Klaus G. Troitzsch Universität Koblenz-Landau, Germany

1. Perspectives and Challenges of Agent-Based Simulation as a Tool for Economics and Other Social Sciences Klaus G. Troitzsch Universität Koblenz-Landau, Germany AAMAS 2009, Budapest

2. Overview • Introduction • What economics and social science can learn from MAS • Predecessors and alternatives • Unfolding, nesting, coupling: reconstructing complexity • Roles • Interactions • Environment • Agent communication • What MAS can learn from economics and social science • A case study on trust in agent societies • Conclusion AAMAS 2009, Budapest

3. Human social systems: objects of economics and social science • are among the most complex systems in our world • consist of human actors which • are autonomous • interact in numerous different modes • take on different roles even at the same time • are conscious of their interactions and roles • communicate in symbolic languages even about the counterfactual AAMAS 2009, Budapest

4. Complex systems AAMAS 2009, Budapest

5. Fields and forces AAMAS 2009, Budapest

6. Adaptation • many systems can adapt to their environment • finding a local minimum of some potential or a concentration maximum, following a concentration gradient • adaptation of a population of systems via evolution (“blind watchmaker” metaphor) • adaptation via norm learning • mutual adaptation via norm emergence and norm innovation AAMAS 2009, Budapest

7. Decision making • in physical particles: according to natural laws or probabilistic (no decision making in any reasonable sense of the word) • in animals: instinct (mechanisms not well understood) • in humans: after deliberation of different possible outcomes of different action alternatives, boundedly rational, often after discussion among groups of actors AAMAS 2009, Budapest

8. Emergence • definable as the supervenience of characteristics of a system that cannot be owned by the parts of this system • atoms and molecules have a velocity, but no temperature, the gas or fluid or solid body has a temperature • families have places where they live, but they do not have a degree of segregation (but the city has) • voters have attitudes, but no attitude distribution (the electorate has) AAMAS 2009, Budapest

9. Emergence, immergence and second-order emergence • emergence of order in slime moulds works via the concentration gradient of some chemical substance • emergence of an attitude distribution (e.g. polarisation of voter attitude during an election campaign) works via communication, persuasion and publication of opinion poll results (as humans have no “objective” measuring instrument for attitude “gradients”) AAMAS 2009, Budapest

10. Micro and macro level • “sociological phenomena penetrate into us by force or at the very least by bearing down more or less heavily upon us” [Durckheim 1895] macro cause macro effect “upward causation” “downward causation” micro cause micro effect [Coleman 1990] AAMAS 2009, Budapest

11. Micro and macro level • “sociological phenomena penetrate into us by force or at the very least by bearing down more or less heavily upon us” [Durckheim 1895] • both interpretations can be applied to physical and to social systems • both interpretations can be applied • to physical systems • macro cause = field, “downward causation” = force, micro effect = movement, “upward causation” = field change • to social systems • macro cause = “social field”, social norms, “downward causation” = immergence, micro effect = norm adoption, “upward causation” = norm innovation macro cause macro effect “upward causation” “downward causation” micro cause micro effect [Coleman 1990] AAMAS 2009, Budapest

12. Micro and macro level • “sociological phenomena penetrate into us by force or at the very least by bearing down more or less heavily upon us” [Durckheim 1895] • but the difference is: • in physical systems • the effect of the “downward causation” is transitory, as is the effect of the “upward causation” as there is usually no memory on either level • in social systems • the effect of the “downward causation” lasts for a long time, it changes the state of the micro entity forever, as it is stored symbolically in his or her memory, and the effect of the “upward causation” also lasts for a long time, as there is a long-term memory in society (folklore, libraries, codes of law …) • the “downward causation” takes only effect after being interpreted by the individual, and this interpretation is dependent of his or her past macro cause macro effect “upward causation” “downward causation” micro cause micro effect [Coleman 1990] AAMAS 2009, Budapest

13. Predecessors and alternatives to ABS • econophysics / sociophysics • game theory • early simulation attempts of the 1960s • sugarscape and its relatives AAMAS 2009, Budapest

14. Econophysics and sociophysics • Social forces and social fields where humans are modelled as particles moving and/or changing their internal states in something like a social field: mean “movements” of voters in their attitude space, determined by the gradient of the attitude distribution, empirical data, Western Germany 1972 • often with the assumption of a vectorialadditivityof the separate force terms reflecting different environmental influences AAMAS 2009, Budapest

