How can we begin to address the global, insidious problem of climate change — a problem that’s too big for any one country to solve? Economist Nicholas Stern lays out a plan, presented to the UN’s Climate Summit in 2014, showing how the world’s countries can work together on climate. It’s a big vision for cooperation, with a payoff that goes far beyond averting disaster. He asks: How can we use this crisis to spur better lives for all?
As hierarchically and centrally controlled computational systems, contemporary political systems have limitations in their information processing and action capacities to face the current social crises and challenges. In contrast, some older cultures whose political structure was more heterarchically organized, such as found in pre-Hispanic Colombia, were adaptive even without advanced scientific knowledge and without powerful top-down control. In this context, we propose that creating and analyzing computer models of their decentralized processes of management can provide a broader perspective on the possibilities of political organization. In terms of self-optimization, this approach seeks the promotion of social systems with a balance of flexibility and robustness, i.e., systems that do not rely on the current ideal of rule-based control of all systemic aspects.
Computational Aspects of Ancient Social Heterarchies: Learning how to Address Contemporary Global Challenges Nathalie Mezza-Garcia, Tom Froese, Nelson Fernández
Humans are unique both in their cognitive abilities and in the extent of cooperation in large groups of unrelated individuals. How our species evolved high intelligence in spite of various costs of having a large brain is perplexing. Equally puzzling is how our ancestors managed to overcome the collective action problem and evolve strong innate preferences for cooperative behaviour. Here, I theoretically study the evolution of social-cognitive competencies as driven by selection emerging from the need to produce public goods in games against nature or in direct competition with other groups. I use collaborative ability in collective actions as a proxy for social-cognitive competencies. My results suggest that collaborative ability is more likely to evolve first by between-group conflicts and then later be utilized and improved in games against nature. If collaborative abilities remain low, the species is predicted to become genetically dimorphic with a small proportion of individuals contributing to public goods and the rest free-riding. Evolution of collaborative ability creates conditions for the subsequent evolution of collaborative communication and cultural learning.
Collective action and the collaborative brain Sergey Gavrilets
Journal of the Royal Society Interface, Volume: 12 Issue: 102
Threshold cascade models have been used to describe the spread of behavior in social networks and cascades of default in financial networks. In some cases, these networks may have multiple kinds of interactions, such as distinct types of social ties or distinct types of financial liabilities; furthermore, nodes may respond in different ways to influence from their neighbors of multiple types. To start to capture such settings in a stylized way, we generalize a threshold cascade model to a multiplex network in which nodes follow one of two response rules: some nodes activate when, in at least one layer, a large enough fraction of neighbors is active, while the other nodes activate when, in all layers, a large enough fraction of neighbors is active. Varying the fractions of nodes following either rule facilitates or inhibits cascades. Near the inhibition regime, global cascades appear discontinuously as the network density increases; however, the cascade grows more slowly over time. This behavior suggests a way in which various collective phenomena in the real world could appear abruptly yet slowly.
Problems come in all kinds and sizes. Small problems call for the use of known tools found in circumscribed fields, whereas big problems call for further research, which may require breaching disciplinary walls. This is because every small problem concerns some separable system whose components are so weakly linked with one another, that it may be reduced to an aggregate, at least to a first approximation. I submit that (a) every problem concerns some system, and (b) analysis works only provided the system components are so loosely linked, that they can be treated as if they were isolated items. These methodological assumptions are key principles of systemism, the philosophy first expounded by d’Holbach in the 18th century, and rescued by Bertalanffy and his companions in the general systems movement in the last century. Systems and systemism are so little known in the philosophical community, that the vast majority of philosophical dictionaries have ignored them. By contrast, all scientists and technologists have practiced systemism – except when they failed for having adopted either of the alternatives to systemism, namely atomism and holism. A number of examples taken from contemporary science and technology are analyzed, from the entanglement typical of quantum physics to the design of social policies. Along the way we define the concept of a system, and note that (a) analysis is the dual of synthesis rather than its opposite; (b) systemism should not be mistaken for holism, because the former recommends combining the bottom-up with the top-down strategies; (c) systemism encourages the convergence or fusion of disciplines rather than reductionism. The recent replacement of GDP with more complex social indicators as the measure of social progress is regarded as a victory of the systemic view of society. Finally, I argue that systemism is no less than a component of the philosophical matrix of scientific and technological research, along with epistemological realism, ontological materialism, scientism, and humanism. I also argue in favor of Anatol Rapoport’s view, that systems theory is not a theory proper but a viewpoint or approach that helps pose problems and place them in their context.
Big Questions Come In Bundles, Hence They Should Be Tackled Systemically Mario Bunge
The last decade and a half has seen an ardent development of self-organised criticality (SOC), a new approach to complex systems, which has become important in many domains of natural as well as social science, such as geology, biology, astronomy, and economics, to mention just a few. This has led many to adopt a generalist stance towards SOC, which is now repeatedly claimed to be a universal theory of complex behaviour. The aim of this paper is twofold. First, I provide a brief and non-technical introduction to SOC. Second, I critically discuss the various bold claims that have been made in connection with it. Throughout, I will adopt a rather sober attitude and argue that some people have been too readily carried away by fancy contentions. My overall conclusion will be that none of these bold claims can be maintained. Nevertheless, stripped of exaggerated expectations and daring assertions, many SOC models are interesting vehicles for promising scientific research.
