomDoor  is  closed • if door  = closed  then factual_hindrance • if no  pincode then no  money • “Natural” rules ≠  Natural law (Naturrecht) – e.g. gravitation force • Natural image or essence of man    →???   behavior Terminology: “factual” and “technical” rules ? Technical rules Natural rules Factual rules
  • 20. 20 3 legal stages 1. Legislative stage Community Produce 3. Judicial stage 2. Stage of the game – everyday life p% Judgement Negotiations, contracts , etc. Rules
  • 21. 2 legislative substages 21 2. Stage of the game People think in roles,  not rules Stage of access – “enter airport” Having meals Passenger Citizen, ticket
  • 22. 2 legislative sub‐stages 22 2. Stage of the game People think in roles,  not rules 1a. Legislative rules General rules 1b. Contract rules Individual rules Buyer Sellere.g. inter partes Stage of access. Like “entering an airport” Having meals Passenger Citizen, ticket
  • 23. Technical rules Causation is formalized with the modus ponens rule. Example.  (pincode → money)  &  pincode ⇒ money (1) Rule(P→Q) (2) Fact(P) Conclusion. Fact(Q)  Modus ponens rule in mathematical logic: Sequent notation: 23 P→Q, P   |– Q P→Q, P ‐‐‐‐‐‐‐‐‐‐‐ QP→Q &  P ⇒ Q If P, then Q. P. Therefore Q. In some domains the following interpretation of a technical rule is aimed: (1) Rule(P→Q) (2) Fact(¬P) Conclusion. Fact(¬Q) Obtained inference Fact(¬P) ⇒ Fact(¬Q) and (1) imply equivalence of P and Q, denoted, P↔Q. Consequently, such reasoning is sound in the case of equivalent facts, only. Lachmayer’s notation: Rule form:
  • 24. Legal rules (1) Permission(P iff Q)   ⇒ Norm(¬P → ¬Q) Example.  green if_and_only_if cross  ⇒ ( red → do_not_cross ) (2) Fact(¬P)          – red is on (3) Fact(Q)            – you cross the street, nevertheless Interpretation. You are simply a bad guy. Nobody can stop you  crossing. Notes: • Here P denotes “green”, Q denotes “cross”, ¬P denotes “red”. • A punishment procedure is exercised with probability p%, e.g.  by a policeman. • P iff Q is also denoted    P⇔Q 24
  • 25. Reputation/energy rules (1) Norm(¬A) (2) Fact(A) Conclusion. Energy reduction by 10% Formalization:  Energy is reduced to  A1, then A2 and so on to An. And at last ¬A. 25 A A1 A2 An ¬A Norm(¬A),   A ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐ A := 0.9*A
  • 26. Spatialization of Norm and Status Virtual space Stage ActionsAvatar Avatar Avatar F. Lachmayer, Grundzüge einer Normentheorie. Duncker & Humblot 1977.  III. Normativer Status
  • 27. Spatialization of Norm and Status Virtual space Stage ActionsAvatar Avatar Avatar F. Lachmayer, Grundzüge einer Normentheorie, 1977, Seite 67 N(A)Norm
  • 28. Spatialization of Norm and Status Virtual space Stage ActionsAvatar Avatar Avatar F. Lachmayer, Grundzüge einer Normentheorie, 1977, Seite 67, 76 N(A)Norm Status N(A) ⇒ O(A)
  • 29. Spatialization of Norm and Status Virtual space Stage ActionsAvatar Avatar Avatar F. Lachmayer, Grundzüge einer Normentheorie, 1977, Seite 89 O(A)e(r1-r2) N(A)r1 N(A)r2 Norm Status
  • 30. Characterization of Normative Status • Suppose a huge set of rules r1, r2,…, r_n. • What is a characterization of the normative status, O , of a  subject (avatar) S? O(subject=S, duty=X,…)(r1 ,r2…,r_n) – Has S a duty X ? – Is S permitted to doY? • “ … the power … does not reside in the inference method;  almost any inference method will do. The power resides in  the knowledge” (Feigenbaum 1984, p.101) 30 Synthesizer of  normative  status r1 r2 r_n O(…)(r1 ,r2…,r_n) black box role, such as “passenger”, “professor”, etc.  N(A1) N(A2) N(A_n)
  • 31. Motivation of learning • “Pro” virtual worlds Learning materials – static, searchable in 2D  for learner’s queries  (Web)  – interactive objects  (virtual worlds) • “Contra” virtual worlds – values? 31 – mono‐sensorial, perceived through computer’s display – multi‐sensorial learning in the real world • human’s brain and senses (seeing, hearing, feel) work concurrently • “learning by doing” when accomplishing real‐world tasks
  • 32. Thank you • Acknowledgements: The whole VirtualLife  consortium, 9 partner organisations,  http://www.ict‐virtuallife.eu 32