Workshop 4: Insect Self-organization and Swarming

(March 14,2011 - March 18,2011 )

Organizers


Madeleine Beekman
Bio sciences, University of Sydney
Vijay Kumar
Engineering and Applied Science, University of Pennsylvania
Stephen Pratt
Life Sciences, Arizona State University
David Sumpter
Interdisciplinary Mathematics, Uppsala University
Chad Topaz
Mathematics, Macalester College

Insect groups generate a wide range of interesting collective patterns and behaviours, for example the formation of ant trails, the building of elaborate nests, collective movement of honey bee swarms and marching locust bands, to name just a few. The complex non-linear nature of the mechanisms underlying such collective behaviour has generated a great deal of theoretical interest from mathematicians and physicists. Collective insect behaviour is one area where mathematical modelling and experiment have lived well side by side.

Collective insect behaviour is interesting from the point of view of evolution because understanding the non-linear dynamics provides insights into self-organization in natural systems which in turn serves as an inspiration for computer algorithms and robots. Many of the emergent collective phenomena involve synchronization where large numbers of individuals move in the same direction or co-ordinate their activities. Lastly, mass movement of insects such as grasshoppers and crickets involve large-scale interactions with the environment, whereby feedback between individuals within a group and their environment determine collective patterns.

Accepted Speakers

Fred Adler
Mathematics and Biology, University of Utah
Andrew Bernoff
Math, Harvey Mudd College
Iain Couzin
EEOB, Princeton University
Jean-Louis Deneubourg
Biology, Universite libre de Bruxelles
Claire Detrain
Biology, Universite libre de Bruxelles
Audrey Dussutour
Research Center on Animal Cognition, Universite Paul Sabatier, CNRS
Nina Fefferman
Department of Ecology, Evolution and Natural Resources, Rutgers University
Jennifer Fewell
Social Dynamics & Complexity, Arizona State University
Nigel Franks
School of Biological Sciences, University of Bristol
Deborah Gordon
Biology, Stanford University
Paulien Hogeweg
Theoretical Biology & Bioinformatics, Utrecht University
Cristian Huepe
Physics, Northwestern University
Leah Keshet
Mathematics, University of British Columbia
P. S. Krishnaprasad
Electrical and Computer Engineering Department, University of Maryland
Naomi Leonard
Mechanical and Aerospace Engineering, Princeton University
James Marshall
Computer Science, Unversity of Sheffield
Alcherio Martinoli
School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Federale de Lausanne
Martin Middendorf
Department of Computer Science, University of Leipzig
Mary Myerscough
School of Mathematics and Statistics, University of Sydney
Radhika Nagpal
Engineering&Applied Sci, Harvard University
Kevin Passino
Electrical and Computer Engineering, The Ohio State University
William Romey
Biology, SUNY College at Potsdam
Tom Seeley
Department of Neurobiology and Behavior, Cornell University
Stephen Simpson
School of Biological Sciences, University of Sydney
Guy Theraulaz
Centre de Recherches sur la Cognition Animale, Universite Paul Sabatier
Craig Tovey
Industrial and Systems Engineering, Georgia Institute of Technology
Monday, March 14, 2011
Time Session
09:30 AM
10:00 AM
Audrey Dussutour - Moving in the crowd: Ants hold the key to traffic chaos
Many animals take part in flow-like collective movements. In most species, however, the flow is unidirectional. Ants are one of the rare group of organisms in which flow-like movements are predominantly bidirectional. This adds to the difficulty of the task of maintaining a smooth, efficient movement. Yet, ants seem to fare well at this task. Do they really? And if so, how do such simple organisms succeed in maintaining a smooth traffic flow, when even humans experience trouble with this task? The experimental study of ant traffic is only a few years old but it has already provided interesting insights into traffic organization and regulation in animals, showing in particular that an ant colony as a whole can be considered as a typical self-organized adaptive and highly flexible system.
10:00 AM
10:30 AM
Jean-Louis Deneubourg - No Title Available
No description available.
10:30 AM
11:00 AM
Nigel Franks - Self-organization in Insect Societies: past, present and future
The application of self-organization theory to social insect studies is, for the most part, barely 20 years old. It has been remarkably successful because much of the new thinking and modelling that self-organization theory has brought to social insect studies has been very provocative, sometimes naive, and often oversimplifying; yet it has, almost invariably, lead to new experiments that have formed foundations for further progress. This has been a tale not of vicious circles but of virtuous ones. They are virtuous because errors and misunderstandings are exposed and corrected. They have gained great momentum from the natural, yet uneasy, tension between mathematical and empirical explanations. But most of all, they have been successful because mathematical modellers and experimentalists have worked together intimately both on the models and the experiments. In this talk, my aim is to illustrate these principles and the success of this endeavour by reviewing certain key examples. My goal is for this celebration of science past to suggest some of what might lie ahead.
02:30 PM
03:00 PM
Paulien Hogeweg - Division of labor and emergent adaptation
I will review some of the aims and thoughts that led me to model insect societies in individual based (agent based) models and study the ensuing (self)organization in a non-supervised manner ca 30 years ago, at the dawn of the then emerging field of emergent phenomena.

The I will discuss some of my much more recent work on the evolution of division of labor and discuss/speculate how the insights of these models might impact on understanding insect societies.
03:00 PM
03:30 PM
Fred Adler - Parallel Work and Parallel Play
In human children, parallel play describes two or more children playing side by side, perhaps using the same toy but for different purposes, and only occasionally modifying their behavior in response to the other. It forms an early stage of social development, following solitary play and generally preceding social and cooperative play.

If a group of ants were overseen by an extremely scientific teacher, how would he or she classify their interactions? I will address this question by studying models of three long-term interactions within and between ant colonies.

1. Ants must allocate effort among tasks such as foraging in different spatial locations, and do so based on information about what others, including nearby competitors, are doing.
2. Ants may need to choose conflict strategies to deal with neighbors of different species with different behaviors and fighting abilities, but without prior knowledge of who they will encounter.
3. Ants must choose strategies to compete with nearby or distant neighbors, potentially acting more or less aggressive toward members of familiar colonies.

Individuals can only base decisions on what they know, whether shaped by personal experience or shared information, ideally contributing to the long-term success of their colony. I will examine how well ants can regulate foraging and conflict with only limited information, and discuss when the resulting behaviors can be considered a coordinated strategy by the colony rather than "parallel work" by socially unsophisticated individuals.
Tuesday, March 15, 2011
Time Session
09:30 AM
10:00 AM
Leah Keshet - Modeling flocks and swarms
I will summarize some work on the link between individual behaviour and the dynamics of the swarm/flock. I will highlight two projects:

