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A general framework for measuring system complexity

In this work, we are motivated by the observation that previous considerations of appropriate complexity measures have not directly addressed the fundamental issue that the complexity of any particular matter or thing has a significant subjective component in which the degree of complexity depends on available frames of reference. Any attempt to remove subjectivity from a suitable measure therefore fails to address a very significant aspect of complexity. Conversely, there has been justifiable apprehension toward purely subjective complexity measures, simply because they are not verifiable if the frame of reference being applied is in itself both complex and subjective. We address this issue by introducing the concept of subjective simplicity—although a justifiable and verifiable value of subjective complexity may be difficult to assign directly, it is possible to identify in a given context what is “simple” and, from that reference, determine subjective complexity as distance from simple. We then propose a generalized complexity measure that is applicable to any domain, and provide some examples of how the framework can be applied to engineered systems.

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Control principles of complex systems

Control principles of complex systems | Emergence anywhere | Scoop.it

A reflection of our ultimate understanding of a complex system is our ability to control its behavior. Typically, control has multiple prerequisites: it requires an accurate map of the network that governs the interactions between the system’s components, a quantitative description of the dynamical laws that govern the temporal behavior of each component, and an ability to influence the state and temporal behavior of a selected subset of the components. With deep roots in dynamical systems and control theory, notions of control and controllability have taken a new life recently in the study of complex networks, inspiring several fundamental questions: What are the control principles of complex systems? How do networks organize themselves to balance control with functionality? To address these questions here recent advances on the controllability and the control of complex networks are reviewed, exploring the intricate interplay between the network topology and dynamical laws. The pertinent mathematical results are matched with empirical findings and applications. Uncovering the control principles of complex systems can help us explore and ultimately understand the fundamental laws that govern their behavior.

 

Control principles of complex systems
Yang-Yu Liu and Albert-László Barabási
Rev. Mod. Phys. 88, 035006


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Fundamental structures of dynamic social networks

We study the dynamic network of real world person-to-person interactions between approximately 1,000 individuals with 5-min resolution across several months. There is currently no coherent theoretical framework for summarizing the tens of thousands of interactions per day in this complex network, but here we show that at the right temporal resolution, social groups can be identified directly. We outline and validate a framework that enables us to study the statistical properties of individual social events as well as series of meetings across weeks and months. Representing the dynamic network as sequences of such meetings reduces the complexity of the system dramatically. We illustrate the usefulness of the framework by investigating the predictability of human social activity.

 

Fundamental structures of dynamic social networks
Vedran Sekara, Arkadiusz Stopczynski, and Sune Lehmann

PNAS vol. 113 no. 36


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The co-evolution of power and friendship networks in an organization

Despite the pivotal role that both power and interpersonal trust play in a multitude of social exchange situations, relatively little is known about their interplay. Moreover, previous theorizing makes competing claims. Do we consider our relatively more powerful exchange partners to be less trustworthy, as rational choice reasoning would suggest? Or do more complex psychological mechanisms lead us to trust them more, as motivated cognition reasoning implies? Extending the latter approach, we develop and empirically test three hypotheses on the interrelation between perceptions of interpersonal trust and power. According to the status value hypothesis, individuals are more likely to befriend those whom they or others perceive as powerful. The status signaling hypothesis states that the friends of people one perceives as powerful will also be seen as powerful. According to the self-monitoring hypothesis, high self-monitors are more likely than low self-monitors to befriend those they or others perceive as powerful. We use multiplex stochastic actor-based models to analyze the co-evolution of trust and power relations among n = 49 employees in a Dutch Youth Care organization. Data covers three waves of a longitudinal sociometric network survey collected over a period of 18 months in the years 2009–2010. In general, we find some support for all three hypotheses, though the effects are weak. Being one of the first organizational field studies on the co-evolution of power and trust, we conclude with discussing the implications of these findings for the study of social exchange processes.

 

The co-evolution of power and friendship networks in an organization
ALONA LABUN, RAFAEL WITTEK, CHRISTIAN STEGLICH
Network Science , Volume 4 , Issue 03 , September 2016, pp 364 - 384
doi: 10.1017/nws.2016.7


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The Evolutionary Origins of Hierarchy

Hierarchy is a ubiquitous organizing principle in biology, and a key reason evolution produces complex, evolvable organisms, yet its origins are poorly understood. Here we demonstrate for the first time that hierarchy evolves as a result of the costs of network connections. We confirm a previous finding that connection costs drive the evolution of modularity, and show that they also cause the evolution of hierarchy. We further confirm that hierarchy promotes evolvability in addition to evolvability caused by modularity. Because many biological and human-made phenomena can be represented as networks, and because hierarchy is a critical network property, this finding is immediately relevant to a wide array of fields, from biology, sociology, and medical research to harnessing evolution for engineering.

