Abstract
Abstract
Recognizing the inherent strengths and weaknesses of human agents and organizations, as well as changing characteristics and behavior of the interacting agents is the key fundamental to better adaptation, leadership, governance, resilience and sustainability. In all human organizations, the agents are human beings each embedded with an intrinsic intense intelligence source that could easily transform their behavioral schemata. Thus, contradictory to the Newtonian/design paradigm, the group/organizational dynamic of human agents is complex, nonlinear, constantly/continuously changing, and can be unpredictable. In addition, complexity in the human world can be relativistic. Consequently, human agent/organization may perceive certain spaces of complexity as spaces of relativistic order – relativistic complexity. In particular, due the presence of the intense mental dimension in humanity – complexity is in the mind of the beholder.
In human existence, leadership and governance are spontaneously emerging key requirements – a primary trait for collective survival. Currently, with more knowledge-intensive and participative new agents (self-powered intrinsic leadership) who possess modified beliefs, values, norms, and expectations that are dissimilar from the older generations, governance and leadership need deeper analysis and redefinition. Traditional governance systems in all categories of organization are manifesting their constraint, vulnerability, and incompetency, in particular, incoherency due to new values and cultural pressure, and their associated self-organizing networks – especially informal networks that demand change, a more commonly observed worldwide phenomenon. In this respect, special attend has to be focus on the highly nonlinear relational parameter is beneficial.
This study adopts the intelligence mindset that concurrently focuses on intelligence/consciousness-centricity, complexity-centricity, and network-centricity as the new strategic path towards better adaptive governance and the new leadership. It concentrates on the self-powered agents that are also intrinsic leaders/actors. The new intelligence leadership focal point include nurturing intense collective intelligence (more actors), the critical ability of self-organizing communications, immersion of leadership nodes in networks/clusters (including e-governance), increasing coherency of complex networks (interdependency of network of networks, network management), exploiting selected spaces of complexity (complexity management), and intelligence-driven self-transcending constructions that better facilitates emergence through ‘multi-lateral’ dynamics (minimizing ‘direct’ governance). This intelligence governance strategy emphasizes that mass lateral collectivity rather than selective enforced hierarchical empowerment is the more effective approach in the present contact. Fundamentally, optimizing the ‘everybody is in charge’ phenomenon frequently is a more viable option.
Keywords
Introduction
Constraints of the newtonian mindset and design paradigm
Inherently, humanity is group instinctive. An enormous number of organizations (communities, corporations, nations, regional institutions) exit in the human world, and unknown number of civilizations have appeared and disappeared. The social and political perspectives of these systems surface spontaneously. In particular, the economic perspective intensified when the industrial revolution started at the end of the eighteenth century (1760s to 1840s) that led to the establishment of a large number of business organizations, first in the UK and subsequently worldwide. Concurrently, with the deeper formalization of structures and processes in humanity, governments, societies, political systems, businesses, and environmental systems, spontaneously governance and governance systems were established. In particular, due to the strong presence of the Newtonian mindset (order, linear, equilibrium, forecasting, and predictability), a machine-based (fundamentalism, reductionist hypothesis, and Cartesian belief) and hierarchical design paradigm (top-down structure, empowerment leadership, and command and control) dominated the structure and dynamics of all current human organizations and their governance systems.
The design paradigm of management (encompassing the cybernetics and systems engineering perspectives) and operations emerges in the middle of the 20th century for organizational problem solving [10]. The key focus of the cybernetics perspective is on the design of organizations as systems with structural stability that could be regulated and controlled by management intervention (using feedback loops) – that is, highly hierarchical and centralized control. The main weaknesses of the models are they do not take into account the non-rational (emotional function) behavior of human beings, the self-powered and intrinsic leadership ability of human agents, the highly nonlinear relational parameter, the emergent aspects of their destructive collective behavior (internal destruction), and the increasing presence of complexity.
The systems engineering perspective comprises the ‘hard’ system approach and the ‘soft’ system approach. The ‘hard’ system approach focuses on the internal consistency of modularized systems (their hierarchical structures and modular organization). The ‘soft’ system approach concentrates on the problem definition of the ‘whole’ for human systems. Its design is to enable all stakeholders to observe the whole, to identify the diverse problem space, and to take a best collective decision. Again, the overall disadvantage of this paradigm is planned management interventions, highly hierarchical leadership, and not all agents are well connected or informed.
However, it is important to recognize that a significant proportion of human dynamics inherently manifest nonlinear behavior and unpredictability within and without the organization. Each human agent is self-centric and autopoietic. In complexity theory, the focus on the assumptions of structural stability has been shifted to the non-equilibrium dynamics of open systems (dynamical equilibrium), and the autopoietic and self-organizing abilities of the interacting agents – that is, an intense focus on spontaneous dynamics – a prominent process in humanity that requires greater attention.
Apparently, orderliness, linearity, equilibrium and predictability are not the only characteristics in the human world. Order and complexity (stasis and turbulence) co-exist. The entire humanity and their sub-systems, including human beings are intrinsic complex adaptive systems (and composite systems). Additional characteristics such as nonlinearity, unpredictability, sensitive dependence on initial conditions, punctuation point, changing rugged/fitness landscape, far-from-equilibrium, interdependency, self-organization (self-transcending constructions), basin of strange attractor, and complexity are all natural characteristics/part of the global human ecosystem. The human world is continuously changing (gradual and sudden), and change is the only thing that never change. In certain aspect, complexity theory is also a theory of change.
The emergence of complexity studies
The study of chaos/complexity both in the natural sciences and its extension to humanities (social, economic, political, military, and the environment) is relatively new. Traditional or exact natural sciences had concentrated on linear and orderly characteristics (for instance, classical mechanics – ordered systems – OS) or infinite random systems (for instance, statistical mechanics – disordered systems – DS) that can be analyzed with high precision. The domain of chaos/complexity concentrates on the range of systems in between the two extreme ends (namely, nonlinear dynamical systems), that is, venturing from low to high finite dimensionality, and their associated new unfamiliar chaos/complexity characteristics and properties arising for certain embedded nonlinear variables/parameters. The latter include deterministic and in-deterministic (unknown unknowns) nonlinear relations and behavior, resulting in unpredictability.
There were several early complexity research pioneers in the sciences and mathematics. Henri Poincare (1854–1912), coined the term bifurcation in 1885, and studied the chaotic motion of three-body systems – discovering the existence of non-periodic state; while Aleksandr Lyapunov (1857–1918), is famous for his development of the stability theory of dynamical systems. Another highly prominent contributor is Ilya Prigogine (1917–2003), who from 1950s to 1980s conducted research on dissipative structures, and conceptualized the dissipative structures theory (concentrated on edge of order – first critical value – new intra-system order emerges) that focused on self-organizing systems [61, 64–66].
In humanities, Joseph Schumpeter (1883–1950), an economist, developed evolutionary economics, theory of economic development, and popularized the term ‘creative destruction’ which is closely associated with self-organization (endogenous) and emergence (emergent of new order); and Friedrich Hayek (1899–1992), another economist, contributed to the concept on spontaneous order in economics – self-organization is due to human action and not human design. In addition, Niklas Luhmann (1927–1998), a sociologist, and a significant contributor to human system theory focused on self-organizing communications – a communication process that generates more communications spontaneously. He also conceived the social spontaneous order theory.
The intensive studies on this new domain re-emerged in the early 1960s focusing on chaos (simplicity to chaotic), starting with rather simple mathematical equations or models. Some main contributors (both mathematical and practical aspects) over the next two decades include Edward Lorenz (Lorenz attractor, sensitive dependence on initial conditions, butterfly effect), Benoit Mandelbrot (fractal geometry, Mandelbrot set), Stephen Smale (nonlinear oscillators, topological transformation) and some others in the 1960s), and David Ruelle (strange attractor), Floris Takens (strange attractor), Mitchell Feigenhaum (bifurcation, Feigenhaum constant), Tien-Yien Li (chaos), James Yorke (chaos), and Robert May (logistic map) in the 1970 [44, 67] (May, 1976). In particular, Lorenz’s confirmation of sensitive dependence on initial conditions, and the first physical observation of the (3-dimensional) strange attractor provided a key foundation for the complexity theory.
