In this paper, we analyze various Decentralized Finance (DeFi) protocols in terms of their token distributions. We propose an iterative mapping process that allows us to split aggregate token holdings from custodial and escrow contracts and assign them to their economic beneficiaries. This method accounts for liquidity-, lending-, and staking-pools, as well as token wrappers, and can be used to break down token holdings, even for high nesting levels. We compute individual address balances for several snapshots and analyze intertemporal distribution changes. In addition, we study reallocation and protocol usage data, and propose wrapping complexity as a proxy for measuring token dependencies and ecosystem integration. The paper offers new insights on DeFi interoperability as well as token ownership distribution and may serve as a foundation for further research.
Volker Strobel, Eduardo CastellĂł Ferrer, Marco Dorigo
Consensus achievement is a crucial capability for robot swarms, for example, for path selection, spatial aggregation, or collective sensing. However, the presence of malfunctioning and malicious robots (Byzantine robots) can make it impossible to achieve consensus using classical consensus protocols. In this work, we show how a swarm of robots can achieve consensus even in the presence of Byzantine robots by exploiting blockchain technology. Bitcoin and later blockchain frameworks, such as Ethereum, have revolutionized financial transactions. These frameworks are based on decentralized databases (blockchains) that can achieve secure consensus in peer-to-peer networks. We illustrate our approach in a collective sensing scenario where robots in a swarm are controlled via blockchain-based smart contracts (decentralized protocols executed via blockchain technology) that serve as "meta-controllers" and we compare it to state-of-the-art consensus protocols using a robot swarm simulator. Additionally, we show that our blockchain-based approach can prevent attacks where robots forge a large number of identities (Sybil attacks). The developed robot-blockchain interface is released as open-source software in order to facilitate future research in blockchain-controlled robot swarms. Besides increasing security, we expect the presented approach to be important for data analysis, digital forensics, and robot-to-robot financial transactions in robot swarms.
Human decision making is often prone to biases and irrationality. Group decisions add dynamic interactions that further complicate the choice process and frequently result in outcomes that are suboptimal for both the individual and the collective. We show that an implementation of a Blockchain protocol improves individualsâ decision strategies and increases the alignment between desires and outcomes. The Blockchain protocol affords (1) a distributed decision, (2) the ability to iterate repeatedly over a choice, (3) the use of feedback and corrective inputs, and (4) the quantification of intrinsic choice attributes (i.e., greed, desire for fairness, etc.). We test our protocolâs performance in the context of the Public Goods Game. The game, a generalized version of the Prisonerâs Dilemma, allows players to maximize their own gain or act in ways that benefit the collective. Empirical evidence shows that participantsâ cooperation in the game typically decreases once a single player favors their own interest at the expense of othersâ. In our Blockchain implementation, âsmart contractsâ are used to safeguard individuals against losses and, consequently, encourage contributions to the public good. Across different tested simulations, the Blockchain protocol increases both the overall trust among the participants and their profits. Agents decision strategies remain flexible while they act as each otherâs source of accountability (which can be seen as formalized distributed âUlysses contractâ). To highlight the contribution of our protocol to society at large we incorporated an entity that represents the public good. This benevolent independent beneficiary of the contributions of all participants (e.g. a charity organization or a tax system) maximized its payoffs when the Blockchain protocol was implemented. We provide a formalized implementation of the Blockchain protocol and discuss potential applications that could benefit society by more accurately capturing individualsâ preferences. For example, the protocol could help maximize profits in groups, facilitate democratic election that better reflect the public opinion, or enable group decision in circumstances where a balance between anonymity, diverse opinions, personal preferences and loss-aversion play a role.
Understanding how complex system components interact and adapt to environment changes is critical for analyzing their emergent behavior and the various positive and negative effects of that emergent behaviors might have. Several modeling languages and frameworks have been proposed for the modeling of complex adaptive systems but few have been applied in practice beyond simple models such as flocks of birds and predator prey. In this paper, we model the adaptive behavior of various entities in a Bitcoin market. We employ CASTLE, a dedicated framework for the modeling of adaptability in complex adaptive systems. Contrary to existing models where realistic details are not included, we introduce the influence of price speculation and news on trader behavior and experiment with different trader behaviors under varying market conditions. Our analysis of a market of 1,000 initial traders shows the feasibility of our approach but also highlights future research challenges.