15. Game theory • Agents make decisions between strategies considering a payoff matrix • tragedy of the commons, prisoners’ dilemma • with fixed rules and fixed payoff matrix • models also apply to both human social systems and animal social systems • no communication among players except for the observation of the strategy chosen by the other player • coalition forming without negotiations • one-dimensional utility functions AAMAS 2009, Budapest

16. Simulmatics and other attempts of the 1960s • Models of voters in presidential election or referendum campaigns, involving large numbers of “agents” of several types (citizens, politicians, media channels) interacting among each other according to fixed rules, but keeping information in their memories for a long time AAMAS 2009, Budapest

17. Sugarscape: Social science from the bottom up • large numbers of agents in an environment, interactions of several types, including communication (sharing observations) • a laboratory in which artificial societies can be generated to find out what keeps them going • still no symbolic interaction • cultural transmission via tags AAMAS 2009, Budapest

18. Different roles in different environments • real world entities can be components of several different systems at the same time • humans typically belong to a family, a peer group, an enterprise department, a military unit at the same time • a level concept is not appropriate any longer: the systems a person belongs to are of different kinds as their structures (the sets of bonding relations between their components) are different • no “level” of social subsystems AAMAS 2009, Budapest

19. Interactions • the pheromone metaphor (chemical substances whose concentration gradient is observed and reacted to) • the telepathy metaphor (agents read other agents’ memories directly) • the message metaphor (messages do not necessarily express the “objective” internal state of the sender agent) AAMAS 2009, Budapest

20. Message metaphor • Software agents in simulations of economic or social processes should be able to exchange messages that hide or counterfeit their internal states. • Messages have to be interpreted by the recipient before they can take any effect. • Agents need a language or symbol system for communicating. • Symbol systems have to refer to the components of agents’ environments and to the actions agents can perform. see the case study at the end AAMAS 2009, Budapest

21. Communication • a language evolves in a population of agents • first attempts 20 years ago [Hutchins and Hazlehurst 1991; 1995]: agents represent patterns with names that they use (more or less!) unequivocally • problem: so far only lexicon, no syntax evolved • agents use a pre-defined language • restricted message templates can be used by agents • problem: templates have to be defined for every new scenario AAMAS 2009, Budapest

22. Environment • Simulated environments allow agents • to interact in a realistic manner • to take actions other than those that directly affect other agents of the same kind (these actions, like harvesting in Sugarscape, affect other agents only indirectly) • to communicate about the environment they “live” in • Environments provide resources and services for agents • e.g. in traffic simulations AAMAS 2009, Budapest

23. EMIL-S • the first (simulated) minute (20 children, random cars • children and cars run into each other, near-collision is interpreted as norm invocation (“You have to stop when I am stepping on the street!”, “You must not step on the street when I am around with my car!”) • several (simulated) minutes later (again 20 children, random cars) • children have learnt that they have to use the striped area for street crossing, car drivers have learnt that they are expected (obliged) to slow down or stop in front of the striped area (which has emerged into an institution after the first successful norm learning happened there) when there are children visible in the neighbourhood • the same, some children have not learnt that the striped area is something special • some children still do not use the striped area but stop for an approaching car • the same with perception sectors (only four children) • approaching the street, children enlarge their perception area; approaching the striped area, cars enlarge their perception area AAMAS 2009, Budapest

24. EMIL-S AAMAS 2009, Budapest

25. EMIL-S • the event board stores messages together with the remembered current state of the environment and the actions that can be taken as a consequence of an event of this type AAMAS 2009, Budapest

26. Immergence and second-order emergence • A: “I don’t like your • smoking here, B!” • norm-invocation messages • motivate individual agents to change the rules controlling their actions • if this happens often enough, “sociological phenomena penetrate into us by force or at the very least by bearing down more or less heavily upon us” [Durckheim 1895] • and as a consequence, these norm invocations – and the resulting behaviour – occur more and more often and become a “sociological phenomenon” A: You must not cross the street when I am approaching in my car, B! • (B abstains from smoking • in the presence of A.) (B abstains from crossing the street when A is approaching with her car.) … and we have programmed something much like this in an agent-based simulation system! (not only B, but others, too, abstain from crossing streets, not only in the presence of A’s car, but in most other cases.) (not only B, but others, too, abstain from smoking, not only in the presence of A, but also on other occasions.) AAMAS 2009, Budapest

