Swarms of robots will revolutionize many industrial applications, from targeted material delivery to precision farming. However, several of the heterogeneous characteristics that make them ideal for certain future applications --- robot autonomy, decentralized control, collective emergent behavior, etc. --- hinder the evolution of the technology from academic institutions to real-world problems. Blockchain, an emerging technology originated in the Bitcoin field, demonstrates that by combining peer-to-peer networks with cryptographic algorithms a group of agents can reach an agreement on a particular state of affairs and record that agreement without the need for a controlling authority. The combination of blockchain with other distributed systems, such as robotic swarm systems, can provide the necessary capabilities to make robotic swarm operations more secure, autonomous, flexible and even profitable. This work explains how blockchain technology can provide innovative solutions to four emergent issues in the swarm robotics research field. New security, decision making, behavior differentiation and business models for swarm robotic systems are described by providing case scenarios and examples. Finally, limitations and possible future problems that arise from the combination of these two technologies are described.
Ecologists warn that the rapid evolution occurring as a result of high-intensity commercial fishing could have significant economic and ecological effects. So far, fishery managers do not take this rapid evolution (called fisheries-induced evolution or FIE) into consideration when determining fishery policy. I model the interactions between the genetics, population structure, and economics of the fishery in order to determine how beneficial altering the fishery managers decision framework to include fisheries induced evolution would be to fishery profit and yield. My model is based on North-East Arctic Cod, which are long lived and for which an abundance of information exists, including proof of FIE. I compare the steady state reached by a `myopic' fishery manager who sets effort and mesh size policy while ignoring evolution, to the steady state reached by a fishery manager who dynamically optimizes his strategy with the knowledge of how evolution will respond. This paper shows that accounting for evolution can increase steady state profits by 29-34%, however this benefit decreases and is eventually eliminated as the discount rate increases from zero. An important auxiliary benefit to accounting for evolution is the effect optimal management has on fishery biomass, maturation rates, and yield.
The following news item is taken in part from the July 22, 2010 issue of The Economist titled “Agents of change,” by Philip Ball. Conventional economic models failed to foresee the financial crisis. Could agent-based modeling (ABM) do better? ABM does not assume that the economy can achieve a settled equilibrium. No order or design is imposed on the economy from the top down. Unlike many models, ABMs are not populated with “representative agents”: identical traders, firms, or households whose individual behavior mirrors the economy as a whole. Rather, an ABM uses a bottom-up approach which assigns particular behavioral rules to each agent. For example, some may believe that prices reflect fundamentals whereas others may rely on empirical observations of past price trends. A link to this article can be found at http://www.economist.com/node/16636121?story_id=16636121. The following news item is taken in part from the August 5, 2010 issue of Nature titled “Link communities reveal multiscale complexity in networks,” by Yong-Yeol Ahn, James P. Bagrow, and Sune Lehmann. Here, we reinvent communities as groups of links rather than nodes and show that this unorthodox approach successfully reconciles the antagonistic organizing principles of overlapping communities and hierarchy. In contrast to the existing literature, which has entirely focused on grouping nodes, link communities naturally incorporate overlap while revealing hierarchical organization. We find relevant link communities in many networks. A link to this article can be found at http://dx.doi.org/10.1038/nature09182. The following news item is taken in part from the August 12, 2010 issue of Science titled “Stability of Ecological Communities and the Architecture of Mutualistic and Trophic Networks,” by Elisa Thébault and Colin Fontaine. Research on the relationship between the architecture of ecological networks and community stability has mainly focused on one type of interaction at a time, making difficult any comparison between different network types. We used a theoretical approach to show that the network architecture favoring stability fundamentally differs between trophic and mutualistic networks. A highly connected and nested architecture promotes community stability in mutualistic networks, whereas the stability of trophic networks is enhanced in compartmented and weakly connected architectures. These theoretical predictions are supported by a meta-analysis on the architecture of a large series of real pollination (mutualistic) and herbivory (trophic) networks. We conclude that strong variations in the