 

Mengistu H, Huizinga J, Mouret J-B, Clune J (2016) The Evolutionary Origins of Hierarchy. PLoS Comput Biol 12(6): e1004829. doi:10.1371/journal.pcbi.1004829


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The new challenges of multiplex networks: measures and models

What do societies, the Internet, and the human brain have in common? The immediate answer might be "not that much", but in reality they are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and at quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.

 

The new challenges of multiplex networks: measures and models
Federico Battiston, Vincenzo Nicosia, Vito Latora

http://arxiv.org/abs/1606.09221


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Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems

In the last years, network scientists have directed their interest to the multi-layer character of real-world systems, and explicitly considered the structural and dynamical organization of graphs made of diverse layers between its constituents. Most complex systems include multiple subsystems and layers of connectivity and, in many cases, the interdependent components of systems interact through many different channels. Such a new perspective is indeed found to be the adequate representation for a wealth of features exhibited by networked systems in the real world. The contributions presented in this Focus Issue cover, from different points of view, the many achievements and still open questions in the field of multi-layer networks, such as: new frameworks and structures to represent and analyze heterogeneous complex systems, different aspects related to synchronization and centrality of complex networks, interplay between layers, and applications to logistic, biological, social, and technological fields.

 

Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems
Stefano Boccaletti, Regino Criado, Miguel Romance and Joaquín J. Torres

Chaos 26, 065101 (2016); http://dx.doi.org/10.1063/1.4953595


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The Braess Paradox in a network of totally asymmetric exclusion processes

We study the Braess paradox in the transport network as originally proposed by Braess with totally asymmetric exclusion processes (TASEPs) on the edges. The Braess paradox describes the counterintuitive situation where adding an additional edge to a road network leads to a user optimum with higher traveltimes for all network users. Traveltimes on the TASEPs are nonlinear in the density and jammed states can occur due to the microscopic exclusion principle. Furthermore the individual edges influence each other. This leads to a much more realistic description of traffic-like transport on the network than in previously studied linear macroscopic mathematical models. Furthermore the stochastic dynamics allows to explore the effects of fluctuations on the network performance. We observe that for low densities the added edge leads to lower traveltimes. For slightly higher densities the Braess paradox in its classical sense occurs in a small density regime. In a large regime of intermediate densities strong fluctuations in the traveltimes dominate the system's behaviour. These fluctuations are due to links that are in a domain wall or coexistence phase. At high densities the added link leads to lower traveltimes. We present a phase diagram predicting in which state the system will be, depending on the global density and crucial length ratios.

 

The Braess Paradox in a network of totally asymmetric exclusion processes
Stefan Bittihn, Andreas Schadschneider

http://arxiv.org/abs/1608.03753


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Open Issues in Evolutionary Robotics

Open Issues in Evolutionary Robotics | Emergence anywhere | Scoop.it

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize controllers for real autonomous robots based only on a task specification. While a number of studies have shown the applicability of evolutionary robotics techniques for the synthesis of behavioral control, researchers have consistently been faced with a number of issues preventing the widespread adoption of evolutionary robotics for engineering purposes. In this article, we review and discuss the open issues in evolutionary robotics. First, we analyze the benefits and challenges of simulation-based evolution and subsequent deployment of controllers versus evolution on real robotic hardware. Second, we discuss specific evolutionary computation issues that have plagued evolutionary robotics: (1) the bootstrap problem, (2) deception, and (3) the role of genomic encoding and genotype-phenotype mapping in the evolution of controllers for complex tasks. Finally, we address the absence of standard research practices in the field. We also discuss promising avenues of research. Our underlying motivation is the reduction of the current gap between evolutionary robotics and mainstream robotics, and the establishment of evolutionary robotics as a canonical approach for the engineering of autonomous robots.