In the 1980s to 2010s, the focus of researchers extended/shifted to complexity, in particular after the establishment of the Santa Fe Institute in 1984 [the study of complex systems (CS), and complex adaptive systems (CAS) – system dynamics vary from complexity to simplicity] by scientists mainly from the Los Alamos National Laboratory. Currently, complexity theory is a domain that encompasses both complexity and chaos. Gradually, its influence and impact on research institutions and all categories of human organizations is intensifying [1, 76].
Meanwhile, a significant development of the complexity studies focuses on the subset of complex systems known as complex adaptive systems that encompasses life (biological, ecological, human). In this case, the interacting agents of the systems are living organisms/beings, and the systems are CAS or composite CAS, such as eco-systems. In all human organizations (social, economics, education, political, military, and others), the interacting agents are human beings each embedded with an intrinsic intense intelligence source. Thus, fundamentally their dynamics involve complex interactions of a collection of intelligence sources. In this respect, all human organizations are intelligent complex adaptive systems that encompass properties/activities such as order, complexity, nonlinearity, intelligence, consciousness (awareness and mindfulness), learning, adaptation, knowledge acquisition, autopoiesis, self-organization, self-transcending constructions, far-from-equilibrium, edge of order, edge of emergence, butterfly effect, biodiversity, unpredictability, emergence, scalability, and attractor [46, 69–71].
In human systems (in particular, business organizations, non-profit organizations, social communities, governments), governance, leadership, strategies, and ethicality are highly interconnected. The complexity of these dynamics is increasing, especially due to the changing needs and expectations of human agents – due to better worldwide education, and intense and swift digital connectivity. Numerous studies have been conducted in this aspect of human organizations [17, 75]. Overall, better understanding, exploring, and exploiting complexity theory by human organizations will have a profound impact on their future leadership and management mindset, governance and governance systems, organizational structures, strategies, dynamics, competitiveness, and sustainability [6, 78] (Goldstein 2010; Overman 1989).
Towards the intelligence mindset and intelligence paradigm
Besides the group instinctive characteristic of the human species, the self-centric and stability-centric aspect (its associated characteristics and dynamics) of every individual human agent must also be better recognized and coalesced. Human agents are rather different from agents in other complex adaptive systems (such as an ant colony, a bee hive, or a pack of wolves – that is, there is a significant different between swarm intelligence and human collective intelligence). The uniqueness, competitiveness, and sustainability of humanity are due substantially to the fact that every human being is embedded with an intense intrinsic intelligence sources (including high awareness and mindfulness, emotional and logical, linear and nonlinear) – the complex human thinking system: the biological brain and abstract mind combined is ‘an emotional system that can also be logical’ (Liang, 1988). This highly valuable asset should be the main focus when analyzing, leading, and managing the survival of agents and organizations/systemsalike.
Thus, the intelligent organization theory (including relativistic complexity for human organizations/systems) focuses on the intrinsic intelligence and consciousness of the individuals (self-powered human agents – ‘intelligent persons’ naturally endowed with intrinsic leadership ability – high adaptive capacity – towards everybody is in charge), and the collective intelligence and organizational consciousness (mindful culture and orgmindfulness) of organizations with respect to intelligence-intelligence linkages, self-organizing communications, network-centricity, self-transcending constructions – highly intelligence/consciousness-centric that encompasses both thinking systems, and ‘feeling systems’ (indicating the significant of intelligence/ consciousness management), and in particular, effective emergent of order – relativistic simplicity [46–51].
Concurrently, the theory also focuses on complexity-intelligence linkages – high complexity-centricity [exploiting the co-existence of order and complexity, exploring and exploiting certain spaces of complexity (risks management), and making preparation for punctuation points (crises management) – that is, a high concentration on complexity management is a crucial part of the new paradigm]. The two linkages, intelligence-intelligence linkages and complexity-intelligence linkages are closely associated with balancing autopoiesis, self-centricity, org-centricity, independency, interdependency, self-organization/self-transcending constructions, and innovative emergent of order. Thus, the new intelligence mindset exploits characteristics, concepts, and tools including organizing around intelligence, intelligent biotic structure, intelligent person (model), mindfulness, orgmindfulness (core of supportive culture), mindful culture, third-order stability (encompassing physical stability, biological stability, and mental stability), co-evolution (nested/composite systems), no global optimality, integrated deliberate and emergent strategy, space of relativistic order, relativistic static equilibrium, and intelligence leadership strategy.
As human agents possess an intense mental dimension (high mental capacity), complexity in the human world becomes relativistic – complexity is in the mind of the beholder. In this respect, all human organizations are relativistic complex adaptive systems (rCAS) constantly seeking emergent of order. In this case, the adaptive abilities of most human organizations are or could be significantly different from other complex adaptive systems – that is, they could be nurtured to function as highly intelligent complex adaptive systems (iCAS). Besides the agent-system micro-structure, embedded within all human complex adaptive systems are complex networks (network of networks) – the meta-structure. Consequently, a better understanding on network theory and network management is also vital for a deeper comprehension on human organizational dynamics.
Further analysis
This study is an analysis on the integration of relativistic complexity (intelligent organization theory), network theory, governance, governance systems, and the strategic intelligence leadership that are closely associated with issues/problems that many human organizations (nations, political systems, communities, business organizations, markets) are encountering due to rapid spontaneous changes that cannot be well managed with traditional knowledge and practices. To identify a path out of the expanding problem space, the following four aspects will be examined: The first aspect concentrates on the boundaries and selective focuses of complexity studies, including the similarities and differences of deterministic chaotic systems and complex (adaptive) systems, and reconfirming that human organizations are intrinsic complex adaptive systems – so that the benefits and criticality of exploiting complex adaptive dynamic can be elevated. The second portion analyzes the normative foundation of governance with respect to its ‘individualistic’ and ‘collective’ characteristics, the increasing complications of traditional governance systems due to rising impact of complexity on its structure (including a large intangible component – informal complex networks) and dynamics (nonlinearity and unpredictability), and the basic requirements for better governance capacity, as well as identifying the intrinsic strengths of human agents/organizations, and the inherent constraints of human organizations – the latter is the key foundation for any new leadership, governance and management thinking and strategy. The next analysis concentrates on the significance and impact of intelligence/consciousness-centricity, complexity-centricity, network-centricity, and the intelligent complex adaptive dynamic has on the new governance thinking (including self-organizing communications, emergent strategy, and criticality of the ‘multi-lateral’ dynamics) – indicating the necessity of a paradigmatic shift. The final component focuses on the intelligence governance strategy – a deeper analysis on the integrated adaptive governance, relativistic complexity, self-organizing governance, and the intelligence leadership dynamics that are highly dependent on better network management and effective exploitation of self-powered self-organizing sub-systems (formal and informal network of networks/local spaces) to elevate the global governance capacity, and achieve higher coherency (among non-acting agents, acting agents, leaders) and sustainability of organizations.
Boundaries of complexity studies
In general, complexity studies is a domain that provides a more ‘realistic’ and ‘complete’ coverage for human organizational studies, management, and operations, as well as any human associated activities. In this respect, a more vivid and deeper comprehension of the correlation between complexity theory and human organizations is highly crucial and beneficial for current and future human organizational functionality, leadership, and governance. An important starting point is to observe where human organizations exist in the systems spectrum – which will lead to a deeper identification of its basic characteristics (dimensionality, degree of freedom, variables/parameters – known and unknown, potential dynamics, inherent strengths, constraints), and better potential paths of exploration.
A simplified systems spectrum
The seven common categories of systems (identified in this analysis) and their characteristics encountered in the exact sciences and complexity studies are as follows:
Ordered systems – OS (Newtonian theory, usually closed systems, usually scientific systems and mathematical models, static equilibrium, orderly, linear, predictable, fixed point, periodic point).
Deterministic Chaotic systems – DCS (Chaos theory, closed or open systems, usually low dimensionality, deterministic, unpredictable, nonlinear, bifurcation, phase transition, strange attractor).
