Alexander V. Oleskin,
Full Professor, General Ecology Department, Biology School, Moscow State University
Full Professor, Philosophy, Biomedical Ethics and Humanities Department, Moscow State University for Medicine & Dentistry. Diractor, Club of Biopolitics, Moscow Society for Natural Sciences
Vladimir S. Kurdyumov
Deputy Director, Institute of Economic Strategies, Russian Academy of Social Sciences
Director General, Non-commercial Joint Stock Organization “Centre for Interdisciplinary Research”
Network structures are a currently fashionable interdisciplinary concept that has been extensively used in natural and social sciences according to several different meanings. A large number of scientists and scholars define a network structure as any system of nodes (vertices) that are connected by links (edges). In terms of this broad sense of the term “network”, mathematical analytical tools have been developed, including the criteria of node centrality in a network (node degree, betweenness, closeness and eigenvector centrality), network density, clustering coefficient, and other important measures in “network science” (Newman 2003, 2012; Barabasi, 2002, and other recent publications).
However, the term “network” is also used according to a narrower meaning, which is particularly frequently applied to human society (Castells, 1996, 2004; Börzel, 1998; Bard & Soderqvist, 2000; Borgatti & Foster, 2003).
Per this narrow meaning, a network is a structure that has no single center (no central leader, pace-maker or dominant element). Its behavior results from cooperative interaction among its elements (nodes), which may include several partial leaders whose influence on the whole structure is limited.
The World Wide Web largely conforms with these organizational principles. In this work, the term “network” will be use according to the narrow meaning. Network structures are contrasted with hierarchical (vertical, central) structures that contain a single activity center as well as with (quasi-)market (competitive) structures where competition among the nodes prevails over cooperation.
Decentralized cooperative network structures have recently emerged in various spheres of human society. Such network represent bodies (e..g., creative labs or networked businesses) that lack a single boss but may include several partial leaders with different specialization.
Cooperation among network members is stimulated by common goals, norms and attitudes that foster the feeling of belonging to the network and promote members’ identification with it. Cooperation is facilitated by skillful moderators (psychological leaders) that make good use of psychological techniques encouraging creative work and cooperative interactivity for the purpose of attaining the whole network’s goals.
In human society, network structures represent “the third alternative to vertical planning” that is typical of centralized hierarchies as well as to “the anarchy of the market” (Powell, 1990; Meulemann, 2008). The principles of network structures can be implemented by commercial enterprises, research centers, social movements, and political bodies. Like a variety of other innovative technological developments (e.g., nuclear energetics or genetic engineering), network structures can produce both positive and negative effects. A threat to the present-day world is posed by the “dark networks” of drug dealers, gangsters, and terrorists. Such network structures, including al-Qaeda and the Islamic State, lack a centralized hierarchy but include influential partial leaders (denoted as hubs in network science) whose removal often inactivates the whole structure.
Nonetheless, there are also a wide variety of beneficial network structures. They are exemplified by hiramas. This abbreviation stands for High-Intensity Research and Management Associations (Oleskin, 1996, 2014a, b; Oleskin & Masters, 1997; Oleskin et al, 2001). This are creative teams which can be set up to deal with an interdisciplinary problem or issue such as Small-Quantity Environmental Pollution Generators or Organized Terrorism. The problem/issue is subdivided into several subproblems. For example, Organized Terrorism can be broken down into:
- Ethological Approach to Terrorism: this subproblem is linked to the evolutionary background of human aggression
- Military Approach to Terrorism: internationally organized terrorism as envisaged as a novel military strategy
- Religious Approach to Terrorism: this subproblem is concerned both with the terrorism-promoting role of religious fanaticism, fundamentalism, and intolerance and with potential methods of discouraging people from terrorism using peace-promoting ideas and dogmas contained, e.g., in Christian, Judaic, or Islamic doctrine.