 

Open Issues in Evolutionary Robotics
Fernando Silva, Miguel Duarte, Luís Correia, Sancho Moura Oliveira, Anders Lyhne Christensen

Evolutionary Computation

Summer 2016, Vol. 24, No. 2, Pages 205-236
Posted Online June 13, 2016.
http://dx.doi.org/10.1162/EVCO_a_00172


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Visualizing the “heartbeat” of a city with tweets

Describing the dynamics of a city is a crucial step to both understanding the human activity in urban environments and to planning and designing cities accordingly. Here, we describe the collective dynamics of New York City (NYC) and surrounding areas as seen through the lens of Twitter usage. In particular, we observe and quantify the patterns that emerge naturally from the hourly activities in different areas of NYC, and discuss how they can be used to understand the urban areas. Using a dataset that includes more than 6 million geolocated Twitter messages we construct a movie of the geographic density of tweets. We observe the diurnal “heartbeat” of the NYC area. The largest scale dynamics are the waking and sleeping cycle and commuting from residential communities to office areas in Manhattan. Hourly dynamics reflect the interplay of commuting, work and leisure, including whether people are preoccupied with other activities or actively using Twitter. Differences between weekday and weekend dynamics point to changes in when people wake and sleep, and engage in social activities. We show that by measuring the average distances to a central location one can quantify the weekly differences and the shift in behavior during weekends. We also identify locations and times of high Twitter activity that occur because of specific activities. These include early morning high levels of traffic as people arrive and wait at air transportation hubs, and on Sunday at the Meadowlands Sports Complex and Statue of Liberty. We analyze the role of particular individuals where they have large impacts on overall Twitter activity. Our analysis points to the opportunity to develop insight into both geographic social dynamics and attention through social media analysis.

 

Visualizing the “heartbeat” of a city with tweets
Urbano França, 


Hiroki Sayama, 


Colin Mcswiggen, Roozbeh Daneshvar, 


Yaneer Bar-Yam

Complexity

Volume 21, Issue 6
July/August 2016
Pages 280–287

http://dx.doi.org/10.1002/cplx.21687


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Evolution of cooperation by the introduction of the probabilistic peer-punishment based on the difference of payoff

There are two types of costly punishment, i.e. peer-punishment and pool-punishment. While peer-punishment applies direct face to face punishment, pool-punishment is based on multi-point, collective interaction among group members. Regarding those two types of costly punishment, peer-punishment is especially considered to have the flaws that it lowers the average payoff of all players as well as pool-punishment does, and facilitates antisocial behaviour like retaliation of a defector on a cooperator. Here, this study proposes the new peer-punishment that punishment to an opponent player works at high probability when an opponent one is uncooperative, and the difference of payoff between a player and an opponent one becomes large in order to prevent such antisocial behaviour. It is natural to think that players of high payoff do not expect to punish others of lower payoff because they do not have any complaints regarding their economic wealth. The author shows that the introduction of the proposed peer-punishment increases both the number of cooperative players and the average payoff of all players in various types of topology of connections between players.

 

Evolution of cooperation by the introduction of the probabilistic peer-punishment based on the difference of payoff
T. Ohdaira
Scientific Reports 6, Article number: 25413 (2016)
http://dx.doi.org/10.1038/srep25413 ;


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Selfishness Is Learned

Selfishness Is Learned | Emergence anywhere | Scoop.it

Many people cheat on taxes—no mystery there. But many people don’t, even if they wouldn’t be caught—now, that’s weird. Or is it? Psychologists are deeply perplexed by human moral behavior, because it often doesn’t seem to make any logical sense. You might think that we should just be grateful for it. But if we could understand these seemingly irrational acts, perhaps we could encourage more of them.

 

http://nautil.us/issue/37/currents/selfishness-is-learned


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Evolution of Swarming Behavior Is Shaped by How Predators Attack

Animal grouping behaviors have been widely studied due to their implications for understanding social intelligence, collective cognition, and potential applications in engineering, artificial intelligence, and robotics. An important biological aspect of these studies is discerning which selection pressures favor the evolution of grouping behavior. In the past decade, researchers have begun using evolutionary computation to study the evolutionary effects of these selection pressures in predator-prey models. The selfish herd hypothesis states that concentrated groups arise because prey selfishly attempt to place their conspecifics between themselves and the predator, thus causing an endless cycle of movement toward the center of the group. Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd. Following this discovery, we show that density-dependent predation provides an abstraction of Hamilton's original formulation of domains of danger. Finally, we verify that density-dependent predation provides a sufficient selective advantage for prey to evolve the selfish herd in response to predation by coevolving predators. Thus, our work corroborates Hamilton's selfish herd hypothesis in a digital evolutionary model, refines the assumptions of the selfish herd hypothesis, and generalizes the domain of danger concept to density-dependent predation.

