Narrow Results

The properties of 2D Ising model on a small world network are investigated. It is found that Curie temperature increases with the increase of small world links. The relations between the Curie temperature and the concentration of small world links are found. The possibility of using Ising model to describe real network is discussed.

In this paper, we use a reduced two-compartment neuron model to investigate the interaction between extracellular subthreshold electric field and synchrony in small world networks. It is observed that network synchronization is closely related to the strength of electric field and geometric properties of the two-compartment model. Specifically, increasing the electric field induces a gradual improvement in network synchrony, while increasing the geometric factor results in an abrupt decrease in synchronization of network. In addition, increasing electric field can make the network become synchronous from asynchronous when the geometric parameter is set to a given value. Furthermore, it is demonstrated that network synchrony can also be affected by the firing frequency and dynamical bifurcation feature of single neuron. These results highlight the effect of weak field on network synchrony from the view of biophysical model, which may contribute to further understanding the effect of electric field on network activity.

A new particle optimization algorithm with dynamic topology is proposed based on small world network. The technique imitates the dissemination of information in a small world network by dynamically updating the neighborhood topology of the Particle Swarm Optimization (PSO). In comparison with other four classic topologies and two PSO algorithms based on small world network, the proposed dynamic neighborhood strategy is more effective in coordinating the exploration and exploitation ability of PSO. Simulations demonstrated that the convergence of the swarms is faster than its competitors. Meanwhile, the proposed method maintains population diversity and enhances the global search ability for a series of benchmark problems.

We model transmission of the Severe Acute Respiratory Syndrome (SARS) associated coronavirus (SARS-CoV) in Hong Kong with a complex small world network. Each node in the network is connected to its immediate neighbors and a random number of geographically isolated nodes. Transmission can only occur along these links. We find that this model exhibits dynamics very similar to those observed during the SARS outbreak in 2003. We derive an analytic expression for the rate of infection and confirm this expression with computational simulations. An immediate consequence of this quantity is that the severity of the SARS epidemic in Hong Kong in 2003 was due to ineffectual infection control in hospitals (i.e. nosocomial transmission). If all infectious individuals were isolated as rapidly as they were identified the severity of the outbreak would have been minimal.

This paper proposes a new complex network: multicenter network. Coupled maps on two different types of network, coupled map lattices with nearest neighbors and multicenter network, are investigated. Both theoretical analysis and numerical simulation show that the synchronization of coupled map is much easier than that on the regular network.

This paper presents a detailed analysis on the stability and instability of a coupled oscillator network with small world connections. This network consists of regular connections, excitatory short-cuts or inhibitory short-cuts. By using the perturbation theory of matrix, we give the upper and lower bounds of maximum and minimum eigenvalues of the coupling strength matrix, and then give the generalized sufficient conditions that guarantee the system complete stability or complete instability. In addition, we analyze the effects of the short-cut possibility, excitatory or inhibitory short-cut strength and time delay on the system stability. We also analyze the instability mechanism and bifurcation modes. In addition, the studies on the robustness stability show that the stability of this network is more robust to change of short-cut connections than the regular network. Compared to the mean-field theory, the stability conditions from the proposed method are more conservational. However, the proposed method can guarantee the complete stability even if the randomness is in the system. They are more useful and adaptive than mean-field theory especially when the excitatory and inhibitory connections exist simultaneously.

We characterize the distributions of short cycles in a large metabolic network previously shown to have small world characteristics and a power law degree distribution. Compared with three classes of random networks, including Erdős–Rényi random graphs and synthetic small world networks of the same connectivity, both the metabolic network and models for the chemical reaction networks of planetary atmospheres have a particularly large number of triangles and a deficit in large cycles. Short cycles reduce the length of detours when a connection is clipped, so we propose that long cycles in metabolism may have been selected against in order to shorten transition times and reduce the likelihood of oscillations in response to external perturbations.