However, despite subdividing the problem/issue into subproblems, the network is not subdivided into parts. Its members work, in parallel, on several (ideally on all) subproblems. Often only one person, the subproblem leader, is explicitly attached to a particular subproblem. The person collects ideas on this subproblem, which are generated by other network members. Many hirama-type network structures also have a psychological leader. The psychological leader provides support, advise, and psychological help that is often sought by other members; he or she creates an atmosphere that promotes efficient work on all subproblems and helps other partial leaders interact with one another, mitigating or—still better—preventing internal conflict. In addition, a hirama typically includes an external leader, also called an “external affairs” leader. The individual with this role is responsible for propagandizing hirama-promoted ideas, establishing contacts with other organizations, and shaping the group’s pastime and leisure activities, thus contributing to the development of informal loyal relationships among members. Hiramas are free to make alterations in the network’s organizational pattern. Additional leadership roles can be introduced, depending on the specific goals of a hirama-type network structure.
Potential applications of network structures in the modern-day world
Network structures are relatively widely used in most countries around the globe. Nonetheless, the authors are convinced that their transformative potential still awaits its full realization in various spheres of human society. Most people are still unaware of networks’ potential advantages with respect to, e.g., research activities, education and business.
1. Interdisciplinary research activities. Network structures are a promising organizational pattern to be used by creative research teams dealing with interdisciplinary issues and innovative research projects.
They are exemplified by the network structure composed of software development teams that adopted the Manifesto for Agile Software Development on February 11–13, 2001, during a meeting at The Lodge at Snowbird ski resort in the Wasatch Mountains of Utah. According to their website (Beck et al., 2001, http://agilemanifesto.org), the conference involved representatives from Extreme Programming, SCRUM, DSDM, and a variety of other firms and organizations. The new network was called The Agile Alliance, and it adopted a “set of values based on trust and respect for each other and promoting organizational models based on people, collaboration, and building the types of organizational communities in which we would want to work” (Ibid.). Software development was broken down into short stages (iterations) carried out by the network structure. The interprofessional dialogue-promoting activities of The Agile Alliance’s are stressed in the principle that “business people and developers must work together daily throughout the project”. In addition, the principle is adopted that “the most efficient and effective method of conveying information to and within a development team is face-to-face conversation”. Therefore, such a development team looks like a primitive hunter–gatherer band engaging in collective hunting or waging war with their neighbors. Most Agile teams usually work in one room (the bullpen). The development team includes the client or his/her representative, as well as testers, designers and managers (Cohn, 2010). The dynamic nature of the network organization, in contrast to the rigid character of bureaucracies, is stressed in the Manifesto, and it is assumed that “the best architectures, requirements and designs emerge from self-organizing teams”. Established over a decade ago, the network of software developers is currently increasing its influence.
Network structures are expected to be of much use in terms of a wide variety of interdisciplinary research projects ranging from military tasks to developments in the field of bio- and nanotechnology, the production of new drugs, and the exploration of the planets of the Solar System.
2. Educational projects. A number of potentially important pilot educational projects have been recently developed. These projects are ultimately aimed at the “networkization” of the whole educational system, ranging from the primary to the secondary and tertiary (college-level) stage of education. This “networkization” facilitates the implementation of novel interactive scenarios of students’ classroom work.
For example, one of the students might be asked to act the role of the CEO of an industrial firm, while another student is in charge of a commission that aims to expose the environmental pollution caused by such firms, and the rest of the group are assigned other important roles (Oleskin et al., 2001). The teacher explains the scenario of the role-playing game: “You live in a city with a high environmental pollution level. I’m showing you data on the concentrations of manganese, cadmium, and lead in the atmosphere. This map shows the location of environment-polluting factories. Your task is to suggest ideas enabling us to prevent an environmental catastrophy”. In a hirama-like fashion, the task is broken down into subproblems such as (1) Making technological changes in the factories in order to improve the environmental situation; (2) Creating economic incentives for stimulating environment-friendly production scenarios; and (3) Restructuring the administrative system in order to facilitate its direct interaction with local industrial agents and environmental activists. A partial creative leader can be assigned to each of the subproblems. There are a large number of alternative ways to subdivide the overall task, and the students can make their choice themselves, with the teacher playing the role of both a facilitator and a consultant.