Complex systems – CS (Complexity theory, open non-living systems, physical variables, high finite dimensionality, nonlinear, deterministic or in-deterministic (mathematically), far-from-equilibrium, dissipative, continuous change, interconnectivity, interdependency, stasis and punctuation points, unpredictable, autocatalytic, self-organizing, symmetry breaking, emergence, strange attractor).
Complex adaptive systems – CAS (Complexity theory, open living systems, living/biological agents, high finite dimensionality, nonlinear, deterministic or in-deterministic (mathematically), far-from-equilibrium, dissipative, continuous change, interconnectivity, interdependency, adaptive, stasis and punctuation points, unpredictable, autopoietic, autocatalytic, self-organizing/self-transcending constructions – presence of latent impetus, symmetry breaking, emergence, strange attractor).
Intelligent human organizations/systems – iCAS (Intelligent organization theory, complexity theory, open systems, high finite dimensionality, human agents, self-powered agents, order and complexity co-exist, nonlinear, interconnectivity, communication, engagement, interdependency, dissipative, far-from-equilibrium, relativistic deterministic and/or in-deterministic (mathematically), uncertainty (unknown unknowns), anticipatory, intelligence/consciousness-centric, continuous change, complexity-centric, network-centric, learning, adaptive, integrated deliberate and emergent strategy, smarter evolver, emergent strategist, self-organizing/self-transcending constructions, intelligence leadership, symmetry breaking, relativistic complexity, relativistic static equilibrium, space of relativistic order, emergence, extropy, ‘equilibrium’ at minimumentropy).
Disordered systems – DS (Statistical mechanics, usually closed systems, infinite dimensionality, randomness, macro-variables, micro-variables, equilibrium at maximum entropy).
In-deterministic chaotic systems – ICS (Catastrophic theory, high dimensionality, nonlinear in-deterministic dynamical systems after crossing the redefined edge of chaos (new – 3rd critical value) will disintegrate [51]. Systems that fail to self-organize and emerge will move into the space of in-deterministic chaos, and they will eventually disintegrate due to in-determinism or high turbulence).
These seven categories of systems can be captured as a simplified systems spectrum (see Fig. 1). This simplified systems spectrum provides a clearer illustration on the spread and relationships of the different systems with respect to dimensionality. The two extreme ends of the spectrum are ordered systems (very low dimensionality) to disordered systems (infinite dimensionality). Systems in between are DS, CS, CAS, and human organizations.
Briefly, all human organizations are intrinsic CAS. As human beings are the most intelligent agents on this planet, human organizations can be nurtured as intelligent complex adaptive systems (iCAS), and complexity is relativistic. However, the intelligent human agents can be highly nonlinear (emotional human thinking systems) due to their self-centric, autopoietic, independency, interdependency, and nonlinear relational characteristics (that is, recognizing the presence and importance of a localized space is vital). Due to the intense presence of individual and collective intelligence (positive or negative), human organizations are relativistic complex adaptive systems that are rather different from other complex adaptive systems.
In general, during the ‘life span’ of these systems when emergent of new order fail, the systems move into a space of in-deterministic chaos and disintegrate. (All systems are composite structures, and any system that is composed will eventually disintegrate.) It is interesting to note that systems with the highest complexity are in the central portion of the spectrum (at high finite dimensionality and not at infinite dimensionality) – including human organizations. Disorder systems are not complex when focusing on average values and macro parameters, and because they move into static equilibrium at maximum entropy.
Key focuses of complexity theory
Primarily, complexity studies focuses on the characteristics, structures, and dynamics of three categories of systems namely, deterministic chaotic systems (DCS), complex systems (CS), and complex adaptive systems (CAS) which also defined the boundaries of the theory. The dynamics involved are deterministic chaotic dynamic and complex (adaptive) dynamic.
Deterministic chaotic dynamic develops from simplicity (low dimensionality) to chaotic through constant bifurcation or phase transition.
Complex (adaptive) dynamic develops from complexity (high finite dimensionality) to simplicity through self-organization/self-transcending constructions, autocatalysis, dissipation, and emergent of new order.
Similarities and differences between DCS and CS/CAS
It is important to recognize the similarities and differences between chaos (DCS) and complexity (CS/CAS) as the two terms have created some confusion during the early period of study due to their restricted meaning or usage in complexity theory – chaos (low dimensionality, deterministic/mathematical chaos, precise mathematical models exist), while complexity (finite high dimensionality, surface pattern still exists although CS/CAS are in-deterministic, may not be captured in precise or complete mathematical models, mathematical models used are estimations. In this case, when analyzing and managing human organizations (CAS), a qualitative approach may be a better option than a quantitative approach – ‘a crude look at the whole’).
a. Key Similarities Both DCS and CS/CAS are nonlinear dynamical systems, and are sensitive dependence on initial conditions – butterfly effect – unpredictable. Both are constantly or continuously changing – far-from-equilibrium, no static equilibrium. Both possess the strange attractor. Both could disintegrate (moving into in-deterministic chaos).
b. Key Differences DCS are low finite dimensionality and deterministic but unpredictable, and will disintegrate. The starting point of chaotic dynamic is simplicity (due to small number of variables involved) to chaotic (due to rapid bifurcation or phase transition). Thus, it is easier to conceive mathematical models for DCS and work on them– low finite dimensionality (all variables, parameters, and path of development can be computed. CS/CAS have high finite dimensionality, ‘relativistic deterministic’ or in-deterministic (in-complete set of variables), unpredictable and will disintegrate. Variables/agents in CS/CAS can be highly interconnected and interdependent – forming localized networks. CAS are CS with living agents. The starting point of CAD is complexity and nonlinearity (due to the large number of agents/variables involved, as well as their changing relationships) to simplicity (emergence of new order) – involving self-organization/self-transcending constructions. CAD develops locally to (to network) ‘globally’ with autocatalysis, feedback, dissipation, self-organization, self-transcending constructions, and emergence. The study of CS/CAS usually (more beneficial) adopts a qualitative (not determining precise solutions which may be difficult or impossible to achieve – no global optimality) longer term approach – focus on whether the system will settle into a particular preferred state after some changes (by identifying potential basin of attraction). Thus, the mathematical model and algorithmic (step-wise) approach used to study CS/CAS (especially for in-deterministic human organizations commonly with in-complete variables and/or unknown unknowns – uncertainty) is only an estimation – for human organizations, their phase space is in-complete (although there are mathematical modeling exploited in complexity studies).
The above analysis reconfirmed that all human organizations are high finite dimensionality nonlinear (relativistic) complex adaptive systems with in-deterministic (relativistic determinism, unknown unknowns) characteristics, encompassing constantly changing landscape and unpredictable behavior (sensitive dependence on initial conditions), and not low dimensional chaotic systems. Similarly, constructive human organizational dynamics are nonlinear complex adaptive self-organizing possesses constantly ‘seeking’ new order (from complexity to simplicity).
Redefined normative foundation of governance
Governance, governance theory, and traditional governance systems
In general, the presence of proper and acceptable governance is crucial whenever a group of human beings coalesce irrespective of their primary functions (communities, governments, business corporations, financial markets, or regional institutions). Therefore, governance is a necessity that surfaces ‘inherently’ when a formal or informal group of human beings and their associated activities need governing for better coherency. This is partially due to the self-centric and stability-centric of the human agents, although, the primary objective of all governance systems is also to establish and implement better and deeper collectivity to achieve more sustainable organizational performance – for both the actors/leaders and non-actors.
For instance, for a nation, effective governance must align leadership decisions/policies with citizens’ interests/survival – concurrently towards a better holistic balanced national (economic, power) development and nurturing citizenry (stability). Similarly, for a business corporation, effective corporate governance must align the various stakeholders’ interests, and top management decisions and actions to achieve a better working environment and higher competitiveness simultaneously. For an ‘informal’ system such as a market, its governance system may not be formal/physical but it is still ‘observable’ – its presence can be felt.
