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[1812.05433] Lenia - Biology of Artificial Life
We report a new model of artificial life called Lenia (from Latin lenis "smooth"), a two-dimensional cellular automaton with continuous space-time-state and generalized local rule. Computer simulations show that Lenia supports a great diversity of complex autonomous patterns or "lifeforms" bearing resemblance to real-world microscopic organisms. More than 400 species in 18 families have been identified, many discovered via interactive evolutionary computation. They differ from other cellular automata patterns in being geometric, metameric, fuzzy, resilient, adaptive, and rule-generic.
We present basic observations of the model regarding the properties of space-time and basic settings. We provide a board survey of the lifeforms, categorize them into a hierarchical taxonomy, and map their distribution in the parameter hyperspace. We describe their morphological structures and behavioral dynamics, propose possible mechanisms of their self-propulsion, self-organization and plasticity. Finally, we discuss how the study of Lenia would be related to biology, artificial life, and artificial intelligence.
artificial-life  representation  cellular-automata  rather-interesting  to-write-about  to-implement  consider:simulation  consider:abstraction
5 days ago by Vaguery
What else is in an evolved name? Exploring evolvable specificity with SignalGP [PeerJ Preprints]
Tags are evolvable labels that provide genetic programs a flexible mechanism for specification. Tags are used to label and refer to programmatic elements, such as functions or jump targets. However, tags differ from traditional, more rigid methods for handling labeling because they allow for inexact references; that is, a referring tag need not exactly match its referent. Here, we explore how adjusting the threshold for how what qualifies as a match affects adaptive evolution. Further, we propose broadened applications of tags in the context of a genetic programming (GP) technique called SignalGP. SignalGP gives evolution direct access to the event-driven paradigm. Program modules in SignalGP are tagged and can be triggered by signals (with matching tags) from the environment, from other agents, or due to internal regulation. Specifically, we propose to extend this tag based system to: (1) provide more fine-grained control over module execution and regulation (e.g., promotion and repression) akin to natural gene regulatory networks, (2) employ a mosaic of GP representations within a single program, and (3) facilitate major evolutionary transitions in individuality (i.e., allow hierarchical program organization to evolve de novo).
artificial-life  genetic-programming  representation  hey-I-know-this-guy  the-mangle-in-practice  to-examine  consider:ReQ
23 days ago by Vaguery
[1811.04960] Molecular computers
We propose the chemlambda artificial chemistry, whose behavior strongly suggests that real molecules which embed Interaction Nets patterns and real chemical reactions which resemble Interaction Nets graph rewrites could be a realistic path towards molecular computers, in the sense explained in the article.
artificial-chemistry  graph-rewriting  artificial-life  rather-interesting  to-simulate  to-write-about  concurrency  consider:the-algorithm-loop-dichotomy  consider:the-edges-of-things
24 days ago by Vaguery
[1807.05283] When Are Two Gossips the Same? Types of Communication in Epistemic Gossip Protocols
We provide an in-depth study of the knowledge-theoretic aspects of communication in so-called gossip protocols. Pairs of agents communicate by means of calls in order to spread information---so-called secrets---within the group. Depending on the nature of such calls knowledge spreads in different ways within the group. Systematizing existing literature, we identify 18 different types of communication, and model their epistemic effects through corresponding indistinguishability relations. We then provide a classification of these relations and show its usefulness for an epistemic analysis in presence of different communication types. Finally, we explain how to formalise the assumption that the agents have common knowledge of a distributed epistemic gossip protocol.
agent-based  graph-theory  communication  artificial-life  collective-behavior  simulation  define-your-terms  rather-interesting  to-simulate
7 weeks ago by Vaguery
[1711.09442] Quantum Artificial Life in an IBM Quantum Computer
We present the first experimental realization of a quantum artificial life algorithm in a quantum computer. The quantum biomimetic protocol encodes tailored quantum behaviors belonging to living systems, namely, self-replication, mutation, interaction between individuals, and death, into the cloud quantum computer IBM ibmqx4. In this experiment, entanglement spreads throughout generations of individuals, where genuine quantum information features are inherited through genealogical networks. As a pioneering proof-of-principle, experimental data fits the ideal model with accuracy. Thereafter, these and other models of quantum artificial life, for which no classical device may predict its quantum supremacy evolution, can be further explored in novel generations of quantum computers. Quantum biomimetics, quantum machine learning, and quantum artificial intelligence will move forward hand in hand through more elaborate levels of quantum complexity.
artificial-life  simulation  quantum  uh-huh
7 weeks ago by Vaguery
Ecological theory provides insights about evolutionary computation [PeerJ Preprints]
Evolutionary algorithms often incorporate ecological concepts to help maintain diverse populations and drive continued innovation. However, while there is strong evidence for the value of ecological dynamics, a lack of overarching theoretical framework renders the precise mechanisms behind these results unclear. These gaps in our understanding make it challenging to predict which approaches will be most appropriate for a given problem. Biologists have been developing ecological theory for decades, but the resulting body of work has yet to be translated into an evolutionary computation context. This paper lays the groundwork for such a translation by applying ecological theory to three different selection mechanisms in evolutionary computation: fitness sharing, lexicase selection, and Eco-EA. First, we use ecological ideas to establish a framework that clarifies how these selection schemes are alike and how they differ. We then build upon this framework by using metrics from ecology to gather empirical data about the underlying differences in the population dynamics that these approaches produce. Specifically, we measure interaction networks and phylogenetic diversity within the population to explore long-term stable coexistence. Notably, we find that selection methods affect phylogenetic diversity differently than phenotypic diversity. These results can inform parameter selection, choice of selection scheme, and the development of new selection schemes.
evolutionary-algorithms  selection  looking-to-see  rather-interesting  hey-I-know-these-folks  artificial-life  feature-construction  community-formation
november 2018 by Vaguery
[1808.05875] Co-evolution of nodes and links: diversity driven coexistence in cyclic competition of three species
When three species compete cyclically in a well-mixed, stochastic system of N individuals, extinction is known to typically occur at times scaling as the system size N. This happens, for example, in rock-paper-scissors games or conserved Lotka-Volterra models in which every pair of individuals can interact on a complete graph. Here we show that if the competing individuals also have a "social temperament" to be either introverted or extroverted, leading them to cut or add links respectively, then long-living state in which all species coexist can occur when both introverts and extroverts are present. These states are non-equilibrium quasi-steady states, maintained by a subtle balance between species competition and network dynamcis. Remarkably, much of the phenomena is embodied in a mean-field description. However, an intuitive understanding of why diversity stabilizes the co-evolving node and link dynamics remains an open issue.
coevolution  theoretical-biology  rather-interesting  population-biology  social-norms  to-write-about  to-simulate  artificial-life  it's-more-complicated-than-you-think  complexology  agent-based
august 2018 by Vaguery
Model of the motion of agents with memory based on the cellular automaton: International Journal of Parallel, Emergent and Distributed Systems: Vol 33, No 3
The article is devoted to the construction of the motion model for agents with memory. Agents can be interpreted, for example, as mobile robots or soldiers. Agents move on the landscape consisting of squares with different passability. The model is based on the cellular automaton with one common to all agents layer corresponding to the landscape and many agent-specific layers corresponding to an agent’s memory. Methods for the random landscape generation are developed. The dependence between configuration entropy of the landscape, efficiency of the path-finding algorithm based on the cellular automaton was found. Also, the dependence of the average speed of the agents’ motion on the landscape configuration entropy was shown.
cannot-read  what-was-that-dude's-name-at-Shippenssburg?  cellular-automata  artificial-life
june 2018 by Vaguery
Navigating with grid-like representations in artificial agents | DeepMind
Most animals, including humans, are able to flexibly navigate the world they live in – exploring new areas, returning quickly to remembered places, and taking shortcuts. Indeed, these abilities feel so easy and natural that it is not immediately obvious how complex the underlying processes really are. In contrast, spatial navigation remains a substantial challenge for artificial agents whose abilities are far outstripped by those of mammals.
ethology  experiment  artificial-life  neural-networks  to-write-about  consider:nudge  consider:pattern-libraries
may 2018 by Vaguery
[1803.05859v3] Neural Network Quine
Self-replication is a key aspect of biological life that has been largely overlooked in Artificial Intelligence systems. Here we describe how to build and train self-replicating neural networks. The network replicates itself by learning to output its own weights. The network is designed using a loss function that can be optimized with either gradient-based or non-gradient-based methods. We also describe a method we call regeneration to train the network without explicit optimization, by injecting the network with predictions of its own parameters. The best solution for a self-replicating network was found by alternating between regeneration and optimization steps. Finally, we describe a design for a self-replicating neural network that can solve an auxiliary task such as MNIST image classification. We observe that there is a trade-off between the network's ability to classify images and its ability to replicate, but training is biased towards increasing its specialization at image classification at the expense of replication. This is analogous to the trade-off between reproduction and other tasks observed in nature. We suggest that a self-replication mechanism for artificial intelligence is useful because it introduces the possibility of continual improvement through natural selection.