An interesting example is also provided by the innovative team-teaching scenario. “Team teaching involves two or more teachers sharing teaching expertise in the classroom and engaging in reflective dialogue with each other” (Chang & Lee, 2010), as exemplified by the collaboration of a computer teacher and an English or a geography teacher in a school in Taiwan. Teachers form a subnetwork (cluster) within the fractally-structured, higher-order network established in the classroom in terms of a project-based interactive teaching scenario. The teachers give the students several different perspectives on the subject, but the problem is that a conflict between these perspectives can potentially arise; special measures are to be taken to cope with this type of conflict. A hirama-type strategy can be used to assign the role of the conflict-mitigating mediator (psychological partial leader) to an additional teacher.
3. Business activities. Network structures in the realm of business can be formed by a non-hierarchical group of firms (such interfirm networks are typified by so-called “strategic alliances”). Strategic alliances are widely spread among all kinds of companies, but they are particularly characteristic of giant firms that aim to gain competitive advantages on the global scale. Several examples were given in the classical work by Powell (1990). Boeing and Rolls Royce formed an alliance to create Boeing 757, and most production processes were carried out in terms of joint projects involving partners in Japan and Italy. Such networks become partly hierarchized if the alliances enlist one or more large firms along with smaller partners. This is exemplified by the alliance that included General Motors and the comparatively smaller company, Teknowledge, which specialized in manufacturing artificial intelligence systems (Powell, 1990).
Alternatively, business network structures may be composed of the structural subdivisions or departments of a single firm if they are granted partial legal independence and allowed to conclude contracts with one another.
While such large-scale business network structures consist of whole firms or their subdivisions, there are also small-sized networks that are composed of individuals or their small groups that establish non-hierarchical relationships. Small-size network structures hold special promise with regard to innovative high-tech developments. As far as drug industry and bio- or nanotechnology are concerned, the market can often be saturated by very low quantities of items. This provides for the competitiveness of small-size networked businesses in branches of industry that are otherwise dominated by industrial giants. Of special interest are networked firms that function as cooperatives (co-ops). They are owned and managed by the by the people who work there or who use their services (these are worker and consumer cooperatives, respectively). A large number of co-ops are characterized by an internal egalitarian structure. These “ethical businesses” dating back to the consumer co-op Rochdale Pioneers (established in 1844 in Britain) espouse the principles of voluntary and open membership, democratic member control, autonomy, and independence (Birchall, 2004), which encourages people to apply analogous principles elsewhere.
According to the «Member’s Guide» of the Consumer Cooperative Society in Hanover, New Hampshire, that operates the Co-op Food Stores, Co-op Community Food Market, and Co-op Service Center: «Members of a cooperative support it with their patronage, participate in decision-making, and share in the profits generated by the organization’s activities». This co-op is affiliated with a higher-order Cooperative Grocer Network that operates in compliance with the principles laid down by the International Co-operative Alliance and represents “… an autonomous association of persons united voluntarily to meet their common economic, social, and cultural needs and aspirations through a jointly owned and democratically controlled enterprise.” (International Co-operative Alliance Statement on the Co-operative Identity; see Cooperative Grocer Network, 2014, http://www.cooperativegrocer.coop).
Many co-ops implement the principle of collective ownership. Therefore, they actually promote some basically socialist ideas and values, despite the capitalist environment in which they are embedded. Importantly, a large number of other networks, despite the pragmatic goals they set themselves, comply with socialist rather than capitalist principles to the extent that, in economic terms, their development entails partial collectivization of the property of their members, i.e., joint control over some of their assets, whereas capitalism per se—at least in its classical form—is based upon the principle of predominantly private property. The “road to socialism” paved by business networks, especially those functioning as co-ops, is in compliance with the structural principles of networks that tend to promote egalitarianism and communalism as well as an equitable redistribution of property.
Establishing networked “strategic alliances” among capitalist enterprises often implies that some of their resources become accessible for all members of the alliance. The development of relationships based upon trust (of social capital, see Putnam, 2000) fosters a collectivist attitude of mind and a sense of belonging. This sets business networks apart from both hierarchical firms and Hobbesian markets where interaction among agents is based on self-interest and often involves misinformation and even guile.
