2017 21st International Conference on Control Systems and Computer Science
Emergence in hierarchical complex systems structured as social networks Aimee - Theodora Dumitrescu, Ecaterina Oltean, Daniel Merezeanu, Radu Dobrescu Politehnica University of Bucharest, Faculty of Automatic Control and Computers, Bucharest, ROMANIA (e-mail:
[email protected]) hyperconnected world, in this paper we consider the model of complex adaptive systems with self-organization as a possible solution to identify mechanisms that enhance the properties of hierarchical emerging social networks. Two particular approaches have been proposed to determine the development framework of this model: multi-agent system organization to observe how the members of such a system relate (for explanation of some elements of social emergence, such as collaboration, consensus, confidence) and respectively scale-free networks for evidence of some elements of organizational emergence (growth mechanisms and degree of interconnectivity).
Abstract — The paper aims to identify the elements that make possible the appearance of emergent properties in organizations structured as social networks, considered as adaptive multi-agent complex systems. The dynamics of these communities (referring primarily to the increasing number of members who interact) was analyzed by the specific rules of scale-free networks growth and the specific principles of multi-agent systems self-organization. Finally, a framework that allow to study the development of such networks according to the preferential attachment rule is introduced. Keywords - Social Networks; Self–organization; Complex Adaptive Systems; Organizational emergence; Scale-free networks
I.
II.
INTRODUCTION
We live today in the era of the Informational Society and for the first time in history the knowledge could be shared to large groups of people. Favorized by the large support of mass-media and of Internet, new ways for interaction and collaboration between individuals has arrived in the form of social networking. The tendency to group a growing number of entities (in particular individuals) is typical for complex systems, and this it done not by cluster size, but by the huge number of mutual interactions between members. Recent (let say the last twenty years) specialty literature indicated that networks are the best fit for this structure, and among these Scale-free Networks (SFN) having both topology and dynamic behavior (expressed by the flow of information) independent of scale representation seem to capture best the behavioral characteristics (Internet remains the best example, with its fractal topology and self-similar patterns exhibited in the informational traffic). In the same time, one can affirm that the main characteristic of complex systems with SFN structure is self-organization. Self-organization is the way due to which following rules (usually simple) of behavior emerges a new organizational structure and new interactions entities also appear in the system. The term self-organization is used to describe the dynamics of complex systems and related emergent processes. Most often the interactions are nonlinear and the evolution is nondeterministic (somehow chaotic), but may lead to a new hierarchical order in the system. In this way, emergence is a step in the evolution of complex systems allowing their adaptation to changes in the environment and can also bring new ordering relationships, for example by limiting the degrees of freedom of movement. Taking into account the needs of those organizations to respond quickly to the specific challenges of a 2379-0482/17 $31.00 © 2017 IEEE DOI 10.1109/CSCS.2017.66
DEFINING EMERGENCE IN COMPLEX SYSTEMS
The emergence is considered the distinguishing feature that makes the difference between complex systems and those that cannot be included in this category. Although there is no universally accepted definition in terms of systems theory, we explain in the simplest way that the emergence represents the appearance of a new property or a new type of behavior as result of the nonlinear interactions between the components (entities, agents) of a system. The concept of emergence has been associated with various structural and functional aspects, like a formal explanation of evolution [1] the logic for potentiating the creative activity [2], as a mean of exploring the capacity to simulate real complex systems [3] or as an indicator of progress in the evolution of a hierarchy [4]. Only in 2009 Bednar has associated the concept of emergence with the organization of socio-cultural complex systems [5]. To date, the meaning of the term was enriched and came to be invoked in very specific applications, such as how emergent patterns in complex systems are related to epistemological and ontological aspects of curious phenomena in modern science and philosophy [6], the application of complexity theory concepts to the social healthcare context [7], optimization of information infrastructure to modernize organization structure of an enterprise [8], emphasizing the role of social housing organizations in neighbourhood regeneration governance networks [9]. In the past few years scholars have used a variety of dynamic systems theories to provide a more complete understanding of organization design, network structuring and strategic adaptation. In addition, the concepts of emergence and self-organization have been used to explain various elements of advanced social management such as strategic decision making, collaborative learning, and organizational changes and adaptation. More recently, it was recognized the potential role of complexity research in 426