1. The behaviour of a 2D flock of aquatic birds, and how Ryan Lukeman (former PhD student, now at St FX University) figured out the underlying individual rules
2. models for social foraging, an ongoing project in my group joint with Nessy Tania, Ben Vanderlei and Joel Heath.
10:00 AM
10:30 AM
Stephen Simpson - From swarms to cannibalism to obesity: lessons from locusts
Locust plagues are one of the most infamous insect scourges, invading vast areas of Africa, Asia, Australia and the Americas. The reason that locusts form plagues is that they have an extraordinary capacity to change from shy, green, harmless grasshoppers into brightly coloured, swarming creatures when they experience crowding. This remarkable change can occur within the life of a single animal: the genome of the insect codes for both forms. I show that an important trigger for the change is bumping into other locusts. Stimulating touch-sensitive hairs on the back legs causes a rapid shift in behaviour, such that locusts become attracted to each other, rather than avoiding one another. Having identified the source of sensory stimulation that induces behavioural gregarization, we next analysed the associated neurochemical pathways involved and have recently shown that a pulse of serotonin causes the shift in behaviour upon crowding. Once a local aggregation reaches a critical number of insects, the locusts suddenly start to move as one. Using self-propelled particles models from statistical physics we have shown that this decision to start migrating does not involve leader locusts, but rather emerges collectively as a result of local interactions between individuals. Continuing to move as a group involves something very sinister, however. To illustrate, I next turn to another swarming animal, the Mormon cricket of North America. The reason these animals form vast marching bands is because they are seeking protein. The most abundant source of protein in a swarm of crickets is other crickets. The reason why they keep marching is that, if an insect stops, it gets cannibalized by the crickets coming from behind: they are on a forced march for protein. The same is true for locusts. The search for protein turns out to be a powerful force in shaping the biology not only of crickets and locusts, but of all animals - including humans. We have shown using experiments based on state-space geometric models for nutrition that many animals have a powerful appetite for protein. I show in humans that this protein appetite plays a key role in obesity. Protein comprising a minor part of our total energy budget, yet its intake is strongly regulated. I show how this combination leads to protein having the power both to drive the development of obesity - and to assuage it. Finally, I consider why it should be that many animals, humans included, should possess specific mechanisms that prevent overconsumption of protein. Using geometric models of nutrition I show that there are costs to over-consuming protein, and that the prevailing view that caloric restriction prolongs life is wrong - in insects at least.
10:30 AM
11:00 AM
Andrew Bernoff - A Primer of Swarm Equilibria
We study equilibrium configurations of swarming biological organisms subject to exogenous and pairwise endogenous forces. Beginning with a discrete dynamical model, we derive a variational description of the continuum population density. Equilibrium solutions are extrema of an energy functional, and satisfy a Fredholm integral equation. We find conditions for the extrema to be local minimizers, global minimizers, and minimizers with respect to infinitesimal Lagrangian displacements of mass. In one spatial dimension, for a variety of exogenous forces, endogenous forces, and domain configurations, we find exact analytical expressions for the equilibria. These agree closely with numerical simulations of the underlying discrete model. The exact solutions provide a sampling of the wide variety of equilibrium configurations possible within our general swarm modeling framework. The equilibria typically are compactly supported and may contain d-concentrations or jump discontinuities at the edge of the support. We apply our methods to a model of locust swarms, which are observed in nature to consist of a concentrated population on the ground separated from an airborne group. Our model can reproduce this configuration; quasi-two-dimensionality of the model plays a critical role.

Work done in collaboration with Chad M. Topaz.
02:00 PM
02:30 PM
Iain Couzin - From democratic consensus to cannibalistic hordes: the mechanism and evolution of collective behavior
No description available.
02:30 PM
03:00 PM
William Romey - The Road from Individual to Group Position to Emergence in Whirligig Swarms
Emergent patterns of flocks and swarms are at once beautiful and mysterious. We ask ourselves: "How and why do individuals coordinate these complicated maneuvers?" More specifically: how does self organization at lower levels influence emergent properties at higher levels? I will present the results of some of my studies addressing these questions using whirligig beetles and computer models to understand the three-part transition from (1) individual behavior to (2) group position to (3) the emergent behavior of swarms. Whirligig beetles make an ideal organism for studying general grouping phenomenon, such as those found in birds and fish, because they are composed of unrelated individuals, unlike bees and ants where altruism and kin-selection complicate the interpretation of emergent properties.