artificial-life  machine-learning  quines  rather-interesting  to-write-about  hey-I-know-this-guy
may 2018 by Vaguery
[1711.08988] Exponential growth for self-reproduction in a catalytic reaction network: relevance of a minority molecular species and crowdedness
Explanation of exponential growth in self-reproduction is an important step toward elucidation of the origins of life because optimization of the growth potential across rounds of selection is necessary for Darwinian evolution. To produce another copy with approximately the same composition, the exponential growth rates for all components have to be equal. How such balanced growth is achieved, however, is not a trivial question, because this kind of growth requires orchestrated replication of the components in stochastic and nonlinear catalytic reactions. By considering a mutually catalyzing reaction in two- and three-dimensional lattices, as represented by a cellular automaton model, we show that self-reproduction with exponential growth is possible only when the replication and degradation of one molecular species is much slower than those of the others, i.e., when there is a minority molecule. Here, the synergetic effect of molecular discreteness and crowding is necessary to produce the exponential growth. Otherwise, the growth curves show superexponential growth because of nonlinearity of the catalytic reactions or subexponential growth due to replication inhibition by overcrowding of molecules. Our study emphasizes that the minority molecular species in a catalytic reaction network is necessary to acquire evolvability at the primitive stage of life.
autocatalysis  artificial-life  origin-of-life  reaction-networks  self-organization  biochemistry  simulation  rather-interesting  to-write-about  to-simulate
february 2018 by Vaguery
[1508.06655] Tapping Into the Wells of Social Energy: A Case Study Based on Falls Identification
Are purely technological solutions the best answer we can get to the shortcomings our organizations are often experiencing today? The results we gathered in this work lead us to giving a negative answer to such question. Science and technology are powerful boosters, though when they are applied to the "local, static organization of an obsolete yesterday" they fail to translate in the solutions we need to our problems. Our stance here is that those boosters should be applied to novel, distributed, and dynamic models able to allow us to escape from the local minima our societies are currently locked in. One such model is simulated in this paper to demonstrate how it may be possible to tap into the vast basins of social energy of our human societies to realize ubiquitous computing sociotechnical services for the identification and timely response to falls.
social-networks  simulation  artificial-life  social-dynamics  to-write-about  to-simulate  performance-measure  exploration-and-exploitation  metaphor  philosophy-of-engineering
january 2018 by Vaguery
[1709.08800] TuringMobile: A Turing Machine of Oblivious Mobile Robots with Limited Visibility and its Applications
In this paper we investigate the computational power of a set of mobile robots with limited visibility. At each iteration, a robot takes a snapshot of its surroundings, uses the snapshot to compute a destination point, and it moves toward its destination. Each robot is punctiform and memoryless, it operates in ℝm, it has a local reference system independent of the other robots' ones, and is activated asynchronously by an adversarial scheduler. Moreover, robots are non-rigid, in that they may be stopped by the scheduler at each move before reaching their destination (but are guaranteed to travel at least a fixed unknown distance before being stopped).
We show that despite these strong limitations, it is possible to arrange 3m+3k of these weak entities in ℝm to simulate the behavior of a stronger robot that is rigid (i.e., it always reaches its destination) and is endowed with k registers of persistent memory, each of which can store a real number. We call this arrangement a TuringMobile. In its simplest form, a TuringMobile consisting of only three robots can travel in the plane and store and update a single real number. We also prove that this task is impossible with fewer than three robots.
Among the applications of the TuringMobile, we focused on Near-Gathering (all robots have to gather in a small-enough disk) and Pattern Formation (of which Gathering is a special case) with limited visibility. Interestingly, our investigation implies that both problems are solvable in Euclidean spaces of any dimension, even if the visibility graph of the robots is initially disconnected, provided that a small amount of these robots are arranged to form a TuringMobile. In the special case of the plane, a basic TuringMobile of only three robots is sufficient.
artificial-life  swarms  rather-interesting  computer-science  computational-complexity  to-simulate  to-write-about  emergent-design  distributed-processing  nudge-targets
january 2018 by Vaguery
[1711.07387] How morphological development can guide evolution
Organisms result from multiple adaptive processes occurring and interacting at different time scales. One such interaction is that between development and evolution. In modeling studies, it has been shown that development sweeps over a series of traits in a single agent, and sometimes exposes promising static traits. Subsequent evolution can then canalize these rare traits. Thus, development can, under the right conditions, increase evolvability. Here, we report on a previously unknown phenomenon when embodied agents are allowed to develop and evolve: Evolution discovers body plans which are robust to control changes, these body plans become genetically assimilated, yet controllers for these agents are not assimilated. This allows evolution to continue climbing fitness gradients by tinkering with the developmental programs for controllers within these permissive body plans. This exposes a previously unknown detail about the Baldwin effect: instead of all useful traits becoming genetically assimilated, only phenotypic traits that render the agent robust to changes in other traits become assimilated. We refer to this phenomenon as differential canalization. This finding also has important implications for the evolutionary design of artificial and embodied agents such as robots: robots that are robust to internal changes in their controllers may also be robust to external changes in their environment, such as transferal from simulation to reality, or deployment in novel environments.
artificial-life  evolved-devo  developmental-biology  representation  rather-interesting  genetic-programming  hey-I-know-this-guy
december 2017 by Vaguery
[1708.06458v1] (Tissue) P Systems with Vesicles of Multisets
We consider tissue P systems working on vesicles of multisets with the very simple operations of insertion, deletion, and substitution of single objects. With the whole multiset being enclosed in a vesicle, sending it to a target cell can be indicated in those simple rules working on the multiset. As derivation modes we consider the sequential mode, where exactly one rule is applied in a derivation step, and the set maximal mode, where in each derivation step a non-extendable set of rules is applied. With the set maximal mode, computational completeness can already be obtained with tissue P systems having a tree structure, whereas tissue P systems even with an arbitrary communication structure are not computationally complete when working in the sequential mode. Adding polarizations (-1, 0, 1 are sufficient) allows for obtaining computational completeness even for tissue P systems working in the sequential mode.
P-systems  formal-languages  artificial-life  to-write-about  to-simulate
november 2017 by Vaguery
[1705.00094] The Impact of Coevolution and Abstention on the Emergence of Cooperation
This paper explores the Coevolutionary Optional Prisoner's Dilemma (COPD) game, which is a simple model to coevolve game strategy and link weights of agents playing the Optional Prisoner's Dilemma game. We consider a population of agents placed in a lattice grid with boundary conditions. A number of Monte Carlo simulations are performed to investigate the impacts of the COPD game on the emergence of cooperation. Results show that the coevolutionary rules enable cooperators to survive and even dominate, with the presence of abstainers in the population playing a key role in the protection of cooperators against exploitation from defectors. We observe that in adverse conditions such as when the initial population of abstainers is too scarce/abundant, or when the temptation to defect is very high, cooperation has no chance of emerging. However, when the simple coevolutionary rules are applied, cooperators flourish.
IPD  agent-based  evolutionary-economics  coevolution  community-formation  simulation  artificial-life  game-theory  to-write-about  to-do
october 2017 by Vaguery
A parallel approach to the Eulerian cycle problem (PDF Download Available)
A novel parallel approach for constructing Eulerian cycles of a given graph is presented. The proposed approach constitutes a combination of genetic algorithms and artificial neural networks. By tackling the Eulerian cycle problem as a constraint optimization problem, Eulerian cycle existence is determined, and either Eulerian cycles (if they exist) or paths encompassing the greatest possible number of edges (maximal traversal of edges, with edges traversed no more than once) are consistently constructed. Apart from being of theoretical interest, Eulerian cycle existence and construction have recently found significant applications in such areas of VLSI circuit design, RAM fault detection, and CPU access. © 2002 Wiley Periodicals, Inc.
algorithms  collective-intelligence  emergent-design  artificial-life  to-write-about
september 2017 by Vaguery
Self-Replicators Emerge from a Self-Organizing Prebiotic Computer World – Complexity Digest
Amoeba, a computer platform inspired by the Tierra system, is designed to study the generation of self-replicating sequences of machine operations (opcodes) from a prebiotic world initially populated by randomly selected opcodes. Point mutations drive opcode sequences to become more fit as they compete for memory and CPU time. Significant features of the Amoeba system include the lack of artificial encapsulation (there is no write protection) and a computationally universal opcode basis set. Amoeba now includes two additional features: pattern-based addressing and injecting entropy into the system. It was previously thought such changes would make it highly unlikely that an ancestral replicator could emerge from a fortuitous combination of randomly selected opcodes. Instead, Amoeba shows a far richer emergence, exhibiting a self-organization phase followed by the emergence of self-replicators. First, the opcode basis set becomes biased. Second, short opcode building blocks are propagated throughout memory space. Finally, prebiotic building blocks can combine to form self-replicators. Self-organization is quantified by measuring the evolution of opcode frequencies, the size distribution of sequences, and the mutual information of opcode pairs.
to-read  artificial-life  abiogenesis  theoretical-biology  simulation  emergence
september 2017 by Vaguery
[1707.06631] Two Results on Slime Mold Computations
In this paper, we present two results on slime mold computations. The first one treats a biologically-grounded model, originally proposed by biologists analyzing the behavior of the slime mold Physarum polycephalum. This primitive organism was empirically shown by Nakagaki et al. to solve shortest path problems in wet-lab experiments (Nature'00). We show that the proposed simple mathematical model actually generalizes to a much wider class of problems, namely undirected linear programs with a non-negative cost vector.