Thus, the traditional governance approach concentrates tremendously on defining/designing rules and actions, control and compliance, and also verifying performance supported by a hierarchical structure and empowerment processes [19, 34]. In this respect, governance is greatly dominated by how an organization is constituted, directed, controlled, and managed – a high level of rigidity. Fundamentally, governance (governance theories and practices) concentrates on three basic entities namely, power (appropriate distribution), interests (intensity of differences and diversification), and conflicts (problems/issues/priorities incoherency, their spread and frequency of emergent) that will greatly affect both the internal and external environment, development, competitiveness, and sustainability of the organizations and their agents – in various aspects, including the entire humanity itself (failure of human global governance). Vividly, a fourth critical parameter involved is relationship among agents, and between agents and their organization – a highly nonlinear and complex parameter that requires deep and subtle management. The above recognition defines the general boundary of governance and its systems.
Apparently, a governance system is a vital sub-system of all human organizations that focuses on the diversification and better management of power, interests, relationships (independency, interdependency, knowledge, issues/policies/problems, coherency, and performance), and objects (largely organizational, including citizenship, service, product) – see Fig. 2. Similar to other types of human systems, very often it also encompasses an important intangible component beyond the formal structure and expected dynamics – that is, involving informal/intangible (‘invisible’) actors. Thus, the critical needs for collectivity and system stability require certain level of leading and co-ordination on thinking, behavioral schemata, and adaptation – largely due to the contradicting self-centric autopoiesis of human agents (leaders/actors and non-actors alike) versus the ‘collective objectives’ of the organization. However, the responsibilities and functions of a traditional governance system very often focuses more on the system’s/organization’s needs and priorities, but not so much on the characteristics and requirements of agents – in particular, the non-actors. In such a situation, as agents’ instability is high, an ‘inherent’ incoherency exists.
In summary, the basic aim of a traditional governance system is the creation of a better order/structure, and always seeks static equilibrium (does not exist in complex adaptive dynamic) by exploiting the design paradigmatic thinking and strategies – hierarchical leadership, empowerment, command and control. In this case, the role of bureaucracies as a source of stability has been highly preferred – also resulting in imbalance between the system and certain clusters/agents within. Consequently, frequent mass discontentment exists, clearly revealing the constraints of the traditional system.
Deeper analysis on traditional governance complications
Basically, a significant requirement in the above stated situation is acceptable power distribution among all agents, according to some formal and informal rules. Conceptually, a holistic consensus is significant – or at least a certain degree of consensus. However, in reality, great differences may surface. For instance, dominant leaders with absolute power render governance meaningless to the masses. In addition, over time with higher living sophistication, governance in humanity encompasses integrated perspectives, including social, political, economic, and even environmental. This integration inevitably escalated the complexity of governance in the human world. In addition, current agents may be better empowered by the social media rather than the leadership.
Increasing stress of space-time compression
In the present context, the first significant aspect of governance complication arises from ‘space-time compression’ which is a consequence of escalation in speed of interactions (accelerating dynamic), and increase in linkages and intensity of coupling (primarily due to globalization, and the swift development of digital/media technology – the substantial increase in information processing capacity, spreading interdependency, and in particular the ‘shrinking’ world effect), leading to rapid elevation in complexity. Human beings become well-informed (or over-informed leading to confusion), and self-organizing communications intensified leading to potential rapid changes in thinking and situation interpretation – the necessity of swift decision making is critical. Under extreme condition, the large space-time compression effect also elevates mental stress, and may ignite an economic-political crisis (punctuation point) due to the unpredictable nonlinear behavior (incoherency) of the agents, clusters and the organization/environment. Thus, the leadership must be aware that what appear to be small perturbations at time may result in enormous irreversible changes (butterfly effect).
Mismatch in leadership mindset and agents redefined expectations
Concurrently, there is also a general increasing mismatch between the leadership mindset/paradigm (high power and hierarchical control correlation) and agents redefined expectations (better power distribution and higher recognition of contributions). Under certain circumstances, with rapid causation informal localized spaces with different expectations and mission emerges (complex networks with new value and cultural pressure). In this case, leaders that attempt to regulate all details by legislation and administrative procedures are no more effective. At the extreme, every intervention may results in unintended consequences as perception, expectations, and values of agents are constantly changing. Inevitably, no recognition and contradicting mindset and thinking (values, belief, ethics, and culture) cause relational/psychological (mental) discomfort. In addition, unfairness (widening of wealth gap, merging of political and economic power confined to a small group) always exists with detailed regulations and only a ‘preferred’ minority (empowerment) will benefit from it.
Thus, another key vulnerability of traditional governance and its systems is the increasing mismatch in thinking, ideology, system structure, practices, and expectations between the leadership and some other agents – resulting in higher skepticism, scrutiny, and criticism. As a primary purpose and focus of governance is ‘stability control’ [ensuring better social cohesion, political consensus, power and economic (wealth) distribution, and higher performance and sustainability of the organizations], constructive mass participation and acceptance (lateral) is beneficial. However, the traditional approach is highly hierarchical/preferential – prominent imposition (over reliance on hierarchical leadership), and empowerment of selective actors (structuralism), while the rest are non-actors. In addition, a system with low and distorted information flow (feedback) possesses low adaptive capacity. For instance, a ‘state-centric’ governance system because of the many veto points creates information deficiency (poor feedback) – apparently a lack of socio-emotional well-being exists. In the process, this leads to unidirectional communication (Abilene paradox) that is disastrous. Subsequently, this situation may leads to internal conflicts, dysfunctional activities or even (‘innovative’) destruction – pushing the system into an in-deterministic chaotic space (already manifesting in some countries). It is important to note that human agents that are trapped in ‘local minima’ (local space with no stability) can be highly frustrated and destructive.
Stakeholders’ needs at different levels not well-balanced
With decades/centuries of existence, many current governance systems are multi-level and highly sophisticated. In practice, at the microscopic level, it is highly significant to recognize that human agents are autopoietic and self-centric (Maturana and Varela, 1993) [77]. The presence and/or perceive the presence of organizational injustice has intensified due to the different beliefs, values, and expectations of the more diversified agents, and the profound lacking in the social and relational dimension (indicated above). In many governance systems, the common problems/issues involved are general welfare, justice, individual (human) rights, or even fundamental belief resulting in conflicts (or weak consensus) between the establishment and challenging groups/networks. As indicated, with the rapid advancement in technologies, and the development of internationalization/ globalization (intense coupling that modifies values), disgruntled agents swiftly self-organized into (informal) disgruntled networks – leading to a larger set of governance complications.
More networks/clusters (self-organizing local spaces) that are not part of the formal structure are also surfacing more rapidly and intensively in the new dynamic [16, 73] – resulting in the emergent of more conflicting decision-making nodes. The informal intrinsic self-powered dynamic substantially redefined the intangible structure, and destabilized/challenge the formal traditional structure. Co-existence issues due to differing self-centricity and stability-centricity at multi-level (agents, networks, organizations) activities also intensify. For instance, policy failure and failing political attempts to improve social and economic conditions, and abrupt ‘local’ catastrophic development are more prominently observed. A weak and/or non-credible political leadership becomes more apparent as the capacity and quality of governance quickly diminishes to an unfavorable level. The overall development (in particular, those contributed by the great dissent of agents) will lead to the more frequent surfacing of spaces of intense complexity (due to ‘destructive’ circular causation and self-organization), as well as punctuation points (abrupt catastrophic events).
At the moment, a vital new trend has been emerging – a more direct confrontation amongst younger agents (new value and cultural pressure) and older leaders. Due to rapid economic development (affluence and independency), and more emphasis on education (as well as easier access to information/knowledge) in many parts of the world, these newer agents possess rather different behavioral schemata – as compared to the older agents. The newer agents are more aware, knowledge intensive, well-informed, interconnected with accelerated communications and interactions, greater cross-perspective influences, and financially independent (resulting in agents with rather different mindset and greater differentiated needs). This is a worldwide phenomenon. They (rightly or wrongly) constantly seek greater organizational/social justice, modified governance arrangements and dynamics (changes) leading to greater complexity and nonlinearity.
