A game theoretical approach to the evolution of structured communication codes

Structured meaning-signal mappings, i.e., mappings that preserve neighborhood relationships by associating similar signals with similar meanings, are advantageous in an environment where signals are corrupted by noise and sub-optimal meaning inferences are rewarded as well. The evolution of these mappings, however, cannot be explained within a traditional language evolutionary game scenario in which individuals meet randomly because the evolutionary dynamics is trapped in local maxima that do not reflect the structure of the meaning and signal spaces. Here we use a simple game theoretical model to show analytically that when individuals adopting the same communication code meet more frequently than individuals using different codes-a result of the spatial organization of the population-then advantageous linguistic innovations can spread and take over the population. In addition, we report results of simulations in which an individual can communicate only with its K nearest neighbors and show that the probability that the lineage of a mutant that uses a more efficient communication code becomes fixed decreases exponentially with increasing K. These findings support the mother tongue hypothesis that human language evolved as a communication system used among kin, especially between mothers and offspring.

The evolution of begging: Signaling and sibling competition

In many species, young solicit food from their parents, which respond by feeding them. Because of the difference in genetic make-up between parents and their offspring and the consequent conflict, this interaction is often studied as a paradigm for the evolution of communication. Existent theoretical models demonstrate that chick signaling and parent responding can be stable if solicitation is a costly signal. The marginal cost of producing stronger signals allows the system to converge to an equilibrium: young beg with intensity that reflects their need, and parents use this information to maximize their own inclusive fitness. However, we show that there is another equilibrium where chicks do not beg and parents’ provisioning effort is optimal with respect to the statistically probable distribution of chicks’ states. Expected fitness for parents and offspring at the nonsignaling equilibrium is higher than at the signaling equilibrium. Because nonsignaling is stable and it is likely to be the ancestral condition, we would like to know how natural systems evolved from nonsignaling to signaling. We suggest that begging may have evolved through direct sibling fighting before the establishment of a parental response, that is, that nonsignaling squabbling leads to signaling. In multiple-offspring broods, young following a condition-dependent strategy in the contest for resources provide information about their condition. Parents can use this information even though it is not an adaptation for communication, and evolution will lead the system to the signaling equilibrium. This interpretation implies that signaling evolved in multiple-offspring broods, but given that signaling is evolutionarily stable, it would also be favored in species which secondarily evolved single-chick broods.

The evolution of controlled multitasked gene networks: The role of introns and other noncoding RNAs in the development of complex organisms

Eukaryotic phenotypic diversity arises from multitasking of a core proteome of limited size. Multitasking is routine in computers, as well as in other sophisticated information systems, and requires multiple inputs and outputs to control and integrate network activity. Higher eukaryotes have a mosaic gene structure with a dual output, mRNA (protein-coding) sequences and introns, which are released from the pre-mRNA by posttranscriptional processing. Introns have been enormously successful as a class of sequences and comprise up to 95% of the primary transcripts of protein-coding genes in mammals. In addition, many other transcripts (perhaps more than half) do not encode proteins at all, but appear both to be developmentally regulated and to have genetic function. We suggest that these RNAs (eRNAs) have evolved to function as endogenous network control molecules which enable direct gene-gene communication and multitasking of eukaryotic genomes. Analysis of a range of complex genetic phenomena in which RNA is involved or implicated, including co-suppression, transgene silencing, RNA interference, imprinting, methylation, and transvection, suggests that a higher-order regulatory system based on RNA signals operates in the higher eukaryotes and involves chromatin remodeling as well as other RNA-DNA, RNA-RNA, and RNA-protein interactions. The evolution of densely connected gene networks would be expected to result in a relatively stable core proteome due to the multiple reuse of components, implying,that cellular differentiation and phenotypic variation in the higher eukaryotes results primarily from variation in the control architecture. Thus, network integration and multitasking using trans-acting RNA molecules produced in parallel with protein-coding sequences may underpin both the evolution of developmentally sophisticated multicellular organisms and the rapid expansion of phenotypic complexity into uncontested environments such as those initiated in the Cambrian radiation and those seen after major extinction events.

The evolution of information suppression in communicating robots with conflicting interests.

