A categorization of simulation work on norms

1. A categorization of simulation work on norms Bastin Tony Roy Savarimuthu Department of Information Science University of Otago Dunedin, New Zealand

2. From the southern most university

3. Software agents research group at Otago Prof. Martin Purvis Assoc. Prof. Stephen Cranefield Dr. Maryam Purvis Dr. Noria Foukia

4. Contents • Mechanisms used in simulation of norms • A life-cycle model for norms • Some pointers on • Open issues in the simulation of norms

5. Motivations • Need for “introductory” material on mechanisms used by researchers in the simulation of norms. • New researchers and students have to look at many places to gather the information. For example, an overview of simulation works on norms can be obtained by reading • Harko Verhagen’s thesis (2000) • Fabiola Lopez’s thesis (2003) • Joseph Pujol’s thesis (2006) • Still, an overview of the simulation works on norms is missing (i.e. condensed summary isn’t available) • Good basis for the “literature review” section of my thesis

6. Norms – definition and examples From the perspective of sociology: • A social norm is a prescribed guide for conduct or action which is generally complied with by the members of the society . -- Edna Ullman-Margalit (1977) • “For the norms to be social, they must be shared by other people and partly sustained by their approval and disapproval” -- Elster (1990) • The human society follows norms such as • tipping in restaurants, • exchange of gifts at Christmas, • driving vehicles on the left or right hand side of the road

7. Why are norms followed? • fear of authority or power (fear of punishment) • rational appeal of the norms (to achieve cooperation, coordination) • emotions such as shame, guilt and embarrassment that arise because of non-adherence. • willingness to follow the crowd

8. Tuomela’s norm categories • Rule norms (laws) • e.g. one has to pay taxes • Moral norms • e.g. norm against polygamy, norm against homophobic behaviour • Social norms • e.g. dinner table etiquette, tipping in restaurants • Prudential norms • e.g. one ought to maximize ones utility. • Several other categorizations exist

9. Norms and Multi-Agent Systems (MAS) • Sociology and MAS complement each other • Agents are modeled using some of the human-like characteristics • Autonomous • Communicate and negotiate (e.g. using speech act) • MAS help sociologists to experiments on their theories. • The intention of the paper is to capture various mechanisms that lead to norm (de)estabilshment. • `

11. Social power based mechanism A powerful agent can motivate, encourage or coerce the followers to take up a norm. • Without punishment -> Leadership mechanism • With punishment -> Sanctioning mechanism • With reward • Not many works have taken this route (e.g. Lopez, 2002)

12. 1. Leadership mechanism • Leadership mechanism • Centralized (e.g. normative advisor) • Decentralized (e.g. distributed role model) • Issues: • Centralized mechanisms not scalable (e.g. virtual worlds) • It is normally assumed in a centralized system what the norm should be “a priori”. This may not be always true. • Alternative • Entrepreneurial leadership (an agent can come up with a norm at run-time). • Example – work of Hoffman (2003)

13. 2. Sanction mechanism • Powerful agents can sanction other agents • Issue - Who bears the sanctioning costs? • Many works on simulation do not consider the cost of sanctioning on the part of a sanctioning agent. Some rely on altruism. • e.g. Lopez et al. (2002) assume that some third party takes care of the cost • It should be noted that Axelrod (1986) quotes that “norms are best at preventing smaller defections where the cost of enforcement are low” (this has been proved by other researchers)

14. 3. Reputation mechanism • Agents store history of interactions and decide based on the available history (e.g. Castelfranchi 1998, Hales 2002) • These two studies have shown • Normative reputation redistributes the costs of norm compliance to agents that follow it and those who do not. • Issue: • Works well for an agent. However, for a cognitive agent that wants to learn the norms of a society, a sanction might be more obvious than the reputation.

15. 4. Imitation mechanism • When in Rome do as Romans do • Positives: • Reduces the computation required by an agent • Agents can use computation saved for other tasks • Negatives: • Norms get entrenched without any thought. • Hard to achieve “co-existing” norms • Normative expectation is missing!

16. 5. Off-line design mechanism • Designer creates the norm and experiments with a society with the “imposed” norm (e.g. food finding game introduced by Conte et al. (1995)). • Positive: • A good mechanism for a centrally controlled institution • Negative: • Not a viable option for open, distributed and decentralized societies.

17. 6. Learning mechanism • Machine learning algorithms • Highest Cumulate Reward (HCR) • Shoham and Tennenholtz (1992) • Kittock (1993) • Walker and Wooldridge (1995) … • Q-learning algorithms • Sen et al. (2006) • Positive: • Straight forward implementation • Negative: • Agents cannot distinguish a strategy from a norm • No notion of “normative expectation”, agents are just utility maximizers

18. 7. Cognitive mechanism • Aims to explore the mental capabilities of an agent (e.g. EMIL project) • Explores • How does an agent know what the norm of a given society is? • How can agents recognize and communicate norms? • Two way norm dynamics (interplay of micro and macro behaviours) • Issues: • At the writing of the paper, the EMIL implementation had the following issues • Agents should be able to consider own experiences as well as observed behaviour • Norm violation is not allowed • Cost of sanctions are not considered

19. 8. Emotion based mechanism • Scheve et al. (2006) have proposed the linkage between emotions at the micro-level causing macro-level “enforcements”. • Fix et al. (2005) have used a Petri-net model to capture the micro-macro linkage (unimplemented). • Issue: • How do you detect emotions on other agents?

