Evolutionary freezing in a competitive population

We show that evolution in a population of adaptive agents, repeatedly competing for a limited resource, can come to an abrupt halt. This transition from evolutionary to non-evolutionary behavior arises as the global resource level is changed, and is reminiscent of a phase transition to a frozen state. Its origin lies in the inductive decision-making of the agents, the limited global information that they possess and the dynamical feedback inherent in the system.Comment: LaTeX file + 4 separate (pdf) figures. Revised version has minor change in Fig. 3 axis labe

Mean Field Equilibria for Competitive Exploration in Resource Sharing Settings

We consider a model of nomadic agents exploring and competing for time-varying location-specific resources, arising in crowdsourced transportation services, online communities, and in traditional location based economic activity. This model comprises a group of agents, and a set of locations each endowed with a dynamic stochastic resource process. Each agent derives a periodic reward determined by the overall resource level at her location, and the number of other agents there. Each agent is strategic and free to move between locations, and at each time decides whether to stay at the same node or switch to another one. We study the equilibrium behavior of the agents as a function of dynamics of the stochastic resource process and the nature of the externality each agent imposes on others at the same location. In the asymptotic limit with the number of agents and locations increasing proportionally, we show that an equilibrium exists and has a threshold structure, where each agent decides to switch to a different location based only on their current location's resource level and the number of other agents at that location. This result provides insight into how system structure affects the agents' collective ability to explore their domain to find and effectively utilize resource-rich areas. It also allows assessing the impact of changing the reward structure through penalties or subsidies.Comment: 17 pages, 1 figure, 1 table, to appear in proceedings of the 25th International World Wide Web Conference(WWW2016

Cost and Resource Level Consideration Under Probabilistic Demand Environment.

This work focuses on quantifying resources in an ever-increasing era of customization and erratic demand

Memory and self-induced shocks in an evolutionary population competing for limited resources

We present a detailed discussion of the role played by memory, and the nature of self-induced shocks, in an evolutionary population competing for limited resources. Our study builds on a previously introduced multi-agent system [Phys. Rev. Lett 82, 3360 (1999)] which has attracted significant attention in the literature. This system exhibits self-segregation of the population based on the `gene' value p (where 0<=p<=1), transitions to `frozen' populations as a function of the global resource level, and self-induced large changes which spontaneously arise as the dynamical system evolves. We find that the large, macroscopic self-induced shocks which arise, are controlled by microscopic changes within extreme subgroups of the population (i.e. subgroups with `gene' values p~0 and p~1).Comment: 27 pages, 31 figure

Theory of Networked Minority Games based on Strategy Pattern Dynamics

We formulate a theory of agent-based models in which agents compete to be in a winning group. The agents may be part of a network or not, and the winning group may be a minority group or not. The novel feature of the present formalism is its focus on the dynamical pattern of strategy rankings, and its careful treatment of the strategy ties which arise during the system's temporal evolution. We apply it to the Minority Game (MG) with connected populations. Expressions for the mean success rate among the agents and for the mean success rate for agents with $k$ neighbors are derived. We also use the theory to estimate the value of connectivity $p$ above which the Binary-Agent-Resource system with high resource level goes into the high-connectivity state.Comment: 24 pages, 3 figures, submitted to PR

Elastic Application Container

The computing resource level architecture allows end-users to directly control its underlying computer resources, such as VM (virtual machine) operations, scaling, networking, etc. However, setting up and maintaining a working environment is complex and time consuming for end-users and resource management is also a heavy-weight task for the providers. In contrast, the application resource level architecture automatically controls its underlying computer resources so that end-users can concentrate on their core business. In this paper, we propose a new architecture called Elastic Application Container (EAC) that enables the end-users to efficiently develop and deliver light-weight, elastic, multi-tenant, and portable applications. The EAC is an abstract representation which hides all its abstractions of the underlying VMs. We believe that our EAC architecture has the potential to become the foundation of future application resource level model in this research area. © 2011 IEEE