Reliable information is a crucial factor influencing decision-making and, thus, fitness in all animals. A common source of information comes from inadvertent cues produced by the behavior of conspecifics. Here we use a system of experimental evolution with robots foraging in an arena containing a food source to study how communication strategies can evolve to regulate information provided by such cues. The robots could produce information by emitting blue light, which the other robots could perceive with their cameras. Over the first few generations, the robots quickly evolved to successfully locate the food, while emitting light randomly. This behavior resulted in a high intensity of light near food, which provided social information allowing other robots to more rapidly find the food. Because robots were competing for food, they were quickly selected to conceal this information. However, they never completely ceased to produce information. Detailed analyses revealed that this somewhat surprising result was due to the strength of selection on suppressing information declining concomitantly with the reduction in information content. Accordingly, a stable equilibrium with low information and considerable variation in communicative behaviors was attained by mutation selection. Because a similar coevolutionary process should be common in natural systems, this may explain why communicative strategies are so variable in many animal species.

Ecology and evolution of soil nematode chemotaxis.

Plants influence the behavior of and modify community composition of soil-dwelling organisms through the exudation of organic molecules. Given the chemical complexity of the soil matrix, soil-dwelling organisms have evolved the ability to detect and respond to these cues for successful foraging. A key question is how specific these responses are and how they may evolve. Here, we review and discuss the ecology and evolution of chemotaxis of soil nematodes. Soil nematodes are a group of diverse functional and taxonomic types, which may reveal a variety of responses. We predicted that nematodes of different feeding guilds use host-specific cues for chemotaxis. However, the examination of a comprehensive nematode phylogeny revealed that distantly related nematodes, and nematodes from different feeding guilds, can exploit the same signals for positive orientation. Carbon dioxide (CO(2)), which is ubiquitous in soil and indicates biological activity, is widely used as such a cue. The use of the same signals by a variety of species and species groups suggests that parts of the chemo-sensory machinery have remained highly conserved during the radiation of nematodes. However, besides CO(2), many other chemical compounds, belonging to different chemical classes, have been shown to induce chemotaxis in nematodes. Plants surrounded by a complex nematode community, including beneficial entomopathogenic nematodes, plant-parasitic nematodes, as well as microbial feeders, are thus under diffuse selection for producing specific molecules in the rhizosphere that maximize their fitness. However, it is largely unknown how selection may operate and how belowground signaling may evolve. Given the paucity of data for certain groups of nematodes, future work is needed to better understand the evolutionary mechanisms of communication between plant roots and soil biota.

Ancient protostome origin of chemosensory ionotropic glutamate receptors and the evolution of insect taste and olfaction.

Ionotropic glutamate receptors (iGluRs) are a highly conserved family of ligand-gated ion channels present in animals, plants, and bacteria, which are best characterized for their roles in synaptic communication in vertebrate nervous systems. A variant subfamily of iGluRs, the Ionotropic Receptors (IRs), was recently identified as a new class of olfactory receptors in the fruit fly, Drosophila melanogaster, hinting at a broader function of this ion channel family in detection of environmental, as well as intercellular, chemical signals. Here, we investigate the origin and evolution of IRs by comprehensive evolutionary genomics and in situ expression analysis. In marked contrast to the insect-specific Odorant Receptor family, we show that IRs are expressed in olfactory organs across Protostomia--a major branch of the animal kingdom that encompasses arthropods, nematodes, and molluscs--indicating that they represent an ancestral protostome chemosensory receptor family. Two subfamilies of IRs are distinguished: conserved "antennal IRs," which likely define the first olfactory receptor family of insects, and species-specific "divergent IRs," which are expressed in peripheral and internal gustatory neurons, implicating this family in taste and food assessment. Comparative analysis of drosophilid IRs reveals the selective forces that have shaped the repertoires in flies with distinct chemosensory preferences. Examination of IR gene structure and genomic distribution suggests both non-allelic homologous recombination and retroposition contributed to the expansion of this multigene family. Together, these findings lay a foundation for functional analysis of these receptors in both neurobiological and evolutionary studies. Furthermore, this work identifies novel targets for manipulating chemosensory-driven behaviours of agricultural pests and disease vectors.

