The collective behavior research group uses a combination of mathematical models and experiments to understand the dynamics of animal and human groups. See http://www2.math.uu.se/~david/web/ for details of their current research. Some current projects on humans involve the study of socially contagious activities, such as audience clapping, and of how individuals integrate social and personal information when trying to answer trivia questions. They collaborate closely in this work with an experimental research group led by Prof. Jens Krause in Berlin.

An opening is currently available for a postdoctoral researcher to work on similar projects, from a perspective that combines mathematical modelling with experimental work.

Full details of the position and how to apply can be found at: http://www2.personalavd.uu.se/ledigaplatser/2450postDOC.html. Applications should be submitted no later than November 18th, 2011. 

What are the principles that keep our society together? This question is even more difficult to answer than the long-standing question, what are the forces that keep our world together. However, the social challenges of humanity in the 21st century ranging from the financial crises to the impacts of globalization, require us to make fast progress in our understanding of how society works, and how our future can be managed in a resilient and sustainable way. This book can present only a few very first steps towards this ambitious goal. However, based on simple models of social interactions, one can already gain some surprising insights into the social, “macro-level'' outcomes and dynamics that is implied by individual, “micro-level'' interactions. Depending on the nature of these interactions, they may imply the spontaneous formation of social conventions or the birth of social cooperation, but also their sudden breakdown. This can end in deadly crowd disasters or tragedies of the commons (such as financial crises or environmental destruction). Furthermore, we demonstrate that classical modeling approaches (such as representative agent models) do not provide a sufficient understanding of the self-organization in social systems resulting from individual interactions. The consideration of randomness, spatial or network interdependencies, and nonlinear feedback effects turns out to be crucial to get fundamental insights into how social patterns and dynamics emerge. Given the explanation of sometimes counter-intuitive phenomena resulting from these features and their combination, our evolutionary modeling approach appears to be powerful and insightful. The chapters of this book range from a discussion of the modeling strategy for socio-economic systems over experimental issues up the right way of doing agent-based modeling. We furthermore discuss applications ranging from pedestrian and crowd dynamics over opinion formation, coordination, and cooperation up to conflict, and also address the response to information, issues of systemic risks in society and economics, and new approaches to manage complexity in socio-economic systems. Selected parts of this book had been previously published in peer reviewed journals.

When selecting a resource to exploit, an insect colony must take into account at least two constraints: the resource must be abundant enough to sustain the whole group, but not too large to limit exploitation costs, and risks of conflicts with other colonies. Following recent results on cockroaches and ants, we introduce here a behavioral mechanism that satisfies these two constraints. Individuals simply modulate their probability to switch to another resource as a function of the local density of conspecifics locally detected. As a result, the individuals gather at the smallest resource that can host the whole group, hence reducing competition and exploitation costs while fulfilling the overall group's needs. Our analysis reveals that the group becomes better at discriminating between similar resources as it grows in size. Also, the discrimination mechanism is flexible and the group readily switches to a better suited resource as it appears in the environment. The collective decision emerges through the self-organization of individuals, that is, in absence of any centralized control. It also requires a minimal individual cognitive investment, making the proposed mechanism likely to occur in other social species and suitable for the development of distributed decision making tools.

The article can be found here: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0019888

Natural systems are a source of inspiration for computer algorithms designed to solve optimisation problems. Yet most ‘nature-inspired’ algorithms take only superficial inspiration from biology, and little is known about how real biological systems solve difficult problems. Moreover, ant algorithms, neural networks and similar methods are usually applied to static problems, whereas most biological systems have evolved to perform under dynamically changing conditions. We used the Towers of Hanoi puzzle to test whether Argentine ants can solve a potentially difficult optimisation problem. We also tested whether the ants can adapt to dynamic changes in the problem. We mapped all possible solutions to the Towers of Hanoi on a single graph and converted this into a maze for the ants to solve. We show that the ants are capable of solving the Towers of Hanoi, and are able to adapt when sections of the maze are blocked off and new sections installed. The presence of exploration pheromone increased the efficiency of the resulting network and increased the ants’ ability to adapt to changing conditions. Contrary to previous studies, our study shows that mass-recruiting ant species such as the Argentine ant can forage effectively in a dynamic environment. Our results also suggest that novel optimisation algorithms can benefit from stronger biological mimicry.

The full version of the article can be found here. 

Stigmergy – the phenomenon of indirect communication mediated by modifications of the environment – was first conceptualized by zoologist Pierre-Paul Grasse in his ground-breaking work on termite colonies (Grasse 1959). It wasn’t until 1999 that Grasse’s work was brought to a wider audience by Eric Bonabeau et al (1999) in a special issue of Artificial Life. Since then interest in stigmergic systems has blossomed with researchers recognizing the application of Grasse’s insights to stock markets, economies, traffic patterns, supply logistics, computer networks, resource allocation, urban sprawl, and cultural memes. New forms of stigmergy have been exponentially expanded through the affordances of digital technology: Google’s recommendation algorithm, Amazon’s filtering algorithm, wiki, open source software, weblogs, and a whole range of “social media” are now deemed as essentially stigmergic.

Though the concept of stigmergy has typically been associated with ant- or swarm-like “agents” with minimal cognitive ability or with creatures of a somewhat higher cognitive capacity such as fish (schooling patterns) or birds (flocking patterns) or sheep (herding behavior), stigmergy offers a powerful tool to be deployed in the human domain. The editors of this special issue are thus looking for contributions that have human-human (social, organizational, and socio-technical) stigmergy as the main focus.

