transforming legal rules into online virtual world rules: a case study in the virtuallife platform
DESCRIPTION
Vytautas Čyras (2010). In: Petros Daras & Oscar Mayora (Eds.) User Centric Media - First International Conference, UCMedia 2009, Venice, Italy, December 9-11, 2009, Revised Selected Papers. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST) vol. 40, pp.279-284. Springer. http://link.springer.com/chapter/10.1007%2F978-3-642-12630-7_34. ABSTRACT. The paper addresses the implementation of legal rules in online virtual world software. The development is performed within a peer-to-peer virtual world platform in the frame of the FP7 VirtualLife project. The goal of the project is to create a serious, secure and legally ruled collaboration environment. The novelty of the platform is an in-world legal framework, which is real world compliant. The approach “From rules in law to rules in artifact” is followed. The development accords with the conception “Code is law” advocated by Lawrence Lessig. The approach implies the transformation of legal rules (that are formulated in a natural language) into machine-readable format. Such a transformation can be viewed as a kind of translation. Automating the translation requires human expert abilities. This is needed in both the interpretation of legal rules and legal knowledge representation.TRANSCRIPT
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Transforming Legal Rules into Online Virtual World Rules: A Case Study in
the VirtualLife Platform
Vytautas ČYRAS
Vilnius University, Lithuania
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Virtual Worlds • Serious, e.g. “Second Life”,
“Active Worlds Educational Universe”
• Not games
– e.g. “World of Warcraft”
• I am neither a proponent nor opponent of them
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– Consider negative factors such as addiction
• Research & software development project
– FP7 ICT VirtualLife project, 3 years from 01.01.2008 – Title “Secure, Trusted and Legally Ruled Collaboration Environment in
Virtual Life”. Acronym “VirtualLife” – Goal: software platform – peer-to-peer architecture – Learning support as a use scenario, e.g. “University Virtual Campus”
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About FP7 VirtualLife project
• Objective – to create a safe, democratic and legally ruled 3D
collaboration environment
• Novelties – issues of security and trust
– in-world legal framework
– a “Supreme Constitution”, a “Virtual Nation Constitution”, a set of contracts
• peer-to-peer network communication architecture
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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 Nation, e.g. “CopyLeft” or “CopyRight”
3. A set of different sample contracts – sales contract – teacher employment contract – student contract
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Learning needs of “digital natives”
• “Pro” and “contra” arguments for virtual worlds • Students of today
– are active Web 2.0 participants – easy create relationships in social networks – like impressing peers with curious facts – enjoy participating in online group activities – function in “multitasking mode” – a new phenomenon: they share the knowledge with
unknown people – do not like memorizing information for later use
• but they are effective in searching
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Sample scenarios Web 2.0 • information as a content • asynchronous communication “University Virtual Campus” • interaction as a content • synchronous communication
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Motivation of learning
• “Pro” virtual worlds Learning materials
– in Web 2.0 are static, searchable in 2D for learner’s queries
– In 3D virtual worlds interactive objects
• “Contra” virtual worlds – values?
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– mono-sensorial, perceived through computer’s display – multi-sensorial learning in the real world
• human’s brain and spinal brain function concurrently • all senses: seeing, hearing, touching, etc. • “learning by doing” when accomplishing real-world tasks
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Learning environment
Constantly gratifying, encouraging social interaction
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From legal rules – to virtual world rules – to rules in software
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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 computer program (script, table). Implemented by triggers which control the avatar
Natural intelligence – a team of • legal expert • virtual world developer
Natural intelligence – a programmer
Translation
Translation
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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.
6. An avatar is forbidden to execute authorized scripts in a certain area.
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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}
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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
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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
– systemic 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.
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Stage
Avatar
Avatar
Avatar Actions
F. Lachmayer’s spatialization
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Virtual space. Frame: constitutive. ~ Theatre
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Stage
Avatar
Avatar
Avatar Actions
F. Lachmayer’s spatialization
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Virtual space. Frame: constitutive. ~ Theatre
Rules-1. Technical.
