On the Necessary Memory to Compute the Plurality in Multi-Agent Systems

We consider the Relative-Majority Problem (also known as Plurality), in which, given a multi-agent system where each agent is initially provided an input value out of a set of $k$ possible ones, each agent is required to eventually compute the input value with the highest frequency in the initial configuration. We consider the problem in the general Population Protocols model in which, given an underlying undirected connected graph whose nodes represent the agents, edges are selected by a globally fair scheduler. The state complexity that is required for solving the Plurality Problem (i.e., the minimum number of memory states that each agent needs to have in order to solve the problem), has been a long-standing open problem. The best protocol so far for the general multi-valued case requires polynomial memory: Salehkaleybar et al. (2015) devised a protocol that solves the problem by employing $O(k 2^k)$ states per agent, and they conjectured their upper bound to be optimal. On the other hand, under the strong assumption that agents initially agree on a total ordering of the initial input values, Gasieniec et al. (2017), provided an elegant logarithmic-memory plurality protocol. In this work, we refute Salehkaleybar et al.'s conjecture, by providing a plurality protocol which employs $O(k^{11})$ states per agent. Central to our result is an ordering protocol which allows to leverage on the plurality protocol by Gasieniec et al., of independent interest. We also provide a $\Omega(k^2)$-state lower bound on the necessary memory to solve the problem, proving that the Plurality Problem cannot be solved within the mere memory necessary to encode the output.Comment: 14 pages, accepted at CIAC 201

Traffic Instabilities in Self-Organized Pedestrian Crowds

In human crowds as well as in many animal societies, local interactions among individuals often give rise to self-organized collective organizations that offer functional benefits to the group. For instance, flows of pedestrians moving in opposite directions spontaneously segregate into lanes of uniform walking directions. This phenomenon is often referred to as a smart collective pattern, as it increases the traffic efficiency with no need of external control. However, the functional benefits of this emergent organization have never been experimentally measured, and the underlying behavioral mechanisms are poorly understood. In this work, we have studied this phenomenon under controlled laboratory conditions. We found that the traffic segregation exhibits structural instabilities characterized by the alternation of organized and disorganized states, where the lifetime of well-organized clusters of pedestrians follow a stretched exponential relaxation process. Further analysis show that the inter-pedestrian variability of comfortable walking speeds is a key variable at the origin of the observed traffic perturbations. We show that the collective benefit of the emerging pattern is maximized when all pedestrians walk at the average speed of the group. In practice, however, local interactions between slow- and fast-walking pedestrians trigger global breakdowns of organization, which reduce the collective and the individual payoff provided by the traffic segregation. This work is a step ahead toward the understanding of traffic self-organization in crowds, which turns out to be modulated by complex behavioral mechanisms that do not always maximize the group's benefits. The quantitative understanding of crowd behaviors opens the way for designing bottom-up management strategies bound to promote the emergence of efficient collective behaviors in crowds.Comment: Article published in PLoS Computational biology. Freely available here: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.100244

Effects of anisotropic interactions on the structure of animal groups

This paper proposes an agent-based model which reproduces different structures of animal groups. The shape and structure of the group is the effect of simple interaction rules among individuals: each animal deploys itself depending on the position of a limited number of close group mates. The proposed model is shown to produce clustered formations, as well as lines and V-like formations. The key factors which trigger the onset of different patterns are argued to be the relative strength of attraction and repulsion forces and, most important, the anisotropy in their application.Comment: 22 pages, 9 figures. Submitted. v1-v4: revised presentation; extended simulations; included technical results. v5: added a few clarification

Collective motion of active Brownian particles in one dimension

We analyze a model of active Brownian particles with non-linear friction and velocity coupling in one spatial dimension. The model exhibits two modes of motion observed in biological swarms: A disordered phase with vanishing mean velocity and an ordered phase with finite mean velocity. Starting from the microscopic Langevin equations, we derive mean-field equations of the collective dynamics. We identify the fixed points of the mean-field equations corresponding to the two modes and analyze their stability with respect to the model parameters. Finally, we compare our analytical findings with numerical simulations of the microscopic model.Comment: submitted to Eur. Phys J. Special Topic

Swarming Behavior in Plant Roots

Interactions between individuals that are guided by simple rules can generate swarming behavior. Swarming behavior has been observed in many groups of organisms, including humans, and recent research has revealed that plants also demonstrate social behavior based on mutual interaction with other individuals. However, this behavior has not previously been analyzed in the context of swarming. Here, we show that roots can be influenced by their neighbors to induce a tendency to align the directions of their growth. In the apparently noisy patterns formed by growing roots, episodic alignments are observed as the roots grow close to each other. These events are incompatible with the statistics of purely random growth. We present experimental results and a theoretical model that describes the growth of maize roots in terms of swarming

A face for all seasons:searching for context-specific leadership traits and discovering a general preference for perceived health