stability of architectural patterns constrain ecological networks toward different architectures, depending on the type of interaction. A link to this article can be found at http://dx.doi.org/10.1126/science.1188321. The following news item is taken in part from the August 5, 2010 issue of arXiv titled “Emergence of Zipf's Law in the Evolution of Communication,” by Bernat Corominas-Murtra, Jordi Fortuny, and Ricard V. Solé. Zipf's law seems to be ubiquitous in human languages and appears to be a universal property of complex communicating systems. Following an early proposal made by Zipf concerning the presence of a tension between the efforts of speaker and hearer in a communication system, we introduce evolution by means of a variational approach to the problem based on Kullback's Minimum Discrimination of Information Principle. Using a formalism fully embedded in the framework of information theory, we demonstrate that Zipf's law is the only expected outcome of an evolving, communicative system under a rigorous definition of the communicative tension described by Zipf. A link to this article can be found at http://arXiv.org/abs/1008.0938. The following news item is taken in part from the August 26, 2010 issue of Nature titled “The evolution of eusociality,” by Martin A. Nowak, Corina E. Tarnita, and Edward O. Wilson. Eusociality, in which some individuals reduce their own lifetime reproductive potential to raise the offspring of others, underlies the most advanced forms of social organization and the ecologically dominant role of social insects and humans. For the past four decades kin selection theory, based on the concept of inclusive fitness, has been the major theoretical attempt to explain the evolution of eusociality. Here, we show the limitations of this approach. We argue that standard natural selection theory in the context of precise models of population structure represents a simpler and superior approach, allows the evaluation of multiple competing hypotheses, and provides an exact framework for interpreting empirical observations. A link to this article can be found at http://dx.doi.org/10.1038/nature09205. The following news item is taken in part from the August 27, 2010 issue of Science titled “Optimally Interacting Minds,” by Bahador Bahrami, Karsten Olsen, Peter E. Latham, Andreas Roepstorff, Geraint Rees, and Chris D. Frith. In everyday life, many people believe that two heads are better than one. Our ability to solve problems together appears to be fundamental to the current dominance and future survival of the human species. But are two heads really better than one? We addressed this question in the context of a collective low-level perceptual decision-making task. For two observers of nearly equal visual sensitivity, two heads were definitely better than one, provided they were given the opportunity to communicate freely, even in the absence of any feedback about decision outcomes. But for observers with very different visual sensitivities, two heads were actually worse than the better one. A link to this article can be found at http://dx.doi.org/10.1126/science.1185718. The following news item is taken in part from the August 19, 2010 issue of Nature titled “Promiscuity and the evolutionary transition to complex societies,” by Charlie K. Cornwallis, Stuart A. West, Katie E. Davis, and Ashleigh S. Griffin. A phylogenetic analysis of breeding behavior in birds shows that cooperation is more likely when promiscuity is low—a circumstance in which helpers can be more certain that they are offering aid to relatives. Intermediate levels of promiscuity favor the ability to distinguish relatives from nonrelatives. At high levels of promiscuity, no form of cooperation is favored. Levels of promiscuity therefore provide an explanation for differences between species in levels of cooperation. A link to this article can be found at http://dx.doi.org/10.1038/nature09335. The following news item is taken in part from the August 23, 2010 issue of arXiv titled “Network Complexity of Foodwebs,” by Russell K. Standish. In previous work, I have developed an information theoretic complexity measure of networks. When applied to several real world foodwebs, there is a distinct difference in complexity between the real foodweb, and randomized control networks obtained by shuffling the network links. One hypothesis is that this complexity surplus represents information captured by the evolutionary process that generated the network. In this paper, I test this idea by applying the same complexity measure to several well-known artificial life models that exhibit ecological networks: Tierra, EcoLab, and Webworld. Contrary to what was found in real networks, the artificial life-generated foodwebs had little information difference between itself and randomly shuffled versions. A link to this article can be found at http://arXiv.org/abs/1008.3800. The following news item is taken in part from the August, 2010 issue of PLoS ONE titled “A New Measure of Centrality for Brain Networks,” by Karen E. Joyce, Paul J. Laurienti, Jonathan H. Burdette, and Satoru Hayasaka. Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. We propose that this metric may be able to identify critical nodes that are highly influential within the network. A link to this article can be found at http://dx.doi.org/10.1371/journal.pone.0012200. The following news item is taken in part from the September 10, 2010 issue of Science titled “Biodiversity Conservation: Challenges Beyond 2010,” by Michael R.W. Rands, William M. Adams, Leon Bennun, Stuart H.M. Butchart, Andrew Clements, David Coomes, Abigail Entwistle, Ian Hodge, Valerie Kapos, Jörn P.W. Scharlemann, William J. Sutherland, and Bhaskar Vira. The continued growth of human populations and of per capita consumption have resulted in unsustainable exploitation of Earth's biological diversity, exacerbated by climate change, ocean acidification, and other anthropogenic environmental impacts. We argue that effective conservation of biodiversity is essential for human survival and the maintenance of ecosystem processes. Despite some conservation successes (especially at local scales) and increasing public and government interest in living sustainably, biodiversity continues to decline. Moving beyond 2010, successful conservation approaches need to be reinforced and adequately financed. However, in addition, more radical changes are required that recognize biodiversity as a global public good, that integrate biodiversity conservation into policies and decision frameworks for resource production and consumption, and that focus on wider institutional and societal changes to enable more effective implementation of policy. A link to this article can be found at http://dx.doi.org/10.1126/science.1189138. The following news item is taken in part from the September 6, 2010 issue of arXiv titled “Are large complex economic systems unstable?,” by Sitabhra Sinha. Although classical economic theory is based on the concept of stable equilibrium, real economic systems appear to be always out of equilibrium. Indeed, they share many of the dynamical features of other complex systems, e.g., ecological foodwebs. We focus on the relation between increasing complexity of the economic network and its stability with respect to small perturbations in the dynamical variables associated with the constituent nodes. Inherent delays and multiple time scales suggest that economic systems will be more likely to exhibit instabilities as their complexity is increased even though the speed at which transactions are conducted has increased many fold through technological developments. Analogous to the birth of nonlinear dynamics from Poincare's work on the question of whether the solar system is stable, we suggest that similar theoretical developments may arise from efforts by econophysicists to understand the mechanisms by which instabilities arise in the economy. A link to this article can be found at http://arXiv.org/abs/1009.0972. The following news item is taken in part from the September 3, 2010 issue of Science titled “The Spread of Behavior in an Online Social Network Experiment,” by Damon Centola. How do social networks affect the spread of behavior? A popular hypothesis states that networks with many clustered ties and a high degree of separation will be less effective for behavioral diffusion than networks in which locally redundant ties are rewired to provide shortcuts across the social space. A competing hypothesis argues that when behaviors require social reinforcement, a network with more clustering may be more advantageous, even if the network as a whole has a larger diameter. I investigated the effects of network structure on diffusion by studying the spread of health behavior through artificially structured online communities. Individual adoption was much more likely when participants received social reinforcement from multiple neighbors in the social network. The behavior spread farther and faster across clustered-lattice networks than across corresponding random networks. A link to this article can be found at http://dx.doi.org/10.1126/science.1185231. The following news item is taken in part from the September, 2010 issue of Cognitive Science titled “Language Acquisition Meets Language Evolution,” by Nick Chater and Morten H. Christiansen. Recent research suggests that language evolution is a process of cultural change, in which linguistic structures are shaped through repeated cycles of learning and use by domain-general mechanisms. This paper draws out the implications of this viewpoint for understanding the problem of language acquisition, which is cast in a new, and much more tractable, form. In essence, the child faces a problem of induction, where the objective is to coordinate with others (C-induction), rather than to model the structure of the natural world (N-induction). We argue that, of the two, C-induction is dramatically easier. More broadly, we argue that understanding the acquisition of any cultural form, whether linguistic or otherwise, during development, requires considering the corresponding question of how that cultural form arose through processes of cultural evolution. This perspective helps resolve the “logical” problem of language acquisition and has far-reaching implications for evolutionary psychology. A link to this article can be found at http://dx.doi.org/10.1111/j.1551-6709.2009.01049.x. The following