27. ABM and policy modelling • Agent-based modelling can also be applied to less simple scenarios: • emergence of loyalties within criminal organisations and collusion between criminals and their victims: the example of extortion rackets • emergence of practices in microfinance • spontaneous formation of teams according to the skills of individual members • emergence of trust in online transactions between sellers, intermediaries and buyers AAMAS 2009, Budapest

28. Adaptation • agents observe each other and • draw conclusions about • which behavioural features are desirable and • which are misdemeanour in the eyes of other agents • necessary • that agents can make abstractions and generalisations from what they observe in order that ambiguities are resolved • to define which kinds of actions can be taken by agents in order that other agents can know what to evaluate as desirable or undesirable actions AAMAS 2009, Budapest

29. Adaptation • Software agents are not embodied in any realistic sense of the word, • thus they must be given something like a virtual embodiment which defines which events and actions are possible, impossible, desirable, undesirable in their virtual worlds. AAMAS 2009, Budapest

30. A final example: Trust in agent societies • Agent-based social simulation allows for modelling beliefs and actions in a both formal and descriptive manner, as agent architectures can be designed to take into account the fact that human decision making is not just converting stimuli into responses with externally set probabilities and/or according to externally set payoff matrices, as is usually done in game-theoretic modelling of the emergence of trust. AAMAS 2009, Budapest

31. Trust in human social systems • Compared to the traditional “trust game” approach, it is a new challenge to use agent-based models in order to simulate social agents who can improve their situation by using trust enhancing mechanisms, such as providing personal data, delivering reliable information, responding promptly, keeping confidentiality, etc. (nothing of this can ever be modelled in game-theoretic approaches). AAMAS 2009, Budapest

32. Simulating human adaptive behaviour • Simulating such a model would reveal chances and risks of social agents to act trustworthy, not trustworthy, or too trustworthy, as well as trustful, distrustful or overly trustful. • Persons can cooperate on achieving group targets, or they can exploit each other in order to achieve individual targets. In both respects they can succeed or fail. • Persons are free to share their knowledge honestly with others or to behave opportunistically, hiding part of their knowledge from others. • This would lay a basis for the specification of appropriate trust enhancing mechanisms in automatically operated transaction systems AAMAS 2009, Budapest

33. Emergence of trust in online transactions • specify instruments (mechanisms) which help individual persons to measure the success of such instruments, to adapt their behaviour to changing trust situations and to help groups to establish norms which support cooperative and impede opportunistic behaviour • use the experience from these simulations to build software that analyses the trustworthiness of clients and that takes measures either to evade or to punish misdemeanor of clients • in the following business situation: AAMAS 2009, Budapest

34. Case study (1) • buyers, an intermediary, sellers • sellers offer their goods and want to make sure that they get their money • buyers order these goods and want to make sure that they get the goods they want • the intermediary guarantees • buyers money back in case of non-delivery and • sellers their money even in case the buyer did not pay • how can the intermediary minimise its risk? AAMAS 2009, Budapest

35. Case study (2) • intermediary • collects the money from the buyer • asks the seller to send the goods • sends the money to the sender when the buyer acknowledges the receipt of the goods, otherwise returns the money to the buyer • complicated, but not risky for all partners • alternative? AAMAS 2009, Budapest

36. Case study (3) • intermediary analyses the past behaviour of both buyer and seller • after several successful transactions according to the “prepaid” model the process is simplified (e.g. credit card charging with the risk that the money is not received in the end but has to be paid to the seller) • correlations between past behaviour and available information about seller/buyer (is reputation justified? how difficult is it to sue a defecting partner?) • intermediary agent builds and updates models of all its clients and treats them accordingly AAMAS 2009, Budapest

37. Conclusion: Can social sciences contribute to the further development of computer science? • the development of self-adapting software could use the insights of social science to construct something such as more co-operative, secure agent societies, for instance on the web • once we succeed in building a valid simulation of a human social system, we have created adaptive software • still a long way to go toward socially-inspired computing in a way that well understood social processes of norm emergence, trust formation and negotiation can be used as design patterns in distributed systems engineering AAMAS 2009, Budapest