MiloĹĄ N. MladenoviÄ, Montasir Abbas, Claudio Roncoli, Sanaz Bozorg Chenani
Development of integrated mobility and traffic management strategies is an important aspect of the ongoing transition of urban mobility systems. Extending from existing credit schemes, this research presents a system design and evaluation of a framework based on the principle of Universal Basic Mobility. In particular, using premises of long-term cooperation and hierarchical self-organization, the system design includes user-based Mobility Credits interrelated with Priority Levels. To complement the cooperation framework, system architecture is formulated in line with the distributed ledger technology. The proposed framework is tested using web-based interaction in the form of stated-preference experiment. Results are analyzed through statistical distributions and a discrete-choice model of user decision-making within the proposed framework. This research concludes that this framework could nudge uses towards reciprocity and altruism in their travelling behavior. In addition, experiment participants have provided a range of comments related to positive features, potential for failure, and further development. Finally, the paper ends by raising several implications for wider citizen participation in the integrated mobility system design and evaluation.
In the current blockchain network, many participants rationally migrate the pool to receive a better compensation according to their contribution in situations where the pools they engage encounter undesirable attacks. The Nash equilibria of attacked pool has been widely analyzed, but the analysis of practical methodology for obtaining it is still inadequate. In this paper, we propose an evolutionary game theoretic analysis of Proof-of-Work (PoW) based blockchain network in order to investigate the mining pool dynamics affected by malicious infiltrators and the feasibility of autonomous migration among individual miners. We formulate a revenue model for mining pools which are implicitly allowed to launch a block withholding attack. Under our mining game, we analyze the evolutionary stability of Nash equilibrium with replicator dynamics, which can explain the population change with time between participated pools. Further, we explore the statistical approximation of successful mining events to show the necessity of artificial manipulation for migrating. Finally, we construct a better response learning based on the required block size which can lead to our evolutionarily stable strategy (ESS) with numerical results that support our theoretical discoveries.
Neoliberalism propels a retreat from state supports for the population and the unfettering of privatization and profit seeking. The collapse of the Soviet Union was believed by many to be proof that Western democracy and capitalist markets had not only conquered the world, but were amongst the greatest achievements of humanity. While neoliberalism has shifted the stateâs stake in the welfare of the population, the state remains important in determining and legitimating the rights of individuals and corporations to pursue a vulturine profit maximizing agenda. The disassembling effects of the gap between political inclusion and social exclusion are particularly evident in urban spaces where health and mortality rates, grocery and school options, and foreclosure signs often differ dramatically across city neighborhoods. A further effect of private profits becoming the primary benchmark for public urban projects is that there are neighborhoods in the city where profits are secured not through investment, but from disinvestment.
In blockchain networks adopting the proof-of-work schemes, the monetary incentive is introduced by the Nakamoto consensus protocol to guide the behaviors of the full nodes (i.e., block miners) in the process of maintaining the consensus about the blockchain state. The block miners have to devote their computation power measured in hash rate in a crypto-puzzle solving competition to win the reward of publishing (a.k.a., mining) new blocks. Due to the exponentially increasing difficulty of the crypto-puzzle, individual block miners tends to join mining pools, i.e., the coalitions of miners, in order to reduce the income variance and earn stable profits. In this paper, we study the dynamics of mining pool selection in a blockchain network, where mining pools may choose arbitrary block mining strategies. We identify the hash rate and the block propagation delay as two major factors determining the outcomes of mining competition, and then model the strategy evolution of the individual miners as an evolutionary game. We provide the theoretical analysis of the evolutionary stability for the pool selection dynamics in a case study of two mining pools. The numerical simulations provide the evidence to support our theoretical discoveries as well as demonstrating the stability in the evolution of miners' strategies in a general case.