Working from the bottom up, we have used computer models and ethograms to examine how differences in (1) individuals influence the lower-level movement rules of the beetles. For example, sex, hunger, and age may influence an animal's preferred distance from others, and the speed with which they swim. These rules then influence the (2) group position which they occupy. For example, depending on predators and water speed, certain classes of whirligigs reliably end up at the edge or front of groups. We have modeled and carried out experiments which show that these positions are consistent with the hypothesis of self-organization: simple movement rules can explain the observed within-group segregation. Also, we present evolutionary optimization models that support the hypothesis that these group positions are individually adaptive. Finally, the group as a whole exhibits measurable behaviors (3) that seem to be an emergent property of levels one and two. These emergent properties include: group speed, turning, stopping, and mass predator escape. In conclusion we will discuss if the behaviors at all three of these levels are evolutionarily adaptive, or whether some might be neutral byproducts of an adaptive response at a different level.
03:00 PM
03:30 AM
Cristian Huepe - Adaptive network models of swarm dynamics
I will present a simple adaptive network model describing recent insect swarming experiments. By exploiting an analogy with human decision-making models and considering network-like interactions, this model captures the experimental dynamics using a low dimensional system of equations that permits analytical investigation. It reproduces several characteristic features of swarms, including: spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, intermittency, and metastable configurations displaying memory effects. By considering only minimal components, and introducing few elements of the spatial dynamics, it highlights the essential elements required to reproduce the observed behavior.
Wednesday, March 16, 2011
Time Session
09:30 AM
10:00 AM
Tom Seeley - Collective decision making by honey bees
I will review what is known about one of the most enchanting forms of collective animal behavior: the skillful choice of a new home by a swarm of honey bees. The challenge has been to understand how the 1.5 kilograms of bees in a swarm, like the 1.5 kilograms of neurons in a brain, are organized so that even though each individual has limited information and limited intelligence, the group as a whole makes first-rate collective decisions. I will describe how this complex phenomenon has been analyzed through a combination of empirical studies (observations and experiments) and mathematical studies (simulation models). In general, the empirical studies have revealed how the bees act and interact to produce the abilities of whole swarms, and the mathematical studies have clarified why the bees behave as they do to create a reliable decision-makng system.
10:00 AM
10:30 AM
Mary Myerscough - Swarm guidance in Apis florea: making decisions on the fly?
Nest site selection and swarm guidance in swarms of Apis mellifera are well studied, both observationally and theoretically, but not nearly so much is known about decision-making behaviour in other species of Apis. The Asian red dwarf honey bee, Apis florea, is an open-nesting honey bee, found in Southeast Asia, India and parts of the Middle East whose nest is a single comb in the midst of a cluster of bees formed around a small, shaded branch. As in A. mellifera, scouts go out from an A. florea colony that is looking for a new home and seek out suitable nest sites. They then return and dance to indicate the location of suitable new nest sites, but their dances are more variable and less intense than those of Apis mellifera and several sites may still be being advertised when the swarm takes off. In A. mellifera, scouts, who are informed about the location of the new nest site guide the swarm to their destination by flying rapidly through the swarm in the direction that the swarm needs to travel. In A. florea it is possible that different groups of scouts are directing the swarm in different directions. Using both observations and models that have been particularly devised for flying bees which have constantly changing speed, we will examine the process of swarm guidance in A. florea and explore what happens in a migrating swarm when different groups of scouts direct the swarm to different nest sites.
10:30 AM
11:00 AM
Kevin Passino - Cohesive Swarm Behavior With Information Flow Constraints
Bacteria, bees, and birds often work together in groups to find food. A group of mobile wheeled robots can be designed to coordinate their activities to achieve a goal. Networked cooperative autonomous air vehicles are being developed for commercial and military applications. In order for such multiagent systems to succeed it is often critical that they can both maintain cohesive behaviors and appropriately respond to environmental stimuli. In this talk, we characterize cohesiveness of discrete-time multiagent systems as a boundedness or stability property of the agents' position trajectories and use a Lyapunov approach to develop conditions under which local agent actions will lead to cohesive group behaviors even in the presence of (i) an interagent "sensing topology'' that constrains information flow, where by "information flow,'' we mean the sensing of positions and velocities of agents, (ii) a random but bounded delay and "noise'' in sensing other agents' positions and velocities, and (iii) noise in sensing a resource profile that represents an environmental stimulus and quantifies the goal of the multiagent system. Simulations are used to illustrate the ideas for multiagent systems and to make connections to synchronization of coupled oscillators.
Thursday, March 17, 2011
Time Session
09:30 AM
10:00 AM
Jennifer Fewell - Organization and regulation of work in the social insect colony
Division of labor, the way in which social groups distribute work among their individual members, is a product of self organization and selection. A basic system of division of labor can be produced even in artificial associations of normally solitary individuals and fits simple rules of interaction. In social insect colonies, however, the process of division of labor reflects the integration of the colony itself. I will discuss how division of labor changes with increased group size within social insect (primarily ant) colonies and discuss a network subgraph approach for capturing colony integration and regulation within social insect colonies s.
10:00 AM
10:30 AM
Nina Fefferman - Evolutionary Constraints on Social Organization from Disease Risks
As social insects have evolved division of labor and colony organization to accomplish tasks necessary to their survival, their social and collaborative environment should make them more and more susceptible to risk from infectious disease. Since they haven't been forced to extinction yet, they're clearly doing something right. Some have evolved individual physiological protections, others have behaviorally mediated individual responses/defenses, and a few have been shown to have collaborative behavioral defenses. In this talk, we'll discuss a set of models that explore whether or not the entire social organization of colonies themselves shows evidence of evolutionary selective pressures from disesase risks.
10:30 AM
11:00 AM
Claire Detrain - Audience and information transfer in ant societies
The ant society is a dynamic network of interacting nestmates of which individual decision rules lead to adaptive and functional patterns at the collective level. The non-linearity of relationships between workers makes those societies displaying properties characterizing other complex systems such as a high sensitivity to the number and/or rates of interactions between system agents. In the case of ant foraging, exploitation patterns strongly depend on colony size in a non-linear and discontinuous way: the density of nestmates' interactions influences the occurrence as well as the transition from one system state to another. However, ants do not passively undergo such a density-dependent structuring effect but instead, can play an active role in tuning feed-backs loops as a function of the density of workers. We shall review the ways workers can "assess" nestmates' density through either direct or indirect cues and then can tune amplification processes such as the laying of trail recruitment according to the social context of food exploitation. Another feature of ant societies as complex dynamic systems is the occurrence of hysteresis in which prior states of ant densities influence the ways through which the whole system can evolve. We shall see how ant individuals could keep track of such prior states and accordingly tune their behaviour and communication to improve their foraging efficiency. Finally, we shall discuss how the number of foragers can also deeply influence collective choices of ant societies between resources of different values and may act in conjunction with the availabilities of food resources of poor quality upon the discriminative abilities of insect societies
02:30 PM
03:00 PM
Guy Theraulaz - Swarms as smart architects: understanding construction dynamics in ant colonies
The amazing abilities of social insects to solve their everyday-life problems, also known as swarm intelligence, have received a considerable attention the past twenty years. Among their collective behaviors, nest building is certainly the most spectacular. Not only the characteristic scale of the nests is typically much larger than the size of the individuals, but some of the architectures built by insect colonies can also be highly complex. All along the evolution of these animals, there has been a whole set of innovations in terms of architectural designs and construction techniques that proved to be efficient to solve a large number of problems such as controlling the nest temperature, ensuring the gas exchanges with the outside environment or adapting the nest structure to various colony sizes. One fundamental question is: how large-scale patterns are generated by the actions and interactions of individual insects? To investigate this issue, we focused on the early stages of nest construction in the ant Lasius niger. This experimental paradigm was used to disentangle the coordinating mechanisms at work and characterize the individual behaviors (transport and assemblage of construction material). We then developed a 3D model implementing the mechanisms detected on the individual level and showed that they correctly explain the construction dynamics and the patterns observed at the collective level for various conditions. The model also revealed that complex helicoidal structures connecting nearby chambers emerge from a constant remodeling process of the nest architecture.
03:00 PM
03:30 PM
James Marshall - Optimality theory in collective behaviour
Twenty years ago, the case for optimality theory in evolutionary biology was set out in a review by Geoff Parker and John Maynard Smith. Thinking of what idealised animals should do if they are behaving optimally has informed behavioural ecology since its inception. With some exceptions, the study and theory of collective behaviour seems to be much more more mechanistic. This is probably because the mechanisms of collective behaviour are much more easily observed than those underlying individual decision-making, and because simple mathematical models and computational simulation often give good descriptions of collective systems. I shall argue that optimality theory is important for collective behaviour, review existing and potential applications of it, and highlight the crucial importance of selecting the right optimality criteria for a particular system.
Friday, March 18, 2011
Time Session
09:30 AM
10:00 AM
Naomi Leonard - Network topology and the evolution of collective migration
Agent-based dynamical models have been used successfully to reproduce a range of observed collective behaviors in biological groups. In these models, agents interact with one another and it has been shown that the topology of the interaction network plays a significant role in emergent outcomes and performance at the level of the group. An important challenge is to understand the tradeoffs, sensitivity to parameters, and different regimes of behavior in these biological models from the perspective of evolution by natural selection. Here we focus our attention on collective migration, defined broadly to represent a class of problems in which individuals in a group respond to an environmental cue and to social interactions. Models of collective migration have shown that a small group of leaders (individuals who invest strongly in the environmental cue) is capable of guiding a larger group of followers (individuals that rely on social interactions). Further, evolutionary simulations of migration models have shown that the speciation of a homogeneous group into leaders and followers is a stable evolutionary outcome when the cost of leadership is sufficiently high. Analytical mean-field evolutionary models using the techniques of adaptive dynamics have confirmed the observations in these simulations. We study the role that the interaction topology plays in the evolutionary outcomes of collective migration. As a point of comparison, we show that our model recovers the (qualitative) results of the mean-field analysis in the limit of all-to-all interconnections. We then demonstrate a minimum connectivity threshold for random interconnection graphs to yield speciated outcomes. We also study the adaptation of nodes on fixed graphs and illustrate the influence of graph topology on emergent outcomes in such adaptive systems.
10:00 AM
10:30 AM
Radhika Nagpal - Engineering Self-Organizing Systems
Biological systems, from embryos to social insects, get tremendous mileage by having vast numbers of cheap and unreliable individuals cooperate to achieve complex goals. We are also rapidly building new kinds of distributed systems with similar characteristics, from multi-modular robots and robot swarms, to vast sensor networks. Can we engineer collective systems to achieve the kind of complexity and self-repair that nature seems to achieve?

