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Collective cognition has received much attention in recent years but most of the empirical work has focused on comparing individuals and groups within single populations, thereby not addressing evolutionary origins of collective cognition. Here, we investigated collective cognition in multiple populations that are subject to different levels of predation. Guppies (Poecilia reticulata) were given a simultaneous choice between an edible and a nonedible stimulus. We found evidence for an improvement in decision accuracy when in groups but only in low-predation guppies. This performance increase was due to a combination of increased private sampling behavior when in groups (compared to being alone) and social information use. In contrast, high-predation fish did not sample more when in groups, nor used social information; hence did not improve decision-accuracy when in groups. The improvement of groups in foraging accuracy in low but not in high-predation sites, suggests that these populations differ in their trade-off between attention dedicated to food and predators. In high-predation sites, investing time in predator detection is more crucial than in low-predation sites, thereby possibly conflicting with food detection. Our results highlight the importance of considering the effects of ecological gradients on collective cognition. [ABSTRACT FROM AUTHOR]

How social groups and organisms decide between alternative feeding sites or shelters has been extensively studied both experimentally and theoretically. One key result is the existence of a symmetry-breaking bifurcation at a critical system size, where there is a switch from evenly distributed exploitation of all options to a focussed exploitation of just one. Here we present a decision-making model in which symmetry-breaking is followed by a symmetry restoring bifurcation, whereby very large systems return to an even distribution of exploitation amongst options. The model assumes local positive feedback, coupled with a negative feedback regulating the flow toward the feeding sites. We show that the model is consistent with three different strains of the slime mold Physarum polycephalum, choosing between two feeding sites. We argue that this combination of feedbacks could allow collective foraging organisms to react flexibly in a dynamic environment. [ABSTRACT FROM AUTHOR]

Over the past decades many countries have experienced rapid changes in their economies, their democratic institutions and the values of their citizens. Comprehensive data measuring these changes across very different countries has recently become openly available. Between country similarities suggest common underlying dynamics in how countries develop in terms of economy, democracy and cultural values. We apply a novel Bayesian dynamical systems approach to identify the model which best captures the complex, mainly non-linear dynamics that underlie these changes. We show that the level of Human Development Index (HDI) in a country drives first democracy and then higher emancipation of citizens. This change occurs once the countries pass a certain threshold in HDI. The data also suggests that there is a limit to the growth of wealth, set by higher emancipation. Having reached a high level of democracy and emancipation, societies tend towards equilibrium that does not support further economic growth. Our findings give strong empirical evidence against a popular political science theory, known as the Human Development Sequence. Contrary to this theory, we find that implementation of human-rights and democratisation precede increases in emancipative values. [ABSTRACT FROM AUTHOR]

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach. [ABSTRACT FROM AUTHOR]

Epidemics, voting behaviour and cascading failures in power grids are examples of natural, social and technological phenomena that can be modelled as dynamical processes on networks. The study of such important complex systems requires approximation, but the assumptions that underpin the standard mean-field approaches are routinely violated by dynamics on real-world networks, leading to uncontrolled errors and even controversial results. Consequently, determining the approximation precision has been recognised as a key challenge. We present a micro-scale foundation for mean-field approximation of a wide range of dynamics on networks that facilitates quantification of approximation error, elucidating its connection to network structure and model dynamics. We show that our coarse-graining approach minimises approximation error and we obtain an upper bound on this uncertainty. We illustrate our approach using epidemic dynamics on real-world networks. Mean-field approximations in complex networks, while useful, often vary in accuracy and can be particularly problematic in the case of dynamical correlations. Here, the authors report the use of approximate lumping to derive low-dimensional mean-field equations and estimate the overall approximation error for real-world networks. [ABSTRACT FROM AUTHOR]

"Collective intelligence" and "wisdom of crowds" refer to situations in which groups achieve more accurate perception and better decisions than solitary agents. Whether groups outperform individuals should depend on the kind of task and its difficulty, but the nature of this relationship remains unknown. Here we show that colonies of Temnothorax ants outperform individuals for a difficult perception task but that individuals do better than groups when the task is easy. Subjects were required to choose the better of two nest sites as the quality difference was varied. For small differences, colonies were more likely than isolated ants to choose the better site, but this relationship was reversed for large differences. We explain these results using a mathematical model, which shows that positive feedback between group members effectively integrates information and sharpens the discrimination of fine differences. When the task is easier the same positive feedback can lock the colony into a suboptimal choice. These results suggest the conditions under which crowds do or do not become wise. [ABSTRACT FROM AUTHOR]

