Transcript Perspectives and Challenges of Agent

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Perspectives and Challenges of AgentBased Simulation as a Tool for
Economics and Other Social Sciences
Klaus G. Troitzsch
Universität Koblenz-Landau, Germany
30/10/2015
AAMAS 2009, Budapest
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Overview
• Introduction
• What economics and social science can learn from MAS
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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
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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
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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 systems
Physical 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
living things which
• are partly autonomous
• interact in several
different modes
• can play different roles
• are not conscious of
their interactions
• 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
• do not communicate
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• communicate in
symbolic languages
even about the
counterfactual
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Fields and forces
Physical particles interact
with the help of
Living things 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
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Human actors additionally
interact with the help of
• by sounds and graphical
symbols (symbolic,
unrestricted lexicon, also
referring to absent or nonexisting 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 innovation
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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
macro effect
“upward
causation”
“downward
causation”
micro cause
micro effect
[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
“downward
causation”
micro cause
• both interpretations can
be applied to physical
and to social systems
• both interpretations can be applied
macro effect
micro effect
“upward
causation”
[Coleman 1990]
 to physical systems
o
macro cause = field, “downward causation” = force, micro effect = movement,
“upward causation” = field change
 to social systems
o
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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
• but the difference is:
 in physical systems
“downward
causation”
micro cause
macro effect
micro effect
“upward
causation”
[Coleman 1990]
o 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
o 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
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•
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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
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• 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
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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.
see the case study at the end
• 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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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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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
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Immergence and second-order emergence
A:You
“I don’t
yourthe street
must like
not cross
• norm-invocation messages A:
here, B!” in my car, B!
whensmoking
I am approaching
• motivate individual agents to change the rules
(B
(B abstains
abstainsfrom
fromcrossing
smoking
controlling their actions
the
street
when Aofis A.)
approaching
in the
presence
with her car.) penetrate
• if this happens often enough, “sociological phenomena
into us by force or at the very least by bearing down more or less
heavily upon us” [Durckheim 1895]
•
… and we have programmed
and assomething
a consequence,
theselike
normthis
invocations –
much
and the resulting behaviour – occur more and
in
an
agent-based
simulation
system!
more often and become a “sociological
(notonly
onlyB,
B,but
butothers,
others,too,
too,abstain
abstainfrom
fromcrossing
smoking,
streets,
phenomenon” (not
not
notonly
onlyininthe
thepresence
presenceofofA’s
A, car,
but also
but in
onmost
otherother
occasions.)
cases.)
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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 buyers
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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 gametheoretic 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 agentbased 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?
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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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