In this talk, I will describe several projects from my group where we have used inspiration from nature -- termites, fireflies, and cells -- to design new kinds of robots and networks. For example, simple robots that collectively build structures without explicit communication, self-adaptive modular robots that respond to the environment, and wireless sensor networks that use firefly-inspired algorithms to achieve high throughput. In each case, we use inspiration from biology to design simple decentralized cooperation, and techniques from computer science to analyze and generalize these algorithms to new tasks. A common theme in all of our work is understanding self-organizing multi-agent systems: how does robust collective behavior arise from many locally interacting agents, and how can we systematically program simple agents to achieve the global behaviors we want.
10:30 AM
11:00 AM
Martin Middendorf - Social Insects and Organic Computing
The relations between social insects and organic computing are discussed in this chapter. The aim of organic computing is to design and study computing systems that consist of many components which often act autonomously and show forms of collective behavior.

Such organic computing systems (OC systems) ideally possess self-x properties (e.g., self- healing, self-managing, self-optimizing), have a decentralized control, and be adaptive to changing requirements of their users. Social insects are a source of inspiration for the design of OC systems. In this talk I present examples from our own research on the relation between OC systems and social insects.
02:00 PM
02:30 PM
Alcherio Martinoli - Multi-Level Modeling and Distributed Control for Miniature Robotic Swarms
In this talk, I will first highlight the challenges related to the design, control, modeling, performance evaluation, and optimization of distributed, mobile, resource-constrained robotic systems. In particular, I will describe a specific distributed control method based on multiple modeling levels which has provided up to date interesting results in several case studies concerned with distributed sensing and manipulation missions, investigated either by us or other research groups worldwide. I will support the discussion with a few concrete examples concerned with aggregation and assembling tasks. Finally, I will revisit our engineering methodology and outline its similarities, differences, and possible links with the world of social insects.
02:30 PM
03:00 PM
Craig Tovey - Workshop 4: Insect Self-organization and Swarming Lecture 5
No description Available.
03:00 PM
03:30 PM
P. S. Krishnaprasad - Variational Principles and Control of Collective Behavior
Geometric methods in control theory have had a useful role in the investigation of dynamics of collectives. In this talk, we build on models from this theory to sketch recent progress in understanding small networks governed by interaction strategies associated with pursuit. We extend these ideas to a broader array of variational principles in networks of interacting systems. Using symmetry and reduction methods, hamiltonian structures, and conservation laws, we explore questions of collective behavior. These results also suggest how such principles may be exploited in the extraction of individual behaviors from movement data on flocks and swarms.
Name Email Affiliation
Adler, Fred adler@math.utah.edu Mathematics and Biology, University of Utah
Alawam, Fatin falawam@hotmail.com Mathematics, University of Alabama at Birmingham
Bassen, Jonathan jbassen@macalester.edu Mathematics, Statistics and Computer Science, Macalester College
Bazazi, Sepideh sepideh.bazazi@zoo.ox.ac.uk Zoology Department, University of Oxford
Beekman, Madeleine madeleine.beekman@sydney.edu.au Bio sciences, University of Sydney
Berdahl, Andrew aberdahl@princeton.edu Ecology and Evolutionary Biology, Princeton University
Berman, Spring sberman@eecs.harvard.edu Computer Science, Harvard University
Bernoff, Andrew andrew.bernoff@gmail.com Math, Harvey Mudd College
Bewick, Sharon sharon_bewick@hotmail.com NIMBioS, University of Tennessee
Buffin, Auralie aubuffin@ulb.ac.be Unit of Social Ecology, Universite libre de Bruxelles
Cho, Gi phil yogofo@naver.com
Cipra, Barry bcipra@rconnect.com Freelance Mathematics Writer
Couzin, Iain icouzin@princeton.edu EEOB, Princeton University
D'Orsogna, Maria dorsogna@csun.edu Department of Mathematics, California State University, Northridge
Deneubourg, Jean-Louis jldeneub@ulb.ac.be Biology, Universite libre de Bruxelles
Detrain, Claire cdetrain@ulb.ac.be Biology, Universite libre de Bruxelles
Diwold, Konrad kdiwold@informatik.uni-leipzig.de Department of Computer Science, University of Leipzig
Dussutour, Audrey dussutou@cict.fr Research Center on Animal Cognition, Universite Paul Sabatier, CNRS
Edelstein-Keshet, Leah keshet@math.ubc.ca Mathematics, University of British Columbia
Fefferman, Nina fefferman@aesop.rutgers.edu Department of Ecology, Evolution and Natural Resources, Rutgers University
Fewell, Jennifer j.fewell@asu.edu Social Dynamics & Complexity, Arizona State University
Franks, Nigel nigel.franks@bristol.ac.uk School of Biological Sciences, University of Bristol
Garnier, Simon sgarnier@princeton.edu Ecology and Evolutionary Biology, Princeton University
Gomes, Gabriela ggomes@igc.gulbenkian.pt Theoretical Epidemiology, Instituto Gulbenkian de Ciencia
Gordon, Deborah DMGordon@stanford.edu Biology, Stanford University
Granovskiy, Boris boris@math.uu.se Department of Mathematics, Uppsala University
Guo , Hang guo.298@osu.edu Department of Mathematics, The Ohio State University
Guttal, Vishwesha vguttal@princeton.edu Ecology and Evolutionary Biology, Princeton University
Ha, Seungmok ums0818@naver.com Mathematics,
Hartnett, Andrew ahartnet@princeton.edu Physics, Princeton University
Herbers, Joan herbers.4@osu.edu Evolution, Ecology, and Organismal Biology, The Ohio State University
Herbert-Read, James james.herbert-read@sydney.edu.au School of Biological Sciences, University of Sydney
Hogan, Patrick patrick.hogan@physics.org Department of Computer Science, University of Sheffield
Hogeweg, Paulien p.hogeweg@bio.uu.nl Theoretical Biology & Bioinformatics, Utrecht University
Huepe, Cristian cristian@northwestern.edu Physics, Northwestern University
Joo, Jaewook jjoo1@utk.edu Physics, University of Tennessee
Kao, Albert akao@princeton.edu Ecology and Evolutionary Biology, Princeton University
Kim, Byul Nim air1227@knu.ac.kr Mathematics, Kyungpook National University
Kim, Yongkuk yongkuk@knu.ac.kr Mathematics, Kyungpook National University
Krishnaprasad, P. S. krishna@umd.edu Electrical and Computer Engineering Department, University of Maryland
Kumar, Vijay kumar@seas.upenn.edu Engineering and Applied Science, University of Pennsylvania
Leblanc, Simon sleblanc@princeton.edu Applied and Computational Mathematics, Princeton University
Lemasson, Bertrand bertrand.h.lemasson@usace.army.mil Cognitive ecology and ecohydraulics, US Army Corps of Engineers
Leonard, Naomi naomi@princeton.edu Mechanical and Aerospace Engineering, Princeton University
Lutz, Matthew mlutz@princeton.edu Ecology and Evolutionary Biology, Princeton University
Ma, Qi qi@math.uu.se Interdisciplinary Mathematics, Uppsala University
Mann, Richard rmann@math.uu.se Mathematics, Uppsala University
Marshall, James j.marshall@dcs.shef.ac.uk Computer Science, Unversity of Sheffield
Martinoli, Alcherio alcherio.martinoli@epfl.ch School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Federale de Lausanne
Merrill, Emily emerrill@alumni.macalester.edu Smith College
Middendorf, Martin middendorf@informatik.uni-leipzig.de Department of Computer Science, University of Leipzig
Miller, Julie jsm349@cornell.edu Neurobiology and Behavior, Cornell University
Myerscough, Mary marymyerscough@gmail.com School of Mathematics and Statistics, University of Sydney
Nagpal, Radhika rad@eecs.harvard.edu Engineering&Applied Sci, Harvard University
Ngonghala, Calistus cngongha@mix.wvu.edu Mathematics, West Virginia University
Nicolis , Stamatios snicolis@math.uu.se Mathematics department, Uppsala University
Nishimori, Hiraku nishimor@math.sci.hiroshima-u.ac.jp Department of Mathematical and Life Sciences, Hiroshima University
Ogunsebikan, Temitope topgunsngs4u@yahoo.com Mathematics, Obafemi Awolowo University
Pais, Darren dpais@princeton.edu MAE, Princeton University
Passino, Kevin passino@ece.osu.edu Electrical and Computer Engineering, The Ohio State University
Pavlic, Theodore pavlic.3@osu.edu Computer Science and Engineering, The Ohio State University
Petersen, Kirstin Kirstin@eecs.harvard.edu EECS, Harvard University
Plowes, Nicola Nicola.Plowes@asu.edu Life Sciences, Arizona State University
Pratt, Stephen Stephen.Pratt@asu.edu Life Sciences, Arizona State University
Ramsch, Kai kairamsch@informatik.uni-leipzig.de Department of Computer Science, University of Leipzig
Reid, Chris christopher.reid@sydney.edu.au University of Sydney, University of Sydney
Richardson, Thomas tom.richardson@bristol.ac.uk Department of Engineering Design and Mathematics, University of the West of England
Romey, William romeywl@potsdam.edu Biology, SUNY College at Potsdam
Rubenstein, Michael mrubenst@seas.harvard.edu School of Engineering & Applied Sciences, Harvard University
Sasaki, Takao tsasaki1@asu.edu Life Sciences, Arizona State University
Schlegel, Thomas Thomas.Schlegel@Bristol.ac.uk School of Biology, University
Seeley, Thomas tds5@cornell.edu Department of Neurobiology and Behavior, Cornell University
Shen, Tian tshen@princeton.edu Mechanical and Aerospace Engineering, Princeton University
Shourijeh, Bahman tabataba@math.susc.ac.ir Department of Mathematics, Shiraz University
Simpson, Stephen stephen.simpson@sydney.edu.au School of Biological Sciences, University of Sydney
Sinha , Kunal knlsinha@gmail.com
Stroeymeyt, Nathalie Nathalie.Stroeymeyt@unil.ch Ecologie et Evolution, University of Lausanne
Strombom, Daniel strombom@math.uu.se Mathematics, Uppsala University
Sumpter, David david@math.uu.se Interdisciplinary Mathematics, Uppsala University
Tanner, Colby colbyjtanner@gmail.com Zoology, Trinity College
Tao, Yun yuntao@ucdavis.edu Environmental Science and Policy, University of California, Davis
Taslim, Cenny taslim.2@osu.edu Statistics/Comprehensive Cancer Center, The Ohio State University
Theraulaz, Guy theraula@cict.fr Centre de Recherches sur la Cognition Animale, Universite Paul Sabatier
Topaz, Chad chad.topaz@gmail.com Mathematics, Macalester College
Torney, Colin ctorney@princeton.edu Ecology and Evolutionary Biology, Princeton University
Tovey, Craig ctovey@isye.gatech.edu Industrial and Systems Engineering, Georgia Institute of Technology
Tunstrom, Kolbjorn tunstrom@princeton.edu Ecology and Evolutionary Biology, Princeton University
Twomey , Colin ctwomey@princeton.edu Ecology and Evolutionary Biology, Princeton University
Vela Perez, Maria mvp_es@yahoo.es Mathematics, Universidad Rey Juan Carlos
Werfel, Justin justin.werfel@wyss.harvard.edu Wyss Institute for Biologically Inspired Engineering, Harvard University
Wiser, Justin jwiser84@gmail.com
Yates, Christian yatesc@maths.ox.ac.uk Centre for Mathematical Biology, University of Oxford
Young, George gfyoung@princeton.edu Mechanical and Aerospace Engineering, Princeton University
Parallel Work and Parallel Play
In human children, parallel play describes two or more children playing side by side, perhaps using the same toy but for different purposes, and only occasionally modifying their behavior in response to the other. It forms an early stage of social development, following solitary play and generally preceding social and cooperative play.