Summary: The evolutionary divergence of sexual signals is often important during the formation of new animal species, but our understanding of the origin of signal diversity is limited []. Sensory drive, the optimization of communication signal efficiency through matching to the local environment, has been highlighted as a potential promoter of diversification and speciation []. The swordtail characin (Corynopoma riisei) is a tropical fish in which males display a flag-like ornament that elicits female foraging behavior during courtship. We show that the shape of the male ornament covaries with female diet across natural populations. More specifically, natural populations in which the female diet is more dominated by ants exhibit male ornaments more similar to the shape of an ant. Feeding experiments confirm that females habituated to a diet of ants prefer to bite at male ornaments from populations with a diet more dominated by ants. Our results show that the male ornament functions as a “fishing lure” that is diversifying in shape to match local variation in female search images employed during foraging. This direct link between variation in female feeding ecology and the evolutionary diversification of male sexual ornaments suggests that sensory drive may be a common engine of signal divergence. [Copyright &y& Elsevier]

Collective motion, where large numbers of individuals move synchronously together, is achieved when individuals adopt interaction rules that determine how they respond to their neighbors' movements and positions. These rules determine how group-living animals move, make decisions, and transmit information between individuals. Nonetheless, few studies have explicitly determined these interaction rules in moving groups, and very little is known about the interaction rules of fish. Here, we identify three key rules for the social interactions of mosquitofish (Gambusia holbrooki): (i) Attraction forces are important in maintaining group cohesion, while we find only weak evidence that fish align with their neighbor's orientation; (ii) repulsion is mediated principally by changes in speed; (iii) although the positions and directions of all shoal members are highly correlated, individuals only respond to their single nearest neighbor. The last two of these rules are different from the classical models of collective animal motion, raising new questions about how fish and other animals self-organize on the move. [ABSTRACT FROM AUTHOR]

One of the main challenges for policymakers aiming to promote cycling in car-oriented transport systems is how to increase social diversity among its users. The under-representation of certain groups makes it difficult to normalise cycling and distribute its benefits throughout society. Many studies have explored cycling levels in relation to age and gender, and to a lesser extent disability and ethnicity. Less attention has been paid to bicycle use related to income and other socioeconomic factors associated with disadvantage. This study aims to better understand the relationship between income and other factors of socioeconomic disadvantage and cycling behaviour. We analysed data from the English National Travel Survey to estimate the likelihood of cycling participation (if individuals cycle at all), cycling frequency ('how often' they cycle), and cycling distance ('how far' they cycle) using regression models. We found that people in lower-income households cycle less for transport, particularly for commuting, than people in higher-income households. However, no income inequalities were found for leisure cycling. In addition, low-income leisure cyclists were found to cycle more often, but higher-income leisure cyclists further. Our findings may have important policy implications. Favouring a broader focus on non-commuting cycling and subsidising cycle access and maintenance could help to diversify cycling. [ABSTRACT FROM AUTHOR]

Associations in mixed-species foraging groups are common in animals, yet have rarely been explored in the context of collective behaviour. Despite many investigations into the social and ecological conditions under which individuals should form groups, we still know little about the specific behavioural rules that individuals adopt in these contexts, or whether these can be generalized to heterospecifics. Here, we studied collective behaviour in flocks in a community of five species of woodland passerine birds. We adopted an automated data collection protocol, involving visits by RFID-tagged birds to feeding stations equipped with antennae, over two winters, recording 91 576 feeding events by 1904 individuals. We demonstrated highly synchronized feeding behaviour within patches, with birds moving towards areas of the patch with the largest proportion of the flock. Using a model of collective decision making, we then explored the underlying decision rule birds may be using when foraging in mixed-species flocks. The model tested whether birds used a different decision rule for conspecifics and heterospecifics, and whether the rules used by individuals of different species varied. We found that species differed in their response to the distribution of conspecifics and heterospecifics across foraging patches. However, simulating decisions using the different rules, which reproduced our data well, suggested that the outcome of using different decision rules by each species resulted in qualitatively similar overall patterns of movement. It is possible that the decision rules each species uses may be adjusted to variation in mean species abundance in order for individuals to maintain the same overall flock-level response. This is likely to be important for maintaining coordinated behaviour across species, and to result in quick and adaptive flock responses to food resources that are patchily distributed in space and time. [ABSTRACT FROM AUTHOR]