Constraints of existing governance theories
Consequently, existing governance theories and practices including agency theory, stakeholder theory, steward theory, and sustainability theory which emphasize hierarchical control and empowerment are revealing their constraints and inefficiency. In general, very often traditional leaders/actors do not fully comprehend the needs of the masses. The newer agents who possess higher ‘mobility’ often challenge such a governance/leadership thinking and structure more aggressively – resulting in greater indifference, political dissatisfaction, organizational injustice (actual or perceived), no loyalty, surfacing of ‘free’ or ‘semi-free’ agents (agents with no long-term commitment, foreigners talents, or part-time/contract staff), and more new demands and rapid changes. The traditional governance system lacking in mass/lateral engagement, convening, and unifying abilities appears to be self-contradicting as the state-society, society-individual, and organization-employee relations become highly complex, and obscure. This heightened incoherency – as both the spread and depth of mass integration is swiftly reducing.
Briefly, a key observation in this analysis is that the traditional features of structural/ hierarchical leadership management concepts and practices, and empowerment supported by existing governance theories are diminishing in efficiency, while the intrinsic self-powered self-organization ability is escalating and demanding deeper attention. Therefore, a crucial thinking that current leaders should embrace is human organizations are more intensely manifesting their complex adaptive characteristics (possess ‘sensitive’ characteristics such as local instability and butterfly effect), and therefore towards a governance system that can manage rapid changes and unpredictability is crucial. Thus, better recognition of the characteristics of human agents, and the presence of the complex networks meta-structure – network of networks, informal local spaces is highly essential in an efficient governance system. The reality of unpredictability can be stressful and fearful – for both actors/leaders and non-actors alike.
Consequently, a highly responsive governance system featuring better integrated adaptability all levels (agents, networks/clusters, organization) are preferred. Thus, focusing on self-organizing communications, self-organizing sub-groups or networks/clusters/local spaces (embedded with localized decision-making nodes) that are incoherent with the global/holistic system, is a new crucial leadership advantage. In this respect, a greater awareness and participation in emergent processes is vital (that is, venturing beyond the deliberate strategy is critical). Apparently, the new abilities embedded in innovative governance capacity must be better comprehended, explored, andexploited.
Towards higher ‘quality’ capacity of governance
In this respect, nurturing a higher quality capacity of governance is essential. The new capacity should encompass redefined multi-perspective thinking and strategies including dynamical complexity, network-centricity, nonlinearity, uncertainty, self-organizing ability, tipping point, and unpredictability. Although many leaders are aware of some of the existing problems/issues but initiating a change is difficult as there is no viable option – in particular, the absence of a ‘common language’, and no acceptable new leadership model or paradigm. In this respect, what are the possible new principles of governance and leadership? A key concern and focal point of the new leadership should be the human thinking systems (agents), the sources of human emotion in particular, anger, fear, and stability-centricity which are highly nonlinear and risk-intensive. Anger and fear of the agents when manifested to a certain level can be destructive to the system.
In general, towards a ‘multi-lateral’ (more collective participation with higher trust and respect for every agent in all perspectives – including informal networks) that reduces relational friction (as there is a great need for closer and more enhancing intelligence-intelligence linkages), instead of a multi-level structure and dynamics is essential. Relational friction is a highly nonlinear variable that could easily shift the system into a punctuation point. Thus, leadership with deeper insights, in particular, focusing on collectivity (with the leadership included as actors in the localized networks/clusters), enhancing interdependency and exploiting independency, and the critical nonlinear relational aspect is inevitable. Consequently, a critical new thinking and transformation is one that must encompass change, adaptation (intelligence and consciousness – orgmindfulness), complexity (new opportunity), network-centricity (interconnectivity, self-organizing communications, truthful engagement), and self-organization/self-transcending constructions (emergence and new order) to elevate the capacity of governance.
In a constantly and rapidly changing environment, the capacity of governance must be ‘sufficient and good enough’ to handle/manage or even exploit the swift changes characterized by high complexity and unpredictability. In what ways can the capacity and quality of governance be further increased? Inevitably, a deeper analysis on the fundamentals of human agents and organizations is essential. In this respect, it becomes crucial to focus on two basic vital and integrated aspects that better define governance and governance system. The first focus is with respect to the characteristics of the human agents – individualistic characteristics. Recognizing the characteristics of the human agents in governance or human systems is the first critical step. They have to be better recognized, comprehended, and subtly exploited. These characteristics include the following aspects: Human agents are intelligence/consciousness intensive. They are self-powered and possess intrinsic leadership ability. They are intrinsically self-centric and stability-centric – autopoietic, and heterogeneous. They are guided by a changing behavioral schemata that are complex, nonlinear, emotional, independent, and interdependent. They encompass an intense mental dimension – mental-stability, and relativistic complexity. They can be unpredictable – the human thinking systems are intrinsic nonlinear/emotional complex adaptive systems that can be logical. They are different from agents in other complex adaptive systems because of their intense intelligence and consciousness (human intelligence, awareness, mindfulness, and relations are nonlinear). They can be intensively interconnected by ICT/digital technology – self-organizing communications, well-informed. Every agent in a governance system is a stakeholder (citizenship, shareholder, employee) that possesses certain rights, interest, and status. Every agent would prefer to be an acting agent (actor rather than non-actor) to certain extent (self-powered, intrinsic leadership) depending on the situation or environment.
Due to the heterogeneity of human agents in human organizations, the performance of different local spaces (networks, clusters) can be rather dissimilar from the global system, resulting in incoherency, especially the presence of local spaces with ‘local minima’ (including no stability at both the agent and network level) that can be disastrous. However, the heterogeneity of agents could also elevate the rate of innovation – indicating that the presence of diversification can beadvantageous.
The second focal point is with respect to the holistic characteristics of organization (the fact that it is intrinsically a composite complex adaptive system) – collectivistic characteristics. The latter includes the following aspects: The components/agents within a human organization can be interconnected and interdependent in varying degrees – due to the presence of certain impetus. There are certain levels of communications and engagement involved – both positive and negative. Networks/clusters (may contradict formal structure) within the organization surfaces spontaneously (self-organizing communications and engagement – network of networks). Thus, human organizations possess self-organization and/or self-transcending constructions ability – driven by the latent impetus (intelligence of the agents and/or collective intelligence of the network and/or system). The emerging dynamic is complex adaptive – exploiting co-existence of order and complexity, continuous learning and adaptation. Co-evolution exists within and without the organization. A human organization is sensitive dependence on initial conditions – butterfly effect – unpredictable. Complexity in human organizations is relativistic if it is highly intelligent – exploiting spaces of relativistic order, reflecting higher innovation and better risk management. Nurturing high collective intelligence in an organization is highly dependent on the presence of intelligence/consciousness-centric leaders, orgmindfulness, and mindful culture. A human organization is a high finite nonlinear dynamical system with incomplete/unknown variables – in-deterministic and unpredictable – no optimal global solution.
Consequently, recognizing the intrinsic strengths of human agents and the constraints of human organizations is a highly crucial and beneficial starting point for new normative governance concepts to emerge. These two fundamentals are summarized as follows:
The intelligent organization theory emphasizes that the inherent strengths of all intelligent human organizations is the intense intelligence and consciousness (awareness and mindfulness) of the interacting agents – this recognition confirms that a paradigmatic shift towards intelligence/consciousness-centricity is inevitable. Next, the constraints of human organizations include the fact that they are intrinsic complex adaptive systems (high finite dimensionality) and possess a very large number of variables or inputs – and not all are well-defined or known. This situation reduces the accuracy of analyzing human organizations (especially mathematically). Thus, formulating a mathematical model of a human system/ organization (for instance, a financial market, a social community) is at best an approximation – an estimated model with estimated outputs (contradicts the Laplace belief). The nonlinear and sensitive dependence on initial conditions characteristics of human organizations further elevated their unpredictability. In this respect, there are only best possible solutions and no global optimality in human organizational dynamic and output, as their phase spaces are never completely known.