The Proof of Work (PoW) consensus algorithm guarantees the safety and dependability of Blockchain systems. Miners can achieve a consensus through the PoW algorithm during the mining process, that is mutual attacking. However, when the miners attack each other, all miners earn less. In this paper, we established a model that mining between two miners is an iterative game, and proposed a subclass of ZD strategy (a pining strategy) to alleviate miners' dilemma, a miner can control another miner's payoff and increase the social revenue through a pinning strategy. Numerical simulation results verify the effectiveness of the proposed strategy. In summary, this work leads to the better understanding and analysis of the PoW algorithm via the game theory, rendering it possible to design a more rational consensus algorithm in the future.
Blockchain Technology Applications and Security
Evolutionary Game Theory and Cooperation
Mathematical and Theoretical Epidemiology and Ecology Models
Abeer ElBahrawy, Laura Alessandretti, Anne Kandler, Romualdo PastorâSatorras ¡ 5 authors
The cryptocurrency market surpassed the barrier of \$100 billion market capitalization in June 2017, after months of steady growth. Despite its increasing relevance in the financial world, however, a comprehensive analysis of the whole system is still lacking, as most studies have focused exclusively on the behaviour of one (Bitcoin) or few cryptocurrencies. Here, we consider the history of the entire market and analyse the behaviour of 1,469 cryptocurrencies introduced between April 2013 and June 2017. We reveal that, while new cryptocurrencies appear and disappear continuously and their market capitalization is increasing (super-)exponentially, several statistical properties of the market have been stable for years. These include the number of active cryptocurrencies, the market share distribution and the turnover of cryptocurrencies. Adopting an ecological perspective, we show that the so-called neutral model of evolution is able to reproduce a number of key empirical observations, despite its simplicity and the assumption of no selective advantage of one cryptocurrency over another. Our results shed light on the properties of the cryptocurrency market and establish a first formal link between ecological modelling and the study of this growing system. We anticipate they will spark further research in this direction.
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.
We reproduce apparently complex cellular automaton behaviour with partial differential equations as developed in the authorâs previous work. Our partial differential equation (PDE) model easily explains behaviour observed in selected scenarios of the cellular automaton wargame ISAAC without resorting to anthropomorphization of autonomous âagentsâ. The insinuation that agents have a reasoning and planning ability is replaced with a deterministic numerical approximation which encapsulates basic motivational factors and demonstrates a variety of spatial behaviours approximating the mean behaviour of the ISAAC scenarios. All scenarios presented here highlight the dangers associated with attributing intelligent reasoning to behaviour shown, when this can be explained quite simply through the effects of the terms in our equations. A continuum of forces is able to behave in a manner similar to a collection of individual autonomous agents, and shows decentralized self-organization and adaptation of tactics to suit a variety of combat situations.
Cellular Automata and Applications
Mathematical and Theoretical Epidemiology and Ecology Models
The following news item is taken in part from the July 22, 2010 issue of The Economist titled âAgents of change,â 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 ârepresentative agentsâ: 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 âLink communities reveal multiscale complexity in networks,â 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 âStability of Ecological Communities and the Architecture of Mutualistic and Trophic Networks,â by Elisa ThĂŠbault 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 âEmergence of Zipf's Law in the Evolution of Communication,â by Bernat Corominas-Murtra, Jordi Fortuny, and Ricard V. SolĂŠ. 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 âThe evolution of eusociality,â 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 âOptimally Interacting Minds,â 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 âPromiscuity and the evolutionary transition to complex societies,â 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 lowâa 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 âNetwork Complexity of Foodwebs,â 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 âA New Measure of Centrality for Brain Networks,â 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 âBiodiversity Conservation: Challenges Beyond 2010,â by Michael R.W. Rands, William M. Adams, Leon Bennun, Stuart H.M. Butchart, Andrew Clements, David Coomes, Abigail Entwistle, Ian Hodge, Valerie Kapos, JĂśrn 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 âAre large complex economic systems unstable?,â 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 âThe Spread of Behavior in an Online Social Network Experiment,â 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 âLanguage Acquisition Meets Language Evolution,â 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 âlogicalâ 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 âEarly warning signals of extinction in deteriorating environments,â 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 âSelf-Stabilizing Decentralized Signal Control of Realistic, Saturated Network Traffic,â 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 âInformation Driven Self-Organization: The Dynamical System Approach to Autonomous Robot Behavior,â 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 IntelligenceâSSCI 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.