Central place foragers, such as many ants, exploit the environment around their nest. The extent of their foraging range is a function of individual movement, but how the movement patterns of large numbers of foragers result in an emergent colony foraging range remains unclear. Here, we introduce a random walk model with stochastic resetting to depict the movements of searching ants. Stochastic resetting refers to spatially resetting at random times the position of agents to a given location, here the nest of searching ants. We investigate the effect of a range of resetting mechanisms and compare the macroscopic predictions of our model to laboratory and field data. We find that all returning mechanisms very robustly ensure that scouts exploring the surroundings of a nest will be exponentially distributed with distance from the nest. We also find that a decreasing probability for searching ants to return to their nest is compatible with empirical data, resulting in scouts going further away from the nest as the number of foraging trips increases. Our findings highlight the importance of resetting random walk models for depicting the movements of central place foragers and nurture novel questions regarding the searching behaviour of ants.

The popularity of relaxed clock Bayesian inference of clade origin timings has generated several recent publications with focal results considerably older than the fossils of the clades in question. Here, we critically examine two such clades: the animals (with a focus on the bilaterians) and the mammals (with a focus on the placentals). Each example displays a set of characteristic pathologies which, although much commented on, are rarely corrected for. We conclude that in neither case does the molecular clock analysis provide any evidence for an origin of the clade deeper than what is suggested by the fossil record. In addition, both these clades have other features (including, in the case of the placental mammals, proximity to a large mass extinction) that allow us to generate precise expectations of the timings of their origins. Thus, in these instances, the fossil record can provide a powerful test of molecular clock methodology, and why it goes astray, and we have every reason to think these problems are general. [Cambrian explosion; mammalian evolution; molecular clocks.]., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society of Systematic Biologists.)

Decision-making and movement of single animals or group of animals are often treated and investigated as separate processes. However, many decisions are taken while moving in a given space. In other words, both processes are optimized at the same time, and optimal decision-making processes are only understood in the light of movement constraints. To fully understand the rationale of decisions embedded in an environment (and therefore the underlying evolutionary processes), it is instrumental to develop theories of spatial decision-making. Here, we present a framework specifically developed to address this issue by the means of artificial neural networks and genetic algorithms. Specifically, we investigate a simple task in which single agents need to learn to explore their square arena without leaving its boundaries. We show that agents evolve by developing increasingly optimal strategies to solve a spatially embedded learning task while not having an initial arbitrary model of movements. The process allows the agents to learn how to move (i.e. by avoiding the arena walls) in order to make increasingly optimal decisions (improving their exploration of the arena). Ultimately, this framework makes predictions of possibly optimal behavioural strategies for tasks combining learning and movement.

Social animals can use the choices made by other members of their groups as cues in decision making. Individuals must balance the private information they receive from their own sensory cues with the social information provided by observing what others have chosen. These two cues can be integrated using decision making rules, which specify the probability to select one or other options based on the quality and quantity of social and non-social information. Previous empirical work has investigated which decision making rules can replicate the observable features of collective decision making, while other theoretical research has derived forms for decision making rules based on normative assumptions about how rational agents should respond to the available information. Here we explore the performance of one commonly used decision making rule in terms of the expected decision accuracy of individuals employing it. We show that parameters of this model which have typically been treated as independent variables in empirical model-fitting studies obey necessary relationships under the assumption that animals are evolutionarily optimised to their environment. We further investigate whether this decision making model is appropriate to all animal groups by testing its evolutionary stability to invasion by alternative strategies that use social information differently, and show that the likely evolutionary equilibrium of these strategies depends sensitively on the precise nature of group identity among the wider population of animals it is embedded within., (Creative Commons Attribution license.)