It should be stipulated that the notion of socialism includes several fundamentally different social systems. Soviet-style state-governed socialism is characterized by the state’s monopoly on most resources, centralized planning, the dictatorship of the apparatus of the party or the government, and the persecution of dissidents. However, this is just one of several kinds of socialism. Moreover, as pointed out by the Russian scholar Alexander Zinoviev, this kind of socialism was “killed” at the very beginning of its historical development, before it could manifest its potentially advantageous features.
It is a different kind of socialism, denoted as self-governed socialism, that is promoted by establishing network structures in business. It is based upon (1) the operation of autonomous self-regulated economic actors, e.g., cooperatives and similar self-governed businesses and (2) a decentralized mechanism where economic and political decisions are made. Despite the wide variety of subtypes of self-governed socialism, which range from worker-owned enterprises in Yugoslavia under Tito to Israeli kibbutzim, they are all characterized by the principle of collective ownership with respect to production means at the enterprise level. Such enterprises are often small in size (or are composed of smaller-sized modules) and are devoid of a rigid centralized bureaucratic hierarchy.
Biological models (paradigms) of network structures and their creative use in human society (with special emphasis on the neural paradigm)
There is a wide variety of different kinds of decentralized network structures in the realm of biology. Some of their organizational scenarios can be creatively modified in order to make good use of them in human society. Different organizational tasks and different spheres of society require different network paradigms. Their prototypes can often be found in living nature, which has tested them during the whole course of biological evolution. The organizational pluralism of network structures, which is associated with the diversity of evolution-molded network scenarios, will be discussed below.
Despite the pluralism, many biological network structures exhibit common important features that can potentially be used by the developers of various kinds of decentralized networks for social, economic, or political purposes.
These features of biological networks will be considered in the example of neural (neuronal) networks. They are composed of nervous cells or their functional analogs. Neurons, the functional nodes of neural network structures, are connected by links including (1) axons, long thin processes transmitting electrical impulses from a neuron’s body (soma) to a synapse, a narrow cleft between the neuron and its neighbor; and (2) branched dendrites transferring the electrical signal from a synapse to the soma. Transmitting impulses across the synaptic cleft involves neuromediators (neurotransmitters) that cross the cleft and bind to the receptors of the postsynaptic neuron’s dendrite or soma. This either stimulates (a stimulatory neurotransmitter) or inhibits (an inhibitory neurotransmitter) the postsynaptic neuron. The postsynaptic neuron summates all stimulatory and inhibitory influences and generates an impulse (is depolarized) if the summated excitation exceeds the threshold value. After transmitting an impulse, a neuron temporarily becomes refractory, i.e., unresponsive, to stimulatory agents. These organizational principles of neural networks are simulated by their artificial analogs (perceptrons, cognitrons, etc.).
In the following, we outline some of the key characteristics of neural networks that can potentially be used in social network structures. Most of these features manifest themselves to an extent in various other kinds of networks in biological systems, including bacterial biofilms, cnidarian colonies, insect families, ape groups, and others. Biological networks of any kind, therefore, can be considered as “quasi-neural networks” in terms of structural and organizational properties.
1. Collective information processing and decision making. Neural networks as well as a large number of other biological networks are often characterized by relatively simple behavior of their nodes (neurons or their analogs). A typical neuron is a binary system that can assume one of the two states digitally denoted as 1 (the active/excited state) and 0 (the inactive state). The 0 → 1 transition takes place if the summated excitatory stimuli received by the neuron exceed the threshold level. The highly complex behavior of the whole network that both recognizes images and makes decisions represents an emergent phenomenon that results from the intricate interaction among its innumerable nodes.
A network structure in human society can be compared to the “thinking brain”. Like this highly sophisticated neuronal network, a network of people formulates problem solutions at the level of the whole collective involved; each network member functions as a neuron analog. A somewhat paradoxical situation often arises: none of the network members possesses the whole information that is at the disposal of the entire network as a coherent “collective brain”.