In recent years, immunization strategies have been developed for stopping epidemics in complex-network-like environments. Yet it still remains a challenge for existing strategies to deal with dynamically-evolving networks that contain community structures, though they are ubiquitous in the real world. In this paper, we examine the performances of an autonomy-oriented distributed search strategy for tackling such networks. The strategy is based on the ideas of self-organization and positive feedback from Autonomy-Oriented Computing (AOC). Our experimental results have shown that autonomous entities in this strategy can collectively find and immunize most highly-connected nodes in a dynamic, community-based network within a few steps.
This chapter presents organizations as a macro-micro notion and device; they presuppose autonomous proactive entities (agents) playing the organizational roles. Agents may have their own powers, goals, relationships (of dependence, trust, etc.). This opens important issues to be discussed: Does cooperation require mentally shared plans? Which is the relationship between individual powers and role powers; personal dependencies and role dependencies; personal goals and assigned goals; personal beliefs and what we have to assume when playing our role; individual actions and organizational actions? What about possible conflicts, deviations, power abuse, given the agentsâ autonomy? MultiAgentSystems discipline should both aim at scientifically modeling human organizations, and at designing effective artificial organizations. Our claim is that for both those aims, one should model a high (risky) degree of flexibility, exploiting autonomy and pro-activity, intelligence and decentralized knowledge of roleplayers, allowing for functional violations of requests and even of rules.Request access from your librarian to read this chapter's full text.
A network community is a special sub-network that contains a group of nodes sharing similar linked patterns. A distributed network community mining problem (D-NCMP) is concerned with finding all such communities from a distributed network. A variety of applications in WWW and ad-hoc networks such as P2P and sensor networks can be formulated into DNCMPs, in which both resources and controls are distributed and/or decentralized. The problem is difficult for some existing methods to deal with because of the fact that their required global topological representations of distributed networks are hard to obtain. In this paper, we present an autonomy oriented computing (AOC) approach [15], in which the nodes and links of a distributed network are distributed among a group of autonomous agents that collectively find global communities hidden in the network. In doing so, the agents maintain only their respective local views and update them through a proposed self-organization process. The effectiveness of the AOC based approach has been validated using network examples.
Bill Hamilton was always at his best in small groups, and I would like to open and close my reflections with some thoughts generated by two small scientific meetings that Bill Hamilton attended. I was not present at the first meeting, in Tvarminne, Finland, but a photograph from the meeting made an impression on me. In the foreground stood Pekka Pamilo, the organizer of the meeting, with a rather worried expression on his face. In the background was Bill Hamilton, skating across the ice. Bill had brought his ice skates, intent on taking every advantage of this visit to the north. It turned out, however, that the ice was quite thin, so the organizers attempted to dissuade Bill. They made a general request that speakers at the meeting should please not attempt any ice skating, at least until after they had delivered their talks. Bill followed the letter of this request, but did not follow its spirit. After giving his talk, he donned his skates and set off. This picture brought to mind all sorts of associations. How can one not remember J. B. S. Haldane's musings about being willing to rescue two drowning brothers, or eight cousins. I hope Pekka will forgive me if I try to imagine his thoughts about the possibility of Bill falling though the ice. â Well, I do not think I am related to him... On the other hand, everyone in Finland is more or less related! But then, he is not from Finland, is he? Still, we may not share many genes, but we do share a great many memes.