The 'many-wrongs hypothesis' predicts that groups improve their decision-making performance by aggregating members' diverse opinions. Although this has been considered one of the major benefits of collective movement and migration, whether and how multiple inputs are in fact aggregated for superior directional accuracy has not been empirically verified in non-human animals. Here we showed that larger homing pigeon flocks had significantly more efficient (i.e. shorter) homing routes than smaller flocks, consistent with previous findings and with the predictions of the many-wrongs hypothesis. However, detailed analysis showed that flock routes were not simply averages of individual routes, but instead that pigeons that more faithfully recapitulated their routes during individual flights had a proportionally greater influence on their flocks' routes. We discuss the implications of our results for possible mechanisms of collective learning as well as for the definition of leadership in animals solving navigational tasks collectively., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Assessing the systemic effects of uncertainty that arises from agents' partial observation of the true states of the world is critical for understanding a wide range of scenarios, from navigation and foraging behavior to the provision of renewable resources and public infrastructures. Yet previous modeling work on agent learning and decision-making either lacks a systematic way to describe this source of uncertainty or puts the focus on obtaining optimal policies using complex models of the world that would impose an unrealistically high cognitive demand on real agents. In this work we aim to efficiently describe the emergent behavior of biologically plausible and parsimonious learning agents faced with partially observable worlds. Therefore we derive and present deterministic reinforcement learning dynamics where the agents observe the true state of the environment only partially. We showcase the broad applicability of our dynamics across different classes of partially observable agent-environment systems. We find that partial observability creates unintuitive benefits in several specific contexts, pointing the way to further research on a general understanding of such effects. For instance, partially observant agents can learn better outcomes faster, in a more stable way, and even overcome social dilemmas. Furthermore, our method allows the application of dynamical systems theory to partially observable multiagent leaning. In this regard we find the emergence of catastrophic limit cycles, a critical slowing down of the learning processes between reward regimes, and the separation of the learning dynamics into fast and slow directions, all caused by partial observability. Therefore, the presented dynamics have the potential to become a formal, yet practical, lightweight and robust tool for researchers in biology, social science, and machine learning to systematically investigate the effects of interacting partially observant agents.

The collective behaviour of animal and human groups emerges from the individual decisions and actions of their constituent members. Recent research has revealed many ways in which the behaviour of groups can be influenced by differences amongst their constituent individuals. The existence of individual differences that have implications for collective behaviour raises important questions. How are these differences generated and maintained? Are individual differences driven by exogenous factors, or are they a response to the social dilemmas these groups face? Here I consider the classic case of patch selection by foraging agents under conditions of social competition. I introduce a multilevel model wherein the perceptual sensitivities of agents evolve in response to their foraging success or failure over repeated patch selections. This model reveals a bifurcation in the population, creating a class of agents with no perceptual sensitivity. These agents exploit the social environment to avoid the costs of accurate perception, relying on other agents to make fitness rewards insensitive to the choice of foraging patch. This provides a individual-based evolutionary basis for models incorporating perceptual limits that have been proposed to explain observed deviations from the Ideal Free Distribution (IFD) in empirical studies, while showing that the common assumption in such models that agents share identical sensory limits is likely false. Further analysis of the model shows how agents develop perceptual strategic niches in response to environmental variability. The emergence of agents insensitive to reward differences also has implications for societal resource allocation problems, including the use of financial and prediction markets as mechanisms for aggregating collective wisdom., Competing Interests: The authors have declared that no competing interests exist.

Background: Prediction of clinical training aptitude in medicine and dentistry is largely driven by measures of a student's intellectual capabilities. The measurement of sensorimotor ability has lagged behind, despite being a key constraint for safe and efficient practice in procedure-based medical specialties. Virtual reality (VR) haptic simulators, systems able to provide objective measures of sensorimotor performance, are beginning to establish their utility in facilitating sensorimotor skill acquisition, and it is possible that they may also inform the prediction of clinical performance., Methods: A retrospective cohort study examined the relationship between student performance on a haptic VR simulator in the second year of undergraduate dental study with subsequent clinic performance involving patients 2 years later. The predictive ability was tested against a phantom-head crown test (a traditional preclinical dental assessment, in the third year of study)., Results: VR scores averaged across the year explained 14% of variance in clinic performance, while the traditional test explained 5%. Students who scored highly on this averaged measure were ~10 times more likely to be high performers in the clinical crown test. Exploratory analysis indicated that single-trial VR scores did not correlate with real-world performance, but the relationship was statistically significant and strongest in the first half of the year and weakened over time., Conclusions: The data demonstrate the potential of a VR haptic simulator to predict clinical performance and open up the possibility of taking a data-driven approach to identifying individuals who could benefit from support in the early stages of training., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. No commercial re-use. See rights and permissions. Published by BMJ.)

