8,252 research outputs found
Evolution of Cooperation and Coordination in a Dynamically Networked Society
Situations of conflict giving rise to social dilemmas are widespread in
society and game theory is one major way in which they can be investigated.
Starting from the observation that individuals in society interact through
networks of acquaintances, we model the co-evolution of the agents' strategies
and of the social network itself using two prototypical games, the Prisoner's
Dilemma and the Stag Hunt. Allowing agents to dismiss ties and establish new
ones, we find that cooperation and coordination can be achieved through the
self-organization of the social network, a result that is non-trivial,
especially in the Prisoner's Dilemma case. The evolution and stability of
cooperation implies the condensation of agents exploiting particular game
strategies into strong and stable clusters which are more densely connected,
even in the more difficult case of the Prisoner's Dilemma.Comment: 18 pages, 14 figures. to appea
Mutual Trust and Cooperation in the Evolutionary Hawks-Doves Game
Using a new dynamical network model of society in which pairwise interactions
are weighted according to mutual satisfaction, we show that cooperation is the
norm in the Hawks-Doves game when individuals are allowed to break ties with
undesirable neighbors and to make new acquaintances in their extended
neighborhood. Moreover, cooperation is robust with respect to rather strong
strategy perturbations. We also discuss the empirical structure of the emerging
networks, and the reasons that allow cooperators to thrive in the population.
Given the metaphorical importance of this game for social interaction, this is
an encouraging positive result as standard theory for large mixing populations
prescribes that a certain fraction of defectors must always exist at
equilibrium.Comment: 23 pages 12 images, to appea
Evolution of trust in structured populations
The trust game, derived from a notable economics experiment, has recently
attracted interest in the field of evolutionary dynamics. In a prevalent
version of the evolutionary trust game, players adopt one of three strategies:
investor, trustworthy trustee, or untrustworthy trustee. Trustworthy trustees
enhance and share the investment with the investor, whereas untrustworthy
trustees retain the full amount, betraying the investor. Following this setup,
we propose a two-player version of the trust game, which is analytically
feasible. Based on weak selection and pair approximation, we explore the
evolution of trust in structured populations, factoring in four strategy
updating rules: pairwise comparison (PC), birth-death (BD), imitation (IM), and
death-birth (DB). Comparing structured populations with well-mixed populations,
we arrive at two main conclusions. First, in the absence of untrustworthy
trustees, there is a saddle point between investors and trustworthy trustees,
with collaboration thriving best in well-mixed populations. The collaboration
diminishes sequentially from DB to IM to PC/BD updating rules in structured
populations. Second, an invasion of untrustworthy trustees makes this saddle
point unstable and leads to the extinction of investors. The 3-strategy system
stabilizes at an equilibrium line where the trustworthy and untrustworthy
trustees coexist. The stability span of trustworthy trustees is maximally
extended under the PC and BD updating rules in structured populations, while it
decreases in a sequence from IM to DB updating rules, with the well-mixed
population being the least favorable. This research adds an analytical lens to
understanding the evolution of trust in structured populations.Comment: 15 pages, 5 figure
Influence of Supply Chain Network Topology on the Evolution of Firm Strategies
This study investigates the influence of the topological structure of a supply chain network (SCN) on the evolution of cooperative and defective strategies adopted by the individual firms. First, a range of topologies representative of SCNs was generated using a fitness-based network growth model, which enabled cross comparisons by parameterising the network topologies with the power law exponent of their respective degree distributions. Then, the inter-firm links in each SCN were considered as repeated strategic interactions and were modelled by the Prisoner’s Dilemma game to represent the self-interested nature of the individual firms. This model is considered an agent-based model, where the agents are bound to their local neighbourhood by the network topology. A novel strategy update rule was then introduced to mimic the behaviour of firms. In particular, the heterogeneously distributed nature of the firm rationality was considered when they update their strategies at the end of each game round. Additionally, the payoff comparison against the neighbours was modelled to be strategy specific as opposed to accumulated payoff comparison analysis adopted in past work. It was found that the SCN topology, the level of rationality of firms and the relative strategy payoff differences are all essential elements in the evolution of cooperation. In summary, a tipping point was found in terms of the power law exponent of the SCN degree distribution, for achieving the highest number of co- operators. When the connection distribution of an SCN is highly unbalanced (such as in hub and spoke topologies) or well balanced (such as in random topologies), more difficult it is to achieve higher levels of co-operation among the firms. It was concluded that the scale-free topologies provide the best balance of hubs firms and lesser connected firms. Therefore, scale-free topologies are capable of achieving the highest proportion of co- operators in the firm population compared to other network topologies
How Effective are Electronic Reputation Mechanisms? An Experimental Investigation
Electronic reputation or "feedback" mechanisms aim to mitigate the moral hazard problems associated with exchange among strangers by providing the type of information available in more traditional close-knit groups, where members are frequently involved in one another's dealings. In this paper, we compare trading in a market with online feedback (as implemented by many Internet markets) to a market without feedback, as well as to a market in which the same people interact with one another repeatedly (partners market). We find that, while the feedback mechanism induces quite a substantial improvement in transaction efficiency, it also exhibits a kind of public goods problem in that, unlike in the partners market, the benefits of trust and trustworthy behavior go to the whole community and are not completely internalized. We discuss the implications of this perspective for improving feedback systems.
Evolution of Cooperation and Coordination in a Dynamically Networked Society
Situations of conflict giving rise to social dilemmas are widespread in society and game theory is one major way in which they can be investigated. Starting from the observation that individuals in society interact through networks of acquaintances, we model the co-evolution of the agents' strategies and of the social network itself using two prototypical games, the Prisoner's Dilemma and the Stag-Hunt. Allowing agents to dismiss ties and establish new ones, we find that cooperation and coordination can be achieved through the self-organization of the social network, a result that is nontrivial, especially in the Prisoner's Dilemma case. The evolution and stability of cooperation implies the condensation of agents exploiting particular game strategies into strong and stable clusters which are more densely connected, even in the more difficult case of the Prisoner's Dilemm
Resilient Autonomous Control of Distributed Multi-agent Systems in Contested Environments
An autonomous and resilient controller is proposed for leader-follower
multi-agent systems under uncertainties and cyber-physical attacks. The leader
is assumed non-autonomous with a nonzero control input, which allows changing
the team behavior or mission in response to environmental changes. A resilient
learning-based control protocol is presented to find optimal solutions to the
synchronization problem in the presence of attacks and system dynamic
uncertainties. An observer-based distributed H_infinity controller is first
designed to prevent propagating the effects of attacks on sensors and actuators
throughout the network, as well as to attenuate the effect of these attacks on
the compromised agent itself. Non-homogeneous game algebraic Riccati equations
are derived to solve the H_infinity optimal synchronization problem and
off-policy reinforcement learning is utilized to learn their solution without
requiring any knowledge of the agent's dynamics. A trust-confidence based
distributed control protocol is then proposed to mitigate attacks that hijack
the entire node and attacks on communication links. A confidence value is
defined for each agent based solely on its local evidence. The proposed
resilient reinforcement learning algorithm employs the confidence value of each
agent to indicate the trustworthiness of its own information and broadcast it
to its neighbors to put weights on the data they receive from it during and
after learning. If the confidence value of an agent is low, it employs a trust
mechanism to identify compromised agents and remove the data it receives from
them from the learning process. Simulation results are provided to show the
effectiveness of the proposed approach
- …