4,894 research outputs found
Conservation Law of Utility and Equilibria in Non-Zero Sum Games
This short note demonstrates how one can define a transformation of a
non-zero sum game into a zero sum, so that the optimal mixed strategy achieving
equilibrium always exists. The transformation is equivalent to introduction of
a passive player into a game (a player with a singleton set of pure
strategies), whose payoff depends on the actions of the active players, and it
is justified by the law of conservation of utility in a game. In a transformed
game, each participant plays against all other players, including the passive
player. The advantage of this approach is that the transformed game is zero-sum
and has an equilibrium solution. The optimal strategy and the value of the new
game, however, can be different from strategies that are rational in the
original game. We demonstrate the principle using the Prisoner's Dilemma
example
Intellectual Property and the Prisoner’s Dilemma: A Game Theory Justification of Copyrights, Patents, and Trade Secrets
In this article, I will offer an argument for the protection of intellectual property based on individual self-interest and prudence. In large part, this argument will parallel considerations that arise in a prisoner’s dilemma game. In brief, allowing content to be unprotected in terms of free access leads to a sub-optimal outcome where creation and innovation are suppressed. Adopting the institutions of copyright, patent, and trade secret is one way to avoid these sub-optimal results
Reinforcement Learning Dynamics in Social Dilemmas
In this paper we replicate and advance Macy and Flache\'s (2002; Proc. Natl. Acad. Sci. USA, 99, 7229–7236) work on the dynamics of reinforcement learning in 2�2 (2-player 2-strategy) social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different transient regimes can be clearly observed. It is shown that the inclusion of small quantities of randomness in players\' decisions can change the dynamics of the model dramatically.Reinforcement Learning; Replication; Game Theory; Social Dilemmas; Agent-Based; Slow Learning
Fairness Emergence in Reputation Systems
Reputation systems have been used to support users in making decisions under uncertainty or risk that is due to the autonomous behavior of others. Research results support the conclusion that reputation systems can protect against exploitation by unfair users, and that they have an impact on the prices and income of users. This observation leads to another question: can reputation systems be used to assure or increase the fairness of resource distribution? This question has a high relevance in social situations where, due to the absence of established authorities or institutions, agents need to rely on mutual trust relations in order to increase fairness of distribution. This question can be formulated as a hypothesis: in reputation (or trust management) systems, fairness should be an emergent property. The notion of fairness can be precisely defined and investigated based on the theory of equity. In this paper, we investigate the Fairness Emergence hypothesis in reputation systems and prove that , under certain conditions, the hypothesis is valid for open and closed systems, even in unstable system states and in the presence of adversaries. Moreover, we investigate the sensitivity of Fairness Emergence and show that an improvement of the reputation system strengthens the emergence of fairness. Our results are confirmed using a trace-driven simulation from a large Internet auction site.Trust, Simulation, Fairness, Equity, Emergence, Reputation System
On Partially Controlled Multi-Agent Systems
Motivated by the control theoretic distinction between controllable and
uncontrollable events, we distinguish between two types of agents within a
multi-agent system: controllable agents, which are directly controlled by the
system's designer, and uncontrollable agents, which are not under the
designer's direct control. We refer to such systems as partially controlled
multi-agent systems, and we investigate how one might influence the behavior of
the uncontrolled agents through appropriate design of the controlled agents. In
particular, we wish to understand which problems are naturally described in
these terms, what methods can be applied to influence the uncontrollable
agents, the effectiveness of such methods, and whether similar methods work
across different domains. Using a game-theoretic framework, this paper studies
the design of partially controlled multi-agent systems in two contexts: in one
context, the uncontrollable agents are expected utility maximizers, while in
the other they are reinforcement learners. We suggest different techniques for
controlling agents' behavior in each domain, assess their success, and examine
their relationship.Comment: See http://www.jair.org/ for any accompanying file
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