If a group of ants were overseen by an extremely scientific teacher, how would he or she classify their interactions? I will address this question by studying models of three long-term interactions within and between ant colonies.

1. Ants must allocate effort among tasks such as foraging in different spatial locations, and do so based on information about what others, including nearby competitors, are doing.
2. Ants may need to choose conflict strategies to deal with neighbors of different species with different behaviors and fighting abilities, but without prior knowledge of who they will encounter.
3. Ants must choose strategies to compete with nearby or distant neighbors, potentially acting more or less aggressive toward members of familiar colonies.

Individuals can only base decisions on what they know, whether shaped by personal experience or shared information, ideally contributing to the long-term success of their colony. I will examine how well ants can regulate foraging and conflict with only limited information, and discuss when the resulting behaviors can be considered a coordinated strategy by the colony rather than "parallel work" by socially unsophisticated individuals.
A Primer of Swarm Equilibria
We study equilibrium configurations of swarming biological organisms subject to exogenous and pairwise endogenous forces. Beginning with a discrete dynamical model, we derive a variational description of the continuum population density. Equilibrium solutions are extrema of an energy functional, and satisfy a Fredholm integral equation. We find conditions for the extrema to be local minimizers, global minimizers, and minimizers with respect to infinitesimal Lagrangian displacements of mass. In one spatial dimension, for a variety of exogenous forces, endogenous forces, and domain configurations, we find exact analytical expressions for the equilibria. These agree closely with numerical simulations of the underlying discrete model. The exact solutions provide a sampling of the wide variety of equilibrium configurations possible within our general swarm modeling framework. The equilibria typically are compactly supported and may contain d-concentrations or jump discontinuities at the edge of the support. We apply our methods to a model of locust swarms, which are observed in nature to consist of a concentrated population on the ground separated from an airborne group. Our model can reproduce this configuration; quasi-two-dimensionality of the model plays a critical role.

