IEEE Technology and Society Magazine - Fall 2014 - 59

expected return (non-monetary, shared
goods), fairness, etc.
In addition, a general incentive framework should account for
phenomena such as reflexivity [14]
and performativity [15], which can
deeply impact dynamics in sociotechnical systems. Non-monetary
incentives have been extensively
studied within the field of behavioral
economics; evidence has been found
that they can play a major role in
decision-making in themes such as,
e.g., energy and climate change [30].
If we pursue the idea that understanding and designing incentives
is the key to engineering SCI systems, we need to build a framework
for exploring various choices in the
design space.
We claim that game theory provides a reasonable framework for
addressing this problem. We now
provide a short tour in some (nonconventional) game-theoretic models
and tools to substantiate our claims.

Towards a Game
Theory-Based Swiss
Army Knife for SCI
Game theory is a broad-scope field
of scientific investigations which,
generally speaking, helps to model
the interaction situation among
agents in which the behavior of
one agent affects the condition of
the others. Hence, game theory can
be conceived of as a mathematical
tool capable of predicting and, up to
some extent, controlling the behavior of interacting agents.3
In 1947, the groundbreaking
work [16] by the father of modern
computers, John von Neumann,
and by Oskar Morgenstern, laid
the foundations of modern game
theory. Research in game theory
was then spurred by John Nash's
celebrated work in 1950 [17], [18],
3

While game theory started as a modeling/descriptive tool, in the past 15 years it has been extensively
used for the engineering, control and optimization
of networked systems, in particular in the fields
of telecommunications and computer science. We
therefore do not discuss further the link between
game theory and system engineering.

that proved the existence of an
equilibrium (since then dubbed as
"Nash equilibrium"), in which no
agent can be better off by unilaterally changing its strategy.
In the early days of this science, the
pillars that mainstream game theory
was built upon were the following:
1) Static game: the interaction
among agents is one-shot, i.e.,
not repeated over time;
2) Rationality: agents have infinite
computational power and are
able to perfectly balance costs
against benefits of their actions;
3) Complete information: information on agents' available strategies and payoffs are available to
all agents;
4) Description, not prescription:
in its early days, game theory
used to describe the outcome
of an interaction situation, but
did not prescribe how to design
the game such that the expected
outcome complies with some
agreeable properties.
Nowadays, many debates about
the effectiveness of game theory in
modeling realistic interaction scenarios are still fueled by the misconception that the limitations I-IV
cannot be overcome. Actually, over
the last few decades, researchers
have expanded the boundaries the
of applicability of game theory in
several directions, encompassing
some that do the trick for a purposeful SCI analysis and design.
Dynamics and Reputation
Most of the social interactions are
not one-shot, but are repeated and
span a certain period of time. In
such situations, people usually react
to the past behavior of the other
agents, and often in an imitative
fashion. Cooperative or antagonistic behaviors of an agent frequently
trigger in the other agents similar
reactions. Furthermore, during the
interaction process the agents build
a reputation for themselves, based
on their own past actions.

IEEE TECHNOLOGY AND SOCIETY MAGAZINE

FALL 2014

Dynamic game theory analyzes
situations in which interactions
among agents are repeated and
take place in a possibly dynamically changing environment. Interestingly, it has been shown [19]
that repeated interactions enforce
a cooperative behavior, primarily
because agents can pose a retaliation threat in the case that other
agents do not act harmoniously
with the society. The famous prisoner's dilemma4 is an elucidating
example of how threats often lead
to a Pareto improvement in the payoff of all agents.
There have been successful
attempts to incorporate reputation
system formalizations into more
standard game theoretical models
[20]. The aim is studying how agents
can build a reputation for themselves,
i.e., how to instill in the other agents
the belief that in the future it will
behave consistently with the past.
Evolutionary games are a peculiar form of dynamic games, as
they were initially conceived by
Smith [21] to explain the evolution of species in a biological context. Basically, evolutionary games
describe the interaction among
agents adopting different behaviors and study the evolution of the
acceptance of each of the considered behaviors inside a society. In
the case of SCI, evolutionary games
may be utilized, e.g., to assess the
extent of the acceptance of some
software by a group of users in an
environment where people suffer
from peer pressure.
Bounded Rationality
As a matter of fact, humans are not
completely rational. Even when our
objective function is perfectly specified, very often people are not capable of devising the most profitable
strategy for themselves. Moreover,
different individuals are not equally
capable of analyzing a situation even
if equipped with the same amount of
information. A clear example is the
4

http://en.wikipedia.org/wiki/Prisoner's_dilemma.

http://en.wikipedia.org/wiki/Prisoner's_dilemma

Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - Fall 2014