For our second result, we consider the discretization of a biologically-inspired model. This model is a directed variant of the biologically-grounded one and was never claimed to describe the behavior of a biological system. Straszak and Vishnoi showed that it can ϵ-approximately solve flow problems (SODA'16) and even general linear programs with positive cost vector (ITCS'16) within a finite number of steps. We give a refined convergence analysis that improves the dependence on ϵ from polynomial to logarithmic and simultaneously allows to choose a step size that is independent of ϵ. Furthermore, we show that the dynamics can be initialized with a more general set of (infeasible) starting points.
collective-intelligence  emergent-design  artificial-life  operations-research  performance-measure  to-write-about  to-simulate
august 2017 by Vaguery
[1512.02832] Connectivity Preserving Network Transformers
The Population Protocol model is a distributed model that concerns systems of very weak computational entities that cannot control the way they interact. The model of Network Constructors is a variant of Population Protocols capable of (algorithmically) constructing abstract networks. Both models are characterized by a fundamental inability to terminate. In this work, we investigate the minimal strengthenings of the latter that could overcome this inability. Our main conclusion is that initial connectivity of the communication topology combined with the ability of the protocol to transform the communication topology plus a few other local and realistic assumptions are sufficient to guarantee not only termination but also the maximum computational power that one can hope for in this family of models. The technique is to transform any initial connected topology to a less symmetric and detectable topology without ever breaking its connectivity during the transformation. The target topology of all of our transformers is the spanning line and we call Terminating Line Transformation the corresponding problem. We first study the case in which there is a pre-elected unique leader and give a time-optimal protocol for Terminating Line Transformation. We then prove that dropping the leader without additional assumptions leads to a strong impossibility result. In an attempt to overcome this, we equip the nodes with the ability to tell, during their pairwise interactions, whether they have at least one neighbor in common. Interestingly, it turns out that this local and realistic mechanism is sufficient to make the problem solvable. In particular, we give a very efficient protocol that solves Terminating Line Transformation when all nodes are initially identical. The latter implies that the model computes with termination any symmetric predicate computable by a Turing Machine of space Θ(n2).
artificial-chemistries  artificial-life  self-organization  distributed-processing  to-understand  to-write-about
april 2017 by Vaguery
[1503.01913] Terminating Distributed Construction of Shapes and Patterns in a Fair Solution of Automata
We consider a solution of automata similar to Population Protocols and Network Constructors. The automata (or nodes) move passively in a well-mixed solution and can cooperate by interacting in pairs. Every such interaction may result in an update of the local states of the nodes. Additionally, the nodes may also choose to connect to each other in order to start forming some required structure. We may think of such nodes as the smallest possible programmable pieces of matter. The model that we introduce here is a more applied version of Network Constructors, imposing physical (or geometrical) constraints on the connections. Each node can connect to other nodes only via a very limited number of local ports, therefore at any given time it has only a bounded number of neighbors. Connections are always made at unit distance and are perpendicular to connections of neighboring ports. We show that this restricted model is still capable of forming very practical 2D or 3D shapes. We provide direct constructors for some basic shape construction problems. We then develop new techniques for determining the constructive capabilities of our model. One of the main novelties of our approach, concerns our attempt to overcome the inability of such systems to detect termination. In particular, we exploit the assumptions that the system is well-mixed and has a unique leader, in order to give terminating protocols that are correct with high probability (w.h.p.). This allows us to develop terminating subroutines that can be sequentially composed to form larger modular protocols. One of our main results is a terminating protocol counting the size n of the system w.h.p.. We then use this protocol as a subroutine in order to develop our universal constructors, establishing that the nodes can self-organize w.h.p. into arbitrarily complex shapes while still detecting termination of the construction.
self-organization  self-assembly  distributed-processing  rather-interesting  artificial-life  simulation  to-write-about  nudge-targets  consider:looking-to-see
april 2017 by Vaguery
[1704.07589] Model of knowledge transfer within an organisation
Many studies show that the acquisition of knowledge is the key to build competitive advantage of companies. We propose a simple model of knowledge transfer within the organization and we implement the proposed model using cellular automata technique. In this paper the organisation is considered in the context of complex systems. In this perspective, the main role in organisation is played by the network of informal contacts and the distributed leadership. The goal of this paper is to check which factors influence the efficiency and effectiveness of knowledge transfer. Our studies indicate a significant role of initial concentration of chunks of knowledge for knowledge transfer process, and the results suggest taking action in the organisation to shorten the distance (social distance) between people with different levels of knowledge, or working out incentives to share knowledge.
organizational-behavior  agent-based  artificial-life  rather-interesting  simulation  to-write-about  consider:looking-to-see
april 2017 by Vaguery
[1603.08269] Equivalence of Deterministic walks on regular lattices on the plane
We consider deterministic walks on square, triangular and hexagonal two dimensional lattices. In each case, there is a scatterer at every site that can be in one of two states that force the walker to turn either to his/her immediate right or left. After the walker is scattered, the scatterer changes state. A lattice with an arrangement of scatterers is an environment. We show that there are only two environments for which the scattering rules are injective, mirrors or rotators, on the three lattices. On hexagonal lattices, B. Z. Webb and E. G. D. Cohen, proved that given an initial position and velocity of the walker and an environment of one type of scatterers, mirrrors or rotators, there is an environment of the other type such that the walks on both environments are equivalent, meaning they visit the same sites at the same time steps. We prove the equivalence of walks on square and triangular lattices and include a proof of the equivalence of walks on hexagonal lattices. The proofs are based both on the geometry of the lattice and the structure of the scattering rule.
cellular-automata  artificial-life  discrete-mathematics  rather-interesting  to-write-about  computational-complexity
april 2017 by Vaguery
[1012.1332] Time-Symmetric Cellular Automata
Together with the concept of reversibility, another relevant physical notion is time-symmetry, which expresses that there is no way of distinguishing between backward and forward time directions. This notion, found in physical theories, has been neglected in the area of discrete dynamical systems. Here we formalize it in the context of cellular automata and establish some basic facts and relations. We also state some open problems that may encourage further research on the topic.
cellular-automata  artificial-life  complexology  computational-complexity  information-theory  representation
april 2017 by Vaguery
[1611.09149] Dynamic landscape models of coevolutionary games
Players of coevolutionary games may update not only their strategies but also their networks of interaction. Based on interpreting the payoff of players as fitness, dynamic landscape models are proposed. The modeling procedure is carried out for Prisoner's Dilemma (PD) and Snowdrift (SD) games that both use either birth--death (BD) or death--birth (DB) strategy updating. The main focus is on using dynamic fitness landscapes as a mathematical model of coevolutionary game dynamics. Hence, an alternative tool for analyzing coevolutionary games becomes available, and landscape measures such as modality, ruggedness and information content can be computed and analyzed. In addition, fixation properties of the games and quantifiers characterizing the interaction networks are calculated numerically. Relations are established between landscape properties expressed by landscape measures and quantifiers of coevolutionary game dynamics such as fixation probabilities, fixation times and network properties.
evolutionary-economics  agent-based  rather-interesting  to-write-about  artificial-life  game-theory
april 2017 by Vaguery
[1309.1837] Evolution and non-equilibrium physics. A study of the Tangled Nature Model
We argue that the stochastic dynamics of interacting agents which replicate, mutate and die constitutes a non-equilibrium physical process akin to aging in complex materials. Specifically, our study uses extensive computer simulations of the Tangled Nature Model (TNM) of biological evolution to show that punctuated equilibria successively generated by the model's dynamics have increasing entropy and are separated by increasing entropic barriers. We further show that these states are organized in a hierarchy and that limiting the values of possible interactions to a finite interval leads to stationary fluctuations within a component of the latter. A coarse-grained description based on the temporal statistics of quakes, the events leading from one component of the hierarchy to the next, accounts for the logarithmic growth of the population and the decaying rate of change of macroscopic variables. Finally, we question the role of fitness in large scale evolution models and speculate on the possible evolutionary role of rejuvenation and memory effects.
theoretical-biology  artificial-life  complexology  ecology  Bak-Sneppen-stuff  fitness-landscapes  Oh  Physics!
march 2017 by Vaguery
[1612.07182] Multi-Agent Cooperation and the Emergence of (Natural) Language
The current mainstream approach to train natural language systems is to expose them to large amounts of text. This passive learning is problematic if we are interested in developing interactive machines, such as conversational agents. We propose a framework for language learning that relies on multi-agent communication. We study this learning in the context of referential games. In these games, a sender and a receiver see a pair of images. The sender is told one of them is the target and is allowed to send a message from a fixed, arbitrary vocabulary to the receiver. The receiver must rely on this message to identify the target. Thus, the agents develop their own language interactively out of the need to communicate. We show that two networks with simple configurations are able to learn to coordinate in the referential game. We further explore how to make changes to the game environment to cause the "word meanings" induced in the game to better reflect intuitive semantic properties of the images. In addition, we present a simple strategy for grounding the agents' code into natural language. Both of these are necessary steps towards developing machines that are able to communicate with humans productively.
artificial-life  neural-networks  agent-based  collective-behavior  language  to-write-about  rather-interesting
march 2017 by Vaguery
[1204.1749] Robust Soldier Crab Ball Gate
Soldier crabs Mictyris guinotae exhibit pronounced swarming behaviour. The swarms of the crabs tolerant of perturbations. In computer models and laboratory experiments we demonstrate that swarms of soldier crabs can implement logical gates when placed in a geometrically constrained environment.
via:futility-closet  biologically-inspired  biological-engineering  collective-behavior  unconventional-computing  swarms  experiment  simulation  artificial-life
march 2017 by Vaguery
[1512.03390] Analysis of the high dimensional naming game with committed minorities
The naming game has become an archetype for linguistic evolution and mathematical social behavioral analysis. In the model presented here, there are N individuals and K words. Our contribution is developing a robust method that handles the case when K=O(N). The initial condition plays a crucial role in the ordering of the system. We find that the system with high Shannon entropy has a higher consensus time and a lower critical fraction of zealots compared to low-entropy states. We also show that the critical number of committed agents decreases with the number of opinions and grows with the community size for each word. These results complement earlier conclusions that diversity of opinion is essential for evolution; without it, the system stagnates in the status quo [S. A. Marvel et al., Phys. Rev. Lett. 109, 118702 (2012)]. In contrast, our results suggest that committed minorities can more easily conquer highly diverse systems, showing them to be inherently unstable.