Work done in collaboration with Chad M. Topaz.
From democratic consensus to cannibalistic hordes: the mechanism and evolution of collective behavior
No description available.
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No description available.
Audience and information transfer in ant societies
The ant society is a dynamic network of interacting nestmates of which individual decision rules lead to adaptive and functional patterns at the collective level. The non-linearity of relationships between workers makes those societies displaying properties characterizing other complex systems such as a high sensitivity to the number and/or rates of interactions between system agents. In the case of ant foraging, exploitation patterns strongly depend on colony size in a non-linear and discontinuous way: the density of nestmates' interactions influences the occurrence as well as the transition from one system state to another. However, ants do not passively undergo such a density-dependent structuring effect but instead, can play an active role in tuning feed-backs loops as a function of the density of workers. We shall review the ways workers can "assess" nestmates' density through either direct or indirect cues and then can tune amplification processes such as the laying of trail recruitment according to the social context of food exploitation. Another feature of ant societies as complex dynamic systems is the occurrence of hysteresis in which prior states of ant densities influence the ways through which the whole system can evolve. We shall see how ant individuals could keep track of such prior states and accordingly tune their behaviour and communication to improve their foraging efficiency. Finally, we shall discuss how the number of foragers can also deeply influence collective choices of ant societies between resources of different values and may act in conjunction with the availabilities of food resources of poor quality upon the discriminative abilities of insect societies
Moving in the crowd: Ants hold the key to traffic chaos
Many animals take part in flow-like collective movements. In most species, however, the flow is unidirectional. Ants are one of the rare group of organisms in which flow-like movements are predominantly bidirectional. This adds to the difficulty of the task of maintaining a smooth, efficient movement. Yet, ants seem to fare well at this task. Do they really? And if so, how do such simple organisms succeed in maintaining a smooth traffic flow, when even humans experience trouble with this task? The experimental study of ant traffic is only a few years old but it has already provided interesting insights into traffic organization and regulation in animals, showing in particular that an ant colony as a whole can be considered as a typical self-organized adaptive and highly flexible system.
Modeling flocks and swarms
I will summarize some work on the link between individual behaviour and the dynamics of the swarm/flock. I will highlight two projects:

1. The behaviour of a 2D flock of aquatic birds, and how Ryan Lukeman (former PhD student, now at St FX University) figured out the underlying individual rules
2. models for social foraging, an ongoing project in my group joint with Nessy Tania, Ben Vanderlei and Joel Heath.
Evolutionary Constraints on Social Organization from Disease Risks
As social insects have evolved division of labor and colony organization to accomplish tasks necessary to their survival, their social and collaborative environment should make them more and more susceptible to risk from infectious disease. Since they haven't been forced to extinction yet, they're clearly doing something right. Some have evolved individual physiological protections, others have behaviorally mediated individual responses/defenses, and a few have been shown to have collaborative behavioral defenses. In this talk, we'll discuss a set of models that explore whether or not the entire social organization of colonies themselves shows evidence of evolutionary selective pressures from disesase risks.
Organization and regulation of work in the social insect colony
Division of labor, the way in which social groups distribute work among their individual members, is a product of self organization and selection. A basic system of division of labor can be produced even in artificial associations of normally solitary individuals and fits simple rules of interaction. In social insect colonies, however, the process of division of labor reflects the integration of the colony itself. I will discuss how division of labor changes with increased group size within social insect (primarily ant) colonies and discuss a network subgraph approach for capturing colony integration and regulation within social insect colonies s.
Self-organization in Insect Societies: past, present and future
The application of self-organization theory to social insect studies is, for the most part, barely 20 years old. It has been remarkably successful because much of the new thinking and modelling that self-organization theory has brought to social insect studies has been very provocative, sometimes naive, and often oversimplifying; yet it has, almost invariably, lead to new experiments that have formed foundations for further progress. This has been a tale not of vicious circles but of virtuous ones. They are virtuous because errors and misunderstandings are exposed and corrected. They have gained great momentum from the natural, yet uneasy, tension between mathematical and empirical explanations. But most of all, they have been successful because mathematical modellers and experimentalists have worked together intimately both on the models and the experiments. In this talk, my aim is to illustrate these principles and the success of this endeavour by reviewing certain key examples. My goal is for this celebration of science past to suggest some of what might lie ahead.
Division of labor and emergent adaptation
I will review some of the aims and thoughts that led me to model insect societies in individual based (agent based) models and study the ensuing (self)organization in a non-supervised manner ca 30 years ago, at the dawn of the then emerging field of emergent phenomena.

The I will discuss some of my much more recent work on the evolution of division of labor and discuss/speculate how the insights of these models might impact on understanding insect societies.
Adaptive network models of swarm dynamics
I will present a simple adaptive network model describing recent insect swarming experiments. By exploiting an analogy with human decision-making models and considering network-like interactions, this model captures the experimental dynamics using a low dimensional system of equations that permits analytical investigation. It reproduces several characteristic features of swarms, including: spontaneous symmetry breaking, noise- and density-driven order-disorder transitions that can be of first or second order, intermittency, and metastable configurations displaying memory effects. By considering only minimal components, and introducing few elements of the spatial dynamics, it highlights the essential elements required to reproduce the observed behavior.
Variational Principles and Control of Collective Behavior
Geometric methods in control theory have had a useful role in the investigation of dynamics of collectives. In this talk, we build on models from this theory to sketch recent progress in understanding small networks governed by interaction strategies associated with pursuit. We extend these ideas to a broader array of variational principles in networks of interacting systems. Using symmetry and reduction methods, hamiltonian structures, and conservation laws, we explore questions of collective behavior. These results also suggest how such principles may be exploited in the extraction of individual behaviors from movement data on flocks and swarms.
Network topology and the evolution of collective migration
Agent-based dynamical models have been used successfully to reproduce a range of observed collective behaviors in biological groups. In these models, agents interact with one another and it has been shown that the topology of the interaction network plays a significant role in emergent outcomes and performance at the level of the group. An important challenge is to understand the tradeoffs, sensitivity to parameters, and different regimes of behavior in these biological models from the perspective of evolution by natural selection. Here we focus our attention on collective migration, defined broadly to represent a class of problems in which individuals in a group respond to an environmental cue and to social interactions. Models of collective migration have shown that a small group of leaders (individuals who invest strongly in the environmental cue) is capable of guiding a larger group of followers (individuals that rely on social interactions). Further, evolutionary simulations of migration models have shown that the speciation of a homogeneous group into leaders and followers is a stable evolutionary outcome when the cost of leadership is sufficiently high. Analytical mean-field evolutionary models using the techniques of adaptive dynamics have confirmed the observations in these simulations. We study the role that the interaction topology plays in the evolutionary outcomes of collective migration. As a point of comparison, we show that our model recovers the (qualitative) results of the mean-field analysis in the limit of all-to-all interconnections. We then demonstrate a minimum connectivity threshold for random interconnection graphs to yield speciated outcomes. We also study the adaptation of nodes on fixed graphs and illustrate the influence of graph topology on emergent outcomes in such adaptive systems.
Optimality theory in collective behaviour
Twenty years ago, the case for optimality theory in evolutionary biology was set out in a review by Geoff Parker and John Maynard Smith. Thinking of what idealised animals should do if they are behaving optimally has informed behavioural ecology since its inception. With some exceptions, the study and theory of collective behaviour seems to be much more more mechanistic. This is probably because the mechanisms of collective behaviour are much more easily observed than those underlying individual decision-making, and because simple mathematical models and computational simulation often give good descriptions of collective systems. I shall argue that optimality theory is important for collective behaviour, review existing and potential applications of it, and highlight the crucial importance of selecting the right optimality criteria for a particular system.
Multi-Level Modeling and Distributed Control for Miniature Robotic Swarms
In this talk, I will first highlight the challenges related to the design, control, modeling, performance evaluation, and optimization of distributed, mobile, resource-constrained robotic systems. In particular, I will describe a specific distributed control method based on multiple modeling levels which has provided up to date interesting results in several case studies concerned with distributed sensing and manipulation missions, investigated either by us or other research groups worldwide. I will support the discussion with a few concrete examples concerned with aggregation and assembling tasks. Finally, I will revisit our engineering methodology and outline its similarities, differences, and possible links with the world of social insects.
Social Insects and Organic Computing
The relations between social insects and organic computing are discussed in this chapter. The aim of organic computing is to design and study computing systems that consist of many components which often act autonomously and show forms of collective behavior.

