IEEE Technology and Society Magazine - Fall 2014 - 60
game of chess. According to classical game theory with perfectly
rational players [22], in chess both
players possess a strategy leading
to the "equilibrium," and guaranteeing to both of them an outcome
at least as good as the equilibrium.
Nevertheless, chess is still a very
popular game, since applying such
strategy would require formidable
computation abilities by the players,
and in fact also for modern computers like IBM's Deep Blue (see previous sections). An interesting and
challenging research thread in game
theory deals with modeling competing agents with bounded rationality,
especially in repeated games [23].
The (limited) complexity of the
promptly utilize the action which is
apparently the best up to that point.
With the aid of what are termed
learning models with expert advice,
which includes Multi-Armed Bandit
problems, it has been shown [25] that
even under such limited information
regime the agents' behavior can converge to an equilibrium situation by
utilizing a simple updating rule on
the strategy played at each step.
Mechanism Design
Perhaps the most successful story in
game theory is that of mechanism
design [26], [27], which can be seen
as a reverse engineering application of a large part of the descriptive
game theory developed over the last
We also need to account for the
unpredictable nature of human
beings and social dynamics, which
hardly fit traditional "programming"
logic employed by computer
scientists and engineers.
strategy implemented by a generic
player is defined via the notion of
a finite automaton. The complexity
of a strategy is defined as the minimum number of states required by
an automaton in order to implement
it. Over the years, researchers have
tried to understand the extent of the
actual benefit for an individual in
having at their disposal powerful
machinery to beat the opponent [24].
Learning
Beyond having limited reasoning
capabilities, players may also not
have complete information on the
opponents' behavior, as well as on the
reward associated to each of its available actions. In this case, agents face
a typical exploration-exploitation
trade-off, where it is not clear up to
what extent an agent should employ
time in trying out all the actions in
order to refine the statistics of the
associated rewards or else should
60
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decades. In practice, mechanism
design studies how to a) build the
rules of the interaction among agents
and b) allocate incentives to agents,
such that the resulting equilibrium
situation satisfies some properties
that are "agreeable" under a global
perspective. A certain - socially
desirable - behavior is said to be
"incentive compatible" whenever it
can be elicited from the agents via
suitable incentives, which are often
interpreted as monetary ones. Typically, in mechanism design, such
behavior is considered as properly
elicited whenever it is a Nash equilibrium. Remarkably, a stronger
form of mechanism design takes
into account instead a refinement of
the Nash equilibrium, and requires
that the strategies are dominating for
all agents, i.e., optimal regardless
of the opponents' behavior. Hence,
there is no apparent reason why
an agent should deviate from that
strategy, thus ruling out any philosophical consideration on whether
the Nash equilibrium should be
actually implemented when agents
have full information on the game.
Prominent applications of mechanism design can be found in online
advertisement auctions, regulation in
monopolies and oligopolies, auctioning of radio frequencies to mobile
phone companies, and the building
of social welfare systems [28].
We posit that, if put in the proper
perspective, mechanism design
plays a fundamental role in SCI systems, since it provides precious help
in designing a) the rules of interactions between humans and machines
and b) the incentives to people in
order to make them feel enticed to
adequately carry out their tasks.
In its classic form, mechanism
design theory studies situations in
which the rule/incentive planner has
no complete information about the
agents in the system. In SCI systems,
the incomplete information about the
agents may concern, for example,
their skills in carrying out a specific
task, or else their sensitivity to monetary incentives, rather than to other
kinds of incentives. Mechanism
design theory is a sufficiently general model that also allows for the
study of incentives that are not only
myopic or solely related to financial
issues, even though monetary incentives can be more easily modeled
and embedded in a game theoretic
model. In SCI, incentives may have
a different and long-term nature,
and are commonly related to the
reputation that individuals build for
themselves. In current crowdsourcing systems, for example, the assignment of tasks is performed according
to the ranking of the users. Thus,
seriously engaging in successfully
carrying out a task is clearly a longterm incentive for a participating
agent to build a good reputation and,
as a direct consequence, to increase
its future revenue.
On the other hand, designing
incentives only for those agents
might not be enough to build a
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