Of special note in this connection are the recently established innovative networks in business. These networks are aimed at collectively solving complex fuzzy problems. They are typically made up of structural modules (subnetworks) that exchange problems to be solved and their solutions. An example of such a network is Procter and Gamble (P&G)’s Connect + Develop network. The sources of innovation in the network are technology entrepreneurs around the world, suppliers and open networks (e.g., NineSigma, YourEncore, and Yet2.com). The problem stories are presented to these groups and anyone with an answer can respond” (Trkman & Desouza, 2012, p.8). Networks can make good use of network-level distributed intelligence; active work within the framework of a “collective neural network” is obviously promoted by collectivist attitudes and a sense of belonging.
2. Combining serial and parallel pathways of information. Neural networks can combine serial processing of information (the input → hidden layer(s) → output sequence) and its parallel processing enabled by the coexistence of several units belonging to the same layer. Within the cerebral cortex, there are at least two parallel systems that process visual information: one system recognizes objects (the ventral stream: the “what” pathway); and the other deals with their arrangement in space and with movement (the dorsal stream: the “where”/“how” pathway). Language is analyzed by three different, although interconnected, networks inside the brain; memory also involves several information-processing systems. Combining serial and parallel information processing is considered an important practical advantage of artificial neural networks. “Since the neural networks are massively parallel in nature, they can perform computations faster and help find solutions in computation-intensive problems” (Balakrishnan & Weil, 1996, p.107).
As for networks made up of human individuals or their groups, their quasi-neural behavior can be illustrated in the example of risk-hedging networks. They pool their members’ resources to share risks (Trkman & Desouza, 2012). In this respect, not only alliances between firms dealing with different production stages (they may be referred to as “vertical alliances” in the literature), but also networks bringing together several firms concerned with the same stage (“horizontal alliances”), are of paramount importance. In a neural network-like fashion, the parallel functioning of several entities performing the same function, i.e., dealing with the same production stage, increases the reliability and resilience of the whole network structure.
3. Combining several functions within the framework of a single network. A biological example is provided by the brain module that is meaningfully referred to as “the reticulate formation”. It consists of “cells, cell aggregations, and fibres that form a network located in the brainstem (the medulla oblongata, the pons, and the midbrain). The reticulate formation is linked to all sense organs as well as to the locomotive and sensory areas of the cortex, thalamus, hypothalamus, and spinal cord. It controls the excitation level and the tone of various parts of the nervous system including the cortex and is involved in regulating the sleep-wake cycle, vegetative functions, and locomotive behaviors” (Dubynin et al., 2003, pp.56-57).
Using network structures in the business sphere often results in establishing analogous multifunctional systems, which are denoted as stable networks. Stable networks in business are typified by alliances of spatially distributed firms, including Japanese keiretsu, that are stable unions of firms of various sizes. They specialize, e.g., in different stages of producing cottonwool textiles. Stable networks are also exemplified by Scandinavian interorganizational alliances composed of large industrial companies such as Volvo, Ericsson, Saab-Scandis, and Fairchild. Analogous stable interfirm alliances are formed in present-day East Europe including Hungary; their degree of hierarchization is highly variable and often situation-dependent.
Different enterprises within a stable network can be functionally differentiated. In this case, they are similar to polyps and medusae within decentralized colonies of cnidarians. Alternatively, they may represent multifunctional uniform modules. Such uniform modules can compete with one another, but the whole business network structure mitigates this competition, and cooperative interaction prevails in the network. Like the brain, the whole network stimulates cooperative interactivity among all its nodes.
4. Creating order from chaos. This subject was considered in S. P. Kurdyumov’s classical works, including those available on the website spkurdyumov.ru. The basic principle is as follows.
Neural networks are characterized by an associative mode of operation. This implies that neural networks can create the image of the whole object based on its fragments. Order is created from chaos due to the cooperation among a large number of network nodes. Different nodes may deal with different features of the objects to be recognized. As for the brain, not only do at least two different systems process visual data in parallel, but tactile information processing (object recognition using the sense of touch) involves the parallel functioning of two brain areas where the neurons are responsible for evaluating the macrogeometric properties (length, width) and the microgeometric peculiarities (surface structure) of objects (Bohlhalter et al., 2002). Brain networks, as well as many artificial neural networks, can create a complete object image by generating details that have not been demonstrated to them. Moreover, they can distinguish the object from the background. This is the reason why visual illusions are possible: the brain can separate the object from the background in several different ways, e.g., recognizing either a vase or two human faces in the same picture.