Such organic computing systems (OC systems) ideally possess self-x properties (e.g., self- healing, self-managing, self-optimizing), have a decentralized control, and be adaptive to changing requirements of their users. Social insects are a source of inspiration for the design of OC systems. In this talk I present examples from our own research on the relation between OC systems and social insects.
Swarm guidance in Apis florea: making decisions on the fly?
Nest site selection and swarm guidance in swarms of Apis mellifera are well studied, both observationally and theoretically, but not nearly so much is known about decision-making behaviour in other species of Apis. The Asian red dwarf honey bee, Apis florea, is an open-nesting honey bee, found in Southeast Asia, India and parts of the Middle East whose nest is a single comb in the midst of a cluster of bees formed around a small, shaded branch. As in A. mellifera, scouts go out from an A. florea colony that is looking for a new home and seek out suitable nest sites. They then return and dance to indicate the location of suitable new nest sites, but their dances are more variable and less intense than those of Apis mellifera and several sites may still be being advertised when the swarm takes off. In A. mellifera, scouts, who are informed about the location of the new nest site guide the swarm to their destination by flying rapidly through the swarm in the direction that the swarm needs to travel. In A. florea it is possible that different groups of scouts are directing the swarm in different directions. Using both observations and models that have been particularly devised for flying bees which have constantly changing speed, we will examine the process of swarm guidance in A. florea and explore what happens in a migrating swarm when different groups of scouts direct the swarm to different nest sites.
Engineering Self-Organizing Systems
Biological systems, from embryos to social insects, get tremendous mileage by having vast numbers of cheap and unreliable individuals cooperate to achieve complex goals. We are also rapidly building new kinds of distributed systems with similar characteristics, from multi-modular robots and robot swarms, to vast sensor networks. Can we engineer collective systems to achieve the kind of complexity and self-repair that nature seems to achieve?

In this talk, I will describe several projects from my group where we have used inspiration from nature -- termites, fireflies, and cells -- to design new kinds of robots and networks. For example, simple robots that collectively build structures without explicit communication, self-adaptive modular robots that respond to the environment, and wireless sensor networks that use firefly-inspired algorithms to achieve high throughput. In each case, we use inspiration from biology to design simple decentralized cooperation, and techniques from computer science to analyze and generalize these algorithms to new tasks. A common theme in all of our work is understanding self-organizing multi-agent systems: how does robust collective behavior arise from many locally interacting agents, and how can we systematically program simple agents to achieve the global behaviors we want.
Cohesive Swarm Behavior With Information Flow Constraints
Bacteria, bees, and birds often work together in groups to find food. A group of mobile wheeled robots can be designed to coordinate their activities to achieve a goal. Networked cooperative autonomous air vehicles are being developed for commercial and military applications. In order for such multiagent systems to succeed it is often critical that they can both maintain cohesive behaviors and appropriately respond to environmental stimuli. In this talk, we characterize cohesiveness of discrete-time multiagent systems as a boundedness or stability property of the agents' position trajectories and use a Lyapunov approach to develop conditions under which local agent actions will lead to cohesive group behaviors even in the presence of (i) an interagent "sensing topology'' that constrains information flow, where by "information flow,'' we mean the sensing of positions and velocities of agents, (ii) a random but bounded delay and "noise'' in sensing other agents' positions and velocities, and (iii) noise in sensing a resource profile that represents an environmental stimulus and quantifies the goal of the multiagent system. Simulations are used to illustrate the ideas for multiagent systems and to make connections to synchronization of coupled oscillators.
The Road from Individual to Group Position to Emergence in Whirligig Swarms
Emergent patterns of flocks and swarms are at once beautiful and mysterious. We ask ourselves: "How and why do individuals coordinate these complicated maneuvers?" More specifically: how does self organization at lower levels influence emergent properties at higher levels? I will present the results of some of my studies addressing these questions using whirligig beetles and computer models to understand the three-part transition from (1) individual behavior to (2) group position to (3) the emergent behavior of swarms. Whirligig beetles make an ideal organism for studying general grouping phenomenon, such as those found in birds and fish, because they are composed of unrelated individuals, unlike bees and ants where altruism and kin-selection complicate the interpretation of emergent properties.

Working from the bottom up, we have used computer models and ethograms to examine how differences in (1) individuals influence the lower-level movement rules of the beetles. For example, sex, hunger, and age may influence an animal's preferred distance from others, and the speed with which they swim. These rules then influence the (2) group position which they occupy. For example, depending on predators and water speed, certain classes of whirligigs reliably end up at the edge or front of groups. We have modeled and carried out experiments which show that these positions are consistent with the hypothesis of self-organization: simple movement rules can explain the observed within-group segregation. Also, we present evolutionary optimization models that support the hypothesis that these group positions are individually adaptive. Finally, the group as a whole exhibits measurable behaviors (3) that seem to be an emergent property of levels one and two. These emergent properties include: group speed, turning, stopping, and mass predator escape. In conclusion we will discuss if the behaviors at all three of these levels are evolutionarily adaptive, or whether some might be neutral byproducts of an adaptive response at a different level.
Collective decision making by honey bees
I will review what is known about one of the most enchanting forms of collective animal behavior: the skillful choice of a new home by a swarm of honey bees. The challenge has been to understand how the 1.5 kilograms of bees in a swarm, like the 1.5 kilograms of neurons in a brain, are organized so that even though each individual has limited information and limited intelligence, the group as a whole makes first-rate collective decisions. I will describe how this complex phenomenon has been analyzed through a combination of empirical studies (observations and experiments) and mathematical studies (simulation models). In general, the empirical studies have revealed how the bees act and interact to produce the abilities of whole swarms, and the mathematical studies have clarified why the bees behave as they do to create a reliable decision-makng system.
From swarms to cannibalism to obesity: lessons from locusts
Locust plagues are one of the most infamous insect scourges, invading vast areas of Africa, Asia, Australia and the Americas. The reason that locusts form plagues is that they have an extraordinary capacity to change from shy, green, harmless grasshoppers into brightly coloured, swarming creatures when they experience crowding. This remarkable change can occur within the life of a single animal: the genome of the insect codes for both forms. I show that an important trigger for the change is bumping into other locusts. Stimulating touch-sensitive hairs on the back legs causes a rapid shift in behaviour, such that locusts become attracted to each other, rather than avoiding one another. Having identified the source of sensory stimulation that induces behavioural gregarization, we next analysed the associated neurochemical pathways involved and have recently shown that a pulse of serotonin causes the shift in behaviour upon crowding. Once a local aggregation reaches a critical number of insects, the locusts suddenly start to move as one. Using self-propelled particles models from statistical physics we have shown that this decision to start migrating does not involve leader locusts, but rather emerges collectively as a result of local interactions between individuals. Continuing to move as a group involves something very sinister, however. To illustrate, I next turn to another swarming animal, the Mormon cricket of North America. The reason these animals form vast marching bands is because they are seeking protein. The most abundant source of protein in a swarm of crickets is other crickets. The reason why they keep marching is that, if an insect stops, it gets cannibalized by the crickets coming from behind: they are on a forced march for protein. The same is true for locusts. The search for protein turns out to be a powerful force in shaping the biology not only of crickets and locusts, but of all animals - including humans. We have shown using experiments based on state-space geometric models for nutrition that many animals have a powerful appetite for protein. I show in humans that this protein appetite plays a key role in obesity. Protein comprising a minor part of our total energy budget, yet its intake is strongly regulated. I show how this combination leads to protein having the power both to drive the development of obesity - and to assuage it. Finally, I consider why it should be that many animals, humans included, should possess specific mechanisms that prevent overconsumption of protein. Using geometric models of nutrition I show that there are costs to over-consuming protein, and that the prevailing view that caloric restriction prolongs life is wrong - in insects at least.
Swarms as smart architects: understanding construction dynamics in ant colonies
The amazing abilities of social insects to solve their everyday-life problems, also known as swarm intelligence, have received a considerable attention the past twenty years. Among their collective behaviors, nest building is certainly the most spectacular. Not only the characteristic scale of the nests is typically much larger than the size of the individuals, but some of the architectures built by insect colonies can also be highly complex. All along the evolution of these animals, there has been a whole set of innovations in terms of architectural designs and construction techniques that proved to be efficient to solve a large number of problems such as controlling the nest temperature, ensuring the gas exchanges with the outside environment or adapting the nest structure to various colony sizes. One fundamental question is: how large-scale patterns are generated by the actions and interactions of individual insects? To investigate this issue, we focused on the early stages of nest construction in the ant Lasius niger. This experimental paradigm was used to disentangle the coordinating mechanisms at work and characterize the individual behaviors (transport and assemblage of construction material). We then developed a 3D model implementing the mechanisms detected on the individual level and showed that they correctly explain the construction dynamics and the patterns observed at the collective level for various conditions. The model also revealed that complex helicoidal structures connecting nearby chambers emerge from a constant remodeling process of the nest architecture.
Workshop 4: Insect Self-organization and Swarming Lecture 5
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Optimality theory in collective behaviour
James Marshall Twenty years ago, the case for optimality theory in evolutionary biology was set out in a review by Geoff Parker and John Maynard Smith. Thinking of what idealised animals should do if they are behaving optimally has informed behavioural ecology sinc

