A Stochastic Approach to Shortcut Bridging in Programmable Matter

In a self-organizing particle system, an abstraction of programmable matter, simple computational elements called particles with limited memory and communication self-organize to solve system-wide problems of movement, coordination, and configuration. In this paper, we consider a stochastic, distributed, local, asynchronous algorithm for "shortcut bridging", in which particles self-assemble bridges over gaps that simultaneously balance minimizing the length and cost of the bridge. Army ants of the genus Eciton have been observed exhibiting a similar behavior in their foraging trails, dynamically adjusting their bridges to satisfy an efficiency trade-off using local interactions. Using techniques from Markov chain analysis, we rigorously analyze our algorithm, show it achieves a near-optimal balance between the competing factors of path length and bridge cost, and prove that it exhibits a dependence on the angle of the gap being "shortcut" similar to that of the ant bridges. We also present simulation results that qualitatively compare our algorithm with the army ant bridging behavior. Our work gives a plausible explanation of how convergence to globally optimal configurations can be achieved via local interactions by simple organisms (e.g., ants) with some limited computational power and access to random bits. The proposed algorithm also demonstrates the robustness of the stochastic approach to algorithms for programmable matter, as it is a surprisingly simple extension of our previous stochastic algorithm for compression.Comment: Published in Proc. of DNA23: DNA Computing and Molecular Programming - 23rd International Conference, 2017. An updated journal version will appear in the DNA23 Special Issue of Natural Computin

Individualization as driving force of clustering phenomena in humans

One of the most intriguing dynamics in biological systems is the emergence of clustering, the self-organization into separated agglomerations of individuals. Several theories have been developed to explain clustering in, for instance, multi-cellular organisms, ant colonies, bee hives, flocks of birds, schools of fish, and animal herds. A persistent puzzle, however, is clustering of opinions in human populations. The puzzle is particularly pressing if opinions vary continuously, such as the degree to which citizens are in favor of or against a vaccination program. Existing opinion formation models suggest that "monoculture" is unavoidable in the long run, unless subsets of the population are perfectly separated from each other. Yet, social diversity is a robust empirical phenomenon, although perfect separation is hardly possible in an increasingly connected world. Considering randomness did not overcome the theoretical shortcomings so far. Small perturbations of individual opinions trigger social influence cascades that inevitably lead to monoculture, while larger noise disrupts opinion clusters and results in rampant individualism without any social structure. Our solution of the puzzle builds on recent empirical research, combining the integrative tendencies of social influence with the disintegrative effects of individualization. A key element of the new computational model is an adaptive kind of noise. We conduct simulation experiments to demonstrate that with this kind of noise, a third phase besides individualism and monoculture becomes possible, characterized by the formation of metastable clusters with diversity between and consensus within clusters. When clusters are small, individualization tendencies are too weak to prohibit a fusion of clusters. When clusters grow too large, however, individualization increases in strength, which promotes their splitting.Comment: 12 pages, 4 figure

Individual Preferences and Social Interactions Determine the Aggregation of Woodlice

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Large scale dynamics of the Persistent Turning Walker model of fish behavior

International audienceThis paper considers a new model of individual displacement, based on fish motion, the so-called Persistent Turning Walker (PTW) model, which involves an Ornstein-Uhlenbeck process on the curvature of the particle trajectory. The goal is to show that its large time and space scale dynamics is of diffusive type, and to provide an analytic expression of the diffusion coefficient. Two methods are investigated. In the first one, we compute the large time asymptotics of the variance of the individual stochastic trajectories. The second method is based on a diffusion approximation of the kinetic formulation of these stochastic trajectories. The kinetic model is a Fokker-Planck type equation posed in an extended phase-space involving the curvature among the kinetic variables. We show that both methods lead to the same value of the diffusion constant. We present some numerical simulations to illustrate the theoretical results

Group Living Enhances Individual Resources Discrimination: The Use of Public Information by Cockroaches to Assess Shelter Quality