for sociological study of self-organizing systems in MAS, the habitus-field theory of Pierre Bourdieu [12]. Bourdieu describes in fact a way of organizing social entities (which he defines as "autonomous fields") specific Multi-Agent Systems (MAS). Through its relationships with social entities, an organization overcomes the entities' cognitive, physical, temporal, formal and informal barriers and thusly, through its formalism of group affiliation, its procedures, objectives and restrictions imposed to its members, it persists despite the fact that, alone or at group level the agents (i.e., the members of a social entity or of an organization) do not interact. Consequently, the organizations enjoy the advantages of its own purely formal structures which can exist independently of the individual objectives, actions and intentions of the agents, which it actually coerces to act in a certain manner, such as through objective differentiation and therefore though material benefits differences, with the objective of reaching the organization's goals. Conversely, an organization's formalism can become its disadvantage due to the inherent rigidity of the roles and structures of its makeup, which every so often are slow to react to open, dynamic and unpredictable environments. In order to compensate this disadvantage, the organization must employ complex social interaction capacities, such as cooperation and negotiation, as well as adaptability and efficiency in overcoming the problems of its members (e.g., loss of interest, lack of interaction etc.). Based on the above, we can conclude that organizations (i.e., systems composed of multiple agents/individuals) exist, react and persist due the common objectives and simultaneous actions of its members, and cannot be modelled like the social systems where, sociologically, the change of the systems' structure is due to the interaction between its members but not due to their intentions, which can be different. In order to in a position to use the advantages of such a system, the definition of self-organization must take into account simultaneously fulfilling the following conditions: (i) the formal existence of social structures which are independent of its members but impose them to act and interact in certain ways; (ii) the genesis, change and adaptation of the organization structure is the result of joint actions and interactions between the members, even in situations where the outcome does not reflect the intentions and objectives of the individual members.
explaining coevolutionary properties and processes. Various complexity models were proposed for studying the multifaced properties of complex adaptive systems and to provide a framework for understanding adaptive ordering in dynamic environments, placed under influence of uncertainties. That is the reason to suggest that “emergence” is a core issue that integrates the main research works in the field of complexity. The only possibility to understand and to model such organized complex systems is to exhibit some organizational principles. The hierarchy theory which allows a decomposition of this kind of systems could improve the understanding of the causal relations of subsequent dynamical processes, for example from a bottom-up perspective like in [10], or proposing a holonic approach to formalize hierarchical systems, in particular in the form of multi-agent system architectures [11]. Complex adaptive systems (CAS) offer an alternative approach for studying the emergent behaviors of agents or populations adapting and coevolving in a computational context. In complex adaptive systems, agents adapt by changing their rules as experience accumulates. In addition, each change of strategy of an agent alters the context in which the next change will be tried and evaluated. When multiple populations of agents are adapting to each other, the result is a coevolutionary process. Studying this emergence process can generate insights about the mutual, simultaneous and nested effects of coevolution. Perhaps more important, CAS as a discipline can help define interaction process that hold across levels, which may allow researchers to identify similar patterns acting in macroevolution and in microevolution. This search for similar patterns across scale can be aided by the mathematics of fractals. The fractal notion of “self-similarity across scales,” and the resulting topological mapping techniques used to uncover those oftenunseen patterns, has been utilized by complexity scholars. Although the operationalization of “fractal dimensions” may not yet be obvious in coevolutionary contexts, the mathematics is a unique way to reveal whole-part relations that are a key to understanding mutual adaptation processes. III.