 

Evolution of Swarming Behavior Is Shaped by How Predators Attack
Randal S. Olson
David B. Knoester
Christoph Adami

Artificial Life

http://dx.doi.org/10.1162/ARTL_a_00206


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Defining emergence: Learning from flock behavior

The idea of emergence originates from the fact that global effects emerge from local interactions producing a collective coherent behavior. A particular instance of emergence is illustrated by a flocking model of interacting “boids” encompassing two antagonistic conducts—consensus and frustration—giving rise to highly complex, unpredictable, coherent behavior. The cohesive motion arising from consensus can be described in terms of three ordered dynamic phases. Once frustration is included in the model, local phases for specific groups of flockmates, and transitions among them, replace the global ordered phases. Following the evolution of boids in a single group, we discovered that the boids in this group will alternate among the three phases. When we compare two uncorrelated groups, the second group shows a similar behavior to the first one, but with a different sequence of phases. Besides the visual observation of our animations with marked boids, the result is evident plotting the local order parameters. Rather than adopting one of the consensus ordered phases, the flock motion resembles more an entangled dynamic sequence of phase transitions involving each group of flockmates.

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Symmetric States Requiring System Asymmetry

Symmetric States Requiring System Asymmetry | Emergence anywhere | Scoop.it

Spontaneous synchronization has long served as a paradigm for behavioral uniformity that can emerge from interactions in complex systems. When the interacting entities are identical and their coupling patterns are also identical, the complete synchronization of the entire network is the state inheriting the system symmetry. As in other systems subject to symmetry breaking, such symmetric states are not always stable. Here, we report on the discovery of the converse of symmetry breaking—the scenario in which complete synchronization is not stable for identically coupled identical oscillators but becomes stable when, and only when, the oscillator parameters are judiciously tuned to nonidentical values, thereby breaking the system symmetry to preserve the state symmetry. Aside from demonstrating that diversity can facilitate and even be required for uniformity and consensus, this suggests a mechanism for convergent forms of pattern formation in which initially asymmetric patterns evolve into symmetric ones.

 

Symmetric States Requiring System Asymmetry
Takashi Nishikawa and Adilson E. Motter
Phys. Rev. Lett. 117, 114101


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The Social Dynamics of Language Change in Online Networks

Language change is a complex social phenomenon, revealing pathways of communication and sociocultural influence. But, while language change has long been a topic of study in sociolinguistics, traditional linguistic research methods rely on circumstantial evidence, estimating the direction of change from differences between older and younger speakers. In this paper, we use a data set of several million Twitter users to track language changes in progress. First, we show that language change can be viewed as a form of social influence: we observe complex contagion for phonetic spellings and "netspeak" abbreviations (e.g., lol), but not for older dialect markers from spoken language. Next, we test whether specific types of social network connections are more influential than others, using a parametric Hawkes process model. We find that tie strength plays an important role: densely embedded social ties are significantly better conduits of linguistic influence. Geographic locality appears to play a more limited role: we find relatively little evidence to support the hypothesis that individuals are more influenced by geographically local social ties, even in their usage of geographical dialect markers.

 

The Social Dynamics of Language Change in Online Networks
Rahul Goel, Sandeep Soni, Naman Goyal, John Paparrizos, Hanna Wallach, Fernando Diaz, Jacob Eisenstein


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Coupled dynamics of node and link states in complex networks: A model for language competition

Inspired by language competition processes, we present a model of coupled evolution of node and link states. In particular, we focus on the interplay between the use of a language and the preference or attitude of the speakers towards it, which we model, respectively, as a property of the interactions between speakers (a link state) and as a property of the speakers themselves (a node state). Furthermore, we restrict our attention to the case of two socially equivalent languages and to socially inspired network topologies based on a mechanism of triadic closure. As opposed to most of the previous literature, where language extinction is an inevitable outcome of the dynamics, we find a broad range of possible asymptotic configurations, which we classify as: frozen extinction states, frozen coexistence states, and dynamically trapped coexistence states. Moreover, metastable coexistence states with very long survival times and displaying a non-trivial dynamics are found to be abundant. Interestingly, a system size scaling analysis shows, on the one hand, that the probability of language extinction vanishes exponentially for increasing system sizes and, on the other hand, that the time scale of survival of the non-trivial dynamical metastable states increases linearly with the size of the system. Thus, non-trivial dynamical coexistence is the only possible outcome for large enough systems. Finally, we show how this coexistence is characterized by one of the languages becoming clearly predominant while the other one becomes increasingly confined to "ghetto-like" structures: small groups of bilingual speakers arranged in triangles, with a strong preference for the minority language, and using it for their intra-group interactions while they switch to the predominant language for communications with the rest of the population.