artificial-life  collective-behavior  simulation  to-write-about  agent-based
february 2017 by Vaguery
[1612.02537] Another phase transition in the Axelrod model
Axelrod's model of cultural dissemination, despite its apparent simplicity, demonstrates complex behavior that has been of much interest in statistical physics. Despite the many variations and extensions of the model that have been investigated, a systematic investigation of the effects of changing the size of the neighborhood on the lattice in which interactions can occur has not been made. Here we investigate the effect of varying the radius R of the von Neumann neighborhood in which agents can interact. We show, in addition to the well-known phase transition at the critical value of q, the number of traits, another phase transition at a critical value of R, and draw a q -- R phase diagram for the Axelrod model on a square lattice. In addition, we present a mean-field approximation of the model in which behavior on an infinite lattice can be analyzed.
hey-I-know-this-guy  evolutionary-economics  artificial-life  simulation  phase-transitions  to-write-about
february 2017 by Vaguery
[1701.09002] The interdependent network of gene regulation and metabolism is robust where it needs to be
The major biochemical networks of the living cell, the network of interacting genes and the network of biochemical reactions, are highly interdependent, however, they have been studied mostly as separate systems so far. In the last years an appropriate theoretical framework for studying interdependent networks has been developed in the context of statistical physics. Here we study the interdependent network of gene regulation and metabolism of the model organism Escherichia coli using the theoretical framework of interdependent networks. In particular we aim at understanding how the biological system can consolidate the conflicting tasks of reacting rapidly to (internal and external) perturbations, while being robust to minor environmental fluctuations, at the same time. For this purpose we study the network response to localized perturbations and find that the interdependent network is sensitive to gene regulatory and protein-level perturbations, yet robust against metabolic changes. This first quantitative application of the theory of interdependent networks to systems biology shows how studying network responses to localized perturbations can serve as a useful strategy for analyzing a wide range of other interdependent networks.
systems-biology  nonlinear-dynamics  robustness  engineering-design  biological-engineering  rather-interesting  to-write-about  multiobjective-optimization  nudge-targets  consider:looking-to-see  simulation  artificial-life  theoretical-biology
february 2017 by Vaguery
[1612.01605] Zipf's law, unbounded complexity and open-ended evolution
A major problem for evolutionary theory is understanding the so called {\em open-ended} nature of evolutionary change. Open-ended evolution (OEE) refers to the unbounded increase in complexity that seems to characterise evolution on multiple scales. This property seems to be a characteristic feature of biological and technological evolution and is strongly tied to the generative potential associated with combinatorics, which allows the system to grow and expand their available state spaces. Several theoretical and computational approaches have been developed to properly characterise OEE. Interestingly, many complex systems displaying OEE, from language to proteins, share a common statistical property: the presence of Zipf's law. Given and inventory of basic items required to build more complex structures Zipf's law tells us that most of these elements are rare whereas a few of them are extremely common. Using Algorithmic Information Theory, in this paper we provide a fundamental definition for open-endedness, which can be understood as {\em postulates}. Its statistical counterpart, based on standard Shannon Information theory, has the structure of a variational problem which is shown to lead to Zipf's law as the expected consequence of an evolutionary processes displaying OEE. We further explore the problem of information conservation through an OEE process and we conclude that statistical information (standard Shannon information) is not conserved, resulting into the paradoxical situation in which the increase of information content has the effect of erasing itself. We prove that this paradox is solved if we consider non-statistical forms of information. This last result implies that standard information theory may not be a suitable theoretical framework to explore the persistence and increase of the information content in OEE systems.
information-theory  open-ended-evolution  artificial-life  emergent-design  emergence  approximation  modeling  to-write-about
december 2016 by Vaguery
[1610.07600] Towards Rock-paper-scissors patterns in the Optional Public Goods Game under random mobility
Social dilemmas concern a natural conflict between cooperation and self interests among individuals in large populations. The emergence of cooperation and its maintenance is the key for the understanding of fundamental concepts about the evolution of species. In order to understand the mechanisms involved in this framework, here we study the Optional Public Good Games with focus on the effects of diffusive aspects in the emergent patterns of cyclic dominance between the strategies. Differently from other works, we showed that rock-paper-scissors (RPS) patterns occur by introducing a simple kind of random mobility in a lattice sparsely occupied. Such pattern has been revealed to be very important in the conservation of the species in ecological and social environments. The goal of this paper is to show that we do not need more elaborated schemes for construction of the neighbourhood in the game to observe RPS patterns as suggested in the literature. As an interesting additional result, in this contribution we also propose an alternative method to quantify the RPS density in a quantitative context of the game theory which becomes possible to perform a finite size scaling study. Such approach can be very interesting to be applied in other games generically.
evolutionary-economics  agent-based  collective-behavior  nonlinear-dynamics  game-theory  artificial-life  simulation  to-write-about  nudge-targets  consider:representation
october 2016 by Vaguery
[1605.03281] Mean-Field-Type Games in Engineering
With the ever increasing amounts of data becoming available, strategic data analysis and decision-making will become more pervasive as a necessary ingredient for societal infrastructures. In many network engineering games, the performance metrics depend on some few aggregates of the parameters/choices. One typical example is the congestion field in traffic engineering where classical cars and smart autonomous driverless cars create traffic congestion levels on the roads. The congestion field can be learned, for example by means of crowdsensing, and can be used for efficient and accurate prediction of the end-to-end delays of commuters. Another example is the interference field where it is the aggregate-received signal of the other users that matters rather than their individual input signal. In such games, in order for a transmitter-receiver pair to determine his best-replies, it is unnecessary that the pair is informed about the other users' strategies. If a user is informed about the aggregative terms given her own strategy, she will be able to efficiently exploit such information to perform better. In these situations the outcome is influenced not only by the state-action profile but also by the distribution of it. The interaction can be captured by a game with distribution-dependent payoffs called mean-field-type games (MFTG). An MFTG is basically a game in which the instantaneous payoffs and/or the state dynamics functions involve not only the state and the action profile of the players but also the joint distributions of state-action pairs. In this article, we propose and analyze engineering applications of MFTGs.
collective-intelligence  collective-behavior  artificial-life  engineering-design  rather-interesting  to-write-about  nudge-targets  consider:looking-to-see
september 2016 by Vaguery
[1603.07991] A Markov Chain Algorithm for Compression in Self-Organizing Particle Systems
Many programmable matter systems have been proposed and realized recently, each often tailored to a specific task or physical setting. In our work on self-organizing particle systems, we abstract away from specific settings and instead describe programmable matter as a collection of simple computational elements (called particles) with limited computational power that each perform fully distributed, local, asynchronous algorithms to solve system-wide problems of movement, configuration, and coordination. Here, we focus on the compression problem, where we seek fully distributed and asynchronous algorithms that lead the system to gather together as tightly as possible, that is, to converge to a configuration with small perimeter, where we measure the perimeter of a configuration by the length of the walk along the configuration boundary. We present a Markov chain based algorithm that solves the compression problem under the geometric amoebot model, using the triangular lattice as the underlying graph, for particle systems that begin in a connected configuration with no holes. The Markov chain M takes as input a bias parameter λ, where λ>1 corresponds to particles favoring inducing more lattice triangles within the particle system. We prove that during the execution of M, the particles stay connected and no holes form. We furthermore prove M is a reversible and ergodic Markov chain, which leads to our main result: for all λ>2+2‾√, there is a constant α>1 such that at stationarity the particles are α-compressed, meaning the perimeter of the particle configuration is at most α times the minimum perimeter for those particles. We additionally show λ>1 is not enough to guarantee compression: for all 0<λ<2.17, there is a constant β<1 such that the perimeter is at least a β fraction of the maximum perimeter.
amoebot  self-organization  programmable-matter  artificial-life  agent-based  rather-interesting  nudge-targets  consider:looking-to-see
july 2016 by Vaguery
[1503.07991] Leader Election and Shape Formation with Self-Organizing Programmable Matter
We consider programmable matter consisting of simple computational elements, called particles, that can establish and release bonds and can actively move in a self-organized way, and we investigate the feasibility of solving fundamental problems relevant for programmable matter. As a suitable model for such self-organizing particle systems, we will use a generalization of the geometric amoebot model first proposed in SPAA 2014. Based on the geometric model, we present efficient local-control algorithms for leader election and line formation requiring only particles with constant size memory, and we also discuss the limitations of solving these problems within the general amoebot model.
geometric-amoebot  nuff-said  artificial-life  self-organization  collective-intelligence  simulation  nudge-targets  consider:looking-to-see
july 2016 by Vaguery
[1606.02837] Spatial neutral dynamics
Neutral models, in which individual agents with equal fitness undergo a birth-death-mutation process, are very popular in population genetics and community ecology. Usually these models are applied to populations and communities with spatial structure, but the analytic results presented so far are limited to well-mixed or mainland-island scenarios. Here we present a new technique, based on interface dynamics analysis, and apply it to the neutral dynamics in one, two and three spatial dimensions. New results are derived for the correlation length and for the main characteristics of the community, like total biodiversity and the species abundance distribution above the correlation length. Our results are supported by extensive numerical simulations, and provide qualitative and quantitative insights that allow for a rigorous comparison between model predictions and empirical data.
