Parrondo games as disordered systems

Parrondo's paradox refers to the counter-intuitive situation where a winning strategy results from a suitable combination of losing ones. Simple stochastic games exhibiting this paradox have been introduced around the turn of the millennium. The common setting of these Parrondo games is that two rules, $A$ and $B$, are played at discrete time steps, following either a periodic pattern or an aperiodic one, be it deterministic or random. These games can be mapped onto 1D random walks. In capital-dependent games, the probabilities of moving right or left depend on the walker's position modulo some integer $K$. In history-dependent games, each step is correlated with the $Q$ previous ones. In both cases the gain identifies with the velocity of the walker's ballistic motion, which depends non-linearly on model parameters, allowing for the possibility of Parrondo's paradox. Calculating the gain involves products of non-commuting Markov matrices, which are somehow analogous to the transfer matrices used in the physics of 1D disordered systems. Elaborating upon this analogy, we study a paradigmatic Parrondo game of each class in the neutral situation where each rule, when played alone, is fair. The main emphasis of this systematic approach is on the dependence of the gain on the remaining parameters and, above all, on the game, i.e., the rule pattern, be it periodic or aperiodic, deterministic or random. One of the most original sides of this work is the identification of weak-contrast regimes for capital-dependent and history-dependent Parrondo games, and a detailed quantitative investigation of the gain in the latter scaling regimes.Comment: 17 pages, 10 figures, 2 table

An investigation of equilibration in small quantum systems: the example of a particle in a 1D random potential

We investigate the equilibration of a small isolated quantum system by means of its matrix of asymptotic transition probabilities in a preferential basis. The trace of this matrix is shown to measure the degree of equilibration of the system launched from a typical state, from the standpoint of the chosen basis. This approach is substantiated by an in-depth study of the example of a tight-binding particle in one dimension. In the regime of free ballistic propagation, the above trace saturates to a finite limit, testifying good equilibration. In the presence of a random potential, the trace grows linearly with the system size, testifying poor equilibration in the insulating regime induced by Anderson localization. In the weak-disorder situation of most interest, a universal finite-size scaling law describes the crossover between the ballistic and localized regimes. The associated crossover exponent 2/3 is dictated by the anomalous band-edge scaling characterizing the most localized energy eigenstates.Comment: 19 pages, 7 figures, 1 tabl

Analysing partner selection through exchange values

Dynamic and resource-constrained environments raise interesting issues for partnership formation and multi-agent systems. In a scenario in which agents interact with each other to exchange services, if computational resources are limited, agents cannot always accept a request, and may take time to find available partners to delegate their needed services. Several approaches are available to solve this problem, which we explore through an experimental evaluation in this paper. In particular, we provide a computational implementation of Piaget's exchange-values theory, and compare its performance against alternatives

Agents in Bioinformatics

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarise and reflect on the presentations and discussions

Balancing Conflict and Cost in the Selection of Negotiation Opponents

Within the context of agent-to-agent purchase negotiations, a problem that has received little attention is that of identifying negotiation opponents in situations where the consequences of conflict and the ability to access resources dynamically vary. Such dynamism poses a number of problems that make it difficult to automate the identification of appropriate opponents. To that end, this paper describes a motivation-based opponent selection mechanism used by a buyer-agent to evaluate and select between an already identified set of seller-agents. Sellers are evaluated in terms of the amount of conflict they are expected to bring to a negotiation and the expected amount of cost a negotiation with them will entail. The mechanism allows trade-offs to be made between conflict and cost minimisation, and experimental results show the effectiveness of the approach

Light scattering from mesoscopic objects in diffusive media

The diffuse intensity propagating in turbid media is sensitive to the presence of any kind of object embedded in the medium, e.g. obstacles or defects. The long-ranged effects of isolated objects can be described by a stationary diffusion equation, the effect of any single object being parametrized in terms of a multipole expansion. An absorbing object is chiefly characterized by a negative charge, while the leading effect of a non-absorbing object is due to its dipole moment. The associated intrinsic characteristics of the object (capacitance $Q$ or effective radius $R_{\rm eff}$, polarizability $P$) can be evaluated within the diffusion approximation for large enough objects. The situation of mesoscopic objects, with a size comparable to the mean free path, requires a more careful treatment, for which the appropriate framework is radiative transfer theory. This formalism is worked out in detail for spheres and cylinders of the following kinds: totally absorbing (black), transparent, and totally reflecting.Comment: 31 pages, 2 tables, 7 figures. To appear in Eur. J. Phys.

Single-spin-flip dynamics of the Ising chain

We consider the most general single-spin-flip dynamics for the ferromagnetic Ising chain with nearest-neighbour influence and spin reversal symmetry. This dynamics is a two-parameter extension of Glauber dynamics corresponding respectively to non-linearity and irreversibility. The associated stationary state measure is given by the usual Boltzmann-Gibbs distribution for the ferromagnetic Hamiltonian of the chain. We study the properties of this dynamics both at infinite and at finite temperature, all over its parameter space, with particular emphasis on special lines and points.Comment: 31 pages, 18 figure

Universality in survivor distributions: Characterising the winners of competitive dynamics

We investigate the survivor distributions of a spatially extended model of competitive dynamics in different geometries. The model consists of a deterministic dynamical system of individual agents at specified nodes, which might or might not survive the predatory dynamics: all stochasticity is brought in by the initial state. Every such initial state leads to a unique and extended pattern of survivors and non-survivors, which is known as an attractor of the dynamics. We show that the number of such attractors grows exponentially with system size, so that their exact characterisation is limited to only very small systems. Given this, we construct an analytical approach based on inhomogeneous mean-field theory to calculate survival probabilities for arbitrary networks. This powerful (albeit approximate) approach shows how universality arises in survivor distributions via a key concept -- the {\it dynamical fugacity}. Remarkably, in the large-mass limit, the survival probability of a node becomes independent of network geometry, and assumes a simple form which depends only on its mass and degree.Comment: 12 pages, 6 figures, 2 table