Current reversal and exclusion processes with history-dependent random walks

A class of exclusion processes in which particles perform history-dependent random walks is introduced, stimulated by dynamic phenomena in some biological and artificial systems. The particles locally interact with the underlying substrate by breaking and reforming lattice bonds. We determine the steady-state current on a ring, and find current-reversal as a function of particle density. This phenomenon is attributed to the non-local interaction between the walkers through their trails, which originates from strong correlations between the dynamics of the particles and the lattice. We rationalize our findings within an effective description in terms of quasi-particles which we call front barriers. Our analytical results are complemented by stochastic simulations.Comment: 5 pages, 6 figure

Psychological Issues in Online Adaptive Task Allocation

Adaptive aiding is an idea that offers potential for improvement over many current approaches to aiding in human-computer systems. The expected return of tailoring the system to fit the user could be in the form of improved system performance and/or increased user satisfaction. Issues such as the manner in which information is shared between human and computer, the appropriate division of labor between them, and the level of autonomy of the aid are explored. A simulated visual search task was developed. Subjects are required to identify targets in a moving display while performing a compensatory sub-critical tracking task. By manipulating characteristics of the situation such as imposed task-related workload and effort required to communicate with the computer, it is possible to create conditions in which interaction with the computer would be more or less desirable. The results of preliminary research using this experimental scenario are presented, and future directions for this research effort are discussed

The use of happiness research for public policy

Research on happiness tends to follow a "benevolent dictator" approach where politicians pursue people's happiness. This paper takes an antithetic approach based on the insights of public choice theory. First, we inquire how the results of happiness research may be used to improve the choice of institutions. Second, we show that the policy approach matters for the choice of research questions and the kind of knowledge happiness research aims to provide. Third, we emphasize that there is no shortcut to an optimal policy maximizing some happiness indicator or social welfare function since governments have an incentive to manipulate this indicator

Canonical phase space approach to the noisy Burgers equation

Presenting a general phase approach to stochastic processes we analyze in particular the Fokker-Planck equation for the noisy Burgers equation and discuss the time dependent and stationary probability distributions. In one dimension we derive the long-time skew distribution approaching the symmetric stationary Gaussian distribution. In the short time regime we discuss heuristically the nonlinear soliton contributions and derive an expression for the distribution in accordance with the directed polymer-replica model and asymmetric exclusion model results.Comment: 4 pages, Revtex file, submitted to Phys. Rev. Lett. a reference has been added and a few typos correcte

Belief-propagation algorithm and the Ising model on networks with arbitrary distributions of motifs

We generalize the belief-propagation algorithm to sparse random networks with arbitrary distributions of motifs (triangles, loops, etc.). Each vertex in these networks belongs to a given set of motifs (generalization of the configuration model). These networks can be treated as sparse uncorrelated hypergraphs in which hyperedges represent motifs. Here a hypergraph is a generalization of a graph, where a hyperedge can connect any number of vertices. These uncorrelated hypergraphs are tree-like (hypertrees), which crucially simplify the problem and allow us to apply the belief-propagation algorithm to these loopy networks with arbitrary motifs. As natural examples, we consider motifs in the form of finite loops and cliques. We apply the belief-propagation algorithm to the ferromagnetic Ising model on the resulting random networks. We obtain an exact solution of this model on networks with finite loops or cliques as motifs. We find an exact critical temperature of the ferromagnetic phase transition and demonstrate that with increasing the clustering coefficient and the loop size, the critical temperature increases compared to ordinary tree-like complex networks. Our solution also gives the birth point of the giant connected component in these loopy networks.Comment: 9 pages, 4 figure

Trichinellosis in Switzerland

Human trichinellosis is a food-borne zoonosis exhibiting significant health and economical problems predominantly in countries with high pork consumption. During the past ten years the number of human outbreaks around the world has increased in certain areas. In Europe, more than 20,000 human cases have been detected between the year 1991 and 2000. Conversely, Trichinella infection has not been reported for many decades among Swiss domestic pigs

Dynamic scaling regimes of collective decision making

We investigate a social system of agents faced with a binary choice. We assume there is a correct, or beneficial, outcome of this choice. Furthermore, we assume agents are influenced by others in making their decision, and that the agents can obtain information that may guide them towards making a correct decision. The dynamic model we propose is of nonequilibrium type, converging to a final decision. We run it on random graphs and scale-free networks. On random graphs, we find two distinct regions in terms of the "finalizing time" -- the time until all agents have finalized their decisions. On scale-free networks on the other hand, there does not seem to be any such distinct scaling regions