Emergence anywhere
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Emergence anywhere
Aug 29, 2017
Curated by Takaya Arita
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Cognitive mechanisms for human flocking dynamics

Low-level “adaptive” and higher-level “sophisticated” human reasoning processes have been proposed to play opposing roles in the emergence of unpredictable collective behaviors such as crowd panics, traffic jams, and market bubbles. While adaptive processes are widely recognized drivers of emergent social complexity, complementary theories of sophistication predict that incentives, education, and other inducements to rationality will suppress it. We show in a series of multiplayer laboratory experiments that, rather than suppressing complex social dynamics, sophisticated reasoning processes can drive them. Our experiments elicit an endogenous collective behavior and show that it is driven by the human ability to recursively anticipate the reasoning of others. We identify this behavior, “sophisticated flocking”, across three games, the Beauty Contest and the “Mod Game” and “Runway Game”. In supporting our argument, we also present evidence for mental models and social norms constraining how players express their higher-level reasoning abilities. By implicating sophisticated recursive reasoning in the kind of complex dynamic that it has been predicted to suppress, we support interdisciplinary perspectives that emergent complexity is typical of even the most intelligent populations and carefully designed social systems.

 

Cognitive mechanisms for human flocking dynamics
Seth Frey, Robert L. Goldstone

Journal of Computational Social Science
September 2018, Volume 1, Issue 2, pp 349–375


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A world created from ideas, as immaterial resources are limitless

We put high hopes on analyzing big data, but we failed as we haven´t found solutions to the essential problems of our society. Questions like: What is the superior way of organisation of our society in the future or what’s the role of democratic principles in the future? - need to be asked and solved. In the past globalisation, optimization, administration, regulation have served us well and brought us to the level where we are but apparently as the economic situation shows now, we are in a stagnation and all those principles have reached their limits. We need new success principles. ‘I think those success principles are co-creation, co-evolution, collective intelligence, self-organization and self-regulation.’ - says Prof. Dr. Dirk Helbing, Computational Social Science, Department of Humanities, Social and Political Sciences, ETH/Zurich


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Can we open the black box of AI?

Can we open the black box of AI? | Emergence anywhere | Scoop.it
Artificial intelligence is everywhere. But before scientists trust it, they first need to understand how machines learn.

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Traffic Games: Modeling Freeway Traffic with Game Theory

We apply game theory to a vehicular traffic model to study the effect of driver strategies on traffic flow. The resulting model inherits the realistic dynamics achieved by a two-lane traffic model and aims to incorporate phenomena caused by driver-driver interactions. To achieve this goal, a game-theoretic description of driver interaction was developed. This game-theoretic formalization allows one to model different lane-changing behaviors and to keep track of mobility performance. We simulate the evolution of cooperation, traffic flow, and mobility performance for different modeled behaviors. The analysis of these results indicates a mobility optimization process achieved by drivers’ interactions.

 

Cortés-Berrueco LE, Gershenson C, Stephens CR (2016) Traffic Games: Modeling Freeway Traffic with Game Theory. PLoS ONE 11(11): e0165381. doi:10.1371/journal.pone.0165381


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Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction

Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction | Emergence anywhere | Scoop.it

Nearly all nontrivial real-world systems are nonlinear dynamical systems. Chaos describes certain nonlinear dynamical systems that have a very sensitive dependence on initial conditions. Chaotic systems are always deterministic and may be very simple, yet they produce completely unpredictable and divergent behavior. Systems of nonlinear equations are difficult to solve analytically, and scientists have relied heavily on visual and qualitative approaches to discover and analyze the dynamics of nonlinearity. Indeed, few fields have drawn as heavily from visualization methods for their seminal innovations: from strange attractors, to bifurcation diagrams, to cobweb plots, to phase diagrams and embedding. Although the social sciences are increasingly studying these types of systems, seminal concepts remain murky or loosely adopted. This article has three aims. First, it argues for several visualization methods to critically analyze and understand the behavior of nonlinear dynamical systems. Second, it uses these visualizations to introduce the foundations of nonlinear dynamics, chaos, fractals, self-similarity and the limits of prediction. Finally, it presents Pynamical, an open-source Python package to easily visualize and explore nonlinear dynamical systems’ behavior.