As G. G. Malinetsky emphasizes in his work on the website spkurdyumov.ru, “in terms of traditional approaches based on neural network theory, the phenomenon of consciousness is regarded as a result of self-organization in neuronal ensembles”. Information is partly delocalized in the brain, i.e., it can simultaneously be processed in a large number of brain structures. Long-term nemory involves “multiple changes in the characteristics of synapses in the neuronal networks of the cortex” whose location can change over time (Dubynin et al., 2003, p.240).
On the whole, the brain is a multi-order “network of networks”. The whole giant network (composed of many billions of neurons) is made up of of smaller, partly autonomous, network structures. Each of the smaller networks performs specific functions in the brain and is subdivided, in its turn, into still smaller functional units—small-size “collectives” of neurons.
Importantly, a large number of such functional units do not represent compact areas in the brain. There are many delocalized neural networks in the brain. Their nodes, although functionally linked with one another, are distributed among different parts of the brain.
An analogous phenomenon in human society is Internet-based communication within an online network structure. Small-size networks form coalitions, giving rise to more global structures. They are exemplified by charitable (humanitarian) networked organizations such as Jubilee 2000 or Make Poverty History.
5. Capacity for adaptive structural transitions (“structural learning”) for finding optimal problem-solving strategies. In the traditional, largely hierarchical, society, any major structural change causes social upheavals often associated with a violent revolution. In contrast, network structures based on neural organizational principles peacefully undergo structural transitions in order to adapt to new challenges and tasks. An important mechanism of such transitions involves readjusting the weights of the links between nodes (neurons or their analogs), i.e. purposefully changing their strength, influence on the neurons’ state (exciting or inhibiting them), the volume of the information transmitted interneuronally, etc. In addition, interneuronal links can be severed and re-established. Link weights and link configurations can be set and modified by the “teacher”, an external agent controlling the operation of the network. However, a network can also “learn by itself”, due to a process resembling natural selection during biological evolution. The features that do not contribute to solving the problem faced by the network can be eliminated.
Of relevance are human social network structures that are denoted as dynamic network. In addition to implementing the quasi-neural principle of adaptive readjusting, these networks resemble loose short-lived fission-fusion groups formed by chimpanzees, capuchins and several other primate species, as well as—to an extent—rhizome (“fungal”) networks that are briefly described below.
In the business world, dynamic networks are typified by temporary interfirm alliances that are formed for achieving a specific goal; thereupon, firms terminate their relationship in order to establish another temporary union (Millner, 2006). Dynamic networks are widely used, for instance, in the fashion industry (van Alstyne, 1997).
6. Role of external controlling agents (chaperones, mediators). As we mentioned above, the weights of links between neurons or their analogs and their arrangement can be initially set by an external “teacher”. Even if a network “learns by itself,” its successful functioning depends on the preparatory work done by a teacher/controller. Hopfield’s recurrent networks converge to the correct image, provided that their characteristics (including feedback link weights) have been adequately set initially. This point is of relevance to the properties of network structures in general. Their seemingly spontaneously efficient behavior may actually depend on the involvement of an external controlling agent. At the molecular level of biological systems, of special interest are chaperones, i.e., molecules that control the assembly and folding of other biological molecules such as proteins. In some cases, a biological molecule includes a part whose function is to secure the correct arrangement of the other parts of the molecule. In human society, there are special organizations that mediate the relationships among networks as well as between networks and other types of social structures (see final section).
7. Enhancing the robustness of a network structure by incorporating redundant functional units and links into it. Some of the aforementioned features of a neural network—the parallel operation of several nodes and the capacity to readjust the network’s configuration—provide for the reliability of the entire structure. If a part of their nodes and links ceases to function, the whole network can be restructured, and some of the intact nodes and collateral connections can at least partially replace the impaired part of the network.