agent-based  evolutionary-algorithms  theoretical-biology  self-organization  Bakism  rather-interesting  to-reproduce  artificial-life
july 2016 by Vaguery
[1606.09488] A weakly universal universal cellular automaton in the heptagrid
In this paper, we construct a weakly universal cellular automaton in the heptagrid, the tessellation {7,3} which is not rotation invariant but which is truly planar. This result, under these conditions, cannot be improved for the tessellations {p,3}.
cellular-automata  artificial-life  rather-interesting  visualization  to-explore
july 2016 by Vaguery
[1603.00802] Flies as Ship Captains? Digital Evolution Unravels Selective Pressures to Avoid Collision in Drosophila
Flies that walk in a covered planar arena on straight paths avoid colliding with each other, but which of the two flies stops is not random. High-throughput video observations, coupled with dedicated experiments with controlled robot flies have revealed that flies utilize the type of optic flow on their retina as a determinant of who should stop, a strategy also used by ship captains to determine which of two ships on a collision course should throw engines in reverse. We use digital evolution to test whether this strategy evolves when collision avoidance is the sole penalty. We find that the strategy does indeed evolve in a narrow range of cost/benefit ratios, for experiments in which the "regressive motion" cue is error free. We speculate that these stringent conditions may not be sufficient to evolve the strategy in real flies, pointing perhaps to auxiliary costs and benefits not modeled in our study
artificial-life  theoretical-biology  genetic-algorithm  rather-interesting  physiology  nudge-targets  consider:representation
july 2016 by Vaguery
Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism | bioRxiv
Background: The human gut contains approximately 10e+14 bacteria, belonging to hundreds of different species. Together, these microbial species form a complex food web that can break down food sources that our own digestive enzymes cannot handle, including complex polysaccharides, producing short chain fatty acids and additional metabolites, e.g., vitamin K. The diversity of microbial diversity is important for colonic health: Changes in the composition of the microbiota have been associated with inflammatory bowel disease, diabetes, obestity and Crohn's disease, and make the microbiota more vulnerable to infestation by harmful species, e.g., Clostridium difficile. To get a grip on the controlling factors of microbial diversity in the gut, we here propose a multi-scale, spatiotemporal dynamic flux-balance analysis model to study the emergence of metabolic diversity in a spatial gut-like, tubular environment. The model features genome-scale metabolic models of microbial populations, resource sharing via extracellular metabolites, and spatial population dynamics and evolution. Results: In this model, cross-feeding interactions emerge readily, despite the species' ability to metabolize sugars autonomously. Interestingly, the community requires cross-feeding for producing a realistic set of short-chain fatty acids from an input of glucose, If we let the composition of the microbial subpopulations change during invasion of adjacent space, a complex and stratifed microbiota evolves, with subspecies specializing on cross-feeding interactions via a mechanism of compensated trait loss. The microbial diversity and stratification collapse if the flux through the gut is enhanced to mimic diarrhea. Conclusions: In conclusion, this in silico model is a helpful tool in systems biology to predict and explain the controlling factors of microbial diversity in the gut. It can be extended to include, e.g., complex food source, and host-microbiota interactions via the gut wall.
systems-biology  microbiology  simulation  artificial-life  theoretical-biology  biodiversity  rather-interesting  to-write-about
july 2016 by Vaguery
[1605.08682] The Dose Makes The Cooperation
Explaining cooperation is one of the greatest challenges for basic scientific research. We proposed an agent-based model to study co-evolution of memory and cooperation. In our model, reciprocal agents with limited memory size play Prisoner's Dilemma Game iteratively. The characteristic of the environment, whether it is threatening or not, is embedded in the payoff matrix. Our findings are as follows. (i) Memory plays a critical role in the protection of cooperation. (ii) In the absence of threat, subsequent generations loose their memory and are consequently invaded by defectors. (iii) In contrast, the presence of an appropriate level of threat triggers the emergence of a self-protection mechanism for cooperation within subsequent generations. On the evolutionary level, memory size acts like an immune response of the population against aggressive defection. (iv) Even more extreme threat results again in defection. Our findings boil down to the following: The dose of the threat makes the cooperation.
evolutionary-economics  prisoners'-dilemma  game-theory  agent-based  artificial-life  it's-more-complicated-than-you-think  nudge-targets  consider:looking-to-see
june 2016 by Vaguery
On the evolutionary origins of equity | bioRxiv
Equity, defined as reward according to contribution, is considered a central aspect of human fairness in both philosophical debates and scientific research. Despite large amounts of research on the evolutionary origins of fairness, the evolutionary rationale behind equity is still unknown. Here, we investigate how equity can be understood in the context of the cooperative environment in which humans evolved. We model a population of individuals who cooperate to produce and divide a resource, and choose their cooperative partners based on how they are willing to divide the resource. Agent-based simulations, an analytical model, and extended simulations using neural networks provide converging evidence that equity is the best evolutionary strategy in such an environment: individuals maximize their fitness by dividing benefits in proportion to their own and their partners' relative contribution. The need to be chosen as a cooperative partner thus creates a selection pressure strong enough to explain the evolution of preferences for equity. We discuss the limitations of our model, the discrepancies between its predictions and empirical data, and how interindividual and intercultural variability fit within this framework.
agent-based  evolutionary-economics  simulation  artificial-life  nudge-targets  consider:looking-to-see  consider:feature-discovery
may 2016 by Vaguery
[1602.06737] Necessity is the mother of invention. The role of collective sensing in group formation
Traditionally, the fields of Sociology and Biology explain how groups could form and remain together if external factors are present, for example an environment that favors group behavior, e.g. avoid predation, or between-group competition. This work investigates the causes of group formation when these external factors are not present and individuals compete for the same resources, i.e. within-group competition.
A motivating example for our research is a recent anthropological study that found evidence for an increase in social tolerance among Homo Sapiens starting from the Middle Pleistocene. Social tolerance is a prerequisite for the creation of large social groups that include unrelated individuals, but at the same time it becomes evolutionarily successful only if interactions with unrelated individuals are frequent.
We argue that lack of information about resource location could have induced frequent interactions between unrelated individuals, as it would enable collective sensing and provide an evolutionary advantage. Collective sensing refers to the ability of a group to sense what is beyond the capabilities of the individual. Collective sensing is present in nature but its role in group formation has never been studied.
We test our hypothesis by means of an agent-based evolutionary model of a foraging task, with which we compare the fitness individuals using different navigation strategies: random walk and herding behavior, i.e. moving towards crowded areas. Although agents are unable to perceive resources at the individual level, interactions between them allow the group of herding agents to collectively locate resources.
Our findings suggest that evolution favors the spontaneous formation of groups, if resources become scarce and information about their location is not available at the individual level but can be inferred from the dynamics of the population.
artificial-life  theoretical-biology  collective-intelligence  swarms  nudge-targets  consider:rediscovery
april 2016 by Vaguery
[1603.05350] Self-organization of vocabularies under different interaction orders
Traditionally, the formation of vocabularies has been studied by agent-based models (specially, the Naming Game) in which random pairs of agents negotiate word-meaning associations at each discrete time step. This paper proposes a first approximation to a novel question: To what extent the negotiation of word-meaning associations is influenced by the order in which the individuals interact? Automata Networks provide the adequate mathematical framework to explore this question. Computer simulations suggest that on two-dimensional lattices the typical features of the formation of word-meaning associations are recovered under random schemes that update small fractions of the population at the same time.
collective-intelligence  self-organization  artificial-life  agent-based  evolutionary-economics  nudge-targets  consider:looking-to-see
april 2016 by Vaguery
[1507.08282] Common Knowledge on Networks
Common knowledge of intentions is crucial to basic social tasks ranging from cooperative hunting to oligopoly collusion, riots, revolutions, and the evolution of social norms and human culture. Yet little is known about how common knowledge leaves a trace on the dynamics of a social network. Here we show how an individual's network properties---primarily local clustering and betweenness centrality---provide strong signals of the ability to successfully participate in common knowledge tasks. These signals are distinct from those expected when practices are contagious, or when people use less-sophisticated heuristics that do not yield true coordination. This makes it possible to infer decision rules from observation. We also find that tasks that require common knowledge can yield significant inequalities in success, in contrast to the relative equality that results when practices spread by contagion alone.
evolutionary-economics  network-theory  artificial-life  game-theory  nudge-targets  consider:looking-to-see
march 2016 by Vaguery
Evolution of multiplayer cooperation on graphs | bioRxiv
There has been much interest in studying evolutionary games in structured populations, often modelled as graphs. However, most analytical results so far have only been obtained for two-player or additive games, while the study of more complex multiplayer games has been usually tackled by computer simulations. Here we investigate evolutionary multiplayer games in regular graphs updated with a Moran process. Using a combination of pair approximation and diffusion approximation, we obtain an analytical condition for cooperation to be favored by natural selection, given in terms of the payoffs of the game and a set of structure coefficients. We show that, for a large class of cooperative dilemmas, graph-structured populations are stronger promoters of cooperation than populations lacking spatial structure. Computer simulations validate our results, showing that the complexity arising from many-person social interactions and spatial structure can be often captured by analytical methods.
