|
|
Seeley, T. D. (2002). When Is Self-Organization Used in Biological Systems? Biol Bull, 202(3), 314–318.
Abstract: Self-organization, or decentralized control, is widespread in biological systems, including cells, organisms, and groups. It is not, however, the universal means of organization. I argue that a biological system will be self-organized when it possesses a large number of subunits, and these subunits lack either the communicational abilities or the computational abilities, or both, that are needed to implement centralized control. Such control requires a well informed and highly intelligent supervisor. I stress that the subunits in a self-organized system do not necessarily have low cognitive abilities. A lack of preadaptations for evolving a system-wide communication network can prevent the evolution of centralized control. Hence, sometimes even systems whose subunits possess high cognitive abilities will be self-organized. N1 -
|
|
|
|
Parrish, J. K., Viscido, S. V., & Grunbaum, D. (2002). Self-Organized Fish Schools: An Examination of Emergent Properties. Biol Bull, 202(3), 296–305.
Abstract: Heterogeneous, “aggregated” patterns in the spatial distributions of individuals are almost universal across living organisms, from bacteria to higher vertebrates. Whereas specific features of aggregations are often visually striking to human eyes, a heuristic analysis based on human vision is usually not sufficient to answer fundamental questions about how and why organisms aggregate. What are the individual-level behavioral traits that give rise to these features? When qualitatively similar spatial patterns arise from purely physical mechanisms, are these patterns in organisms biologically significant, or are they simply epiphenomena that are likely characteristics of any set of interacting autonomous individuals? If specific features of spatial aggregations do confer advantages or disadvantages in the fitness of group members, how has evolution operated to shape individual behavior in balancing costs and benefits at the individual and group levels? Mathematical models of social behaviors such as schooling in fishes provide a promising avenue to address some of these questions. However, the literature on schooling models has lacked a common framework to objectively and quantitatively characterize relationships between individual-level behaviors and group-level patterns. In this paper, we briefly survey similarities and differences in behavioral algorithms and aggregation statistics among existing schooling models. We present preliminary results of our efforts to develop a modeling framework that synthesizes much of this previous work, and to identify relationships between behavioral parameters and group-level statistics. N1 -
|
|
|
|
Silk, J. B. (2002). Kin Selection in Primate Groups. Int. J. Primatol., 23(4), 849–875.
Abstract: Altruism poses a problem for evolutionary biologists because natural selection is not expected to favor behaviors that are beneficial to recipients, but costly to actors. The theory of kin selection, first articulated by Hamilton (1964), provides a solution to the problem. Hamilton's well-known rule (br > c) provides a simple algorithm for the evolution of altruism via kin selection. Because kin recognition is a crucial requirement of kin selection, it is important to know whether and how primates can recognize their relatives. While conventional wisdom has been that primates can recognize maternal kin, but not paternal kin, this view is being challenged by new findings. The ability to recognize kin implies that kin selection may shape altruistic behavior in primate groups. I focus on two cases in which kin selection is tightly woven into the fabric of social life. For female baboons, macaques, and vervets maternal kinship is an important axis of social networks, coalitionary activity, and dominance relationships. Detailed studies of the patterning of altruistic interactions within these species illustrate the extent and limits of nepotism in their social lives. Carefully integrated analyses of behavior, demography, and genetics among red howlers provide an independent example of how kin selection shapes social organization and behavior. In red howlers, kin bonds shape the life histories and reproductive performance of both males and female. The two cases demonstrate that kin selection can be a powerful source of altruistic activity within primate groups. However, to fully assess the role of kin selection in primate groups, we need more information about the effects of kinship on the patterning of behavior across the Primates and accurate information about paternal kin relationships.
|
|
|
|
Borgatti, S. P., Everett, M.G., Freeman, L.C. (2002). Ucinet for Windows: Software for Social Network Analysis.
|
|
|
|
Hemelrijk, C. K. (2002). Self-Organization and Natural Selection in the Evolution of Complex Despotic Societies. Biol Bull, 202(3), 283–288.