Collective decisions can emerge from individual-level interactions between members of a group. These interactions are often seen as social feedback rules, whereby individuals copy the decisions they observe others making, creating a coherent group decision. The benefit of these behavioral rules to the individual agent can be understood as a transfer of information, whereby a focal individual learns about the world by gaining access to the information possessed by others. Previous studies have analyzed this exchange of information by assuming that all agents share common goals. While differences in information and differences in preferences have often been conflated, little is known about how differences between agents' underlying preferences affect the use and efficacy of social information. In this paper, I develop a model of social information use by rational agents with differing preferences, and demonstrate that the resulting collective behavior is strongly dependent on the structure of preference sharing within the group, as well as the quality of information in the environment. In particular, I show that strong social responses are expected by individuals that are habituated to noisy, uncertain environments where private information about the world is relatively weak. Furthermore, by investigating heterogeneous group structures, I demonstrate a potential influence of cryptic minority subgroups that may illuminate the empirical link between personality and leadership., Competing Interests: The author declares no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

The fossil record of the origins of major groups such as animals and birds has generated considerable controversy, especially when it conflicts with timings based on molecular clock estimates. Here, we model the diversity of "stem" (basal) and "crown" (modern) members of groups using a "birth-death model," the results of which qualitatively match many large-scale patterns seen in the fossil record. Typically, the stem group diversifies rapidly until the crown group emerges, at which point its diversity collapses, followed shortly by its extinction. Mass extinctions can disturb this pattern and create long stem groups such as the dinosaurs. Crown groups are unlikely to emerge either cryptically or just before mass extinctions, in contradiction to popular hypotheses such as the "phylogenetic fuse". The patterns revealed provide an essential context for framing ecological and evolutionary explanations for how major groups originate, and strengthen our confidence in the reliability of the fossil record., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

We present a non-parametric extension of the conditional logit model, using Gaussian process priors. The conditional logit model is used in quantitative social science for inferring interaction effects between personal features and choice characteristics from observations of individual multinomial decisions, such as where to live, which car to buy or which school to choose. The classic, parametric model presupposes a latent utility function that is a linear combination of choice characteristics and their interactions with personal features. This imposes strong and unrealistic constraints on the form of individuals' preferences. Extensions using non-linear basis functions derived from the original features can ameliorate this problem but at the cost of high model complexity and increased reliance on the user in model specification. In this paper we develop a non-parametric conditional logit model based on Gaussian process logit models. We demonstrate its application on housing choice data from over 50,000 moving households from the Stockholm area over a two year period to reveal complex homophilic patterns in income, ethnicity and parental status., Competing Interests: The authors have declared that no competing interests exist.

Survivorship biases can generate remarkable apparent rate heterogeneities through time in otherwise homogeneous birth-death models of phylogenies. They are a potential explanation for many striking patterns seen in the fossil record and molecular phylogenies. One such bias is the "push of the past": clades that survived a substantial length of time are likely to have experienced a high rate of early diversification. This creates the illusion of a secular rate slow-down through time that is, rather, a reversion to the mean. An extra effect increasing early rates of lineage generation is also seen in large clades. These biases are important but relatively neglected influences on many aspects of diversification patterns in the fossil record and elsewhere, such as diversification spikes after mass extinctions and at the origins of clades; they also influence rates of fossilization, changes in rates of phenotypic evolution and even molecular clocks. These inevitable features of surviving and/or large clades should thus not be generalized to the diversification process as a whole without additional study of small and extinct clades, and raise questions about many of the traditional explanations of the patterns seen in the fossil record., (© 2018 The Author(s). Evolution published by Wiley Periodicals, Inc. on behalf of The Society for the Study of Evolution.)