The question of how innovation comes about, and the circumstances under which it prospers, is a question that has continued to be the focus of academic research on innovation creation. Recent works have focused on how the capacity to innovate hinges on the ability to form networks; hence, the importance of the research. The article therefore answers the research question of whether the network relationships between similar ethnic, education background, and geographically clustered groups plays a significant role in innovation creation or not? Using the theoretical concept of a Small World Network, we use case study methodology to study the origins of several companies from the technology sector, who in recent years have emerged as successful businesses. The article examines the small world network dimensions of ethnicity, education, geography and the binding subject matter interest that initially formed these relationships, as well as the impact that these relationships have on innovation creation and business success.

In this chapter, an introductory analysis of human intelligence and consciousness is executed to establish a conceptual foundation for the intelligent organization theory. Fundamentally, the new intelligence mindset and thinking, and intelligence paradigm focus on high intelligence/ consciousness-centricity. It concentrates on human intrinsic intelligence/ consciousness sources (its intense intelligence and consciousness — awareness and mindfulness), and stipulates that organizing around intelligence (a strategic component of the complexity-intelligence strategy) is the new strategic direction to be adopted by all human organizations in the present knowledge-intensive, fast changing, and not always predictable environment (limited predictability). In addition, the characteristics and variation in capabilities of intelligence and consciousness are further scrutinized using an intelligence spectrum (compared to other biological intelligence sources on this planet — encompassing proto-intelligence, basic life-intelligence, basic human intelligence and advanced human intelligence). In the intelligent organization theory, consciousness (awareness, mindfulness) only exists in the living/biological world, and mindfulness is confined to humanity.

Subsequently, intelligence/consciousness management and its associated dynamics that are critical activities of intelligent human organization (iCAS) are more deeply examined with respect to complexity-centricity (encompassing attributes such as stability-centricity, autopoiesis, symbiosis, self-centric, network-centric, org-centric, independency, interdependency, intelligence-intelligence linkage, engagement, self-organization/ self-transcending constructions, local space, complex networks, constructionist hypothesis and emergence). Concurrently, the urgency and impact for nurturing the new intelligence mindset and intelligence paradigm is also discussed. In a situation with escalating complexity density, this paradigmatic shift in mindset and thinking in leadership, governance and management of human organizations is highly significant for higher functionality and coherency — to all human interacting agents (both leaders and followers), as well as the organizations themselves.

Another component of the complexity-intelligence strategy examined is the nurturing of an intelligent biotic and complex adaptive macro-structure that will serve all human organizations better (towards higher coherency, synergy and structural capacity). The analysis clearly indicates the necessity of systemic transformation or structural reform that is more coherent with intelligence/consciousness, and information processing and knowledge acquisition capability. In this case, a greater operational/ practical utility and higher structural capacity can be achieved with the presence of the intelligent biotic macro-structure and agent-agent/ system micro-structure (principle of locality) that concurrently supports the intelligent complex adaptive dynamic (iCAD) better — a finer synchrony between structure and dynamic — higher intelligence advantage.

The first section of this chapter is an introduction to relativistic complexity (a significant component of the intelligent organization theory). The presence of intense intelligence/consciousness-centricity and 3^rd order stability-centricity in the human world renders complexity relativistic. The impact of the human mental space is so tremendous that complexity is in the mind of the beholder, and predictability becomes highly subjective. In this situation, the state of relativistic static equilibrium may be beneficial. Certain spaces of complexity appear as spaces of relativistic order with surface patterns becoming more apparent. Such spaces must be creatively explored and exploited (higher exploratory capacity) leading to a more advanced level of intelligence advantage. In this respect, effective self-transcending constructions, high self-organizing capacity and emergence-intelligence capacity are significant attributes that the new leadership and governance system in intelligent human organizations must exploit. Holistically, the two strategies focus on concurrent exploitation of intelligence/consciousness-centricity and relative complexity, and optimizing the more comprehensive contributions of the integrated deliberate and emergent strategy.