â The other thought that comes to my mind is one that has been noted frequently since Bill's recent death from malaria. Bill Hamilton was a risk taker, not just in his life, but also in his science. In his work, too, he sometimes skated on thin ice, traveling where others would not. E. O. Wilson once used exactly this metaphor to describe a certain kind of scientist who is always drawn to the dangerous or to the forbidden: âThey are the taboo breakers who enjoy the whiff of grapeshot and the crackle of thin iceâ (Wilson, 1978: 283). When Bill skated on thin ice in Finland, the results were satisfactory: the ice may have crackled but it did not give way. When he skated on thin scientific ice, the results were usually not just satisfactory, but glorious. Bill Hamilton's most glorious ideas were kin selection and inclusive fitness. Talking about Hamilton's contributions to inclusive fitness is a bit like talking about Isaac Newton's contributions to dynamics or Charles Darwin's contributions to natural selection. He invented the idea, and he developed most of its important implications. There were some forerunners, as there always are in science. I think of Hamilton's contribution as a fusing of two traditions. First, there was population genetics. Hamilton didn't completely invent the idea of kin selection. The idea was foreshadowed by Haldane (1955), Fisher (1958), and Williams (Williams and Williams, 1957). However, none of them developed it in any detail, perhaps because they did not appreciate its general importance in nature. For that we can thank animal behaviorists, particularly those like Wynne-Edwards (1962) and Emerson (1960), who believed that cooperation was very common in nature. Bill neatly hybridized the two traditions. If cooperation and altruism were important in nature, then we needed an explanation that was consistent with population genetics, and so inclusive fitness was born. Hamilton was well suited to make this match. Those acquainted with Hamilton only through his best-known papers may think of him as a theoretician and might conclude that he had the theoretician's superficial knowledge of the natural world. But in fact it was his mathematical skills that were hard won, while as a natural historian he was, well, a natural. You can see evidence of this in many of his papers, but it comes out particularly in his lesser known papers on insects under bark (Hamilton, 1978) and on fig wasps (Hamilton, 1979). So was Hamilton's contribution a simple merging of the insights of an ethologist and a population geneticist? No, it wasn't that simple, for several reasons. First, there was a lot of thin ice between these areas, and skating from one to the other was not encouraged in the early 1960s. Geneticists were leery of anything that smacked of eugenics. That included any application of population genetics to behavior. It included most of all applications to understanding social behavior, something we have always been a little touchy about. If nature was nasty, rude, or bawdy, better not to know about it, let alone let the public know. Let me illustrate the idea in an unconventional way, with a bit of verse that I call âFamily Valuesâ: Would I jump in a lake To save my drowning cousin? It's not a risk I'd take For him plus half a dozen. But if you raise the stake And make the prize my brother? Now that's a deal I'll make... If you'll just toss in another. If this poem, and the Haldane quip it is based upon, elicit chuckles, it is in large part because they treat a topic that is uncomfortable for us. Most humor is built on discomfort of one form or another. In this case, we recognize that we make unconscious judgments akin to these, with awkward balances of self-interest and family interest, but we don't like to see ourselves as calculating self-servers. Now throw in a good dash of genetics, and the mixture becomes truly taboo. Perhaps that's why Haldane and others did not pursue the topic. Bill's recollections of his graduate career (Hamilton, 1996) describe the price that he paid for his desire to be where the ice is thin. He had difficulty finding advisors. He had no desk. He had no invitations to talk about his work. It was not even clear that his thesis work, which would produce some of the most heavily cited papers in evolutionary biology (Hamilton, 1964a,b), would be acceptable for a Ph.D. For someone who was not socially outgoing in the first place, the effect of this isolation was severe. He feared that he might be a crank; why else would all these manifestly smart people fail to see the interest in what he was doing? He took to working in train stations and public parks simply to have some minimal level of human interaction. Mary Jane West Eberhard made a telling point in her talk about Bill at a recent meeting (West-Eberhard, 2000). She noted that Bill's life serves as a counter-example to those critics who said that sociobiological knowledge was dangerous. He was proof that one can see all that is grim in the depths of our nature and still live a life of decency and kindness. Bill would have been uncomfortable with hagiography. His writings allude to a knowledge of the dark side of human nature, obvious to him through introspection, so clearly his thoughts were not always saintly. But whatever dark thoughts swirled in his mind, on the surfaceâand this