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Moving in the crowd: Ants hold the key to traffic chaos
Audrey Dussutour Many animals take part in flow-like collective movements. In most species, however, the flow is unidirectional. Ants are one of the rare group of organisms in which flow-like movements are predominantly bidirectional. This adds to the difficulty of t

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Network topology and the evolution of collective migration
Naomi Leonard Agent-based dynamical models have been used successfully to reproduce a range of observed collective behaviors in biological groups. In these models, agents interact with one another and it has been shown that the topology of the interaction network

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Self-organization in Insect Societies: past, present and future
Nigel Franks The application of self-organization theory to social insect studies is, for the most part, barely 20 years old. It has been remarkably successful because much of the new thinking and modelling that self-organization theory has brought to social inse

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Engineering Self-Organizing Systems
Radhika Nagpal Biological systems, from embryos to social insects, get tremendous mileage by having vast numbers of cheap and unreliable individuals cooperate to achieve complex goals. We are also rapidly building new kinds of distributed systems with similar chara

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Parallel Work and Parallel Play
Fred Adler In human children, parallel play describes two or more children playing side by side, perhaps using the same toy but for different purposes, and only occasionally modifying their behavior in response to the other. It forms an early stage of social de

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Multi-Level Modeling and Distributed Control for Miniature Robotic Swarms
Alcherio Martinoli In this talk, I will first highlight the challenges related to the design, control, modeling, performance evaluation, and optimization of distributed, mobile, resource-constrained robotic systems. In particular, I will describe a specific distributed

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Modeling flocks and swarms
Leah Edelstein-Keshet I will summarize some work on the link between individual behaviour and the dynamics of the swarm/flock. I will highlight two projects:

1. The behaviour of a 2D flock of aquatic birds, and how Ryan Lukeman (former PhD student, now a

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Workshop 4: Insect Self-organization and Swarming Lecture 5
Craig Tovey No description Available.

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From swarms to cannibalism to obesity: lessons from locusts
Stephen Simpson Locust plagues are one of the most infamous insect scourges, invading vast areas of Africa, Asia, Australia and the Americas. The reason that locusts form plagues is that they have an extraordinary capacity to change from shy, green, harmless grassho

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Variational Principles and Control of Collective Behavior
P. S. Krishnaprasad Geometric methods in control theory have had a useful role in the investigation of dynamics of collectives. In this talk, we build on models from this theory to sketch recent progress in understanding small networks governed by interaction strategies

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A Primer of Swarm Equilibria
Andrew Bernoff We study equilibrium configurations of swarming biological organisms subject to exogenous and pairwise endogenous forces. Beginning with a discrete dynamical model, we derive a variational description of the continuum population density. Equilibrium

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No Title Available
Jean-Louis Deneubourg No description available.

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The Road from Individual to Group Position to Emergence in Whirligig Swarms
William Romey Emergent patterns of flocks and swarms are at once beautiful and mysterious. We ask ourselves: "How and why do individuals coordinate these complicated maneuvers?" More specifically: how does self organization at lower levels influence emer

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Adaptive network models of swarm dynamics
Cristian Huepe I will present a simple adaptive network model describing recent insect swarming experiments. By exploiting an analogy with human decision-making models and considering network-like interactions, this model captures the experimental dynamics using a

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Collective decision making by honey bees
Thomas Seeley I will review what is known about one of the most enchanting forms of collective animal behavior: the skillful choice of a new home by a swarm of honey bees. The challenge has been to understand how the 1.5 kilograms of bees in a swarm, like the 1.5

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Swarm guidance in Apis florea: making decisions on the fly?
Mary Myerscough Nest site selection and swarm guidance in swarms of Apis mellifera are well studied, both observationally and theoretically, but not nearly so much is known about decision-making behaviour in other species of Apis. The Asian red dwarf honey bee, Apis

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Cohesive Swarm Behavior With Information Flow Constraints
Kevin Passino Bacteria, bees, and birds often work together in groups to find food. A group of mobile wheeled robots can be designed to coordinate their activities to achieve a goal. Networked cooperative autonomous air vehicles are being developed for commercial

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Organization and regulation of work in the social insect colony
Jennifer Fewell Division of labor, the way in which social groups distribute work among their individual members, is a product of self organization and selection. A basic system of division of labor can be produced even in artificial associations of normally solitar

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Evolutionary Constraints on Social Organization from Disease Risks
Nina Fefferman As social insects have evolved division of labor and colony organization to accomplish tasks necessary to their survival, their social and collaborative environment should make them more and more susceptible to risk from infectious disease. Since the

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Audience and information transfer in ant societies
Claire Detrain The ant society is a dynamic network of interacting nestmates of which individual decision rules lead to adaptive and functional patterns at the collective level. The non-linearity of relationships between workers makes those societies displaying pro

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Swarms as smart architects: understanding construction dynamics in ant colonies
Guy Theraulaz The amazing abilities of social insects to solve their everyday-life problems, also known as swarm intelligence, have received a considerable attention the past twenty years. Among their collective behaviors, nest building is certainly the most spect