 

Coupled dynamics of node and link states in complex networks: A model for language competition
Adrián Carro, Raúl Toral, Maxi San Miguel


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Influence of selfish and polite behaviours on a pedestrian evacuation through a narrow exit: A quantitative characterisation

We study the influence of selfish vs. polite behaviours on the dynamics of a pedestrian evacuation through a narrow exit. To this end, experiments involving about 80 participants with distinct prescribed behaviours are performed; reinjection of participants into the setup allowed us to improve the statistics. Notwithstanding the fluctuations in the instantaneous flow rate, we find that a stationary regime is almost immediately reached. The average flow rate increases monotonically with the fraction c\_s of vying (selfish) pedestrians, which corresponds to a "faster-is-faster" effect in our experimental conditions; it is also positively correlated with the average density of pedestrians in front of the door, up to nearly close-packing. At large c\_s , the flow displays marked intermittency, with bursts of quasi-simultaneous escapes. In addition to these findings, we wonder whether the effect of cooperation is specific to systems of intelligent beings, or whether it can be reproduced by a purely mechanical surrogate. To this purpose, we consider a bidimensional granular flow through an orifice in which some grains are made "cooperative" by repulsive magnetic interactions which impede their mutual collisions.

 

Influence of selfish and polite behaviours on a pedestrian evacuation through a narrow exit: A quantitative characterisation
Alexandre Nicolas, Sebastián Bouzat, Marcelo Kuperman

http://arxiv.org/abs/1608.04863


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Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems

In the last years, network scientists have directed their interest to the multi-layer character of real-world systems, and explicitly considered the structural and dynamical organization of graphs made of diverse layers between its constituents. Most complex systems include multiple subsystems and layers of connectivity and, in many cases, the interdependent components of systems interact through many different channels. Such a new perspective is indeed found to be the adequate representation for a wealth of features exhibited by networked systems in the real world. The contributions presented in this Focus Issue cover, from different points of view, the many achievements and still open questions in the field of multi-layer networks, such as: new frameworks and structures to represent and analyze heterogeneous complex systems, different aspects related to synchronization and centrality of complex networks, interplay between layers, and applications to logistic, biological, social, and technological fields.

 

Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems
Stefano Boccaletti, Regino Criado, Miguel Romance and Joaquín J. Torres

Chaos 26, 065101 (2016); http://dx.doi.org/10.1063/1.4953595


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Fruitful symbioses between termites and computers

The living-together of distinct organisms in a single termite nest along with the termite builder colony, is emblematic in its ecological and evolutionary significance. On top of preserving biodiversity, these interspecific and intraspecific symbioses provide useful examples of interindividual associations thought to underly transitions in organic evolution. Being interindividual in nature, such processes may involve emergent phenomena and hence call for analytical solutions provided by computing tools and modelling, as opposed to classical biological methods of analysis. Here we provide selected examples of such solutions, showing that termite studies may profit from a symbiotic-like link with computing science to open up wide and new research avenues in ecology and evolution.

 

Fruitful symbioses between termites and computers
Og DeSouza, Elio Tuci, Octavio Miramontes

http://arxiv.org/abs/1608.05367


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Open-Ended Evolution: Perspectives from the OEE Workshop in York

We describe the content and outcomes of the First Workshop on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1), held during the ECAL 2015 conference at the University of York, UK, in July 2015. We briefly summarize the content of the workshop's talks, and identify the main themes that emerged from the open discussions. Two important conclusions from the discussions are: (1) the idea of pluralism about OEE—it seems clear that there is more than one interesting and important kind of OEE; and (2) the importance of distinguishing observable behavioral hallmarks of systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits those hallmarks. We summarize the different hallmarks and mechanisms discussed during the workshop, and list the specific systems that were highlighted with respect to particular hallmarks and mechanisms. We conclude by identifying some of the most important open research questions about OEE that are apparent in light of the discussions. The York workshop provides a foundation for a follow-up OEE2 workshop taking place at the ALIFE XV conference in Cancún, Mexico, in July 2016. Additional materials from the York workshop, including talk abstracts, presentation slides, and videos of each talk, are available at http://alife.org/ws/oee1

 

Open-Ended Evolution: Perspectives from the OEE Workshop in York
Tim Taylor, Mark Bedau, Alastair Channon, et al.