20. 9. Network topology based mechanisms • People/agents are not connected to each other at random. They have a particular topology (e.g. work group, church group). • So, topology of a network governs how a norm spreads in an agent society.

21. Static network topology • In a static network topology, the network does not evolve over time • No addition of new nodes • Rewiring is not possible. • Can be used to experiment different types of other mechanisms using network topologies • Examples • Kitttock (1995) used learning mechanism (HCR) on top of a regular lattice • Pujol (2006) has experimented with learning mechanism (HCR) on top of random, small-world and scale-free networks

22. Dynamic network topology • In real world, news links are constantly formed and rewiring between nodes happen all the time. • Not many researchers have experimented with how the norms of an agent society change when the topology changes. • At Otago, we have experimented with the role of norm emergence on norm emergence

23. Dynamic network topology

24. Dynamic network topology Bastin Tony Roy Savarimuthu, Stephen Cranefield, Martin K. Purvis, Maryam Purvis: Norm Emergence in Agent Societies Formed by Dynamically Changing Networks. IAT 2007: 464-470

25. 10. Cultural and evolutionary mechanisms • Cultural mechanism • Boyd and Richerson • Vertical transmission (from parents to offspring) • Oblique transmission (from a leader of a society to the followers) • Horizontal transmission (from peer to peer interactions) • Verhagen’s model (2000) is based on oblique transmission • Evolutionary mechanism • Parent to progeny • Examples : Axelrod 1986, Chalub 2006

26. Categorization of mechanisms based on norm life-cycle • No consensus among sociologists on the phases of norm life-cycle • We believe that a norm life-cycle consists of the following four phases (broadly) • Norm creation phase • Norm spreading phase • Norm enforcement phase • Norm emergence phase • All these four phases need not be present for a norm to exist. For example a norm created by a church leader can spread through the church through vertical transmission.

27. Mechanisms for norm creation • A norm in the society can been created using the following mechanisms • Off-line design mechanism • Leadership mechanism • Entrepreneurship mechanism • Cognitive mechanism

28. Norm spreading mechanisms • Leadership mechanism • Learning mechanisms • Imitation • Machine Learning • Culture and evolutionary mechanisms

29. Norm enforcement mechanisms • Sanction (and reward) mechanisms • Reputation mechanisms • Emotion based mechanisms

30. Norm emergence A norm can be said to have emerged if it has spread (i.e. it is followed by a considerable proportion, of an agent society (say X%) and this fact is recognized by most agents). Emergence brings about the “existence” of a norm in a society. In that sense emergence can be thought of as the creation of the norm at the “society level”.

32. Phase-wise comparison of simulation works

33. A scenario • Park littering http://news.bbc.co.uk/2/hi/in_pictures/6619625.stm

34. Futuristic scenario in a virtual environment • Tara Minsky, a new second life resident wants to explore the rich interactions offered by the new medium. She wants to go to a virtual park and relax by the fountain hearing chirping birds. She flies to the virtual park and sees people lying down and enjoying the sun. She notices some water fountains and some soft-drink fountains from the sponsor of the park. She would like to get a drink, but does not know what the norm associated with using the fountain. • She wonders if she should get a cup from the information desk or does she need to acquire the skill of making the cup object. Once she fills the cup with the drink, can she enjoy her drink in all areas of the park or is she restricted to a particular zone. And finally, what should she be doing with the empty cup? What is the norm associated with littering in the park? Can she drop it anywhere for an autonomous robot to pick it once the cup was dropped or should she find a rubbish bin and drop it? Will a norm that she inferred from a previous park be applicable in this situation? Can two norms co-exist?

35. Desired characteristics of agent based simulations (and the scope for future works) • Dynamic change of norms • Explicit communication of norms • Facilitating a truly open environment • Richer representations of norms • Adjustable agent autonomy • Consideration of network topology • Experimenting with co-existence of norms in agent societies • Implicit norm inference • Pluggable component framework for norm life-cycle • Bringing it all together : A norm simulation architecture

36. Conclusions • Several mechanisms have been used by researchers in the study of norms • Mechanisms have pros and cons • A life-cycle model was proposed and the mechanisms were categorized based on the phases that they belong to • More work needs to be done • Comparison of simulation works based on agent characteristics (comparison table will be useful) • Scenarios for each of the future issues

38. Work on norms in MAS • Norms in multi-agent systems are treated as constraints on behaviour, goals to be achieved or as obligations. • Two main branches of research • The first branch deals with normative system architectures, norm representations and norm adherence and the associated punitive or incentive measures. (Lopez et al., Castelfranchi et al., Boella et al.) • The second branch deals with the spreading of norms • Spreading and internalizing of norms (top-down approach) • Emergence of norms (bottom-up approach)