Previous research indicates that followers tend to contingently match particular leader qualities to evolutionarily consistent situations requiring collective action (i.e., context-specific cognitive leadership prototypes) and information processing undergoes categorization which ranks certain qualities as first-order context-general and others as second-order context-specific. To further investigate this contingent categorization phenomenon we examined the “attractiveness halo”—a first-order facial cue which significantly biases leadership preferences. While controlling for facial attractiveness, we independently manipulated the underlying facial cues of health and intelligence and then primed participants with four distinct organizational dynamics requiring leadership (i.e., competition vs. cooperation between groups and exploratory change vs. stable exploitation). It was expected that the differing requirements of the four dynamics would contingently select for relatively healthier- or intelligent-looking leaders. We found perceived facial intelligence to be a second-order context-specific trait—for instance, in times requiring a leader to address between-group cooperation—whereas perceived health is significantly preferred across all contexts (i.e., a first-order trait). The results also indicate that facial health positively affects perceived masculinity while facial intelligence negatively affects perceived masculinity, which may partially explain leader choice in some of the environmental contexts. The limitations and a number of implications regarding leadership biases are discussed

Congestion in a macroscopic model of self-driven particles modeling gregariousness

International audienceWe analyze a macroscopic model with a maximal density constraint which describes short range repulsion in biological systems. This system aims at modeling finite-size particles which cannot overlap and repel each other when they are too close. The parts of the fluid where the maximal density is reached behave like incompressible fluids while lower density regions are compressible. This paper investigates the transition between the compressible and incompressible regions. To capture this transition, we study a one-dimensional Riemann problem and introduce a perturbation problem which regularizes the compressible-incompressible transition. Specific difficulties related to the non-conservativity of the problem are discussed

Unforeseen Costs of Cutting Mosquito Surveillance Budgets

A budget proposal to stop the U.S. Centers for Disease Control and Prevention (CDC) funding in surveillance and research for mosquito-borne diseases such as dengue and West Nile virus has the potential to leave the country ill-prepared to handle new emerging diseases and manage existing ones. In order to demonstrate the consequences of such a measure, if implemented, we evaluated the impact of delayed control responses to dengue epidemics (a likely scenario emerging from the proposed CDC budget cut) in an economically developed urban environment. We used a mathematical model to generate hypothetical scenarios of delayed response to a dengue introduction (a consequence of halted mosquito surveillance) in the City of Cairns, Queensland, Australia. We then coupled the results of such a model with mosquito surveillance and case management costs to estimate the cumulative costs of each response scenario. Our study shows that halting mosquito surveillance can increase the management costs of epidemics by up to an order of magnitude in comparison to a strategy with sustained surveillance and early case detection. Our analysis shows that the total costs of preparedness through surveillance are far lower than the ones needed to respond to the introduction of vector-borne pathogens, even without consideration of the cost in human lives and well-being. More specifically, our findings provide a science-based justification for the re-assessment of the current proposal to slash the budget of the CDC vector-borne diseases program, and emphasize the need for improved and sustainable systems for vector-borne disease surveillance

Reduction in the risk of human breast cancer by selective cyclooxygenase-2 (COX-2) inhibitors

BACKGROUND: Epidemiologic and laboratory investigations suggest that nonsteroidal anti-inflammatory drugs (NSAIDs) have chemopreventive effects against breast cancer due to their activity against cyclooxygenase-2 (COX-2), the rate-limiting enzyme of the prostaglandin cascade. METHODS: We conducted a case control study of breast cancer designed to compare effects of selective and non-selective COX-2 inhibitors. A total of 323 incident breast cancer patients were ascertained from the James Cancer Hospital, Columbus, Ohio, during 2003–2004 and compared with 649 cancer free controls matched to the cases at a 2:1 ratio on age, race, and county of residence. Data on the past and current use of prescription and over the counter medications and breast cancer risk factors were ascertained using a standardized risk factor questionnaire. Effects of COX-2 inhibiting agents were quantified by calculating odds ratios (OR) and 95% confidence intervals. RESULTS: Results showed significant risk reductions for selective COX-2 inhibitors as a group (OR = 0.29, 95% CI = 0.14–0.59), regular aspirin (OR = 0.49, 95% CI = 0.26–0.94), and ibuprofen or naproxen (0.36, 95% CI = 0.18–0.72). Acetaminophen, a compound with negligible COX-2 activity and low dose aspirin (81 mg) produced no significant change in the risk of breast cancer. CONCLUSION: Selective COX-2 inhibitors (celecoxib and rofecoxib) were only recently approved for use in 1999, and rofecoxib (Vioxx) was withdrawn from the marketplace in 2004. Nevertheless, even in the short window of exposure to these compounds, the selective COX-2 inhibitors produced a significant (71%) reduction in the risk of breast cancer, underscoring their strong potential for breast cancer chemoprevention

Active Brownian Particles. From Individual to Collective Stochastic Dynamics

We review theoretical models of individual motility as well as collective dynamics and pattern formation of active particles. We focus on simple models of active dynamics with a particular emphasis on nonlinear and stochastic dynamics of such self-propelled entities in the framework of statistical mechanics. Examples of such active units in complex physico-chemical and biological systems are chemically powered nano-rods, localized patterns in reaction-diffusion system, motile cells or macroscopic animals. Based on the description of individual motion of point-like active particles by stochastic differential equations, we discuss different velocity-dependent friction functions, the impact of various types of fluctuations and calculate characteristic observables such as stationary velocity distributions or diffusion coefficients. Finally, we consider not only the free and confined individual active dynamics but also different types of interaction between active particles. The resulting collective dynamical behavior of large assemblies and aggregates of active units is discussed and an overview over some recent results on spatiotemporal pattern formation in such systems is given.Comment: 161 pages, Review, Eur Phys J Special-Topics, accepte