 

Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction
Geoff Boeing

Systems 2016, 4(4), 37; doi:10.3390/systems4040037


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Marcelo Errera's curator insight, December 3, 2016 12:02 PM
Though not directly related to Constructal Law, it s a very interesting tool to communicate studies in complexity.
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SFINA - Simulation Framework for Intelligent Network Adaptations

SFINA - Simulation Framework for Intelligent Network Adaptations | Emergence anywhere | Scoop.it

The introduction of ICT in techno-socio-economic systems, such as Smart Grids, traffic management, food supply chains and others, transforms the role of simulation as a scientific method for studying these complex systems. The scientific focus and challenge in simulations move from understanding system complexity to actually prototyping online and distributed regulatory mechanisms for supporting system operations. Existing simulation tools are not designed to address the challenges of this new reality, however, simulation is all about capturing reality at an adequate level of detail. This paper fills this gap by introducing a Java-based distributed simulation framework for inter-connected and inter-dependent techno-socio-economic system: SFINA, the Simulation Framework for Intelligent Network Adaptations. Three layers outline the design approach of SFINA: (i) integration of domain knowledge and dynamics that govern various techno-socio-economic systems, (ii) system modeling with dynamic flow networks represented by temporal directed weighted graphs and (iii) simulation of generic regulation models, policies and mechanisms applicable in several domains. SFINA aims at minimizing the fragmentation and discrepancies between different simulation communities by allowing the interoperability of SFINA with several other existing domain backends. The coupling of three such backends with SFINA is illustrated in the domain of Smart Grids and disaster mitigation. It is shown that the same model of cascading failures in Smart Grids is developed once and evaluated with both MATPOWER and InterPSS backends without changing a single line of application code. Similarly, application code developed in SFINA is reused for the evaluation of mitigation strategies in a backend that simulates the flows of a disaster spread. Results provide a proof-of-concept for the high modularity and reconfigurability of SFINA and puts the foundations of a new generation of simulation tools that prototype and validate online decentralized regulation in techno-socio-economic systems.

 

SFINA - Simulation Framework for Intelligent Network Adaptations

Evangelos Pournaras, Ben-Elias Brandt, Manish Thapa, Dinesh Acharya, Jose Espejo-Uribe, Mark Ballandies, Dirk Helbing

Simulation Modelling Practice and Theory
Volume 72, March 2017, Pages 34–50


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Detection of timescales in evolving complex systems

Detection of timescales in evolving complex systems | Emergence anywhere | Scoop.it

Most complex systems are intrinsically dynamic in nature. The evolution of a dynamic complex system is typically represented as a sequence of snapshots, where each snapshot describes the configuration of the system at a particular instant of time. This is often done by using constant intervals but a better approach would be to define dynamic intervals that match the evolution of the system’s configuration. To this end, we propose a method that aims at detecting evolutionary changes in the configuration of a complex system, and generates intervals accordingly. We show that evolutionary timescales can be identified by looking for peaks in the similarity between the sets of events on consecutive time intervals of data. Tests on simple toy models reveal that the technique is able to detect evolutionary timescales of time-varying data both when the evolution is smooth as well as when it changes sharply. This is further corroborated by analyses of several real datasets. Our method is scalable to extremely large datasets and is computationally efficient. This allows a quick, parameter-free detection of multiple timescales in the evolution of a complex system.

 

Detection of timescales in evolving complex systems
Richard K. Darst, Clara Granell, Alex Arenas, Sergio Gómez, Jari Saramäki & Santo Fortunato

Scientific Reports 6, Article number: 39713 (2016)
doi:10.1038/srep39713


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Association of Facebook Use With Compromised Well-Being: A Longitudinal Study

Face-to-face social interactions enhance well-being. With the ubiquity of social media, important questions have arisen about the impact of online social interactions. In the present study, we assessed the associations of both online and offline social networks with several subjective measures of well-being. We used 3 waves (2013, 2014, and 2015) of data from 5,208 subjects in the nationally representative Gallup Panel Social Network Study survey, including social network measures, in combination with objective measures of Facebook use. We investigated the associations of Facebook activity and real-world social network activity with self-reported physical health, self-reported mental health, self-reported life satisfaction, and body mass index. Our results showed that overall, the use of Facebook was negatively associated with well-being. For example, a 1-standard-deviation increase in "likes clicked" (clicking "like" on someone else's content), "links clicked" (clicking a link to another site or article), or "status updates" (updating one's own Facebook status) was associated with a decrease of 5%-8% of a standard deviation in self-reported mental health. These associations were robust to multivariate cross-sectional analyses, as well as to 2-wave prospective analyses. The negative associations of Facebook use were comparable to or greater in magnitude than the positive impact of offline interactions, which suggests a possible tradeoff between offline and online relationships.