Artificial Life

http://dx.doi.org/10.1162/ARTL_a_00210


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Models and people: An alternative view of the emergent properties of computational models

Computer models can help humans gain insight into the functioning of complex systems. Used for training, they can also help gain insight into the cognitive processes humans use to understand these systems. By influencing humans understanding (and consequent actions) computer models can thus generate an impact on both these actors and the very systems they are designed to simulate. When these systems also include humans, a number of self-referential relations thus emerge which can lead to very complex dynamics. This is particularly true when we explicitly acknowledge and model the existence of multiple conflicting representations of reality among different individuals. Given the increasing availability of computational devices, the use of computer models to support individual and shared decision making could potentially have implications far wider than the ones often discussed within the Information and Communication Technologies community in terms of computational power and network communication. We discuss some theoretical implications and describe some initial numerical simulations.

 

Models and people: An alternative view of the emergent properties of computational models
Fabio Boschetti

Complexity

Volume 21, Issue 6
July/August 2016
Pages 202–213

http://dx.doi.org/10.1002/cplx.21680


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Cultural evolution as a nonstationary stochastic process

We present an individual based model of cultural evolution, where interacting agents are coded by binary strings standing for strategies for action, blueprints for products or attitudes and beliefs. The model is patterned on an established model of biological evolution, the Tangled Nature Model (TNM), where a “tangle” of interactions between agents determines their reproductive success. In addition, our agents also have the ability to copy part of each other's strategy, a feature inspired by the Axelrod model of cultural diversity. Unlike the latter, but similarly to the TNM, the model dynamics goes through a series of metastable stages of increasing length, each characterized by mutually enforcing cultural patterns. These patterns are abruptly replaced by other patterns characteristic of the next metastable period. We analyze the time dependence of the population and diversity in the system, show how different cultures are formed and merge, and how their survival probability lacks, in the model, a finite average life-time. Finally, we use historical data on the number of car manufacturers after the introduction of the automobile to the market, to argue that our model can qualitatively reproduce the flurry of cultural activity which follows a disruptive innovation

 

Cultural evolution as a nonstationary stochastic process
Authors
Arwen E. Nicholson, 


Paolo Sibani

Complexity

Volume 21, Issue 6
July/August 2016
Pages 214–223

http://dx.doi.org/10.1002/cplx.21681 ;


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Peer review and competition in the Art Exhibition Game

Competition is an essential mechanism in increasing the effort and performance of human groups in real life. However, competition has side effects: it can be detrimental to creativity and reduce cooperation. We conducted an experiment called the Art Exhibition Game to investigate the effect of competitive incentives in environments where the quality of creative products and the amount of innovation allowed are decided through peer review. Our approach is general and can provide insights in domains such as clinical evaluations, scientific admissibility, and science funding. Our results show that competition leads to more innovation but also to more unfair reviews and to a lower level of agreement between reviewers. Moreover, competition does not improve the average quality of published works.

 

Peer review and competition in the Art Exhibition Game
Stefano Baliettia,b,c,1, Robert L. Goldstoned, and Dirk Helbing

PNAS

http://dx.doi.org/10.1073/pnas.1603723113


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Small groups and long memories promote cooperation

Complex social behaviors lie at the heart of many of the challenges facing evolutionary biology, sociology, economics, and beyond. For evolutionary biologists the question is often how group behaviors such as collective action, or decision making that accounts for memories of past experience, can emerge and persist in an evolving system. Evolutionary game theory provides a framework for formalizing these questions and admitting them to rigorous study. Here we develop such a framework to study the evolution of sustained collective action in multi-player public-goods games, in which players have arbitrarily long memories of prior rounds of play and can react to their experience in an arbitrary way. We construct a coordinate system for memory-m strategies in iterated n-player games that permits us to characterize all cooperative strategies that resist invasion by any mutant strategy, and stabilize cooperative behavior. We show that, especially when groups are small, longer-memory strategies make cooperation easier to evolve, by increasing the number of ways to stabilize cooperation. We also explore the co-evolution of behavior and memory. We find that even when memory has a cost, longer-memory strategies often evolve, which in turn drives the evolution of cooperation, even when the benefits for cooperation are low.

 

Small groups and long memories promote cooperation
Alexander J. Stewart & Joshua B. Plotkin
Scientific Reports 6, Article number: 26889 (2016)
http://dx.doi.org/10.1038/srep26889


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