Jon Husband is an old friend, and I have been planning to involve him in Socialogy since I started the project, but the timing hadn't worked out until now. Jon describes himself in this way: I am a coach, consultant, writer and public speaker…
june holley's insight:
1. Adequate elbow room.
The sense that we are our own boss and that, except in exceptional circumstances, we do not have some boss breathing down our necks. However, not too much elbow room so that we don’t know what to do next.
2. Continuous Learning.
Such learning is possible only when people are able to (a) set goals that are reasonable challenges for them and (b) get accurate feedback in time for them to correct their behaviour. This learning drives innovation.
3. An optimal level of variety.
The ability to vary our work so as to avoid boredom and fatigue and so as to gain the best advantages from settling into a satisfying rhythm of work.
4. Mutual support and respect.
People need to be able to automatically get and give help from their work mates. There also needs to be respect for the contribution made regardless of matters such as IQ.
We need a sense that our work contributes to social welfare in some way. That is, it should not be something that might just as well be done by a trained monkey. Nor should it be something that society would be better without. Meaningfulness includes both the worth of the work, and having knowledge of the whole product or service.
6. A desirable future.
Work that will continue to allow for personal growth and increasing skills.
I heard IBM’s general manager of design speak at an IBM event in New York City recently, and I was impressed by his observations about trying to inject design thinking into IBM’s product planning. In...
The goal of this thematic series is to provide a discussion venue about recent advances in the study of networks and their applications to the study of collective behavior in socio-technical systems. The series includes contributions exploring the intersection between data-driven studies of complex networks and agent-based models of collective social behavior. Particular attention is devoted to topics aimed at understanding social behavior through the lens of data about technology-mediated communication. These include: modeling social dynamics of attention and collaboration, characterizing online group formation and evolution, and studying the emergence of roles and interaction patterns in social media environments.
Collective behaviors and networks Giovanni Luca Ciampaglia, Emilio Ferrara and Alessandro Flammini
It is well known that cooperation cannot be an evolutionary stable strategy for a non-iterative game in a well-mixed population. In contrast, structured populations favor cooperation since cooperators can benefit each other by forming local clusters. Previous studies have shown that scale-free networks strongly promote cooperation. However, little is known about the invasion mechanism of cooperation in scale-free networks. To study microscopic and macroscopic behaviors of cooperators' invasion, we conducted computational experiments of the evolution of cooperation in scale-free networks where, starting from all defectors, cooperators can spontaneously emerge by mutation. Since the evolutionary dynamics are influenced by the definition of fitness, we tested two commonly adopted fitness functions: accumulated payoff and average payoff. Simulation results show that cooperation is strongly enhanced with the accumulated payoff fitness compared to the average payoff fitness. However, the difference between the two functions decreases as the average degree increases. Moreover, with the average payoff fitness, low-degree nodes play a more important role in spreading cooperative strategies compared to the case of the accumulated payoff fitness.
Invasion of cooperation in scale-free networks: Accumulated vs. average payoffs Genki Ichinose, Hiroki Sayama
Cooperative behaviors are defined as the production of common goods benefitting all members of the community at the producer's cost. They could seem to be in contradiction with natural selection, as non-cooperators have an increased fitness compared to cooperators. Understanding the emergence of cooperation has necessitated the development of concepts and models (inclusive fitness, multilevel selection, etc.) attributing deterministic advantages to this behavior. In contrast to these models, we show here that cooperative behaviors can emerge by taking into account only the stochastic nature of evolutionary dynamics: when cooperative behaviors increase the population size, they also increase the genetic drift against non-cooperators.
Network optimality has been described in genes, proteins and human communicative networks. In the latter, optimality leads to the efficient transmission of information with a minimum number of connections. Whilst studies show that differences in centrality exist in animal networks with central individuals having higher fitness, network efficiency has never been studied in animal groups. Here we studied 78 groups of primates (24 species). We found that group size and neocortex ratio were correlated with network efficiency. Centralisation (whether several individuals are central in the group) and modularity (how a group is clustered) had opposing effects on network efficiency, showing that tolerant species have more efficient networks. Such network properties affecting individual fitness could be shaped by natural selection. Our results are in accordance with the social brain and cultural intelligence hypotheses, which suggest that the importance of network efficiency and information flow through social learning relates to cognitive abilities.
Social networks in primates: smart and tolerant species have more efficient networks • Cristian Pasquaretta, Marine Levé, Nicolas Claidière, Erica van de Waal, Andrew Whiten, Andrew J. J. MacIntosh, Marie Pelé, Mackenzie L. Bergstrom, Christèle Borgeaud, Sarah F. Brosnan, Margaret C. Crofoot, Linda M. Fedigan, Claudia Fichtel, Lydia M. Hopper, Mary Catherine Mareno, Odile Petit, Anna Viktoria Schnoell, Eugenia Polizzi di Sorrentino, Bernard Thierry, Barbara Tiddi et al.
Professor and researcher Carol Dweck recently gave a TEDx Talk shared by TED titled “The power of believing that you can improve.” I’ve embedded it below, but you can also see it on the TED site at the previous link.