CHARACTERIZATION OF THE SOCIAL NETWORKS AS COMPLEX ADAPTIVE SYSTEMS
A. Emergence in self-organizing multi-agent systems Self-organization is a concept whose meaning is disputed by various scientific disciplines, but its appurtenance predominate in the AI (Artificial Intelligence). Different theoretical approaches have in common the idea that a selforganizing system may alter its internal structure changes depending on the environment (or context). This definition fits equally well with the adaptive systems (or sensitive to context - context-aware systems). However this approach is not entirely correct. A self-organizing system not only responds to changes in environmental factors, but rather change their organization in a continuous dynamic process, to optimize the relationship with contextual environment. This approach seems to fit best in the description of the relationship of social entities (groups, networks, organizations). In this spirit, we will mention a new concept
B. A conceptual model for emergence By utilizing a conceptual approach to modelling, based on relevant publications used to determine which characteristics should be included into an organization creation model, a new model was identified. This resulting model, due to the method used to obtain it, does not explain in detail the process of creation but it does offer a strategy, a blueprint of the main concepts and how they are connected. McDonald and Weir [13] have introduced the concept of organization creation meta-classes as elements of selforganized systems: (1) they are the product of non-linear interactions, (2) they are independent of the field where they
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Fig. 1 describes a conceptual model of emergence influenced, as can be seen, different combinations of metaclasses without requiring that every phenomenon emerging derive from a specific class or each meta-class is a component of the emerging phenomenon. The model was necessarily speculative and it is subject to change during the investigation because the model order was challenging existing conventions and developing a framework of operation for emerging useful in real systems. The concepts on which we can build a good conceptual model are: - meeting basic scientific conditions of simplicity, accuracy and generality for expressing working hypotheses; - representing the essential characteristics (objects, relations, events) of field research; - differentiation and classification of phenomena in a manner that will lead to significant research problems - providing a useful framework for investigating emergence in real complex systems.
activate, (3) they represent the underlying structure for system interactions and (4) though their mere existence they create new potential in the system. Self-organization, where the detailed organizational structure and the new interactions exist as a result of behavior rules, ever so often defined simplistically for each entity of the system, is a welldocumented characteristic of the real complex system. The word organization, as opposed to self-organization, is therefore used preferentially and is defined as the structural change of a complex system, which results from a non-linear interaction, possibly influenced by environment factors. In case this structural change is a collection of constructive elements, with ordered asymmetrical relations, it is defined as hierarchy. This concept is well documented and is often becomes visible by restricting a complex system's degree of freedom. Similar changes have taken place in history as new social units emerged. In consequence, we define novelty as the appearance of a new sustainable entity, with interaction models which are distinct and different form its predecessors', and we define the memory meta-class as a fixed structure or process which emerges due to non-linear interaction. An entity's capacity to probe its environment and use the synergic opportunities is an example of that which is called emergence of functionality. Before any entity can use other entities or processed, it must be capable of realizing its own existence. When a new environment probing capacity is generated by the interactions, we can define it as an emergence of measurement. Reported the occurrence of memory, the question of its availability in different entities of the complex system. If nonlinear actions or processes make this memory system is limited to certain elements (entities), we describe this as location. If the memory or localized process are used differently in the system, then we can speak of the emergence context. We thus define contextawareness, the ability of entities to recognize patterns that trigger a specific behavior that adds significantly complex system creativity.
IV.
A SCALE-FREE MODEL FOR SOCIAL NETWORKS
The basic model of a social network is a multi-agent structure similar to a graph where for each agent i is assigned a node i. The link (i, j) between the node i and the node j represents the social tie between the agent i and the agent j. The strength of this tie can be expressed by a link weight wi,j. The probability that a link has a weight wi,j follows a power law: P(wi,j)=cwi,j-Ȝ (1) where exponent¬ has usualy values between 2,1…2,7 and c is a normalization factor. The power law is specific for a heterogeneous pattern of agents interactions, but in the same time related to the agents hierarchy in the social organization. An example of the adequacy of the above discussed model was presented in [14] for so called open source software (OSS) communities. OSSs are networks of interacting agents having similar features or common interest, whose size can vary from a few tens or hundreds of individuals bound by professional obligations in the same office or company to large professional groups such as Linkedin, sharing in common information and knowledge or even collaborating to large projects. Let consider that this collaboration implies the exchange of messages between agents (or friends) via e-mail. We use the notation Fi,j (t) to describe the dynamics of messaging flow between agents i and j. Fi,j (t) is 1 if agent i replies to agent j at time t, and 0 otherwise. We consider that the link weight wi,j represents the amount of mail traffic flowing from agent i to agent j: T
wi , j = ¦ Fi , j (t )
(2)
t =0
where T is the timespan of messages exchange. According to equ. (1) we define
G 1− λ P ( wi , j ) ~ wi , j as
the cumulative distribution function (cdf) of P( wi , j ) and
H P( wi , j ) = 1 − P( wi , j )
Fig.1. A conceptual model of emergence
as the complementary cumulative
distribution function (ccdf), or simply the tail distribution.