Abstract: Differences between related species are usually explained as separate adaptations produced by individual selection. I discuss in this paper how related species, which differ in many respects, may evolve by a combination of individual selection, self-organization, and group-selection, requiring an evolutionary adaptation of only a single trait. In line with the supposed evolution of despotic species of macaques, we take as a starting point an ancestral species that is egalitarian and mildly aggressive. We suppose it to live in an environment with abundant food and we put the case that, if food becomes scarce and more clumped, natural selection at the level of the individual will favor individuals with a more intense aggression (implying, for instance, biting and fierce fighting). Using an individual-centered model, called DomWorld, I show what happens when the intensity of aggression increases. In DomWorld, group life is represented by artificial individuals that live in a homogeneous world. Individuals are extremely simple: all they do is flock together and, upon meeting one another, they may perform dominance interactions in which the effects of winning and losing are self-reinforcing. When the intensity of aggression in the model is increased, a complex feedback between the hierarchy and spatial structure results; via self-organization, this feedback causes the egalitarian society to change into a despotic one. The many differences between the two types of artificial society closely correspond to those between despotic and egalitarian macaques in the real world. Given that, in the model, the organization changes as a side effect of the change of one single trait proper to an egalitarian society, in the real world a despotic society may also have arisen as a side effect of the mutation of a single trait of an egalitarian species. If groups with different intensities of aggression evolve in this way, they will also have different gradients of hierarchy. When food is scarce, groups with the steepest hierarchy may have the best chance to survive, because at least a small number of individuals in such a group may succeed in producing offspring, whereas in egalitarian societies every individual is at risk of being insufficiently fed to reproduce. Therefore, intrademic group selection (selection within an interbreeding group) may have contributed to the evolution of despotic societies. N1 -
|
|
|
|
Hemelrijk, C. K. (2002). Understanding Social Behaviour with the Help of Complexity Science (Invited Article). Ethology, 108(8), 655–671.
Abstract: Abstract In the study of complexity, a new kind of explanation has been developed for social behaviour. It shows how patterns of social behaviour can arise as a side-effect of the interaction of individuals with their social or physical environment (e.g. by self-organization). This development may influence our ideas about the direct causation and evolution of social behaviour. Furthermore, it may influence our theories about the integration of different traits. This new method has been made possible by the increase in computing power. It is now applied in many areas of science, such as physics, chemistry, sociology and economics. However, in zoology and anthropology it is still rare. The major aim of this paper is to make this method more generally accepted among behavioural scientists.
|
|
|
|
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network Motifs: Simple Building Blocks of Complex Networks. Science, 298(5594), 824–827.
Abstract: Complex networks are studied across many fields of science. To uncover their structural design principles, we defined “network motifs,” patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.
|
|
|
|
Katherine Faust, & John Skvoretz. (2002). Comparing Networks Across Space and Time, Size and Species. Socio Meth, 32(1), 267–299.
Abstract: We describe and illustrate methodology for comparing networks from diverse settings. Our empirical base consists of 42 networks from four kinds of species (humans, nonhuman primates, nonprimate mammals, and birds) and covering distinct types of relations such as influence, grooming, and agonistic encounters. The general problem is to determine whether networks are similarly structured despite their surface differences. The methodology we propose is generally applicable to the characterization and comparison of network2013level social structures across multiple settings, such as different organizations, communities, or social groups, and to the examination of sources of variability in network structure. We first fit a p* model (Wasserman and Pattison 1996) to each network to obtain estimates for effects of six structural properties on the probability of the graph. We then calculate predicted tie probabilities for each network, using both its own parameter estimates and the estimates from every other network in the collection. Comparison is based on the similarity between sets of predicted tie probabilities. We then use correspondence analysis to represent the similarities among all 42 networks and interpret the resulting configuration using information about the species and relations involved. Results show that similarities among the networks are due more to the kind of relation than to the kind of animal.
|
|
|
|
Soproni, K., Miklósi, Á., Topál, J., & Csányi, V. (2002). Dogs' (Canis familiaris) responsiveness to human pointing gestures. J Comp Psychol, 116(1), 27–34.
Abstract: In a series of 3 experiments, dogs (Canis familiaris) were presented with variations of the human pointing gesture: gestures with reversed direction of movement, cross-pointing, and different arm extensions. Dogs performed at above chance level if they could see the hand (and index finger) protruding from the human body contour. If these minimum requirements were not accessible, dogs still could rely on the body position of the signaler. The direction of movement of the pointing arm did not influence the performance. In summary, these observations suggest that dogs are able to rely on relatively novel gestural forms of the human communicative pointing gesture and that they are able to comprehend to some extent the referential nature of human pointing.
|
|
|
|
Fehr, E., & Gachter, S. (2002). Altruistic punishment in humans. Nature, 415(6868), 137–140.
Abstract: Human cooperation is an evolutionary puzzle. Unlike other creatures, people frequently cooperate with genetically unrelated strangers, often in large groups, with people they will never meet again, and when reputation gains are small or absent. These patterns of cooperation cannot be explained by the nepotistic motives associated with the evolutionary theory of kin selection and the selfish motives associated with signalling theory or the theory of reciprocal altruism. Here we show experimentally that the altruistic punishment of defectors is a key motive for the explanation of cooperation. Altruistic punishment means that individuals punish, although the punishment is costly for them and yields no material gain. We show that cooperation flourishes if altruistic punishment is possible, and breaks down if it is ruled out. The evidence indicates that negative emotions towards defectors are the proximate mechanism behind altruistic punishment. These results suggest that future study of the evolution of human cooperation should include a strong focus on explaining altruistic punishment.
|
|