 

Association of Facebook Use With Compromised Well-Being: A Longitudinal Study.
Shakya HB, Christakis NA. Am J Epidemiol. 2017 Jan 16. doi: 10.1093/aje/kww189


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Updating Darwin: Information and entropy drive the evolution of life

The evolution of species, according to Darwin, is driven by struggle – by competition between variant autonomous individuals for survival of the fittest and reproductive advantage; the outcome of this struggle for survival is natural selection. The Neo-Darwinians reframed natural selection in terms of DNA: inherited genotypes directly encode expressed phenotypes; a fit phenotype means a fit genotype – thus the evolution of species is the evolution of selfish, reproducing individual genotypes.

Four general characteristics of advanced forms of life are not easily explained by this Neo-Darwinian paradigm: 1) Dependence on cooperation rather than on struggle, manifested by the microbiome, ecosystems and altruism; 2) The pursuit of diversity rather than optimal fitness, manifested by sexual reproduction; 3) Life’s investment in programmed death, rather then in open-ended survival; and 4) The acceleration of complexity, despite its intrinsic fragility.

Here I discuss two mechanisms that can resolve these paradoxical features; both mechanisms arise from viewing life as the evolution of information. Information has two inevitable outcomes; it increases by autocatalyis and it is destroyed by entropy. On the one hand, the autocalalysis of information inexorably drives the evolution of complexity, irrespective of its fragility. On the other hand, only those strategic arrangements that accommodate the destructive forces of entropy survive – cooperation, diversification, and programmed death result from the entropic selection of evolving species. Physical principles of information and entropy thus fashion the evolution of life.

 

Updating Darwin: Information and entropy drive the evolution of life
Irun R. Cohen

Version 1. F1000Res. 2016; 5: 2808.
Published online 2016 Dec 1. doi:  10.12688/f1000research.10289.1


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The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations

With the large-scale penetration of the internet, for the first time, humanity has become linked by a single, open, communications platform. Harnessing this fact, we report insights arising from a unified internet activity and location dataset of an unparalleled scope and accuracy drawn from over a trillion (1.5$\times 10^{12}$) observations of end-user internet connections, with temporal resolution of just 15min over 2006-2012. We show how these data can be used to provide scientific insights in diverse fields such as technological diffusion, chronobiology and economics.  To our knowledge, our study is the first of its kind to use online/offline activity of the entire internet to infer such insights, demonstrating the potential of the internet as a quantitative social data-science platform.


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Prediction and explanation in social systems

Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.

 

Prediction and explanation in social systems
Jake M. Hofman, Amit Sharma, Duncan J. Watts

Science  03 Feb 2017:
Vol. 355, Issue 6324, pp. 486-488
DOI: 10.1126/science.aal3856


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A solution to the single-question crowd wisdom problem

Once considered provocative, the notion that the wisdom of the crowd is superior to any individual has become itself a piece of crowd wisdom, leading to speculation that online voting may soon put credentialed experts out of business. Recent applications include political and economic forecasting, evaluating nuclear safety, public policy, the quality of chemical probes, and possible responses to a restless volcano. Algorithms for extracting wisdom from the crowd are typically based on a democratic voting procedure. They are simple to apply and preserve the independence of personal judgment. However, democratic methods have serious limitations. They are biased for shallow, lowest common denominator information, at the expense of novel or specialized knowledge that is not widely shared. Adjustments based on measuring confidence do not solve this problem reliably. Here we propose the following alternative to a democratic vote: select the answer that is more popular than people predict. We show that this principle yields the best answer under reasonable assumptions about voter behaviour, while the standard ‘most popular’ or ‘most confident’ principles fail under exactly those same assumptions. Like traditional voting, the principle accepts unique problems, such as panel decisions about scientific or artistic merit, and legal or historical disputes. The potential application domain is thus broader than that covered by machine learning and psychometric methods, which require data across multiple questions.