In group-living organisms, consensual decision of site selection results from the interplay between individual responses to site characteristics and to group-members. Individuals independently gather personal information by exploring their environment. Through social interaction, the presence of others provides public information that could be used by individuals and modulates the individual probability of joining/leaving a site. The way that individual's information processing and the network of interactions influence the dynamics of public information (depending on population size) that in turn affect discrimination in site quality is a central question. Using binary choice between sheltering sites of different quality, we demonstrate that cockroaches in group dramatically outperform the problem-solving ability of single individual. Such use of public information allows animals to discriminate between alternatives whereas isolated individuals are ineffective (i.e. the personal discrimination efficiency is weak). Our theoretical results, obtained from a mathematical model based on behavioral rules derived from experiments, highlight that the collective discrimination emerges from competing amplification processes relying on the modulation of the individual sheltering time without shelters comparison and communication modulation. Finally, we well demonstrated here the adaptive value of such decision algorithm. Without any behavioral change, the system is able to shift to a more effective strategy when alternatives are present: the modification of the spatio-temporal distributions of individuals leading to the collective selection of the best resource. This collective discrimination implying such parsimonious and widespread mechanism must be shared by many group living-species

Spatial effects, sampling errors, and task specialization in the honey bee

Task allocation patterns should depend on the spatial distribution of work within the nest, variation in task demand, and the movement patterns of workers, however, relatively little research has focused on these topics. This study uses a spatially explicit agent based model to determine whether such factors alone can generate biases in task performance at the individual level in the honey bees, Apis mellifera. Specialization (bias in task performance) is shown to result from strong sampling error due to localized task demand, relatively slow moving workers relative to nest size, and strong spatial variation in task demand. To date, specialization has been primarily interpreted with the response threshold concept, which is focused on intrinsic (typically genotypic) differences between workers. Response threshold variation and sampling error due to spatial effects are not mutually exclusive, however, and this study suggests that both contribute to patterns of task bias at the individual level. While spatial effects are strong enough to explain some documented cases of specialization; they are relatively short term and not explanatory for long term cases of specialization. In general, this study suggests that the spatial layout of tasks and fluctuations in their demand must be explicitly controlled for in studies focused on identifying genotypic specialists

How Group Size Affects Vigilance Dynamics and Time Allocation Patterns: The Key Role of Imitation and Tempo

In the context of social foraging, predator detection has been the subject of numerous studies, which acknowledge the adaptive response of the individual to the trade-off between feeding and vigilance. Typically, animals gain energy by increasing their feeding time and decreasing their vigilance effort with increasing group size, without increasing their risk of predation (‘group size effect’). Research on the biological utility of vigilance has prevailed over considerations of the mechanistic rules that link individual decisions to group behavior. With sheep as a model species, we identified how the behaviors of conspecifics affect the individual decisions to switch activity. We highlight a simple mechanism whereby the group size effect on collective vigilance dynamics is shaped by two key features: the magnitude of social amplification and intrinsic differences between foraging and scanning bout durations. Our results highlight a positive correlation between the duration of scanning and foraging bouts at the level of the group. This finding reveals the existence of groups with high and low rates of transition between activies, suggesting individual variations in the transition rate, or ‘tempo’. We present a mathematical model based on behavioral rules derived from experiments. Our theoretical predictions show that the system is robust in respect to variations in the propensity to imitate scanning and foraging, yet flexible in respect to differences in the duration of activity bouts. The model shows how individual decisions contribute to collective behavior patterns and how the group, in turn, facilitates individual-level adaptive responses

Implications of Behavioral Architecture for the Evolution of Self-Organized Division of Labor

Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio. Our results indicate that architectural constraints play an important role for the outcome of evolution. With the simplest network, only genetically determined specialization is possible. This imposes several limitations on worker specialization. Moreover, in order to minimize idleness, networks evolve a biased work ratio, even when an unbiased work ratio would be optimal. By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range. Optimal work ratios are more easily achieved with the self-feedback network, but still provide a challenge when combined with worker specialization

Local Enhancement Promotes Cockroach Feeding Aggregations

Communication and learning from each other are part of the success of animal societies. Social insects invest considerable effort into signalling to their nestmates the locations of the most profitable resources in their environment. Growing evidence also indicates that insects glean such information through cues inadvertently provided by their conspecifics. Here, we investigate social information use in the foraging decisions by gregarious cockroaches (Blattella germanica L.). Individual cockroaches given a simultaneous choice in a Y-olfactometer between the odour of feeding conspecifics and the mixed odour of food plus non-feeding conspecifics showed a preference for the arm scented with the odour of feeding conspecifics. Social information (the presence of feeding conspecifics) was produced by cockroaches of all age classes and perceived at short distance in the olfactometer arms, suggesting the use of inadvertently provided cues rather than signals. We discuss the nature of these cues and the role of local enhancement (the selection of a location based on cues associated with the presence of conspecifics) in the formation of feeding aggregations in B. germanica. Similar cue-mediated recruitments could underpin a wide range of collective behaviours in group-living insects