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Game-Theoretic Foundations for Norms

Game-Theoretic Foundations for Norms. Guido Boella 1 and Leendert van der Torre 2 1 University of Torino, Italy 2 University of Luxembourg, Luxembourg. Game-Theoretic Foundations for Norms. Guido Boella 1 and Leendert van der Torre 2

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Game-Theoretic Foundations for Norms

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  1. Game-Theoretic Foundations for Norms Guido Boella1 and Leendert van der Torre2 1 University of Torino, Italy2 University of Luxembourg, Luxembourg

  2. Game-Theoretic Foundations for Norms Guido Boella1 and Leendert van der Torre2 1 University of Torino, Italy2 University of Luxembourg, Luxembourg new 20 prof. staff, student grants & jobs, large AI lab. cooperation agreement uni.lu and IPI-PAN looking for master + PhD students

  3. A Normative MultiAgent System (NMAS) • is a multiagent system with normative systems • in which agents can decide whether to follow the explicitly represented norms, and • the normative systems specify how and in which extent the agents can modify the norms.’’ G. Boella, L. van der Torre, H. Verhagen, Introduction to normative multiagent systems. Computational and Mathematical Organization Theory, double special issue on normative multiagent systems, 2006. • Many kinds of norms, e.g., legal, moral, social norms

  4. The Need for Norms • Autonomous agents make decisions • Knowledge and goals (Newell and Simon) • Beliefs, desires, intentions (Bratman, C&L, R&G) • Agents in multiagent systems interact • New: cooperation, negotiation, coordination • New conceptualization: norms and related concepts • Obligations, permissions, conventions, social goals, collective intentions, group commitments, … • New requirements on degree of formalization • Philosophy, sociology, economics, computer science, AI

  5. Ontological Problem • Distinct interpretations of popular concepts. • Also within sociology, philosophy, economics, CS, AI. • Need foundations from first principles. • Examples: • What is utility? (Savage 54: representation theorem) • Consensus only after centuries of debate • What is a goal? (Simon 61: utility aspiration level) • Again problematized in agent programming • What is a norm? (no consensus yet) • (Precedes problem how to represent norms)

  6. Layout of This Talk • Norms in economics and game theory • Game-theoretic foundations for norms • Towards a qualitative cognitive model • Six clauses for obligation • Two clauses for permission • Talk explains motivation only, in paper: • Brafman & Tennenholtz model • Technical details

  7. Economics (Gneezy & Rustichini, Levitt) • Suppose you are manager day-care center • Policy: children must be picked up by 4 p.m. • Reality: very often parents are late. • Problem: What to do?

  8. Economics (Gneezy & Rustichini, Levitt) • Suppose you are manager day-care center. • Policy: children must be picked up by 4 p.m. • Reality: very often parents are late. • Problem: What to do? • Economist’s solution: fine the tardy parents.

  9. Economics (Gneezy & Rustichini, Levitt) • Study of 10 day-care centers in Haifa. • The study lasted 20 weeks. • Week 1-4: 8 late pickups / week / day-center. • Week 5: more than 10 minutes late: $3 / child • Fee added monthly bill, roughly $380. • Then, late pickups went …

  10. Economics (Gneezy & Rustichini, Levitt) • Study of 10 day-care centers in Haifa. • The study lasted 20 weeks. • Week 1-4: 8 late pickups / week / day-center. • Week 5: more than 10 minutes late: $3 / child • Fee added monthly bill, roughly $380. • Then, late pickups went … up to 20 / week

  11. Economics (Gneezy & Rustichini, Levitt) • Study of 10 day-care centers in Haifa. • The study lasted 20 weeks. • Week 1-4: 8 late pickups / week / day-center. • Week 5: more than 10 minutes late: $3 / child • Fee added monthly bill, roughly $380. • Then, late pickups went … up to 20 / week • Week 9: fine retracted,

  12. Economics (Gneezy & Rustichini, Levitt) • Study of 10 day-care centers in Haifa. • The study lasted 20 weeks. • Week 1-4: 8 late pickups / week / day-center. • Week 5: more than 10 minutes late: $3 / child • Fee added monthly bill, roughly $380. • Then, late pickups went … up to 20 / week • Week 9: fine retracted, 20 late pickups / w.

  13. Economics is the Study of Incentives • How people get what they want, or need, • especially when other people want or need the same. • Use same model of incentives for artificial systems too. • Economic, social, and moral incentives. • The addition of $3-per-pack “sin tax” is a strong economic incentive against buying cigarettes. • The banning of cigarettes in bars is a social incentive. • U.S. government assertion that terrorists raise money by selling black-market cigarettes is a moral incentive.

  14. Requirements for Foundations of Norms • Norms influence behavior. • Norm is mechanism to obtain desired system behavior. • Norms can and will (sometimes) be violated. • E.g., if other agents violate the norm too. • Norms are soft constraints in detective control systems. • Typically, a norm must be fulfilled by • norm internalizing agents, • respectful agents fulfilling norms if possible, and • selfish agents obeying norms only due to the sanctions. • Norms & sanctions should not be too strong.

  15. Methodology • Classical game theory? • For example, social laws of Tennenholtz • Discussion on quantitative model in paper • It does not satisfactorily distinguish agent types • It does not combine well with qualitative norms • Cognitive agents, qualitative game theory.

  16. Obligation of agent A towards B to do p • B desires p (“your wish is my command”).

  17. Obligation of agent A towards B to do p • B desires p (“your wish is my command”). • Absence of p is considered as violation of A • Anderson’s reduction of deontic logic to modal logic. • B desires that there are no violations.

  18. Obligation of agent A towards B to do p • B desires p (“your wish is my command”). • Absence of p is considered as violation of A • Anderson’s reduction of deontic logic to modal logic. • B desires that there are no violations. • If violation, then B is motivated to sanction • B does not like to sanction • A does not like being sanctioned.

  19. Permission of A towards B to do p • B does not desires p • The absence of p is not violation of A • Permission is simpler than obligation, • since they cannot lead to violations & sanctions. • Exception reading based on Bulygin 86

  20. Summary of This Talk • Agents in multiagent systems interact: norms • Norms for interaction thus game theory • Economics is more than MEU and auction theory! • Norms work for all kinds of agents • Motivation (for norm internalizing agents) • Counts as violation (for respectful agents) • Control system & sanctions (for selfish agents) • Permission simpler, modeled as exception • Game-theoretic analysis shows adequacy

  21. Use of Foundations • Applications (see http://www.boid.info) • Fraud and deception, trust and security • Contracting, electronic commerce • Virtual communities, knowledge management • Legal knowledge based systems • Organizations (http://www.powerjava.org) • Roles and role based communication • Dynamic specification organization • Further research: also constitutive norms

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