game-theory  theoretical-biology  evolutionary-economics  artificial-life  network-theory  nudge-targets  consider:feature-discovery
march 2016 by Vaguery
[1511.02079] Emergence of proto-organisms from bistable stochastic differentiation and adhesion
The rise of multicellularity in the early evolution of life represents a major challenge for evolutionary biology. Guidance for finding answers has emerged from disparate fields, from phylogenetics to modelling and synthetic biology, but little is known about the potential origins of multicellular aggregates before genetic programs took full control of developmental processes. Such aggregates should involve spatial organisation of differentiated cells and the modification of flows and concentrations of metabolites within well defined boundaries. Here we show that, in an environment where limited nutrients and toxic metabolites are introduced, a population of cells capable of stochastic differentiation and differential adhesion can develop into multicellular aggregates with a complex internal structure. The morphospace of possible patterns is shown to be very rich, including proto-organisms that display a high degree of organisational complexity, far beyond simple heterogeneous populations of cells. Our findings reveal that there is a potentially enormous richness of organismal complexity between simple mixed cooperators and embodied living organisms.
artificial-life  hey-I-know-this-guy  origin-of-life  simulation  nudge-targets  consider:exploration
december 2015 by Vaguery
[1512.05199] Extension of cellular automata by introducing an algorithm of recursive estimation of neighbors
This study focuses on an extended model of a standard cellular automaton (CA) that includes an extra index consisting of a radius that defines a perception area for each cell in addition to the radius defined by the CA rule. Extended standard CA rules form a sequence ordered by this index, which includes the CA rule as its first term. This extension aims at constructing a model that can be used within the CA framework to study the relationship between information processing and pattern formation in collective systems. Although the extension presented here is merely an extrapolation to a CA with a larger rule neighborhood, the extra radius can be interpreted as an individual difference of each cell, which provides a new perspective to CA. Some pattern formations in extended one-dimensional elementary CAs and two-dimensional Life-like CAs are presented. It is expected that the extended CA can be applied to various simulations of complex systems and other fields.
cellular-automata  self-organization  pattern-formation  artificial-life  Wolframism  to-do  nudge-targets  consider:looking-to-see
december 2015 by Vaguery
[1511.07740] Spatial social dilemmas: dilution, mobility and grouping effects with imitation dynamics
We present an extensive, systematic study of the Prisoner's Dilemma and Snowdrift games on a square lattice under a synchronous, noiseless imitation dynamics. We show that for both the occupancy of the network and the (random) mobility of the agents there are intermediate values that may increase the amount of cooperators in the system and new phases appear. We analytically determine the transition lines between these phases and compare with the mean field prediction and the observed behavior on a square lattice. We point out which are the more relevant microscopic processes that entitle cooperators to invade a population of defectors in the presence of mobility and discuss the universality of these results.
artificial-life  evolutionary-economics  simulation  rather-interesting  parameter-sweep  experiment
december 2015 by Vaguery
[1505.04920] Novel Multidimensional Models of Opinion Dynamics in Social Networks
Unlike many complex networks studied in the literature, social networks rarely exhibit regular unanimous behavior, or consensus of opinions. This requires a development of mathematical models that are sufficiently simple to be examined and capture, at the same time, the complex behavior of real social groups, where opinions and the actions related to them may form clusters of different sizes. One such model, proposed in [1], deals with scalar opinions and extends the idea in [2] of iterative pooling to take into account the actors' prejudices, caused by some exogenous factors and leading to disagreement in the final opinions. In this paper, we offer a novel multidimensional extension, which represents the dynamics of agents' opinions on several topics, and those topic-specific opinions are interdependent. As soon as opinions on several topics are affected simultaneously by the same influence networks, they automatically become related. However, we introduce an additional relation, interdependent topics, by which the opinions being formed on one topic are functions of the opinions held on other topics. We examine rigorous convergence properties of the proposed model and find explicitly the steady opinions of the agents. Although our model assumes synchronous communication among the agents, we show that the same final opinion may be reached "on average" via asynchronous gossip-based protocols.
social-norms  social-networks  community-formation  artificial-life  collective-behavior  nudge-targets  simulation  just-so
december 2015 by Vaguery
[1502.01375] Moving in a crowd: human perception as a multiscale process
The strategic behaviour of pedestrians is largely determined by how they perceive and react to neighbouring people. This issue is addressed in this paper by a model which combines, in a time and space-dependent way, discrete and continuous effects of pedestrian interactions. Numerical simulations and qualitative analysis suggest that human perception, and its impact on crowd dynamics, can be effectively modelled as a multiscale process based on a dual microscopic/macroscopic representation of groups of agents.
artificial-life  social-dynamics  planning  collective-intelligence  collective-behavior  swarms  nudge-targets
december 2015 by Vaguery
[1506.08168] Shaping the Growth Behaviour of Biofilms Initiated from Bacterial Aggregates
Bacterial biofilms are usually assumed to originate from individual cells deposited on a surface. However, many biofilm-forming bacteria tend to aggregate in the planktonic phase so that it is possible that many natural and infectious biofilms originate wholly or partially from pre-formed cell aggregates. Here, we use agent-based computer simulations to investigate the role of pre-formed aggregates in biofilm development. Focusing on the initial shape the aggregate forms on the surface, we find that the degree of spreading of an aggregate on a surface can play an important role in determining its eventual fate during biofilm development. Specifically, initially spread aggregates perform better when competition with surrounding unaggregated bacterial cells is low, while initially rounded aggregates perform better when competition with surrounding unaggregated cells is high. These contrasting outcomes are governed by a trade-off between aggregate surface area and height. Our results provide new insight into biofilm formation and development, and reveal new factors that may be at play in the social evolution of biofilm communities.
biofilms  emergent-design  microbiology  physics  aggregation  nudge-targets  simulation  community-assembly  artificial-life
december 2015 by Vaguery
[1505.02020] Influence of Luddism on innovation diffusion
artificial-life  evolutionary-economics  simulation  agent-based  amusing  innovation  social-dynamics  to-do
december 2015 by Vaguery
[1312.4803] Multiscaling edge effects in an agent-based money emergence model
An agent-based computational economical toy model for the emergence of money from the initial barter trading, inspired by Menger's postulate that money can spontaneously emerge in a commodity exchange economy, is extensively studied. The model considered, while manageable, is significantly complex, however. It is already able to reveal phenomena that can be interpreted as emergence and collapse of money as well as the related competition effects. In particular, it is shown that - as an extra emerging effect - the money lifetimes near the critical threshold value develop multiscaling, which allow one to set parallels to critical phenomena and, thus, to the real financial markets.
agent-based  evolutionary-economics  simulation  barter  collective-intelligence  that-Menger-BS  artificial-life  nudge-targets  consider:motivations  consider:reworking
december 2015 by Vaguery
[1407.3501] Robots that can adapt like animals
As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury.
robotics  machine-learning  adaptive-control  adaptive-behavior  artificial-life  nudge-targets  consider:rediscovery
november 2015 by Vaguery
[1501.05113] Two dimensional outflows for cellular automata with shuffle updates
In this paper, we explore the two-dimensional behavior of cellular automata with shuffle updates. As a test case, we consider the evacuation of a square room by pedestrians modeled by a cellular automaton model with a static floor field. Shuffle updates are characterized by a variable associated to each particle and called phase, that can be interpreted as the phase in the step cycle in the frame of pedestrian flows. Here we also introduce a dynamics for these phases, in order to modify the properties of the model. We investigate in particular the crossover between low- and high-density regimes that occurs when the density of pedestrians increases, the dependency of the outflow in the strength of the floor field, and the shape of the queue in front of the exit. Eventually we discuss the relevance of these results for pedestrians.
pedestrians  simulation  artificial-life  cellular-automata  statistical-mechanics  rather-interesting
november 2015 by Vaguery
[1501.02315] Long-term causal effects in multiagent economies
The effect of a treatment in a multiagent economy, e.g., a price increase, is causal if the treated economy would be different, e.g., in terms of revenue, relative to the control economy. Causal effects measured in an equilibrium of the economy, the long-term causal effects, are more representative of the value of such treatments. However, the statistical estimation of long-term causal effects is difficult because it has to rely, for practical reasons, on experimental data where agents are randomly assigned to the treated or the control economy, and their actions are observed before an equilibrium is reached. We propose a methodology to define and estimate long-term causal effects, which relies on a model of agent behaviors that plays a two-fold role. First, it predicts how agents would behave under different assignments, and, second, it predicts how agents would behave in equilibrium. These two prediction tasks enable the estimation of long-term causal effects under suitable assumptions, which we state explicitly.
philosophy  agent-based  causality  economics  models-and-modes  Hollandism  artificial-life  rather-interesting
november 2015 by Vaguery
[1408.0686] A random walk model to study the cycles emerging from the exploration-exploitation trade-off
We present a model for a random walk with memory, phenomenologically inspired in a biological system. The walker has the capacity to remember the time of the last visit to each site and the step taken from there. This memory affects the behavior of the walker each time it reaches an already visited site modulating the probability of repeating previous moves. This probability increases with the time elapsed from the last visit. A biological analog of the walker is a frugivore, with the lattice sites representing plants. The memory effect can be associated with the time needed by plants to recover its fruit load. We propose two different strategies, conservative and explorative, as well as intermediate cases, leading to non intuitive interesting results, such as the emergence of cycles.
ethology  artificial-life  simulation  complexology  exploration-exploitation
october 2015 by Vaguery
[1508.01577] Automata networks model for alignment and least effort on vocabulary formation
Can artificial communities of agents develop language with scaling relations close to the Zipf law? As a preliminary answer to this question, we propose an Automata Networks model of the formation of a vocabulary on a population of individuals, under two in principle opposite strategies: the alignment and the least effort principle. Within the previous account to the emergence of linguistic conventions (specially, the Naming Game), we focus on modeling speaker and hearer efforts as actions over their vocabularies and we study the impact of these actions on the formation of a shared language. The numerical simulations are essentially based on an energy function, that measures the amount of local agreement between the vocabularies. The results suggests that on one dimensional lattices the best strategy to the formation of shared languages is the one that minimizes the efforts of speakers on communicative tasks.