With a clearer comprehension of these two ontological fundamentals a transformation in mindset and paradigm (key thinking) is necessary. Recognizing the strengths and characteristics of the agents, the constraints and dynamics of the system, and the rapid increase in complexity in its environment (internal and external) vividly indicates that a transformation in governance mindset is also inevitable – basically encompassing a new strategic thinking, involving all intelligence sources, lateral and changing structure, interactive and new unifying processes, and collective decision and practices is essential. In general, a larger and better quality governance capacity that helps to elevate the flexibility, resilience, and sustainability of the system must include the integrated intelligence/consciousness management, complexity management, and network management approach.
Towards a more effective governance system: Exploiting the intelligent complex adaptive dynamic
Basic requirements of ‘quality’ governance capacity
Current leaders and managers (government, community, and business leadership) must recognize that centralized, deliberate, and hierarchical authority is no longer feasible or acceptable in totality. The new leadership thinking and governance theory must concentrates on the inherent strengths of agents, constraints of the organizations, spontaneous self-organizing dynamic of local space, exploitation of networks, and emergent of new order. The spontaneous adaptive and emergent dynamics (constant/continuous change) in the human world that encompasses self-organizing communications, self-organization (self-transcending constructions) – formation of networks/clusters/local spaces, leads to the creation of more new decision-making nodes (that may not be part of the formal structure), and emergence. Ideally, the dynamics orchestrated in human organizations must drift towards more integrative multi-lateral interactions (engaging individual agents and networks, including informal networks) by the intelligence leadership – that is, away from the design paradigm and more into the intelligence paradigm.
As a general guide, there are certain changing characteristics in human organizations and their governance systems that can be more cautiously explored and exploited. First, every agent should be directly or indirectly associated with the governance holistic engagement – that is, more actors is beneficial (this is also a new preference of agents – especially the younger agents with higher independency). Next, the new governance dynamic should continuously encompass change – that is, the governance dynamic with continuous collective learning and adaptive ability, exploit changing complexity and new networks, create new coherency, and the generation of fresh innovative ideas is essential. Concurrently, it is vital to recognize that human relationship is highly nonlinear (a nonlinear parameter that increases complexity and unpredictability). Thus, better governance could be achieved only if the following features are also presence namely, quality interconnectivity, holistic truthful engagement, and constantly elevating collective intelligence (intelligence-intelligence linkages). All these indicators are important characteristics closely associated with the nonlinear relational parameter (see Fig. 3).
In the current situation, self-organization and/or self-transcending constructions is affected by the speed of interactions of agents, multiplication of linkages, networks formation, and global space-time compression. The self-organizing process is a significant spontaneous ability that responds to change. Thus, possessing the self-organizing ability is beneficial when there are rapid changes – establishing better global coordination and synergy, although there is no global optimality. However, organizational dynamic that occurs without a central control is not acceptable with the present leadership mindset. Thus, a change in the leadership behavioral schemata and expectation is essential. As constructive self-transcending constructions dynamic is closely linked to the collective intelligence of the local spaces, as well as the organization, the intelligence paradigmatic shift is a necessity for better governance and governance systems. Instead of absolute control and hierarchical empowerment, switching towards orchestrating innovative collectivity, encompassing local spaces/networks and self-organizing possesses with more intrinsic leaders or actors participation is a preferred intelligent dynamic. The latter is a significant component of the intelligent complex adaptive dynamic (iCAD).
The integrated intelligence/consciousness management, complexity management and network management approach
The above analysis vividly indicates that effective intelligence/consciousness management, complexity management, and network management are essential for changing governance dynamics. The integration of the three management processes is a highly significant component/option of the intelligent organization dynamics. Adopting the process is a critical strategic path (a significant component of the holistic complexity-intelligence strategy) for better adaptive and collective governance management. In this respect, the intelligent organization theory provides a better conceptual foundation that facilitates better comprehension and exploitation of human organizational dynamics that encompass never ending changes. A critical aspect of the theory indicates that competitiveness, resilience, and sustainability can be best initiated with intelligence/consciousness-centricity as human organizations are systems driven by a constant inflow of intelligence (including exploiting latent intelligence, innovative ideas and new expertise that elevates its collective intelligence).
Intelligence/consciousness-centricity
Being intelligence/consciousness-centric is a vital starting point towards better intelligence/consciousness management and the nurturing of an intelligent organization. As there is a growing awareness of current governance deficiencies, a key requirement is the recognition that human agents are natural sources of intense (highest) nonlinear intelligence on this planet, and this natural endowment has to be more effectively engaged and utilized. In this respect, intelligence/consciousness management attempts to optimize the microscopic agents-system structure and dynamics, as well as the network meta-structure of human organizations. In particular, how can latent intelligence be extracted and utilized is a key concern. In this case, human systems dynamics are not totally identical to the general complex adaptive dynamic of other biological systems due to the presence of intense intelligence and consciousness (awareness and mindfulness) of the interacting agents – hence, redefining autopoiesis and self-stability (mental stability) in humanity.
Collectivity in human organizations is more complex. For instance, human sociality is an important attribute for collective survival. In human organizations, it is a biological attractor. For some insects with colonies, it is encrypted in their genes (usually fixed within a certain time frame, orderly, linear, and pre-determined, generation after generation). However, for humanity the relational parameter is highly nonlinear and constantly changing – more so with younger new agents. Thus, sociality is a vital evolutionary attribute, and there is a significant different between human collective intelligence and swarm intelligence. This difference arises from the fact that, in general, complex adaptive dynamics can be uniquely and innovatively modified by the presence of intense intelligence, awareness, and mindfulness (especially during crisis – more apparent) that elevates the adaptive characteristic in the human world – mindfulness is a unique mental function confined only to humanity.
In any human organizations, all agents are inherent stakeholders in some levels or forms, and this characteristic has to be recognized and acknowledged by whoever are the leaders, policy makers, decision makers, and managers. This mindset and thinking provides a redefined foundation for better synergy, and hence the establishment of a more effective governance capacity and governance system. Every agent has an individual right, interest, and status in the organization that s/he is a member/player – consequently heightening the percentage of actors is useful and significant. In this case, it is the responsibility of the organization (leaders) to take care of the welfare of the agents, and ensure the sustainability of the agents. However, many organizations today are much more externally focus rather than internally focus – for instance, customers versus employees, and citizens versus foreign talents.
Complexity-centricity
As stipulated earlier, it is important to recognize (micro-structure) that all human organizations are highly complex adaptive systems intertwined with a large number of agents, linear and nonlinear variables (known and unknown), and different perspectives (including social, economic, political and environment) – resulting in complex cascading issues/problems. In addition, human complex adaptive systems are also history dependent – earlier interventions will lead to unintended consequences. In addition, relational complexity in the social perspective by itself is enormous, and this is further heightened by the tremendous intensity and impact of sudden economic and political turmoil. This integrated dynamic (interdependent processes) constantly gives rise to many spaces of complexity and unpredictable punctuation points at any time.
With complexity management, certain spaces of complexity can be exploited when better comprehended – a significant aspect of risk management. These spaces are unexplored territories that are embedded with gold nuggets. With the appropriate intelligence/consciousness, these spaces of complex can be transformed into spaces of relativistic order – when certain surface patterns are observed and comprehended. Concurrently, effective complexity management also focuses on punctuation points during stasis – towards better crisis management and constructive transition – a more open form of ‘futuring’ even when the exact event or destination is unknown (sometimes a known destination may become valueless even before reaching it – due to the changing landscape). This aspect of complexity management makes preparation for unpredictability through elevating collective intelligence, intensive engagement of self-powered agents/networks, and better global self-transcending constructions. Thus, inherent complexity in the human world is relativistic due to the presence of intense intelligence, consciousness, and effective complexity management – indicating that forecasting is still useful during relativistic order (also making preparation for unpredictability). (The network management aspect will be analyzed in the next section.)