 

A solution to the single-question crowd wisdom problem

Dražen Prelec, H. Sebastian Seung & John McCoy

Nature 541, 532–535 (26 January 2017) doi:10.1038/nature21054

 


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Identifying Critical States through the Relevance Index

The identification of critical states is a major task in complex systems, and the availability of measures to detect such conditions is of utmost importance. In general, criticality refers to the existence of two qualitatively different behaviors that the same system can exhibit, depending on the values of some parameters. In this paper, we show that the relevance index may be effectively used to identify critical states in complex systems. The relevance index was originally developed to identify relevant sets of variables in dynamical systems, but in this paper, we show that it is also able to capture features of criticality. The index is applied to two prominent examples showing slightly different meanings of criticality, namely the Ising model and random Boolean networks. Results show that this index is maximized at critical states and is robust with respect to system size and sampling effort. It can therefore be used to detect criticality.

 

Identifying Critical States through the Relevance Index
Andrea Roli, Marco Villani, Riccardo Caprari and Roberto Serra

Entropy 2017, 19(2), 73; doi:10.3390/e19020073


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Cognitive mechanisms for human flocking dynamics

Low-level “adaptive” and higher-level “sophisticated” human reasoning processes have been proposed to play opposing roles in the emergence of unpredictable collective behaviors such as crowd panics, traffic jams, and market bubbles. While adaptive processes are widely recognized drivers of emergent social complexity, complementary theories of sophistication predict that incentives, education, and other inducements to rationality will suppress it. We show in a series of multiplayer laboratory experiments that, rather than suppressing complex social dynamics, sophisticated reasoning processes can drive them. Our experiments elicit an endogenous collective behavior and show that it is driven by the human ability to recursively anticipate the reasoning of others. We identify this behavior, “sophisticated flocking”, across three games, the Beauty Contest and the “Mod Game” and “Runway Game”. In supporting our argument, we also present evidence for mental models and social norms constraining how players express their higher-level reasoning abilities. By implicating sophisticated recursive reasoning in the kind of complex dynamic that it has been predicted to suppress, we support interdisciplinary perspectives that emergent complexity is typical of even the most intelligent populations and carefully designed social systems.

 

Cognitive mechanisms for human flocking dynamics
Seth Frey, Robert L. Goldstone

Journal of Computational Social Science
September 2018, Volume 1, Issue 2, pp 349–375


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Azteca chess: Gamifying a complex ecological process of autonomous pest control in shade coffee

Azteca chess: Gamifying a complex ecological process of autonomous pest control in shade coffee | Emergence anywhere | Scoop.it

Science-based board games can help people grasp the ecological complexity of autonomous pest control (APC) in the shade-coffee agroecosystem. Azteca Chess is a board-game that captures in a stylized way the fascinating natural history and the dynamics of a complex network of direct, indirect and cascading trait-mediated interactions among five species of arthropods dwelling in shade coffee bushes (a coffee-scale, an ant, an adult and larval lady beetle, a parasitoid wasp and a parasitoid fly). In exchange for honey-dew, the Azteca ant protects scale-insects that help control the devastating coffee-rust disease. The ant repels the adult ladybeetle but inadvertently protects its larvae, which devour scales to local extinction. The head-hunting fly paralyzes Azteca and opens a window of opportunity for the adult beetle to oviposit under scales, but also for a parasitoid wasp to kill the beetle larvae. Interactions can cascade or not towards APC. Experimental test-driving shows Azteca Chess meets good modeling and game-design standards and is proved statistically to enhance understanding and application of relevant complex ecological processes.

 

Azteca chess: Gamifying a complex ecological process of autonomous pest control in shade coffee
Luis García-Barriosa, Ivette Perfecto, John Vandermeer

Agriculture, Ecosystems & Environment
Volume 232, 16 September 2016, Pages 190–198


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Multiplex Modeling of the Society

The society has a multi-layered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, a sufficient amount of inter-layer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multi-layer WSN model, where the indirect inter-layer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved. Furthermore, the network of social interactions can be considered as a multiplex from another point of view too: each layer corresponds to one communication channel and the aggregate of all them constitutes the entire social network. However, usually one has information only about one of the channels, which should be considered as a sample of the whole. Here we show by simulations and analytical methods that this sampling may lead to bias. For example, while it is expected that the degree distribution of the whole social network has a maximum at a value larger than one, we get with reasonable assumptions about the sampling process a monotonously decreasing distribution as observed in empirical studies of single channel data. We analyse the far-reaching consequences of our findings.