Many issues/problems that present human organizations (nations, political systems, communities, business organizations, markets) are encountering due to accelerating changes (mindset, thinking, values, perceptions, expectations, redefined boundaries and high interactive dynamics) that cannot be well-managed with traditional knowledge and hierarchical practices are affecting governance and governance systems. Fundamentally, governance deals with power, interest, and conflict. The traditional governance systems are hierarchical, highly directed, controlled and managed, and the relational aspect has not been allocated sufficient priority resulting in extensive disparities. In the current complex dynamical and high interdependency environment, its weaknesses and constraints are highly apparent. The latter includes ‘space-time compression’; incoherency in thinking, values, perceptions, and expectations between the leadership and the other agents; diversification in stakeholders’ needs not accommodated; and constraints of current governance theories. Thus, a new theory that provides a more ‘realistic’ foundation is essential for deeper contemplation.

Primarily, recognizing the inherent strengths of human agents and the fundamental constraints/weaknesses of human organizations is a key foundation towards better adaptation, leadership, governance, resilience and sustainability. In all human organizations, the agents are intrinsic intense intelligence/consciousness sources that could easily transform their behavioral schemata. This observation contradicts the Newtonian/design paradigm, as the organizational dynamic of human agents is complex, nonlinear, constantly/continuously changing with limited predictability. In addition, human agents are self-centric, self-powered, stability-centric, independent and interdependent, network-centric and self-organizing due to high awareness. In this situation, high self-organizing capacity and emergence-intelligence capacity are new niches. However, this phenomenon can create new opportunities, innovation, and elevates competiveness; or destruction.

In particular, effective leadership and governance are spontaneously emerging key requirements in all human groupings — a primary trait for human collective survival. Historically, many organizations disintegrated because of the weaknesses in leadership and governance. Currently, with more knowledge-intensive and higher participative new agents (self-powered intrinsic leadership) possessing modified attributes that are dissimilar from the older generations (also due to the deeper integration of the economic, social, political, and environmental perspective), reduces consensus and collaboration, and renders governance and leadership even more nonlinear or dysfunctional. In particular, the traditional governance systems of more organizations are manifesting their constraints and incompetency, including incoherency due to new values and cultural pressure, and the wider spread of self-organizing networks. The emergent of informal networks is a more commonly observed phenomenon worldwide. Apparently, a deeper comprehension on the diminishing effect of the traditional organizational thinking (political, social, economic), governance capacity, precise strategic planning, decision making, hierarchical structure, communications and engagement, empowerment leadership, management, operations, and the highly nonlinear relational parameter is essential. Apparently, new principles of governance must emerge (intelligent human organization > thinking system + feeling system).

The new paradigmatic path of the intelligence governance strategy that exploits intelligence/consciousness-centricity, complexity-centricity, and network-centricity concurrently, introduces a new basic strategic path towards better adaptive governance and acceptance governance. The latter focuses on integrating self-powered self-organizing governance, reducing direct governance, and increasing e-governance and network-centric governance as a new necessity. In this case, the merits of adopting the intelligence leadership strategic approach simultaneously are more apparent. Hence, the new governance focal points must include more and better interconnected actors, the critical ability of self-organizing communications (supported by mobile/social media development), immersion of leadership nodes in networks (better exploitation of e-governance), increasing coherency of complex networks (exploiting interdependency of network of networks, and better network management), and elevating self-transcending constructions capability (higher self-organizing governance capacity and emergence-intelligence capacity) that better facilitates emergence through multi-level and ‘multi-lateral’ dynamics (complex adaptive networks <=> intelligent networks). Thus, the intelligence governance strategy emphasizes that mass lateral collectivity (acceptance governance) rather than selective enforced hierarchical empowerment as the more constructive approach in the present contact. In particular, the stabilityinducing role of leaders and institutions are critical. Apparently, optimizing the ‘everybody is in charge’ phenomenon (whenever necessary) is a more viable option.