agent-based  models  artificial-life  collective-intelligence  social-norms  nudge-targets  consider:representation
september 2015 by Vaguery
[1506.08133] A Study on the Effect of Exit Widths and Crowd Sizes in the Formation of Arch in Clogged Crowds
The arching phenomenon is an emergent pattern formed by a c-sized crowd of intelligent, goal-oriented, autonomous, heterogeneous individuals moving towards a w-wide exit along a long W-wide corridor, where W>w. We collected empirical data from microsimulations to identify the combination effects of~c and~w to the time~T of the onset of and the size~S of the formation of the arch. The arch takes on the form of the perimeter of a half ellipse halved along the minor axis. We measured the~S with respect to the lengths of the major~M and minor~m axes of the ellipse, respectively. The mathematical description of the formation of this phenomenon will be an important information in the design of walkways to control and easily direct the flow of large crowds, especially during panic egress conditions.
swarms  crowds  artificial-life  collective-behavior  ethology  simulation  rather-interesting
september 2015 by Vaguery
[1504.02022] Flocking at the edge of chaos
Recent investigations have provided important insights into the complex structure and dynamics of collectively moving flocks of living organisms. Two intriguing observations are, scale-free correlations in the velocity fluctuations, in the presence of a high degree of order, and topological distance mediated interactions. Understanding these features, especially, the origin of fluctuations, appears to be challenging in the current scheme of models. It has been argued that flocks are poised at criticality. We present a self-propelled particle model where neighbourhoods and forces are defined through topology based rules. The force fluctuations occur spontaneously, and gives rise to scale-free correlations in the absence of noise and in the presence of alignment of velocities. We characterize the behaviour of the model through power spectral densities and the Lyapunov spectrum. Our investigations suggest self-organized criticality as a probable route to the existence of criticality in flocks.
boids  self-organization  artificial-life  power-laws  animats
september 2015 by Vaguery
[1505.00429] Urban skylines from Schelling model
We propose a metapopulation version of the Schelling model where two kinds of agents relocate themselves, with unconstrained destination, if their local fitness is lower than a tolerance threshold. We show that, for small values of the latter, the population redistributes highly heterogeneously among the available places. The system thus stabilizes on these heterogeneous skylines after a long quasi-stationary transient period, during which the population remains in a well mixed phase. Varying the tolerance passing from large to small values, we identify three possible global regimes: microscopic clusters with local coexistence of both kinds of agents, macroscopic clusters with local coexistence (soft segregation), macroscopic clusters with local segregation but homogeneous densities (hard segregation). The model is studied numerically and complemented with an analytical study in the limit of extremely large node capacity.
Schelling-model  artificial-life  simulation  evolutionary-economics  nudge-targets  consider:feature-discovery
september 2015 by Vaguery
[1507.02293] COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately, ignoring their co-evolutionary dynamics.
We propose a temporal point process model, COEVOLVE, for such joint dynamics, allowing the intensity of one process to be modulated by that of the other. This model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. Furthermore, we also develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. We experimented with both synthetic data and data gathered from Twitter, and show that our model provides a good fit to the data as well as more accurate predictions than alternatives.
social-networks  simulation  artificial-life  social-norms  cultural-dynamics  network-theory
september 2015 by Vaguery
[1401.8290] Control of asymmetric Hopfield networks and application to cancer attractors
The asymmetric Hopfield model is used to simulate signaling dynamics in gene/transcription factor networks. The model allows for a direct mapping of a gene expression pattern into attractor states. We analyze different control strategies aiming at disrupting attractor patterns using selective local fields representing therapeutic interventions. The control strategies are based on the identification of signaling bottlenecks, which are single nodes or strongly connected clusters of nodes that have a large impact on the signaling. We provide a theorem with bounds on the minimum number of nodes that guarantee controllability of bottlenecks consisting of strongly connected components. The control strategies are applied to the identification of sets of proteins that, when inhibited, selectively disrupt the signaling of cancer cells while preserving the signaling of normal cells. We use an experimentally validated non-specific network and a specific B cell interactome reconstructed from gene expression data to model cancer signaling in lung and B cells, respectively. This model could help in the rational design of novel robust therapeutic interventions based on our increasing knowledge of complex gene signaling networks.
artificial-life  theoretical-biology  systems-biology  control-theory  rather-interesting  nudge-targets  self-organization
september 2015 by Vaguery
[1502.00809] The consensus in the two-feature two-state one-dimensional Axelrod model revisited
The Axelrod model for the dissemination of culture exhibits a rich spatial distribution of cultural domains, which depends on the values of the two model parameters: F, the number of cultural features and q, the common number of states each feature can assume. In the one-dimensional model with F=q=2, which is closely related to the constrained voter model, Monte Carlo simulations indicate the existence of multicultural absorbing configurations in which at least one macroscopic domain coexist with a multitude of microscopic ones in the thermodynamic limit. However, rigorous analytical results for the infinite system starting from the configuration where all cultures are equally likely show convergence to only monocultural or consensus configurations. Here we show that this disagreement is due simply to the order that the time-asymptotic limit and the thermodynamic limit are taken in the simulations. In addition, we show how the consensus-only result can be derived using Monte Carlo simulations of finite chains.
evolutionary-economics  hey-I-know-this-guy  agent-based  economics  cultural-dynamics  simulation  artificial-life  to-read
september 2015 by Vaguery
[1310.4975] Competitive dynamics of lexical innovations in multi-layer networks
We study the introduction of lexical innovations into a community of language users. Lexical innovations, i.e., new terms added to people's vocabulary, play an important role in the process of language evolution. Nowadays, information is spread through a variety of networks, including, among others, online and offline social networks and the World Wide Web. The entire system, comprising networks of different nature, can be represented as a multi-layer network. In this context, lexical innovations diffusion occurs in a peculiar fashion. In particular, a lexical innovation can undergo three different processes: its original meaning is accepted; its meaning can be changed or misunderstood (e.g., when not properly explained), hence more than one meaning can emerge in the population; lastly, in the case of a loan word, it can be translated into the population language (i.e., defining a new lexical innovation or using a synonym) or into a dialect spoken by part of the population. Therefore, lexical innovations cannot be considered simply as information. We develop a model for analyzing this scenario using a multi-layer network comprising a social network and a media network. The latter represents the set of all information systems of a society, e.g., television, the World Wide Web and radio. Furthermore, we identify temporal directed edges between the nodes of these two networks. In particular, at each time step, nodes of the media network can be connected to randomly chosen nodes of the social network and vice versa. In so doing, information spreads through the whole system and people can share a lexical innovation with their neighbors or, in the event they work as reporters, by using media nodes. Lastly, we use the concept of "linguistic sign" to model lexical innovations, showing its fundamental role in the study of these dynamics. Many numerical simulations have been performed.
collective-intelligence  social-norms  agent-based  neural-networks  artificial-life  economics  rather-odd  to-read
september 2015 by Vaguery
[1412.5716] Naming game with learning errors in communications
Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network topology. By pair-wise iterative interactions, the population reaches a consensus state asymptotically. In this paper, we study naming game with communication errors during pair-wise conversations, where errors are represented by error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks with different parameters, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the whole population increases linearly as the value of the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold value of the learning errors which impairs the convergence. The new findings help to better understand the role of learning errors in naming game as well as human language development from a network science perspective.
collective-intelligence  game-theory  social-norms  social-networks  artificial-life  simulation  nudge-targets  consider:robustness
august 2015 by Vaguery
[1507.08467] A Model for Foraging Ants, Controlled by Spiking Neural Networks and Double Pheromones
A model of an Ant System where ants are controlled by a spiking neural circuit and a second order pheromone mechanism in a foraging task is presented. A neural circuit is trained for individual ants and subsequently the ants are exposed to a virtual environment where a swarm of ants performed a resource foraging task. The model comprises an associative and unsupervised learning strategy for the neural circuit of the ant. The neural circuit adapts to the environment by means of classical conditioning. The initially unknown environment includes different types of stimuli representing food and obstacles which, when they come in direct contact with the ant, elicit a reflex response in the motor neural system of the ant: moving towards or away from the source of the stimulus. The ants are released on a landscape with multiple food sources where one ant alone would have difficulty harvesting the landscape to maximum efficiency. The introduction of a double pheromone mechanism yields better results than traditional ant colony optimization strategies. Traditional ant systems include mainly a positive reinforcement pheromone. This approach uses a second pheromone that acts as a marker for forbidden paths (negative feedback). This blockade is not permanent and is controlled by the evaporation rate of the pheromones. The combined action of both pheromones acts as a collective stigmergic memory of the swarm, which reduces the search space of the problem. This paper explores how the adaptation and learning abilities observed in biologically inspired cognitive architectures is synergistically enhanced by swarm optimization strategies. The model portraits two forms of artificial intelligent behaviour: at the individual level the spiking neural network is the main controller and at the collective level the pheromone distribution is a map towards the solution emerged by the colony.