The patterns and mechanisms of collective decision making in humans and animals have attracted both empirical and theoretical attention. Of particular interest has been the variety of social feedback rules and the extent to which these behavioral rules can be explained and predicted from theories of rational estimation and decision making. However, models that aim to model the full range of social information use have incorporated ad hoc departures from rational decision-making theory to explain the apparent stochasticity and variability of behavior. In this paper I develop a model of social information use and collective decision making by fully rational agents that reveals how a wide range of apparently stochastic social decision rules emerge from fundamental information asymmetries both between individuals and between the decision makers and the observer of those decisions. As well as showing that rational decision making is consistent with empirical observations of collective behavior, this model makes several testable predictions about how individuals make decisions in groups and offers a valuable perspective on how we view sources of variability in animal, and human, behavior., Competing Interests: The author declares no conflict of interest

While collective movement is ecologically widespread and conveys numerous benefits on individuals, it also poses a coordination problem: who controls the group's movements? The role that animal 'personalities' play in this question has recently become a focus of research interest. Although many animal groups have distributed leadership (i.e. multiple individuals influence collective decisions), studies linking personality and leadership have focused predominantly on the group's single most influential individual. In this study, we investigate the relationship between personality and the influence of multiple leaders on collective movement using homing pigeons, Columba livia , a species known to display complex multilevel leadership hierarchies during flock flights. Our results show that more exploratory (i.e. 'bold') birds are more likely to occupy higher ranks in the leadership hierarchy and thus have more influence on the direction of collective movement than less exploratory (i.e. 'shy') birds during both free flights around their lofts and homing flights from a distant site. Our data also show that bold pigeons fly faster than shy birds during solo flights. We discuss our results in light of theories about the evolution of personality, with specific reference to the adaptive value of heterogeneity in animal groups.This article is part of the theme issue 'Collective movement ecology'., (© 2018 The Author(s).)

Social and economic systems produce complex and nonlinear relationships in the indicator variables that describe them. We present a Bayesian methodology to analyze the dynamical relationships between indicator variables by identifying the nonlinear functions that best describe their interactions. We search for the 'best' explicit functions by fitting data using Bayesian linear regression on a vast number of models and then comparing their Bayes factors. The model with the highest Bayes factor, having the best trade-off between explanatory power and interpretability, is chosen as the 'best' model. To be able to compare a vast number of models, we use conjugate priors, resulting in fast computation times. We check the robustness of our approach by comparison with more prediction oriented approaches such as model averaging and neural networks. Our modelling approach is illustrated using the classical example of how democracy and economic growth relate to each other. We find that the best dynamical model for democracy suggests that long term democratic increase is only possible if the economic situation gets better. No robust model explaining economic development using these two variables was found.

The overall purpose of this study is to contribute to bridging the gap between people- and place-oriented approaches in the study of crime causation. To achieve this we will explore some core hypotheses derived from Situational Action Theory about what makes young people crime prone and makes places criminogenic, and about the interaction between crime propensity and criminogenic exposure predicting crime events. We will also calculate the expected reduction in aggregate levels of crime that will occur as a result of successful interventions targeting crime propensity and criminogenic exposure. To test the hypotheses we will utilize a unique set of space-time budget, small area community survey, land-use and interviewer-led questionnaire data from the prospective longitudinal Peterborough Adolescent and Young Adult Development Study (PADS+) and an artificial neural network approach to modelling. The results show that people's crime propensity (based on their personal morals and abilities to exercise self-control) has the bulk of predictive power, but also that including criminogenic exposure (being unsupervised with peers and engaged in unstructured activities in residential areas of poor collective efficacy or commercial centres) demonstrates a substantial increase in predictive power (in addition to crime propensity). Moreover, the results show that the probability of crime is strongest when a crime-prone person is in a criminogenic setting and, crucially, that the higher a person's crime propensity the more vulnerable he or she is to influences of criminogenic exposure. Finally, the findings suggest that a reduction in people's crime propensity has a much bigger impact on their crime involvement than a reduction in their exposure to criminogenic settings.