Adaptive governance, relativistic complexity, and intelligence leadership
Agents as they interact change their ‘fitness’, as well as the ‘fitness’ of other agents, that is, the rugged/fitness landscape is constantly/continuously changing. In addition, a relatively optimal strategy or solution has a limited space-time constraint because once another organization or competitor changes their strategy the landscape is again redefined. Acting agents with intense intelligence/consciousness-centric and complexity-centric characteristics render complexity relativistic – that is, possesses high ability to recognize or establish relativistic order. In particular, the flexibility and adaptation of the leadership is crucial – creating a constantly exploring and exploiting environment that increases innovative adaptive governance capacity. A leader must always be a leader (lateral but respected) and not a ‘slave leader’. In particular, a primary concern is whether a leader as a key actor (with latent leadership) is able to orchestrate the system (including incoherent networks) into the ‘basin of a preferred attractor’ – a unifying action. A leader that is able to directly or indirectly (latent leaders/actors – the invisible hand of the ‘leadership field’) orchestrate the above path possesses a unique intelligence leadership advantage.
Some finer aspects of the integrated deliberate and emergent approach
Flexible and adaptive exploitation of the deliberate and emergent characteristics is significant in a highly complex environment – the subtle balancing of the two characteristics is the new critical leadership ability. Thus, it is significant to cautiously exploit the contribution of the integrated deliberate and emergent strategy (a component of the complexity-intelligence strategy) with respect to adaptive governance. Deliberate strategy can be exploited with respect to relativistic order (space of relativistic order), although there is no static equilibrium (absolute order) in complex adaptive dynamic. Relativistic order can be strategic (risk management), and there must be a certain focus on this strategic component with more planned strategic options – including the ‘futuring’ approach.
However, this aspect alone does not or cannot accommodate punctuation points that are sudden and unpredictable. More sustainable development is greatly dependent on the fact that punctuation points (crisis management) are better ‘self-managed’ – depending on the effectiveness of the emergent strategy nurtured. The latter is closely associated with high collective intelligence through better orgmindfulness, effective latent leadership, better self-organization/self-transcending constructions, and emergence. In this respect, the emergent approach and innovative adaptive capacity of systems/organizations are closely interrelated. Vividly, the presence and performance of the emergent strategy reflects the intensity of collective intelligence and the quality of adaptive capacity of the human organization.
In this respect, there are certain constraints with the traditional deliberate strategic approach alone – that has to be restructured/nurtured and enhanced with relativistic complexity concepts – encompassing relativistic order, recursive sub-dynamics, networks, and constantly learning, changing, and adapting. In addition, a separate and higher level (global) and/or more constructive self-organization/self-transcending constructions and emergent component have to be nurtured concurrently. Therefore, a better exploitation of complex networks (the changing meta-structure and dynamic) is beneficial – towards more holistic engagement. Consequently, the nurturing a deliberate-emergent auto-switch is vital, and it is not purely a ‘binary switch’. It must possess a combination of characteristics such as spontaneous (automatic), adaptive (gradual change), and binary (sudden change). These abilities are possible only if there is high collectivity in the organization – that is, depending on the interconnectivity of the intrinsic intelligence sources (human agents), and the presence of intense collective intelligence (including network/cluster intelligence). Thus, leaders must also be able to identify new focal points for higher constructions, better coherency and synergetic with respect to network-centricity (able to identify key drivers of longer-term capability changes).
The intelligence governance strategy: Encompassing network-centric self-powered self-organizing governance
The network-centric approach focuses on interconnectivity, inter-relationships and interdependency, and it perceives human organizations as sophisticated networked patterns of interaction – the meta-structure and it dynamic. The rapid development of information/communication/media technology very much favored and strengthened the formation of ‘networked organizations’. In this case, complexity is further escalated because certain interactive networked clusters exist although there may be no formal structure (or not part of the formal structure). In addition, networks with respect to the organizational objectives can be both constructive and destructive. Therefore, overall, human beings exist in a complex world with multi-level interconnected and interdependent networks. An interdependent networks system could enhance developments or escalate cascading failures – hence, they must be carefully managed. Concurrently, the above analysis indicates that network-centric self-organizing governance is a new critical process in contemporary organizational dynamics.
Although, human agents are intrinsically self-centric, the characteristic of stability-centric is often better achieved with networking – a balance between autopoiesis and towards local space dynamics (it is a complex shared influence among interdependent agents). This is true for ‘ordinary’ agents (non-actors), as well as key actors (leaders) – the latter could subtly offer a deeper knowledge on leading and organizing in the complex context. In this case, the new effective leaderships have to be network-centric. Thus, the constructive dynamic must encompass several (mental space) characteristics including mutual understanding and respect (collective social consciousness), and the process itself. In the current environment, leaders/actors that are not part of (not able to engage) a particular network/cluster or network-centric will not be effective with the localized context as network-centric self-organization occurs when nodes/agents/actors/leaders in a network/cluster must possess localized self-organizing capacity – orchestrated by their individual and network collective intelligence (self-transcending constructions) within the local space (exhibiting a level of confinement effect).
Vividly, spontaneous self-organizing communications, elevation in engagement, emergence of localized networks and networks of networks, establishing clusters with dissipative ability, self-organization with latent impetus, and mass lateral participation are inherent processes in the human intelligent complex adaptive dynamics. As different levels of localized spaces of order emerge in space-time (from individual agent to network/cluster to network of networks to the entire organization/system), the immersion of leaders/actors into lower levels processes (agent-agent interaction, agent-network interaction) must be part of the intelligence governance system so that its overall adaptive capacity to be elevated. The presence of such a diversified and yet integrated dynamic (synergetic) is a critical requirement of an intelligence governance system – and intelligent human adaptive dynamics in general.
Graph theory, network theory, and small world theory
Thus, a more comprehensive and deeper understanding of networks and their associated characteristics is highly valuable to the current leadership. Basic network concepts originate from graph theory. The latter was formalized as a domain when a related paper was published by Leonhard Euler (1707–1783), in 1736. Subsequently, the foundation of network theory is derived from graph theory – initiated later by Paul Erdos, 1913–1996, and Alfred Renyi, 1921–1970. They focused mainly on random networks. Besides randomness (hypothetical, evenly distributed, static, no growth or preferential attachment characteristic, for instance a bell-curve), networks can be scale free (‘real world’ networks, exhibit growth and preferential attachment characteristics, large number of nodes and links). Subsequently, network applications (‘real world’ networks) have spread to numerous domains, including sociology (social networks), biology (molecular networks), economics (market internal relations networks), and computer science (artificial neural networks). In human networks (complex adaptive networks), focusing on the links is crucial. Links can be relational or intangible, and nonlinear.
In addition, the small world theory, a subset of network theory focuses on a unique characteristic of scale free ‘real world’ networks. The latter is not totally random but followed a predictable pattern of order and growth. As they form clusters and grow, early nodes possess preferential attachment – first mover advantage. In addition, their links are differentiated as strong ties and weak ties, and interestingly the latter provides a high percentage of contribution to the entire network (first discovered by Mark Granovetter [26] in 1973, in his paper entitled ‘The strength of the weak ties’). The weak tie phenomenon is highly significant with the relational parameter.
Currently, network theory is also regarded and analyzed as a subset of complexity theory – complex systems encompass complex networks. In this respect, nodes in networks are the equivalent of agents in complex adaptive systems where information is consumed (internalized), created and transmitted (externalized) – depending on the details characteristics of the nodes/agents (real or artificial) concerned. In this case, every node/human agent is subject to constraints and enablers. The primary purpose of links is to connect nodes/agents and transmit information – that is, to establish communications and engagement. In this respect, they form localized networks/clusters with nodes that possess preferential attachment and interdependency.
Network management: Exploiting the inherent network-centric dynamics
Currently, human beings and their organizations exist in a complex world progressing towards multi-level complex networks with interdependent agents and encompassing multiple interconnected perspectives including social networks, information networks, market networks, governance networks, and technical networks. Complex networks are ‘real world’ networks that possess complexity characteristics. All human networks are complex, nonlinear, communicating, engaged, self-organizing, and emerging. A significant and critical characteristic of such a system of networks is that the networks involved can be interdependent and scale free. They also exhibit the properties of clustering and forming a ‘small world’ or local space. Interdependent networks are networks with nodes that are interdependent across networks. Such interdependency could either enhance developments or escalate cascading failures. In this case, a key strategy is to render the multi-level structure and dynamics more ‘multi-lateral’ – a primary focus of the intelligence leadership.