Factual limitations, e.g. to fence the grass.
Regimes, paradigms, ethics, professional morality
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Stage
Avatar
Avatar
Avatar Actions
F. Lachmayer’s spatialization
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Virtual space. Frame: constitutive. ~ Theatre
Rules-1. Technical.
Factual limitations, e.g. to fence the grass.
Rules-2. Legal. Obligations, permissions, prohibitions, veto. – Primary rules.
Regimes, paradigms, ethics, professional morality
Sanction is a secondary rule
Office. Virtual procedures. E.g. online dispute resolution
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Stage
Avatar
Avatar
Avatar Actions
F. Lachmayer’s spatialization
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Virtual space. Frame: constitutive. ~ Theatre
Rules-1. Technical.
Factual limitations, e.g. to fence the grass.
Rules-2. Legal. Obligations, permissions, prohibitions, veto. – Primary rules.
Rules-3. Reputation.
Economic, social, civic.
Rules-n. Energy.
…
Regimes, paradigms, ethics, professional morality
Sanction is a secondary rule
Office. Virtual procedures. E.g. online dispute resolution
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Principles of construction (1)
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Rules 1. Technical
Rules 2. Legal
Rules 3. Reputation
Rules n. Energy …
Avatar Avatar
Avatar
Core ontology
Special ontology 1
Special ontology 2
Special ontology 3
Special ontology n
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Principles of construction (2)
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Rules 1. Technical
Rules 2. Legal
Rules 3. Reputation
Rules n. Energy …
Avatar Avatar
Avatar
Core ontology
Special ontology 1
Special ontology 2
Special ontology 3
Special ontology n
Different modes of effectiveness (Wirkung) or relevance
Barrier. Strict
Occasional. Probability p%
Step-by-step.
“Entering without STOP is REFUSED”
“Policeman fines for stepping the grass”.
This happens with p% probability – if you do
not succeed.
“Reputation/energy is decreased by 10
points”
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Example of a technical rule
• E-law project, Austria
if document.XML_format = OK
then put_on_legislative_workflow ( document )
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Legislative workflow in Austria
“Running sushi” transport belt
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Room Door is
closed
• if door = closed then factual_hindrance • if no XXXX_pin_code_to_cash_machine then no access_to_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
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3 legal stages
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1. Legislative stage
Community
Produce
3. Judicial stage
2. Stage of the game – everyday life
% Judgement
Negotiations, storytelling (see H. Prakken)
Rules
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2 legislative substages
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2. Stage of the game
People think in roles, not rules
Stage of access – “enter airport” Having meals
Passenger Citizen, ticket
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2 legislative substages
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2. Stage of the game
People think in roles, not rules
1a. Legislative rules General rules
1b. Contract rules Individual rules Buyer Seller e.g.
inter partes
Stage of access – “enter airport” Having meals
Passenger Citizen, ticket
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Technical rules Causation is formalized with the modus ponens rule.
Example. no_code & (no_code → no_money) no_money
(1) Rule(P→Q)
(2) Fact(P)
Conclusion. Fact(Q)
Modus ponens rule in mathematical logic:
Sequent notation:
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P→Q, P |– Q P→Q, P ----------- Q P→Q & P Q
If P, then Q. P. Therefore Q.
In some domains the following interpretation of the technical rule is aimed: (1) Rule(P→Q) (2) Fact(¬P) Conclusion. Fact(¬Q)
Obtained inference Fact(¬P) Fact(¬Q) and rule (2) imply equivalence of P and Q, formally, P Q. Denoted also P~Q. This is good for cash machine (ATM). But this may be unacceptable for other technical domains, see rule-based knowledge representation.
Lachmayer’s notation: Rule form:
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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”.
• With probability p% a punishment procedure is exercised. This is done, for example, by a policeman.
• P iff Q is also denoted P Q
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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.
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A
A1
A2
An
¬A
Norm(¬A), A ------------------- A := 0.9*A
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Thank you
• Acknowledgements: The whole VirtualLife consortium, 9 partner organisations
• http://www.ict-virtuallife.eu
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