Collective intelligence is the ability of a group to perform more effectively than any individual alone. Diversity among group members is a key condition for the emergence of collective intelligence, but maintaining diversity is challenging in the face of social pressure to imitate one's peers. Through an evolutionary game-theoretic model of collective prediction, we investigate the role that incentives may play in maintaining useful diversity. We show that market-based incentive systems produce herding effects, reduce information available to the group, and restrain collective intelligence. Therefore, we propose an incentive scheme that rewards accurate minority predictions and show that this produces optimal diversity and collective predictive accuracy. We conclude that real world systems should reward those who have shown accuracy when the majority opinion has been in error., Competing Interests: The authors declare no conflict of interest.

Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public's perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit "big data."

Recent research in animal behaviour has contributed to determine how alignment, turning responses, and changes of speed mediate flocking and schooling interactions in different animal species. Here, we propose a complementary approach to the analysis of flocking phenomena, based on the idea that animals occupy preferential, anysotropic positions with respect to their neighbours, and devote a large amount of their interaction responses to maintaining their mutual positions. We test our approach by deriving the apparent alignment and attraction responses from simulated trajectories of animals moving side by side, or one in front of the other. We show that the anisotropic positioning of individuals, in combination with noise, is sufficient to reproduce several aspects of the movement responses observed in real animal groups. This anisotropy at the level of interactions should be considered explicitly in future models of flocking and schooling. By making a distinction between interaction responses involved in maintaining a preferred flock configuration, and interaction responses directed at changing it, our work provides a frame to discriminate movement interactions that signal directional conflict from interactions underlying consensual group motion.

There is increasing evidence that animal groups can maintain coordinated behaviour and make collective decisions based on simple interaction rules. Effective collective action may be further facilitated by individual variation within groups, particularly through leader-follower polymorphisms. Recent studies have suggested that individual-level personality traits influence the degree to which individuals use social information, are attracted to conspecifics, or act as leaders/followers. However, evidence is equivocal and largely limited to laboratory studies. We use an automated data-collection system to conduct an experiment testing the relationship between personality and collective decision-making in the wild. First, we report that foraging flocks of great tits (Parus major) show strikingly synchronous behaviour. A predictive model of collective decision-making replicates patterns well, suggesting simple interaction rules are sufficient to explain the observed social behaviour. Second, within groups, individuals with more reactive personalities behave more collectively, moving to within-flock areas of higher density. By contrast, proactive individuals tend to move to and feed at spatial periphery of flocks. Finally, comparing alternative simulations of flocking with empirical data, we demonstrate that variation in personality promotes within-patch movement while maintaining group cohesion. Our results illustrate the importance of incorporating individual variability in models of social behaviour.

The approach presented in this article represents a generalizable and adaptable methodology for identifying complex interactions in educational systems and for investigating how manipulation of these systems may affect educational outcomes of interest. Multilayer Minimum Spanning Tree and Monte-Carlo methods are used. A virtual Sandbox University is created in order to facilitate effective identification of successful and stable initiatives within higher education, which can affect students' credits and student retention - something that has been lacking up until now. The results highlight the importance of teacher feedback and teacher-student rapport, which is congruent with current educational findings, illustrating the methodology's potential to provide a new basis for further empirical studies of issues in higher education from a complex systems perspective.

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture the observed locality of interactions. Traditional self-propelled particle models fail to capture the fine scale dynamics of the system. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics, while maintaining a biologically plausible perceptual range. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects., (© 2012 Mann et al.)

The emergence of similar collective patterns from different self-propelled particle models of animal groups points to a restricted set of "universal" classes for these patterns. While universality is interesting, it is often the fine details of animal interactions that are of biological importance. Universality thus presents a challenge to inferring such interactions from macroscopic group dynamics since these can be consistent with many underlying interaction models. We present a Bayesian framework for learning animal interaction rules from fine scale recordings of animal movements in swarms. We apply these techniques to the inverse problem of inferring interaction rules from simulation models, showing that parameters can often be inferred from a small number of observations. Our methodology allows us to quantify our confidence in parameter fitting. For example, we show that attraction and alignment terms can be reliably estimated when animals are milling in a torus shape, while interaction radius cannot be reliably measured in such a situation. We assess the importance of rate of data collection and show how to test different models, such as topological and metric neighbourhood models. Taken together our results both inform the design of experiments on animal interactions and suggest how these data should be best analysed.