artificial-life  net-logo  agent-based  simulation  rather-interesting  machine-learning  unsupervised-learning  nudge-targets
august 2015 by Vaguery
[1507.03877] The Informational Architecture Of The Cell
In his celebrated book "What is Life?" Schrodinger proposed using the properties of living systems to constrain unknown features of life. Here we propose an inverse approach and suggest using biology as a means to constrain unknown physics. We focus on information and causation, as their widespread use in biology is the most problematic aspect of life from the perspective of fundamental physics. Our proposal is cast as a methodology for identifying potentially distinctive features of the informational architecture of biological systems, as compared to other classes of physical system. To illustrate our approach, we use as a case study a Boolean network model for the cell cycle regulation of the single-celled fission yeast (Schizosaccharomyces Pombe) and compare its informational properties to two classes of null model that share commonalities in their causal structure. We report patterns in local information processing and storage that do indeed distinguish biological from random. Conversely, we find that integrated information, which serves as a measure of "emergent" information processing, does not differ from random for the case presented. We discuss implications for our understanding of the informational architecture of the fission yeast cell cycle network and for illuminating any distinctive physics operative in life.
theoretical-biology  artificial-life  information-theory  network-theory  philosophy-of-science  to-read
august 2015 by Vaguery
Self-organization of Computation in Neural Systems | bioRxiv
When learning a complex task our nervous system self-organizes large groups of neurons into coherent dynamic activity patterns. During this, a cell assembly network with multiple, simultaneously active, and computationally powerful assemblies is formed; a process which is so far not understood. Here we show that the combination of synaptic plasticity with the slower process of synaptic scaling achieves formation of such assembly networks. This type of self-organization allows executing a difficult, six degrees of freedom, manipulation task with a robot where assemblies need to learn computing complex non-linear transforms and - for execution - must cooperate with each other without interference. This mechanism, thus, permits for the first time the guided self-organization of computationally powerful sub-structures in dynamic networks for behavior control.
self-organization  neural-networks  robotics  rather-interesting  artificial-life  engineering-design  nudge-targets  consider:representation
july 2015 by Vaguery
[1210.7019] Labyrinthine clustering in a spatial rock-paper-scissors ecosystem
The spatial rock-paper-scissors ecosystem, where three species interact cyclically, is a model example of how spatial structure can maintain biodiversity. We here consider such a system for a broad range of interaction rates. When one species grows very slowly, this species and its prey dominate the system by self-organizing into a labyrinthine configuration in which the third species propagates. The cluster size distributions of the two dominating species have heavy tails and the configuration is stabilized through a complex, spatial feedback loop. We introduce a new statistical measure that quantifies the amount of clustering in the spatial system by comparison with its mean field approximation. Hereby, we are able to quantitatively explain how the labyrinthine configuration slows down the dynamics and stabilizes the system.
rock-paper-scissors  roshambo  artificial-life  agent-based  self-organization  pattern-formation  hey-I-know-this-guy  nudge-targets
july 2015 by Vaguery
[1401.0036] Cyclic and Coherent States in Flocks with Topological Distance
A simple model of the two dimensional collective motion of a group of mobile agents have been studied. Like birds, these agents travel in open free space where each of them interacts with the first n neighbors determined by the topological distance with a free boundary condition. Using the same prescription for interactions used in the Vicsek model with scalar noise it has been observed that the flock, in absence of the noise, arrives at a number of interesting stationary states. In the single sink state' the entire flock maintains perfect cohesion and coherence. In the cyclic state' every agent executes a uniform circular motion, and the entire flock executes a pulsating dynamics i.e., expands and contracts periodically between a minimum and a maximum size of the flock. When refreshing rate of the interaction zone is the fastest, the entire flock gets fragmented into smaller clusters of different sizes. On introduction of scalar noise a crossover is observed when the agents cross over from a ballistic motion to a diffusive motion. Expectedly the crossover time is dependent on the strength of the noise η and diverges as η→0. In simpler version the translational degrees of freedom of the agents are suppressed but their angular motion are retained. Here agents are the spins, placed at the sites of a square lattice with periodic boundary condition. Every spin interacts with its n = 2, 3 or 4 nearest neighbors. In the stationary state the entire spin pattern moves as a whole when interactions are anisotropic with n = 2 and 3; but it is completely frozen when the interaction is isotropic with n=4. These spin configurations have vortex-antivortex pairs whose density increases as the noise η increases and follows an excellent finite-size scaling analysis.
boids  flocking  theoretical-biology  agent-based  artificial-life  biological-engineering  simulation  nudge-targets
july 2015 by Vaguery
Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues. | bioRxiv
Development is a process that needs to tightly coordinated in both space and time. Cell tracking and lineage tracing have become important experimental techniques in developmental biology and allow us to map the fate of cells and their progeny in both space and time. A generic feature of developing (as well as homeostatic) tissues that these analyses have revealed is that relatively few cells give rise to the bulk of the cells in a tissue; the lineages of most cells come to an end fairly quickly. This has spurned the interest also of computational and theoretical biologists/physicists who have developed a range of modelling -- perhaps most notably are the agent-based modelling (ABM) --- approaches. These can become computationally prohibitively expensive but seem to capture some of the features observed in experiments. Here we develop a complementary perspective that allows us to understand the dynamics leading to the formation of a tissue (or colony of cells). Borrowing from the rich population genetics literature we develop genealogical models of tissue development that trace the ancestry of cells in a tissue back to their most recent common ancestors. We apply this approach to tissues that grow under confined conditions --- as would, for example, be appropriate for the neural crest --- and unbounded growth --- illustrative of the behaviour of 2D tumours or bacterial colonies. The classical coalescent model from population genetics is readily adapted to capture tissue genealogies for different models of tissue growth and development. We show that simple but universal scaling relationships allow us to establish relationships between the coalescent and different fractal growth models that have been extensively studied in many different contexts, including developmental biology. Using our genealogical perspective we are able to study the statistical properties of the processes that give rise to tissues of cells, without the need for large-scale simulations.
theoretical-biology  developmental-biology  evo-devo  artificial-life  population-biology  self-organization  rather-interesting  morphology  fitness-landscapes  nudge-targets  consider:detailed-reexamination
july 2015 by Vaguery
[1403.6333] Range Expansion of Heterogeneous Populations
Risk spreading in bacterial populations is generally regarded as a strategy to maximize survival. Here, we study its role during range expansion of a genetically diverse population where growth and motility are two alternative traits. We find that during the initial expansion phase fast growing cells do have a selective advantage. By contrast, asymptotically, generalists balancing motility and reproduction are evolutionarily most successful. These findings are rationalized by a set of coupled Fisher equations complemented by stochastic simulations.
population-biology  microbiology  rather-interesting  simulation  artificial-life  nudge-targets  consider:rediscovery  consider:feature-discovery  consider:looking-to-see
july 2015 by Vaguery
[1506.06698] Emergent collective chemotaxis without single-cell gradient sensing
Many eukaryotic cells chemotax, sensing and following chemical gradients. However, even if single cells do not chemotax significantly, small clusters may still follow a gradient; this behavior is observed in neural crest cells and during border cell migration in Drosophila, but its origin remains puzzling. Here, we study this "collective guidance" analytically and computationally. We show collective chemotaxis can exist without single-cell chemotaxis if contact inhibition of locomotion (CIL), where cells polarize away from cell-cell contact, is regulated by the chemoattractant. We present explicit formulas for how cluster velocity and chemotactic index depend on the number and organization of cells in the cluster. Pairs of cells will have velocities that are strongly dependent on the cell pair's orientation: this provides a simple test for the presence of collective guidance in neural crest cells and other systems. We also study cluster-level adaptation, amplification, and cohesion via co-attraction.
theoretical-biology  collective-intelligence  pattern-formation  adaptive-control  simulation  artificial-life  nudge-targets  consider:rediscovery
june 2015 by Vaguery
[1506.01634] Structural Properties of Realistic Cultural Space Distributions
An interesting sociophysical research problem consists of the compatibility between collective social behavior in the short term and cultural diversity in the long term. Recently, it has been shown that, when studying a model of short term collective behavior in parallel with one of long term cultural diversity, one is lead to the puzzling conclusion that the 2 aspects are mutually exclusive. However, the compatibility is restored when switching from the randomly generated cultural space distribution to an empirical one for specifying the initial conditions in those models. This calls for understanding the extent to which such a compatibility restoration is independent of the empirical data set, as well as the relevant structural properties of such data. Firstly, this work shows that the restoration patterns are largely robust across data sets. Secondly, it provides a possible mechanism explaining the restoration, for the special case when the cultural space is formulated only in terms of nominal variables. The proposed model assumes that a realistic distribution in cultural space is governed by the existence of several "cultural prototype", a hypothesis already used in previous work, provided that every individual's sequence of cultural traits is a combination of the sequences associated to the prototypes. This can be considered indirect empirical evidence in favor of social science theories having inspired the model.
social-norms  cultural-norms  simulation  artificial-life  replicate-replicate  social-networks
june 2015 by Vaguery
proceedings [CIG 2014]
CIG 2014 in Dortmund, Germany
IEEE Conference on Computational Intelligence and Games
games  machine-learning  artificial-life  proceedings  rather-interesting
june 2015 by Vaguery
[1501.01501] Autonomous Fault Detection in Self-Healing Systems using Restricted Boltzmann Machines
Autonomously detecting and recovering from faults is one approach for reducing the operational complexity and costs associated with managing computing environments. We present a novel methodology for autonomously generating investigation leads that help identify systems faults, and extends our previous work in this area by leveraging Restricted Boltzmann Machines (RBMs) and contrastive divergence learning to analyse changes in historical feature data. This allows us to heuristically identify the root cause of a fault, and demonstrate an improvement to the state of the art by showing feature data can be predicted heuristically beyond a single instance to include entire sequences of information.
artificial-intelligence  self-image  learning-by-being  learning-by-watching  control-theory  artificial-life  nudge-targets  consider:ephemerality
april 2015 by Vaguery
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