news item is taken in part from the September 8, 2010 issue of Nature titled “Early warning signals of extinction in deteriorating environments,” by John M. Drake and Blaine D. Griffen. Although understanding the causes of population extinction has been a central problem in theoretical biology for decades, the ability to anticipate extinction has remained elusive. Here, we argue that the causes of a population's decline are central to the predictability of its extinction. Specifically, environmental degradation may cause a tipping point in population dynamics, corresponding to a bifurcation in the underlying population growth equations, beyond which decline to extinction is almost certain. A link to this article can be found at http://dx.doi.org/10.1038/nature09389. The following news item is taken in part from the September, 2010 issue of SFI Working Papers titled “Self-Stabilizing Decentralized Signal Control of Realistic, Saturated Network Traffic,” by Stefan Lammer and Dirk Helbing. A coordination of vehicle flows is usually reached by a cyclical operation of traffic lights, and by synchronizing these cycles. However, the typical conditions, for which traffic lights are normally optimized for, never occur exactly. Large fluctuations in the number of vehicles arriving during one cycle time may lead to an inefficient usage of green times, which are often either too short or too long. The method we propose here allows for variable adjustments not only of the duration, but also of the order of green phases, while it reaches at least the same intersection throughput capacity as an optimized fixed-time controller. A link to this article can be found at http://www.santafe.edu/research/working-papers/abstract/67d8c997e841b3a 2e253aacad4e2851b/. The following news item is taken in part from the September, 2010 issue of SFI Working Papers titled “Information Driven Self-Organization: The Dynamical System Approach to Autonomous Robot Behavior,” by Nihat Ay, Ralf Der, and Mikhail Prokopenko. In recent years, information theory has come into the focus of researchers interested in the sensorimotor dynamics of both robots and living beings. One root for these approaches is the idea that living beings are information processing systems and that the optimization of these processes should be an evolutionary advantage. Apart from these more principal questions, there is much interest recently in the question how a robot can be equipped with an internal drive for innovation or curiosity that may serve as a drive for an open ended, self-determined development of the robot. The success of these approaches depends essentially on the choice of a convenient measure for the information. This paper studies in some detail the use of the predictive information of the sensorimotor process. A link to this article can be found at http://www.santafe.edu/research/working-papers/abstract/b67e300041 30f486027ea324c080a058/. Winter Meeting on Statistical Physics, Taxco, Guerrero, Mexico, 2011/1/4-7 https://sites.google.com/site/wintermeetingstatphys IWSOS 2011, Fifth International Workshop on Self-Organizing Systems, Karlsruhe, Germany, 2011/02/23-25 http://iwsos2011.tm.kit.edu/ IEEE Symposium Series on Computational Intelligence—SSCI 2011, Paris, France, 2011/04/11-15 http://www.ieee-ssci.org/ EVOSTAR 2011, Torino, Italy, 2011/4/27-29 http://evostar.org/ International Conference on Complex Systems (ICCS 2011), Boston, MA, USA, 2011/06/26-07/01 http://www. necsi.edu/events/iccs2011/ GECCO 2011: Genetic and Evolutionary Computation Conference, Dublin, Ireland, 2011/07/12-16 http://www. sigevo.org/gecco-2011/ IJCAI 2011, The 22nd International Joint Conference on Artificial Intelligence, Barcelona, Spain, 2011/07/16-22 http://ijcai-11.iiia.csic.es/ ECAL 11: European Conference on Artificial Life, Paris, France, 2011/08/8-12 http://www.ecal11.org/
Stuart M. Allen, Gualtiero B. Colombo, Roger M. Whitaker
We address the problem of cooperation in decentralized systems, specifically looking at interactions between independent pairs of peers where mutual exchange of resources (e.g., updating or sharing content) is required. In the absence of any enforcement mechanism or protocol, there is no incentive for one party to directly reciprocate during a transaction with another. Consequently, for such decentralized systems to function, protocols for self-organization need to explicitly promote cooperation in a manner where adherence to the protocol is incentivized. In this article we introduce a new generic model to achieve this. The model is based on peers repeatedly interacting to build up and maintain a dynamic social network of others that they can trust based on similarity of cooperation. This mechanism effectively incentivizes unselfish behavior, where peers with higher levels of cooperation gain higher payoff. We examine the model's behavior and robustness in detail. This includes the effect of peers self-adapting their cooperation level in response to maximizing their payoff, representing a Nash-equilibrium of the system. The study shows that the formation of a social network based on reflexive cooperation levels can be a highly effective and robust incentive mechanism for autonomous decentralized systems.