Proposals are invited from social scientists, social epistemologists, cognitive scientists, economists, group decision theorists, collective intentionality theorists, computational sociologists, network theorists, multi-agent modelers, and indeed researchers from any discipline that has social complexity and coordination as a core topic.

Papers that are theoretical, experimental, or computational in orientation are welcome. Please send proposals of no more than 300 words to lesliemarsh [at] gmail [dot] com with “Stigmergy/Cognitive Systems Research” in the subject line. The deadline for proposals is Nov 1, 2010.

All papers will be subject to double blind review by a least two referees and accepted papers will be published in a special issue of Cognitive Systems Research

Click here to view the embedded video.

The original video can be found here on the Radiolab blog.

Start date: Mid-October 2010 or sooner

A postdoctoral position is available in the Collective Animal Behavior Laboratory at Princeton University to study collective information processing using schooling fish as a model system. This is part of a multidisciplinary investigation of decision-making by animal groups. Specifically we aim to explore the interplay between individual heterogeneity, spatial organization, and collective decision-making dynamics.

While a comprehension of modeling techniques may be useful this is first and foremost an experimental position and as such you must have experience of working with animals (ideally, but not essentially, with fish). The CouzinLab is a highly inter-disciplinary environment with an integrated research program involving automated tracking, computational modeling and controlled experimentation. See http://www.princeton.edu/~icouzin for further details.

You will be a team-player to capitalize on these technologies and collaborative possibilities with a passion for biology and the capability to provide leadership in our experimental research program.

To apply, please send a CV and a statement of research interests to Prof. Iain D. Couzin at icouzin@princeton.edu with "Schooling" in the subject line.

Princeton University is an Equal Opportunity/Affirmative Action employer and particularly welcomes nominations of women and members of underrepresented minority groups.

- Collective Behavior and Information Transfer in Human Crowds -

Starting Date: Ideally as soon as possible

A two year postdoctoral position is available in collective behavior of human crowds in the CouzinLab at Princeton University. This is part of an ongoing project for the UK Home Office in collaboration with Oxford Risk, a University of Oxford spinout company, and will involve analysis of kinematic data sets from pedestrian crowds as well as the development and validation of individual-based models of the motion and behavior of people in urban environments. Strong programming skills are essential and comprehension of computer vision techniques would be beneficial (although expert knowledge in this area is not required).

You will be integrated within a highly multi-disciplinary research environment and should be enthusiastic, motivated with good interpersonal skills. The position will be based in Princeton but you must be able to travel to the UK periodically to undertake experiments and for meetings with the Oxford Risk team.

Please see the links below for further information about the CouzinLab and Oxford Risk:

• http://www.princeton.edu/~icouzin
• http://www.oxfordrisk.com/

To apply, please send a CV and a statement of research interests to Prof. Iain D. Couzin at icouzin@princeton.edu with "Crowds" in the subject line.

Princeton University is an Equal Opportunity/Affirmative Action employer and particularly welcomes nominations of women and members of underrepresented minority groups.

Hereafter is the summary of this book:

One of the greatest discoveries of recent times is that the complex patterns we find in life are often produced when all of the individuals in a group follow the same simple rule. This process of “self-organization” reveals itself in the inanimate worlds of crystals and seashells, but as Len Fisher shows, it is also evident in living organisms, from fish to ants to human beings. The coordinated movements of fish in shoals, for example, arise from the simple rule: “Follow the fish in front.” Traffic flow arises from simple rules: “Keep your distance” and “Keep to the right.”

Now, in his new book, Fisher shows how we can manage our complex social lives in an ever more chaotic world. His investigation encompasses topics ranging from “swarm intelligence” to the science of parties and the best ways to start a fad. Finally, Fisher sheds light on the beauty and utility of complexity theory. An entertaining journey into the science of everyday life, The Perfect Swarm will delight anyone who wants to understand the complex situations in which we so often find ourselves.

Review in nature can be found here: http://www.nature.com/nature/journal/v464/n7285/full/464035a.html.

Review in Scientific American Mind can be found here: http://www.scientificamerican.com/article.cfm?id=mind-reviews-the-perfect-swarm.

Position: Computer Scientist in Spatial Analysis and Data Integration
Location: IRD Reunion Island, France
Duration: 17 months from June 2010 to December 2011
Deadline: 31st of May 2010
Salary Range: ~ 3000€ by month

IRD (Institute for Reasearch and Development) is seeking for a young scientific researcher (PhD) with strong quantitative analytical skills and good knowledge in spatial analyses including experience in data modelling (developed mathematical approaches by stochastic differential equations or individual-based models) and programming (R, Splus, C++ or Matlab). You should possess good interactive web development skills to manage databases using JavaScript (AJAX) and PHP/Python in an Apache/Linux environment. Knowledge in marine biology and telemetry are suitable. You will work on electronic tagging data coming from experiments dedicated to the understanding of gregarious fish behaviours under FADs (Fish Aggregating Device). Spatial data with high resolution acquired by different telemetry methods are already available. You will contribute to purchase and consolidate the analyses by confront field data to model outputs. You should have to participate to tagging experiment in the open-field. You need good verbal and written presentation and communication skills in English, good working knowledge of Word and Excel and databases and proven ability to work as part of an interdisciplinary team.

For additional information and proposal, please contact:
Dr M. Soria, Research engineer
Tel: 00 262 (0) 262 29 93 42
Email: marc.soria@ird.fr