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We will use the simplest term of “group” because in nowadays society, most relations are not between individuals, but between groups of people. When these groups are at their turn connected to each other, often forming cliques, an emergent process of self-organization can start and finally will lead to a network of bigger size clusters. The growth of the social network follows the principle of preferential attachment highlighted by Barabasi and Albert [15] for scale-free networks, which states new nodes connect preferentially to the most connected nodes already in the network. We can resume these conclusions in the following: 1. Social networks consists of densely connected small groups that overlaps due to individuals with multiple affiliations, forming cliques. 2. Some groups have many overlaps to other different groups, so creating large size clusters. 3. The connection of an individual to a group and of a group to a cluster is made according to the preferential attachment principle. 4. The extension of the network is characterized by a cumulative overlap size distribution which is close to a power law for each network, with a rather large exponent. These remarks leaded us to consider that the most suitable topology of a social network is that of a scale free network [16]. We designed an algorithm that generates subsets of a scale-free network similar to a real social network. The algorithm is implemented in 5 steps: 1. set a node count N and the exponent of distribution degree Ȝ (between 2 and 3) 2. compute the optimal number of nodes per degree 3. create manually a basic subnetwork of 3 nodes 4. for each node from 4 to N, call add node procedure: 4.1. according to the preferential attachment, compute the degree of the parent node 4.2. compute the number of links to establish between the new node and the descendants of its future parent 4.3. chose a parent (randomly) from the nodes with the degree computed in step 4.2 4.4. compute the list of descendants of the new parent 5. create the new node and links 6. for each descendant create the corresponding links 7. save network description file and lunch exit procedure Our application is able to handle very large collections of nodes, to control the generation of network cycles, and the number of isolated nodes. The generated topology consists of three types of nodes: - Routers, defined as nodes with several links, which are unable to initiate traffic and do not accept connections. - Servers, defined as nodes with one or maximum two connections; servers accept traffic connections but do not initiate traffic. - Customers (end-users) defined as nodes that have only one connection, which can initiate traffic connections towards servers at random moments. In fig.3 is shown the topology of a free scale network model with 128 nodes that started from an initial core of 3 nodes; in the connection of other nodes we have applied the
In fig.2 are represented the tails for the distribution of the number of friends per user in a simulation of a small social network with around 500 users, were each user is represented as a node in a graph. Each node is encoded with an integer. The analysis of the graph was made for two cases, depending of the friendship relation between two users A and B, which can be symmetric or asymmetric. An asymmetric relation (fig.2 a) means that if node A has an edge to node B, this does not mean that B also has an edge to A. Of course, in the symmetric relation (fig. 2 b) there is a bidirectional link between A and B.
a
b Fig,2 ccdf of the relationship graph: a) asymmetric; b) symmetric
Both distributions seem to be heavy tailed, which means that the properties of the graph do not change if the edges in the graph are directed or undirected. For this reason in all other simulations we have considered only the asymmetric version of the graph, which is more appropriate with the real case of OSS. Our aim was to find out how strongly the graph is connected, i.e, how many nodes can be deleted before the graph becomes unconnected. The main conclusion was that in a graph with asymmetric interactions we find a characteristic pattern, where few strong nodes dominate the activity of the other OSS nodes. We have also observed that the distribution of links in OSS follows a power-law. Our supposition is that most social networks contain parts (subnetworks) in which the nodes are more highly connected. The sets of such nodes are usually called clusters, modules, or groups.