In this analysis, the concept of
As an intelligent human organization adopts an intelligence/consciousness-centric strategy focusing on the nodes (intense intelligence sources), concurrently, the links (for instance, a set of dyadic ties in social networks) in the organization must also be optimized with better network management (relational-centric). This aspect is vital as the characteristics of the links will influence/determine how the related nodes will act and/or react. It is significant to note that constructive human dynamics encompassing several critical characteristics including mutual understanding and respect, meaningful relations, and the development of the process itself are information dependent, as well as the ‘common language’ adopted. This exploitation is part of the characteristics of the new leadership, and must be incorporated in the new intelligence governance strategy as well – through better enhanced self-organizing governance dynamic.
Engaging self-powered self-organizing governance and the intelligence leadership strategy
As indicated above, focusing on self-organization/self-transcending constructions does not imply that planned strategies are invalid. The ability to orchestrate constructive self-organizing dynamic subtly (must be constantly adjusting) is a critical new intelligence leadership characteristic. Concurrently, the ability to integrate informal self-organizing networks is a new leadership criticality. It enhances the role of an emergent strategist in the intelligence leadership. Hence, the intelligence leadership strategy constantly orchestrates constructive self-organization (integrated self-transcending constructions towards global coherency) by engaging all intrinsic leadership sources. Collectivity and ‘right’ connectedness also diminishes structural and dynamic (‘direct’) control. This is a basic approach for integrating diversification (may be incoherent) by synergetic.
Therefore, a significant process in the intelligence governance strategy is the constructive management and integration (including informal networks) of self-powered self-organizing governance. Although, the latter may or may not be part of the formal structure and dynamics but it encompasses certain processes that must be better comprehended and exploited by the holistic governance and global leadership. Currently, many leaders, managers, and decision-makers are consciously/ unconsciously suppressing self-organization which is a destructive and costly act as micro control requires a substantial amount of resources and escalates incoherency. However, an intelligence leader recognizes that the local activities (localized self-organizing networks) of the agents (citizens, employees, members) produce collective/network/global effect (either a positive or negative impact).
In this respect, constantly in the mindset of an intelligence leader is ‘can the contributions of these local spaces (irrespective if they are formal or informal) be elevated and better integrated’? It is the state where collective intelligence significantly determined how potential self-transcending constructions will be manifested. For intelligent complex adaptive dynamic, these local interactions will eventually increase in coherency through self-transcending constructions with the right orchestration. Thus, it is vital that leaders (as committed actors) must be immersed in all levels of processes – that is, they must be active nodes (through intensive dialogue) in all the three levels of processes (within a local network ->network of networks ->global/entire organization) to orchestrate the final emergent of new order.
Inevitably, the collective/lateral leadership path that is a more relational-centric (links focus) and integrates networks (formal and informal) is a favored approach. As more power sharing is a new requirement, this development redefines the effective leadership and governance characteristics – away from intense hierarchical control and empowerment, and towards highly intense network-based self-powered ability, and intrinsic leadership of every agent. In such a situation, towards minimal direct governance can be an ideal conceptual path. The integration can be achieved by effective continuous self-organizing dynamic – that can change and adapt automatically when complexity (possessing risk management ability) increases and also in time of crisis (possessing crisis management ability). Thus, the basic ideology of all governance systems, including corporate governance systems must encompass community-minded (socio-emotional functionality), value-based, and collectivity-oriented thinking as a significant part of their primary foundation, the nurturing of a ‘diversified concerned commitment’ (‘abstract’ coherency) of all agents (if necessary) is a better option as the approach also increases the relationships capacity of thesystem.
Based on the above analysis, increasing governance adaptive capacity is closely associated with the better engagement of the self-powered self-organizing components with well-connected information processing networks. The latter that realign governance relationship (e-governance) must be effectively exploited. This ability can be better facilitated by a ‘common language’ that manages the complex nonlinear relational parameter better. A ‘common language’ inherently encompasses a social and relational dimension (including cognition and socio-emotional well-beings – respect, recognition, and trust) that is not sufficiently focused upon in many organizations/nations today. The presence of a ‘common language’ with deeper ‘relational synchrony’ and ‘mental coherency’ is a new advantage – towards a deeper intelligence advantage. In particular, any forms of recognition and mutual reciprocity are essential at all time. Nurturing this component is a new challenge for allleaders.
Finally, as the quality of human organizational dynamics can be improved by its rate/quality of self-transcending constructions – continuous learning effects, achieving desired capabilities and aspiration levels, and quick re-learning and responses during crisis are features that must be nurtured and updated continuously. In such a situation, complexity becomes relative. The latent leadership – the ‘invisible hand in the organization’, orchestrates the intrinsic intelligence of the agents (encompassing as many actors as possible), and the collective intelligence (as holistic as possible) of the system. With respect to complexity concepts the implication of the new thinking is that effective leaders must be able to visualize and ‘move’ the organization/system into a new stable attractor basin – one that is still constantly changing and adjusting within a certain defined boundary – that is, a desired strange attractor (see Fig. 4). In this situation, possessing the new intelligence/consciousness-centric thinking and ability (significantly towards nurturing a ‘feeling system’, that is beyond merely a ‘thinking system’) is a critical requirement for leading any competitive human organizations. Thus, intelligence leaders are complex adaptive leaders exploiting an integrated path encompassing the deliberate, emergent, complexity-centric, network-centric, and self-organizing approach.
Briefly, the thinking and processes discussed are summarized as follows: Focusing on intelligence/consciousness management, intensive self-organizing interconnectivity, communications, and engagement – exploiting the power of information networks. Focusing on intelligence/consciousness-driven self-transcending constructions – optimizing collective intrinsic self-powered leadership through latent leadership. Forming, transforming, and integrating networks/clusters. Reduce diverse behavior and enhance interdependency within the complex network with reflexivity loops that better align agents, networks and the organization – enhancing coherency. Balancing between localized self-organizing interactions (formal and informal) and global/holistic emergence – emergent of new order. Leadership must be actors within informal networks/clusters as well – orchestrating better alignedness by allowing bottom up absorption of new spatial structures. Orchestrating collectivity and multi-lateral dynamics, including lateral leadership – minimizing direct governance, towards higher innovative governance capacity. Focusing on nurturing constructive collective intelligence (both at network and global level), enhance the pool of actors, and high ‘quality’ governance capacity at all time – possessing the ability of identifying new basin of stable attractor. Progressing towards a ‘feeling system’, beyond just a thinking(logic-based) system.
Conclusion
This analysis indicates that a general transformation in the governance paradigm encompassing integrated intelligence/consciousness-centricity, complexity-centricity and network-centricity provides a better adaptive option. This fundamental shift from centralized bureaucratic (hierarchical, autocratic, control, selective empowerment) to a more lateral self-powered self-organizing (more balance collective large-scale participation and power distribution, self-powered intrinsic leadership, ‘everybody is in charge’ dynamic) approach with a spontaneous emergent dynamic (encompassing autocatalysis, networks integration, self-organization/self-transcending constructions, recognizing potential stable attractor basin ability) is a new critical requirement. The heightening in flexibility, reflexivity, and adaptive (constantly exploring, learning, and exploiting) characteristics, effective integration of localized spaces (including informal networks), and minimizing ‘direct’ governance greatly elevates the innovative governance capacity.
However, self-powered self-organizing governance, a basic characteristic of the new governance system is just one significant component of the intelligent complex adaptive dynamics (iCAD) that is evolving in the human world. In particular, the presence of new leadership ability in balancing the agents’/networks’ interests and the system’s interests is critical (the presence of latent leadership with problem-solving structures knowledge) – synergetic dynamic. In particular, nurturing ‘feeling systems/organizations’ (higher constructive connectedness) with higher consciousness is another better option – closely correlated with elevating the critical trait of innovative adaptive capacity that emerges from intrinsic intelligence sources. In this respect, all human organizations that wish to enhance their resilience and sustainability will have to recognize the above advantages – possessing high collective intelligence and intense orgmindfulness (mindful culture) that allows the organization to adjust spontaneously.