 

Multiplex Modeling of the Society

Janos Kertesz, Janos Torok, Yohsuke Murase, Hang-Hyun Jo, Kimmo Kaski


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nukem777's curator insight, November 12, 2016 10:58 AM
Big takeaway: geographical distance is not "dead" and the channels matter!
Luciano Lampi's curator insight, November 13, 2016 11:53 AM
Voting standards are very complex!

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Science of the World Wide Web

Ten years ago, Wikipedia was still in its infancy (and totally dismissed by the establishment), Facebook was still restricted to university users, Twitter was in beta testing, and improving search capabilities was the topic that dominated Web conference research agendas. There were virtually no smartphones, online surveillance of activity and data storage was largely unknown beyond security services, and no one knew that being a data scientist was one day going to be “the sexiest job in the world”

 

Science of the World Wide Web
James Hendler, Wendy Hall
Science  11 Nov 2016:
Vol. 354, Issue 6313, pp. 703-704
DOI: 10.1126/science.aai9150


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On Macrostates in Complex Multi-Scale Systems

A characteristic feature of complex systems is their deep structure, meaning that the definition of their states and observables depends on the level, or the scale, at which the system is considered. This scale dependence is reflected in the distinction of micro- and macro-states, referring to lower and higher levels of description. There are several conceptual and formal frameworks to address the relation between them. Here, we focus on an approach in which macrostates are contextually emergent from (rather than fully reducible to) microstates and can be constructed by contextual partitions of the space of microstates. We discuss criteria for the stability of such partitions, in particular under the microstate dynamics, and outline some examples. Finally, we address the question of how macrostates arising from stable partitions can be identified as relevant or meaningful.

 

On Macrostates in Complex Multi-Scale Systems
Harald Atmanspacher

Entropy 2016, 18(12), 426; doi: 10.3390/e18120426


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Characteristics of the evolution of cooperation by the probabilistic peer-punishment based on the difference of payoff

Regarding costly punishment of two types, especially peer-punishment is considered to decrease the average payoff of all players as well as pool-punishment does, and to facilitate the antisocial punishment as a result of natural selection. To solve those problems, the author has proposed the probabilistic peer-punishment based on the difference of payoff. In the limited condition, the proposed peer-punishment has shown the positive effects on the evolution of cooperation, and increased the average payoff of all players.

Based on those findings, this study exhibits the characteristics of the evolution of cooperation by the proposed peer-punishment. Those characteristics present the significant contribution to knowledge that for the evolution of cooperation, a limited number of players should cause severe damage to defectors at the large expense of their payoff when connections between them are sparse, whereas a greater number of players should share the responsibility to punish defectors at the relatively small expense of their payoff when connections between them are dense.

 

Characteristics of the evolution of cooperation by the probabilistic peer-punishment based on the difference of payoff

Tetsushi Ohdaira

Chaos, Solitons & Fractals
Volume 95, February 2017, Pages 77–83


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Mapping the Themes, Impact, and Cohesion of Creativity Research over the Last 25 Years

Mapping the Themes, Impact, and Cohesion of Creativity Research over the Last 25 Years | Emergence anywhere | Scoop.it

 

This article describes the themes found in the past 25 years of creativity research. Computational methods and network analysis were used to map keyword theme development across ~1,400 documents and ~5,000 unique keywords from 1990 (the first year keywords are available in Web of Science) to 2015.

 

Mapping the Themes, Impact, and Cohesion of Creativity Research over the Last 25 Years
Rich Williams, Mark A. Runco & Eric Berlow
Creativity Research Journal.Volume 28, 2016 - Issue 4 Pages 385-394 | Published online: 14 Nov 2016


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Dynamics on expanding spaces: modeling the emergence of novelties

Novelties are part of our daily lives. We constantly adopt new technologies, conceive new ideas, meet new people, experiment with new situations. Occasionally, we as individuals, in a complicated cognitive and sometimes fortuitous process, come up with something that is not only new to us, but to our entire society so that what is a personal novelty can turn into an innovation at a global level. Innovations occur throughout social, biological and technological systems and, though we perceive them as a very natural ingredient of our human experience, little is known about the processes determining their emergence. Still the statistical occurrence of innovations shows striking regularities that represent a starting point to get a deeper insight in the whole phenomenology. This paper represents a small step in that direction, focusing on reviewing the scientific attempts to effectively model the emergence of the new and its regularities, with an emphasis on more recent contributions: from the plain Simon's model tracing back to the 1950s, to the newest model of Polya's urn with triggering of one novelty by another. What seems to be key in the successful modelling schemes proposed so far is the idea of looking at evolution as a path in a complex space, physical, conceptual, biological, technological, whose structure and topology get continuously reshaped and expanded by the occurrence of the new. Mathematically it is very interesting to look at the consequences of the interplay between the "actual" and the "possible" and this is the aim of this short review.