In the world of business, this feature of quasi-neural networks can be illustrated in the example of risk-hedging networks that were mentioned above. “A single entity in a supply chain can be vulnerable to supply disruption, but if there is more than one supply source or if alternative supply resources are available, then the risk of disruption would be reduced” (Lee, 2002, p.114).
In sum, a number of important features of neural networks and their analogs seem to be potentially applicable to network structures that are composed of human individuals or their groups. Moreover, the structural pattern of neural networks is of much interest in terms of social technology, including educational projects.
Network structures can be set up directly in the classroom in the form of decentralized creative students’ teams that are cemented by a common goal set by the teacher. The teams should include partial leaders that help coordinate the team’s work on specific subgoals that are included in the general goal.
One of the authors (AVO) used the network scenario during discussions concerning Genetic Engineering: the Pros and Cons at the State Management Department and the Global studies department of Moscow State University (see Oleskin, 2014a, b). During these discussions, the students set up a networked team with three partial creative leaders performing the following functions:
- Leaders 1 and 2 collected arguments presented by all students in the group for and against genetic manipulations, respectively;
- Leader 3 helped students critique both kinds of arguments and produce a well-balanced, unambiguous final document.
The students were encouraged to interact with any of the leaders, depending on whether they supported genetic engineers, protested against their activities, or preferred a middle-ground attitude.
In a similar fashion, network structures can be established to facilitate interactive teaching during classes in various subjects at a school or college. Teachers can vary both the organizational pattern and the subjects, which can include, for instance, environmental concerns, issues in biomedical ethics, or problems caused by the ineptness and corruption of the state apparatus.
Emphasis on the neural paradigm of network structures could encourage parallel information handling by creative subgroups within the whole network composed of school/college students. Similar to a neural network, a network composed of students can begin to piece together the solution of a given problem (as proposed by the teacher) on the basis of fragments supplied by individual students and creative subgroups. In terms of the quasi-neural scenario of a student network’s operation, special attention is to be given to the creative learning at the levels of the individuals, subgroups, and of the whole multilevel network as a “collective brain”. While dealing with a problem/task, students in the classroom can simulate the operation of a neural network. According to this paradigm, students can form several distinct “layers” (i.e., subgroups). One subgroup can specialize in collecting task-related information, in an analogy to the neural network’s input layer. Another subgroup can process the information received from the “input layer”, i.e. function as the “hidden layer”. A third subgroup—the “output layer”—can generalize and verbalize the result obtained by the “hidden layer” subgroup and report it to the teacher. The scenario would obviously be still more interesting if the “output layer” could send messages back to the “input layer” and the “hidden layer”, providing guidelines for their activity on the basis of the result already obtained. This would transform the “neural network” composed of students (as neuron analogs) into a Hopfield-type recurrent network structure.
Diversity of organizational patterns and scenarios of networks in biological systems.
Neural network structures and their functional analogs are not the only organizational option in biology. To reiterate, biological systems are characterized by the diversity of the network paradigms they use. These different paradigms can also provide much food for thought for those who investigate network structures in human society or aim to develop them for various purposes. In addition to neural networks, network structures created for pedagogical purposes in the classroom can make good use of the organizational pattern of the egalitarian paradigm that is used by social groups formed by a number of primate species.
Some advocates of network organizational principles in the political sphere have paid attention to the organizational pluralism of biological network structures. The adherents of antiglobalism or alter-globalism that organized effective protest action against the IMF and the World Bank, as well the related Zapatista movement in the south of Mexico, compared political networked movements to neural networks and, to a still larger extent, to the social structures of insects such as ants.
“The ants can teach us that by working locally and continually sharing our local stories globally, by connecting everything and creating a plethora of feedback loops, we don’t need to – indeed cannot — ‘organize’ the global network, it will regulate itself, swarm-like, lifelike, if we develop the right structures and conditions” (Networks, 2003).
In the following, a number of important paradigms that underlie the organizational pattern of various biological network structures is outlined. Their more detailed description was given in a number of previous publications (see Oleskin, 2014a, b).