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law of the preferential attachment. The distribution of the connectivity degree was computed in descending order starting from the most connected node. For the simulations we used Ns2 simulator, more exactly its pdns version which allow parallel processing [17].
We believe that by combining the scale-free network model with virtual distributions of connectivity degree in limited zones of social networks we managed to find a way of investigating the concept of emergence in complex systems. First, we considered the evidence of a naturally looking macroscopic emergent property, and not of one resulted from a general process of calculation. Secondly, we considered that the experimental measurements effectuated in the simulated environment allow an accurate bottom-up characterization, in terms of concepts associated to elements constituting the system. Thus, we have shown that there is a relationship between the macroscopic observed emerging property and the topological and dimensional constraints of the support network. V.
CONCLUSION
This paper was aimed at identifying factors that can lead to the emergence achievement in social networks. They were analyzed both elements of social organization such as collaboration, communication, interactions and specific elements for the dynamics of complex adaptive systems such as flexibility, responsiveness, stability. All these elements were linked in a model of the process of emergence, which was then customized for highlighting emerging phenomena in social networks This framework corresponds also to the characteristics of complex organizations, where emergence lead to structural changes in the form of self-organization. Therefore, we have applied these principles for the study of self-organization in multi-agent systems, refer to emergence characteristics such as large number of agents, diversity, hierarchical interactions, etc. As model for multi-agent social organizations we have proposed a graph where agent relationships are represented with nodes and links, from analysis of the behavior of an important class of social networks, called open-source software (OSS) communities we deduced that the topology corresponds to a scale-free network. In order to study the similarities between a social network and a typical scale-free network we developed an algorithm for scale-free model generation and we used the model to simulate a particular social network whose members have multiple affiliations. Further research is oriented on validation of the framework for other applications, focusing on the correlation between the size and the scaling ratio of a social group. In this regard, we consider that the study of service ecosystems and service innovation systems is highly important: for example, viewing innovation as a collaborative process occurring in A2A networks and combining existing resources - including knowledge, competences and information - to generate new ones, Lusch and Nambisan [18] propose a service innovation framework composed of (i) service ecosystems, as emergent A2A structures, (ii) ITbased service platforms, as modular structures of resources facilitating the interaction of actors and rapid resources recombination and (iii) value cocreation as the process
Fig. 3 A scale free network with 128 nodes having 5 hubs In order to verify if social networks are indeed scale-free, we used our algorithm to compute the degree distribution P(k) in a subnetwork of the interconnected network of some Romanian bloggers. The computed distribution corresponds to a scale-free network power law with the exponential: Ȝ=2.65. As expected, almost all bloggers have also accounts on other larger social networks such as Facebook or Twitter. From the first most influent 100 blogs, we find in July 2016, 47 bloggers having also Twitter accounts. Figure 4 illustrates the interconnection between 25 from these blogger profiles which are also interconnected on Twitter. One can easy observe the scale-free topology.
Fig. 4 The network of 25 Romanian bloggers profiles (interconnected on Twitter too)
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through which, based on a service offer, value in exchange and value in use are balanced and the beneficiary can becomes itself value creator and resource integrator in the A2A network. A particular possible research objective is to investigate the conditions facilitating the transformation of the role of an organization [19], in a (scale-free) network of service systems entities, into a hub, and what are the factors involved in the dynamics of this role. Generality and completeness of the framework are also important to test in future work.
[17] V.E. Oltean, „On qualitative behaviours of a class of piecewise-linear control systems (Part I: Basic models)”, Rev. Roum. Sci. Techn. – Électrotechn. et Énerg., 54(1) , 2009, pp. 99– 108 [18] R.L. Lusch, S. Nambisa, “Service innovation; a service dominant logic perspective”, MIS Quarterly, 39(1), 155-175, 2015. [19] K. Lyons, S. Tracy, “Characterizing organizations as service systems”. Human factors and Ergonomics in Mnaufacturing&Service Industries, 00(0), 1-9, 2012.
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