 

Dynamics on expanding spaces: modeling the emergence of novelties
Vittorio Loreto, Vito D. P. Servedio, Steven H. Strogatz, Francesca Tria


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Teams vs. Crowds: A Field Test of the Relative Contribution of Incentives, Member Ability, and Collaboration to Crowd-Based Problem Solving Performance

Teams vs. Crowds: A Field Test of the Relative Contribution of Incentives, Member Ability, and Collaboration to Crowd-Based Problem Solving Performance | Emergence anywhere | Scoop.it

Organizations are increasingly turning to crowdsourcing to solve difficult problems. This is often driven by the desire to find the best subject matter experts, strongly incentivize them, and engage them with as little coordination cost as possible. A growing number of authors, however, are calling for increased collaboration in crowdsourcing settings, hoping to draw upon the advantages of teamwork observed in traditional settings. The question is how to effectively incorporate team-based collaboration in a setting that has traditionally been individual-based. We report on a large field experiment of team collaboration on an online platform, in which incentives and team membership were randomly assigned, to evaluate the influence of exogenous inputs (member skills and incentives) and emergent collaboration processes on performance of crowd-based teams. Building on advances in machine learning and complex systems theory, we leverage new measurement techniques to examine the content and timing of team collaboration. We find that temporal “burstiness” of team activity and the diversity of information exchanged among team members are strong predictors of performance, even when inputs such as incentives and member skills are controlled. We discuss implications for research on crowdsourcing and team collaboration.

 

Preprint at SSRN.


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Alejandro Lopez laverde's curator insight, April 28, 2017 12:20 PM
I find this interesting becuase it sheds light on aspects of team-based work, which is the foundation of cooperative and collaborative work, and it facilitates me information about how teams can work depending on the strength of its integrants and the way they exchange information to reach a solution to a problem.
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Sequence Memory Constraints Give Rise to Language-Like Structure through Iterated Learning

Human language is composed of sequences of reusable elements. The origins of the sequential structure of language is a hotly debated topic in evolutionary linguistics. In this paper, we show that sets of sequences with language-like statistical properties can emerge from a process of cultural evolution under pressure from chunk-based memory constraints. We employ a novel experimental task that is non-linguistic and non-communicative in nature, in which participants are trained on and later asked to recall a set of sequences one-by-one. Recalled sequences from one participant become training data for the next participant. In this way, we simulate cultural evolution in the laboratory. Our results show a cumulative increase in structure, and by comparing this structure to data from existing linguistic corpora, we demonstrate a close parallel between the sets of sequences that emerge in our experiment and those seen in natural language.

 

Cornish, H., Dale, R., Kirby, S. & Christiansen, M.H. (2017). Sequence memory constraints give rise to language-like structure through iterated learning. PLoS ONE 12(1): e0168532.


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The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

Recently, there has been an increasing interest in the control community in studying large-scale distributed systems. Several techniques have been developed to address the main challenges for these systems, such as the amount of information needed to guarantee the proper operation of the system, the economic costs associated with the required communication structure, and the high computational burden of solving for the control inputs for largescale systems.


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Multi-Agent Foraging: state-of-the-art and research challenges

Background

The foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of robots has to search and transport objects to specific storage point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from several perspectives that we analyze in depth.

Results

First, we define the Foraging Problem according to literature definitions. Then we analyze previously proposed taxonomies, and propose a new foraging taxonomy characterized by four principal axes: Environment, Collective, Strategy and Simulation, summarize related foraging works and classify them through our new foraging taxonomy. Then, we discuss the real implementation of MAF and present a comparison between some related foraging works considering important features that show extensibility, reliability and scalability of MAF systems

Conclusions

Finally we present and discuss recent trends in this field, emphasizing the various challenges that could enhance the existing MAF solutions and make them realistic.

 

Multi-Agent Foraging: state-of-the-art and research challenges
Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi and Giancarlo Fortino
Complex Adaptive Systems Modeling 2017 5:3
DOI: 10.1186/s40294-016-0041-8


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