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Perspectives and Challenges of Agent-Based Simulation as a Tool for Economics and Other Social Sciences. Klaus G. Troitzsch Universität Koblenz-Landau, Germany. Overview. Introduction What economics and social science can learn from MAS Predecessors and alternatives - PowerPoint PPT Presentation
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Informatik
Perspectives and Challenges of Agent-Based Simulation as a Tool for Economics and Other Social Sciences
Klaus G. TroitzschUniversität Koblenz-Landau, Germany
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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
• Conclusion21/04/23 AAMAS 2009, Budapest
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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
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Complex systemsPhysical systems consist of
Living systems consist of
Human social systems consist of
particles which• obey natural laws• interact only in a
few different modes
• have no roles
• are not conscious of their interactions
• do not communicate
living things which• are partly
autonomous• interact in several
different modes• can play different
roles
• are only partly conscious of their roles and interactions (but not all are at all)
• communicate only in a very restricted manner (and never about counterfactuals)
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
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Fields and forcesPhysical particles interact with the help of
Living things additionally interact with the help of
Human actors additionally interact with the help of
• (a small number of different) forces
• fields which can change due to the movements of particles
• chemical substances and their concentration gradients
• by sounds (halfway symbolic, very restricted lexicon)
• by observing each other and predicting next moves
• by sounds and graphical symbols (symbolic, unrestricted lexicon, also referring to absent or non-existing things, e.g. unicorns and angels)
• by observing each other, predicting next moves and deriving regularities from what they observed (but they can also learn about regularities from others)
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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 innovation21/04/23 AAMAS 2009, Budapest
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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
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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)
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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”)
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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
micro cause micro effect
macro effect
“downwardcausation”
“upwardcausation”
[Coleman 1990]
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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
micro cause micro effect
macro effect
“downwardcausation”
“upwardcausation”
[Coleman 1990]
• both interpretations can be applied to physical and to social systems• both interpretations can be applied to physical systems
o macro cause = field, “downward causation” = force, micro effect = movement, “upward causation” = field change
to social systemso macro cause = “social field”, social norms, “downward causation”
= immergence, micro effect = norm adoption, “upward causation” = norm innovation
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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
micro cause micro effect
macro effect
“downwardcausation”
“upwardcausation”
[Coleman 1990]• but the difference
is: in physical systemso 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 systemso 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 …)
o the “downward causation” takes only effect after being interpreted by the individual, and this interpretation is dependent of his or her past
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Predecessors and alternatives to ABS• econophysics / sociophysics• game theory• early simulation attempts of the 1960s• sugarscape and its relatives
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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 vectorial additivity of the separate force terms reflecting different environmental influences
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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
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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
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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
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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 21/04/23 AAMAS 2009, Budapest
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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)
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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.
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see the case study at the end
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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
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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
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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
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EMIL-S
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• 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
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EMIL-S
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(not only B, but others, too, abstain from smoking, not only in the presence of A, but also on other occasions.)
(not only B, but others, too, abstain from crossing streets,not only in the presence of A’s car, but in most other cases.)
Immergence and second-order emergence• 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”
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A: “I don’t like your smoking here,
B!”(B abstains from smoking in the presence of A.)
… and we have programmed something much like this in an agent-based simulation system!
A: You must not cross the streetwhen I am approaching in my car, B!
(B abstains from crossingthe street when A is approachingwith her car.)
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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 buyers21/04/23 AAMAS 2009, Budapest
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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
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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.
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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.
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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).
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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
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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:
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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?
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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?21/04/23 AAMAS 2009, Budapest
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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
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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
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