The establishment of communication systems depends on the scale of competition

How communication systems emerge and remain stable is an important question in both cognitive science and evolutionary biology. For communication to arise, not only must individuals cooperate by signaling reliable information, but they must also coordinate and perpetuate signals. Most studies on the emergence of communication in humans typically consider scenarios where individuals implicitly share the same interests. Likewise, most studies on human cooperation consider scenarios where shared conventions of signals and meanings cannot be developed de novo. Here, we combined both approaches with an economic experiment where participants could develop a common language, but under different conditions fostering or hindering cooperation. Participants endeavored to acquire a resource through a learning task in a computer-based environment. After this task, participants had the option to transmit a signal (a color) to a fellow group member, who would subsequently play the same learning task. We varied the way participants competed with each other (either global scale or local scale) and the cost of transmitting a signal (either costly or noncostly) and tracked the way in which signals were used as communication among players. Under global competition, players signaled more often and more consistently, scored higher individual payoffs, and established shared associations of signals and meanings. In addition, costly signals were also more likely to be used under global competition; whereas under local competition, fewer signals were sent and no effective communication system was developed. Our results demonstrate that communication involves both a coordination and a cooperative dilemma and show the importance of studying language evolution under different conditions influencing human cooperation.

Evolution of Ba and Role of Absorptive Capacity in Collaboration between MNCs and Chinese Universities in China

During the recent years, collaboration with Chinese universities has aroused growing interest among multinational companies (MNCs). Cross-cultural university-industry (U-I) collaboration creates various challenges in collaborative knowledge creation and innovation due to the differences e.g. between university and company motivation, objectives and activities. Also different values, norms, and means of actions result often in collisions and misunderstandings. This thesis examines the establishment of the relationships and the evolution of the collaboration between MNCs and Chinese universities. Empirical findings underscore that the partners in collaboration are required to possess research interest as well as capability to acquire, assimilate and exploit new external knowledge. Time and communication have a critical role in the evolution of the collaboration. In China the personal relationships, guanxi, play an important role. Collaborative knowledge creation requires a platform, Ba, which enables the creation of common understanding, commitment, trust and mutual respect. Empirical data has been collected through interviewing company experts and academe of Chinese universities from ICT and forest industries as well as attending panel discussions and meetings with the experts from the field of study.

Direct observations of the evolution of polar cap ionization patches

Patches of ionization are common in the polar ionosphere where their motion and associated density gradients give variable disturbances to High Frequency (HF) radio communications, over-the-horizon radar location errors, and disruption and errors to satellite navigation and communication. Their formation and evolution are poorly understood, particularly under disturbed space weather conditions. We report direct observations of the full evolution of patches during a geomagnetic storm, including formation, polar cap entry, transpolar evolution, polar cap exit, and sunward return flow. Our observations show that modulation of nightside reconnection in the substorm cycle of the magnetosphere helps form the gaps between patches where steady convection would give a “tongue” of ionization (TOI).

A novel approach for distributed application scheduling based on prediction of communication events

The evolution of commodity computing lead to the possibility of efficient usage of interconnected machines to solve computationally-intensive tasks, which were previously solvable only by using expensive supercomputers. This, however, required new methods for process scheduling and distribution, considering the network latency, communication cost, heterogeneous environments and distributed computing constraints. An efficient distribution of processes over such environments requires an adequate scheduling strategy, as the cost of inefficient process allocation is unacceptably high. Therefore, a knowledge and prediction of application behavior is essential to perform effective scheduling. In this paper, we overview the evolution of scheduling approaches, focusing on distributed environments. We also evaluate the current approaches for process behavior extraction and prediction, aiming at selecting an adequate technique for online prediction of application execution. Based on this evaluation, we propose a novel model for application behavior prediction, considering chaotic properties of such behavior and the automatic detection of critical execution points. The proposed model is applied and evaluated for process scheduling in cluster and grid computing environments. The obtained results demonstrate that prediction of the process behavior is essential for efficient scheduling in large-scale and heterogeneous distributed environments, outperforming conventional scheduling policies by a factor of 10, and even more in some cases. Furthermore, the proposed approach proves to be efficient for online predictions due to its low computational cost and good precision. (C) 2009 Elsevier B.V. All rights reserved.

MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modi. cations in their intrinsic organization. For instance, the network of transportation routes between cities and towns ( hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways ( paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation ( in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

Cross-situational and supervised learning in the emergence of communication

Scenarios for the emergence or bootstrap of a lexicon involve the repeated interaction between at least two agents who must reach a consensus on how to name N objects using H words. Here we consider minimal models of two types of learning algorithms: cross-situational learning, in which the individuals determine the meaning of a word by looking for something in common across all observed uses of that word, and supervised operant conditioning learning, in which there is strong feedback between individuals about the intended meaning of the words. Despite the stark differences between these learning schemes, we show that they yield the same communication accuracy in the limits of large N and H, which coincides with the result of the classical occupancy problem of randomly assigning N objects to H words.