is where it countsâhe was basically a gentle man. Despite his highly critical mind, I never heard him criticize anyone in anything but the kindest, most self-effacing manner. He did not judge people by credentials and had time for people that others might consider to be amateurs or even crackpots, George Price being a notable example. And while he no doubt appreciated the recognition he eventually received, particularly given his lonely days as a graduate student, he did not seem to crave recognition excessively. Dawkins reported one example where Bill gave credit to someone else for an idea that was really his own and had to be confronted with the evidence from his own paper (Dawkins, 2000). Then, as Dawkins described it with an adverbial tour de force, Bill â eeyorishlyâ admitted that, yes, he'd had the idea, but the other fellow had put it much better. I can give another small illustration from my own experience. In 1985 I published a paper using Price's rule to obtain a new expression for inclusive fitness (Queller, 1985). Alan Grafen then chided me (Grafen, 1985), quite rightly, for having neglected to cite Hamilton's paper using Price's rule (Hamilton, 1970). My only excuse is that Bill had read my paper in manuscript without ever pointing out the omission, which he must have noticed. For that matter, I had learned about Price's rule directly from Bill in seminars at the University of Michigan. If I remembered Price's rule well and forgot Bill's uses of it, it is partly because of the selfless way that Bill taught the subject. There is a another reason that Hamilton's contribution cannot be viewed as a simple merging of naturalist and theoretical traditions. He did not just come up with any old theoretical model. For example, one could model the evolution of altruism for some particular limited set of conditions (George and Doris Williams had already done this; Williams and Williams, 1957), but then one has to wonder how general the conclusions are. And it is also possible, as other modelers later showed, to add so many mathematical bells and whistles that we lose track of the general theme. In contrast, what Hamilton came up with was a theory that was not only basically true, but also beautiful and elegant. I'm not speaking of the mathematical derivation in his 1964 paper, which was actually rather gruesome. I'm speaking of the result, what has come to be known as Hamilton's rule. It is so simple that even a non-mathematical mind can easily understand it and wield it, and so general that it can often be applied to new social evolution problems without any fresh mathematical modeling. How was this simple elegance achieved? I think there are two main reasons. First, Hamilton was willing to make assumptions that allowed the result to be simple without seriously compromising the biology. For example, he assumed that selection would be weak. Stronger selection has the effect of distorting the relatednesses away from their familiar values, and it makes them dependent on genetic details such as dominance. Hamilton's assumption was justified because weak selection is presumably common. For that matter, it's probably not so bad an approximation for stronger selection. A little distortion of correlation coefficients doesn't matter too much to someone interested in the real world, where estimates of parameters are typically only good to about one significant digit anyway. The second reason inclusive fitness is so useful is its inversion of fitness calculation methods. Instead of grouping together all effects of others on x's fitness, it calculated all the inclusive effects of x on others' fitnesses. This actor-centered approach is what makes the method so easy to apply. In Hamilton's own words: âThe social behaviour of a species evolves in such a way that in each distinct behaviour-evoking situation the individual will seem to value his neighbors' fitness against his own according to the coefficients of relationship appropriate to the situationâ (Hamilton, 1964b: 19). It has become clear in recent years that the same behaviors can also often be understood as a form of group selectionânot the old group selection of Wynne-Edwards, but nevertheless a method that involves partitioning of selection into within-group and between-group components (see Sober and Wilson, 1998). But the fact remains that almost no one uses these methods much to think about and solve interesting problems. Each of the two methods can dissect social evolution into component parts, but where inclusive fitness divides nature neatly at the joints, other methods seem to hack clumsily through the long bones. Inclusive fitness and kin selection were important on several levels. First, of course, they provided an explanation for the evolution of altruism. We still don't know whether Hamilton's famous haplodiploid hypothesis, based on three-quarters relatedness (Hamilton, 1964b, 1972), explains the origin of eusociality. But it seems certain that the answer does lie within his more general framework of relatedness, costs, and benefits. For the study of social insects, another result of inclusive thinking was perhaps even more interesting. The theory did not simply explain the altruism that we already knew about. It also predicted something we did not know much about: conflicts within colonies. Because inclusive fitness interests often differ even among close relatives (Hamilton, 1972), there can be conflicts over who should be queen, conflicts over who should lay the male-destined eggs, and conflicts over sex ratios (reviewed in Queller and Strassmann, 1998). Studies in these areas have amply satisfied the requirement that a good theory should not just explain what is known, but also make novel and successful predictions. I think a parallel phenomenon occurs in the world beyond social insects. Perhaps even more important than the explanation of altruism itself was the general validation given to selfish gene models. If selfish genes are to be of any value in explaining the evolution of social behavior, they simply must be able to explain the cases where the behavior is not phenotypically selfish. Otherwise the method must be counted as a failure. So kin-selected explanations of altruistic behavior gave life to selfish gene explanation in areas where kinship was not paramount. Hamilton's own work clearly shows this. He didn't stop with altruism. He made pioneering contributions in many other areas. The accompanying pieces in this issue describe his contributions to the study of sexual selection and parasites, but his work also included important contributions to senescence theory (Hamilton, 1966), sex ratios (Hamilton, 1967), selfish herds (Hamilton, 1971), dispersal (Hamilton and May, 1977), tit-for-tat cooperation (Axelrod and Hamilton, 1981), and within-individual conflict (Hamilton, 1967). This truly formidable list of accomplishments, and the whole selfish gene tradition of which it is a part, emerged from a confidence based on Hamilton's success in solving the potentially fatal problem of altruism. Finally, in recent years it has become increasingly clear that a theory of altruism and cooperation is important for a much grander reason than solving the annoying puzzle of the social insects. It is also needed to explain a much more pervasive kind of cooperation; the evolution of the organism itself (Maynard Smith and SzathmĂĄry, 1995) why do cells cooperate in a body? Why do formerly independent bacteria evolve into organelles? How did replicators get together in the first place? Organisms, though they compete selfishly with each other, are themselves cooperative entities. Cooperation is therefore fundamental to all of life. I began with a small scientific meeting in Finland. Let me close with another one, in Castiglioncello, Italy. The highest scientific compliment I have ever received was one that Bill delivered there, actually to my wife and collaborator Joan Strassmann. Bill had, many years previously, done field work in Brazil on the troubling question of how sociality could be maintained in wasps with many queens. We had recently helped show, with molecular tools that had not been available to Bill, how relatedness was kept at levels consistent with kin selection (Queller et al., 1988, 1993; West Eberhard, 1978). What he said to Joan was âNow I will have to think up a different question to ask St. Peter when I meet him.â Of course, this was ridiculously inflated praise, a reflection of Bill's generosity rather than his acumen. He was no doubt signaling this exaggeration by his use of the religious reference, since Bill did not seem to be a conventionally religious man. Instead, he is some one who saw his afterlife more in terms of burying beetles (Hamilton, 2000) than in terms of meeting St. Peter. Still, I'd like to run with idea for just a moment. In the sad days after Bill died, the thought of him interrogating St. Peter gave me a certain amount of solace, and perhaps even pleasure. It's not that I can imagine what Bill's question was. Nor was it the thought of him receiving a satisfactory answer. Instead, what appeals to me is the impact on old St. Peter. I imagine him at first flummoxed because he couldn't answer the question, then annoyed because he had never thought of it himself, and finally intrigued by the implications. I imagine him spending his free moments over the next few centuries thinking about it, making new observations on the teeming life below, scribbling some population genetic equations in the margins of his heavenly register, and perhaps running some simulations on God's fastest supercomputer. Perhaps I overestimate St. Peter's curiosity, but Bill's questions have always had that kind of effect. That they will long continue to do so is his legacy to us.